Ai-driven flexible bioelectronics for various applications

Abstract

The present disclosure describes electrically stimulating biological systems using meshes, wherein the mesh comprises nanoscale wires and / or nanoelectronics. These may be embedded as a scaffold in biological structures, such as tissues, organoids, organs, organisms, and the like. These may be connectable to an external device to determine a property of the scaffold and / or to apply a stimulus to the biological structure. Certain embodiments are Al-driven systems designed for applications such as drug screening, stem cell therapy, or the like. In addition, some embodiments are directed to predictive models that can be used to predict and / or control the biological structure. For example, certain cells may be caused to mature more quickly, and / or the functionality of the biological structure may be enhanced or inhibited by using such models to apply suitable electrical stimuli to the biological structure, and / or portions of the biological structure.

Description

[0001]AI-DRIVEN FLEXIBLE BIOELECTRONICS FOR VARIOUS APPLICATIONS RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 681,627, filed August 9, 2024, entitled “AI-Driven Flexible Bioelectronics for Various Applications,” by Liu, et al., incorporated herein by reference in its entirety. GOVERNMENT FUNDING This invention was made with government support under DK130673 and LM014465 awarded by National Institutes of Health (NIH) and under 2112085 awarded by National Science Foundation (NSF). The government has certain rights in this invention. FIELD The following disclosure generally describes nanoscale wires and nanoelectronics as applied to biological systems. Non-limiting embodiments are generally directed to AI-driven systems designed for applications such as drug screening, stem cell therapy, or the like. BACKGROUND Cardiovascular diseases, neurological conditions, and pancreatic ailments have consistently ranked among the primary culprits of mortality, accounting for over 32% of all deaths in the past decade. The pressing need for more effective treatments is evident, especially when considering the limitations of current medical and clinical interventions. At present, many treatments only benefit a subset of patients and can lead to a range of adverse effects, largely due to the reliance on diagnosing based solely on clinical symptoms and observable pathological changes. Over a decade ago, the derivation of induced pluripotent stem cells (iPSCs) ignited significant excitement for the potential development of novel human disease models, enhanced platforms for drug discovery, and the broader application of autologous cell-based therapy. Early studies employing directed differentiation of iPSCs often revealed cell-level phenotypes in monogenic diseases. However, the transition to understanding tissue-level and organ-level diseases necessitated the evolution of more intricate, 3D, multicellular systems. Organoids and human-rodent chimaeras have been developed to more accurately reflect the multifaceted cellular ecosystems of complex tissues, spanning infectious diseases, genetic disorders, and even cancer. With respect to cardiovascular diseases in particular, these diseases remain a leading global cause of mortality. The realm of heart tissue engineering holds tremendous potential for forthcoming cardiac disease treatments through the customization of heart tissues. Decellularized mouse hearts have been repopulated with human iPSC-derived multipotential cardiovascular progenitor cells. However, a key hurdle persists: the predominant immaturity of these cells. This immaturity presents a substantial obstacle to the clinical implementation of cardiomyocyte cell therapies for heart disease. To tackle this challenge, a variety of methodologies, such as prolonged culture periods, co-culturing, and adjustments in chemical, electrical, and mechanical culture conditions, have been under development. For example, electrical stimulation may be able to significantly advance the maturation of human cardiac tissue derived from PSCs. Following a four-week culture period, cardiac tissues may exhibit gene expression profiles akin to adult tissue, organized ultrastructure, appropriate sarcomere length, and efficient calcium handling. However, their electrical properties are ambiguous, which is pivotal for the heart tissue's functionality. Additionally, the fixed stimulation condition is inadequate in precisely stimulating the cells based on their developmental stage. Thus, such cardiac tissues may not be suitable. In the quest to address these challenges, personalized drug screening and stem cell therapy have emerged as promising avenues. Personalized drug screening integrates comprehensive individual health data into the drug screening process, aiming to swiftly pinpoint therapeutic compounds with heightened efficacy and reduced toxicity for each patient. On the other hand, stem cell therapy, encompassing human pluripotent stem cells (hPSCs) and multipotent mesenchymal stem cells (MSCs), has rapidly emerged as pivotal players in the realm of regenerative medicine. However, further developments are still needed to fully answer these challenges. SUMMARY The following disclosure generally describes nanoscale wires and nanoelectronics as applied to biological systems. Non-limiting embodiments are generally directed to AI-driven systems designed for applications such as drug screening, stem cell therapy, or the like. The subject matter of the present disclosure involves, in some cases, interrelated products, alternative solutions to a particular problem, and / or a plurality of different uses of one or more systems and / or articles. In one set of embodiments, the present disclosure generally describes electrically stimulating biological systems using meshes, wherein the mesh comprises nanoscale wires and / or nanoelectronics. In some case, these may be embedded, e.g., as a scaffold, in biological structures of the biological system, such as tissues, organoids, organs, organisms, and the like. These may be connectable, in certain embodiments, to an external device, e.g., to determine a property of the scaffold (e.g., an electrical property), and / or to apply a stimulus (e.g., an electrical stimulus) to the biological structure. Certain non-limiting embodiments are generally directed to AI-driven systems designed for applications such as drug screening, stem cell therapy, or the like. In addition, some embodiments are generally directed to predictive models that can be used to predict and / or control the biological structure, for example, if the biological structure contains electroactive cells. For example, certain cells may be caused to mature more quickly, and / or the functionality of the biological structure may be enhanced or inhibited by using such models to apply suitable electrical stimuli to the biological structure, and / or portions of the biological structure. One aspect is generally directed to a system. In one set of embodiments, the system comprises an organoid; a mesh comprising a plurality of nanoelectrodes comprising a stimulator and a recording sensor, wherein at least one nanoelectrode of the plurality of nanoelectrodes is configured to electrically couple to a cell of the organoid; and a computer- readable medium configured to process a first signal based on a first current having a first peak shape to determine a second current having a second peak shape, different from the first peak shape, to apply to the organoid. The system, in another set of embodiments, comprises an organoid comprising less mature cells and more mature cells; a mesh comprising a plurality of nanoelectrodes comprising a stimulator and a recording sensor, wherein at least one nanoelectrode of the plurality of nanoelectrodes is electrically coupled to the less mature cells and at least one nanoelectrode of the plurality of nanoelectrodes is electrically coupled to the more mature cells; and a computer-readable medium configured to process a first signal based on a first current having a first peak shape from the less mature cells and to provide a second current having a second peak shape, different from the first peak shape, to the more mature cells. In yet another set of embodiments, the system comprises an organoid; a device comprising a plurality of nanoelectrodes comprising a stimulator and a recording sensor, wherein at least one nanoelectrode of the plurality of nanoelectrodes is configured to electrically couple to a cell of the organoid; and a computer-readable medium configured to process a first signal based on a first current having a first peak shape to determine a second current having a second peak shape, different from the first peak shape, to apply to the organoid. According to still another set of embodiments, the system comprises an organoid comprising less mature cells and more mature cells; a mesh comprising a plurality of nanoelectrodes comprising a stimulator and a recording sensor, wherein at least one nanoelectrode of the plurality of nanoelectrodes is electrically coupled to the less mature cells and at least one nanoelectrode of the plurality of nanoelectrodes is electrically coupled to the more mature cells; and a computer-readable medium configured to process a first signal based on a first current having a first peak shape from the less mature cells and to provide a second current having a second peak shape, different from the first peak shape, to the more mature cells. Another aspect is generally drawn to a method. According to one set of embodiments, the method comprises providing a tissue containing a scaffold defining at least a portion of an electrical circuit and comprising a plurality of electrodes; and applying electrical stimuli to portions of the tissue using one or more of the electrodes within the scaffold, wherein the electrical stimuli are generated using a predictive model programmed by electrically stimulating the tissue using one or more of the electrodes and determining a tissue response based on the electrical stimulation. The method, in another set of embodiments, comprises providing human induced pluripotent stem cell-derived cardiomyocytes contained within a cell scaffold defining at least a portion of an electrical circuit and comprising a plurality of electrodes; and applying electrical stimuli to the cardiomyocytes using one or more of the electrodes within the cell scaffold to accelerate maturation of the cardiomyocytes, wherein the electrical stimuli are generated using a predictive model programmed by repeatedly stimulating the tissue using one or more of the electrodes and determining a tissue response based on the stimulation. According to yet another set of embodiments, the method comprises providing electroactive cells contained within a cell scaffold defining at least a portion of an electrical circuit and comprising a plurality of electrodes; and applying electrical stimuli to the electroactive cells using one or more of the electrodes within the cell scaffold, wherein the electrical stimuli are generated using a predictive model programmed by repeatedly stimulating the tissue using one or more of the electrodes and determining a tissue response based on the stimulation. In still another set of embodiments, the method comprises providing human induced pluripotent stem cell-derived cardiomyocytes contained within a cell scaffold defining at least a portion of an electrical circuit and comprising a plurality of electrodes; and applying electrical stimuli to the cardiomyocytes using one or more of the electrodes within the cell scaffold to accelerate maturation of the cardiomyocytes, wherein the electrical stimuli are generated using a predictive model programmed by repeatedly stimulating the tissue using one or more of the electrodes and determining a tissue response based on the stimulation. The method, in yet another set of embodiments, comprises providing electroactive cells contained within a cell scaffold defining at least a portion of an electrical circuit and comprising a plurality of electrodes; and applying electrical stimuli to the electroactive cells using one or more of the electrodes within the cell scaffold, wherein the electrical stimuli are generated using a predictive model programmed by repeatedly stimulating the tissue using one or more of the electrodes and determining a tissue response based on the stimulation. In one set of embodiments, the method is a method for stimulating a biological system comprising an organoid. In some cases, the method comprises, to a mesh comprising a plurality of nanoelectrodes comprising a stimulator and a recording sensor, wherein at least one nanoelectrode of the plurality of nanoelectrodes is electrically coupled to a cell of the organoid: applying a first current to the organoid having a first peak shape; recording a first signal with the recording sensor; processing the first signal within a computer-readable medium; and stimulating the organoid with the stimulator, wherein stimulating comprises applying a second current to the organoid having a second peak shape different from the first peak shape, wherein the second peak shape is determined, at least in part, based on processing the first signal. In another set of embodiments, the method is a method for stimulating a biological system comprising an organoid. In some cases, the method comprises, to a device comprising a plurality of nanoelectrodes comprising a stimulator and a recording sensor, wherein at least one nanoelectrode of the plurality of nanoelectrodes is electrically coupled to a cell of the organoid: applying a first current to the organoid having a first peak shape; recording a first signal with the recording sensor; processing the first signal within a computer-readable medium; and stimulating the organoid with the stimulator, wherein stimulating comprises applying a second current to the organoid having a second peak shape different from the first peak shape, wherein the second peak shape is determined, at least in part, based on processing the first signal. Some embodiments are generally directed to integrating AI-driven flexible bioelectronics with multimodal sensors and actuators within biological systems. This may, in certain cases, allow for closed-loop control in a real-time, bidirectional, and / or long-term stable system that can interrogate and / or intervene in cellular activities, for example, across a three-dimensional (3D) volume of tissue networks, e.g., at single-cell resolutions and / or millisecond temporal resolutions. This may allow for access and control over certain biological systems. Non-limiting examples include cardiovascular research, pancreatic pathology, stem cell therapies, regenerative medicine, or other applications. One aspect is generally directed to systems and methods of mapping and / or controlling AI-driven biological systems, for example, by using AI-driven flexible bioelectronics, e.g., having multimodal sensors, actuators, etc., within biological systems. For instance, certain embodiments are directed to bioelectronics-based sensing and / or actuating systems, for example, that can be used or integrated with various biological systems. These may be, for example, capable of recording and controlling tissue-wide, single-cell activities, e.g., in a long-term stable manner, for example, without interrupting natural tissue development, differentiation, proliferation, etc. of the tissue. In addition, some embodiments are generally directed to predictive models that can be used to predict underlying cellular molecular activities. This may be performed in certain instances in real time. In some cases, the models may use physical sensing data. Additionally, certain embodiments are generally directed to reinforcement learning-driven control systems, for example, that can make decisions based on the sensing data, and / or provide feedback stimulus to one or more cells, e.g., through the tissue-embedded actuators. These can be used, for example, to guide, promote, and ameliorate the whole-tissue level functions and dysfunctions. In another aspect, certain embodiments encompass systems and methods of manufacturing nanoelectrode arrays. In some embodiments, a device may include a nanoelectrode array comprising sensors and / or actuators. In some such embodiments, these can be multimodal. These nanoelectrodes may provide an interface to stimulate and / or record electrical activity in biological tissue. In another aspect, certain embodiments encompass systems and methods of targeting biological systems, including but not limited to cardiomyocytes, neurons, pancreatic cells, etc., in both in vitro and in vivo applications. The biological systems can be, for example, cultured tissues, implanted tissues or organoids, cyborg organoids, or the like. In another aspect, certain embodiments encompass pharmacological and clinical systems and methods that can be applied to AI-drug screening and stem-cell therapy. In another aspect, certain embodiments encompass reinforcement learning methods. These can be used, for example, to access biological data, e.g., to provide reinforcement learning-driven control system that can make decisions based on the sensing data and / or provide feedback stimulus to one or more of cells. These may be performed, for example, using tissue-embedded actuators. These can be used, for example, to guide, promote, and / or ameliorate the whole-tissue level functions and dysfunctions. In another aspect, the present disclosure encompasses methods of making one or more of the embodiments described herein, for example, nanoscale wires and nanoelectronics, e.g., which may be controlled using AI-driven systems. In still another aspect, the present disclosure encompasses methods of using one or more of the embodiments described herein, for example, nanoscale wires and nanoelectronics, e.g., which may be controlled using AI- driven systems. Other advantages and novel features of the present disclosure will become apparent from the following detailed description of various non-limiting embodiments of the disclosure when considered in conjunction with the accompanying figures. BRIEF DESCRIPTION OF THE DRAWINGS Non-limiting embodiments of the present disclosure will be described by way of example with reference to the accompanying figures, which are schematic and are not intended to be drawn to scale. In the figures, each identical or nearly identical component illustrated is typically represented by a single numeral. For purposes of clarity, not every component is labeled in every figure, nor is every component of each embodiment of the disclosure shown where illustration is not necessary to allow those of ordinary skill in the art to understand the disclosure. In the figures: Fig.1 illustrates nanoelectronics used for electrical measurements under various stimulation conditions, including no stimulation, fixed stimulation, and AI-driven stimulation, in accordance with certain embodiments; Figs.2A-2H illustrate examples of the design, fabrication, and characterization of stretchable mesh nanoelectronics in certain embodiments, including various designs, photographs, microscope images, and data showing electrochemical impedance and long- term electrical stability; Figs.3A-3C illustrates the differentiation of IPSC-CMs and their subsequent integration with certain nanoelectronics to form organoids, including insights into the differentiation process and the structural details of the integrated system, in certain embodiments; Figs.4A-4E illustrate long-term recording capabilities, in accordance with certain embodiments, including capturing the development and maturation trajectories of hiPSC- ECs, electrical activities, waveform profiles, and extracted electrical features during different stages of differentiation; Figs.5A-5C illustrate certain electrical waveform features, in certain embodiments, which may suggest that AI-driven stimulation can be used to accelerates electrical maturation of hiPSC-CMs, e.g., when compared to fixed stimulation and no stimulation methods, as shown through projections, density plots, and comparisons across different stimulation groups; Figs.6A-6D illustrate an AI model using Gaussian Processes and Bayesian Optimization, showing that IPSC-CMs subjected to AI-driven stimulation may tend to mature faster than control groups, showing pseudotime values, their increments, and uncertainties as the training sample size increases, in accordance with some embodiments; Figs.7 and 8 illustrate cell scaffolds embedded within organoids, in accordance with certain embodiments; Fig.9 illustrates a method of producing a cell scaffold in accordance with one embodiment; Fig.10 is a block diagram of an example special purpose computer system improved by the functions and / or processes disclosed herein, in certain embodiments; Figs.11A-11E show an overview of AI-driven cardiac organoids, in certain embodiments; Figs.12A-12M illustrate long-term electrical recording and stimulation of cardiac activity, in some embodiments; Figs.13A-13C illustrate Bayesian optimization for cardiac modulation control policy generation, in certain embodiments; Figs.14A-14G illustrate advancing human cardiac organoid functional maturation through an embedded system in another embodiment; Figs.15A-15F illustrate functional maturation characterizations in cardiac organoids, in yet another embodiment; Figs.16A-16D illustrates fabrication of flexible stretchable mesh nanoelectronics with sensing and stimulation electrodes, in still another embodiment; Figs.17A-17D illustrate integration of flexible stretchable mesh nanoelectronics with IPSC-CMs for human cardiac organoids, in another embodiment; Figs.18A-18E illustrate stimulation and recording of human cardiac organoids, in yet another embodiment; Figs.19A-19B illustrate a recording of human cardiac organoids, in another embodiment; Figs.20A-20C show immunofluorescence images of human cardiac organoids with no stimulation, fixed stimulation policy, and AI-driven stimulation policy, in another embodiment; Figs.21A-21C illustrate comparative analysis of electrical activity and conduction velocity in human cardiac organoids under different stimulation conditions, in still another embodiment; and Figs.22A-22C illustrate representative groups of human cardiac organoid whole- tissue maturation through system, in yet another embodiment. DETAILED DESCRIPTION The present disclosure generally describes devices for providing electrical stimulation to a biological system. Due at least in part to the scale and size of the biological system, the device may comprise nanoscale wires and nanoelectronics (e.g., interconnects, resists) and may be in electrical communication (e.g., contact) with at least one cell (e.g., a pancreatic cell, a cardiomyocyte, etc.). The cell may be a part of a tissue (e.g., pancreatic tissue, cardiac tissue, etc.) or an organoid. In some case, these may be embedded and form a network or scaffolding within the biological system along with tissues, organoids, organs, or organisms of the biological system. These may be connectable in certain embodiments to an external device, for example, an external device (e.g., a computer) that can determine a property of the biological system (e.g., an electrical property), and / or to apply a stimulus (e.g., an electrical stimulus) to the biological system. In addition, certain non-limiting embodiments are generally directed to AI-driven systems designed for applications such as cell differentiation, drug screening, stem cell therapies, or the like. For example, the AI-driven systems may comprise a computer-readable medium configured to process data transmitted and / or received (e.g., electrical data, impendence data) using Gaussian and / or Bayesian processes, and / or other machine learning processes. Based, at least in part, on the data transmitted and / or received, the computer- readable medium can adjust an input or output (e.g., a subsequent electrical output) to improve the biological system, for example, by improving cell differentiation of one or more cells within the biological system. Moreover, some embodiments are generally directed to predictive models that can be used to predict and / or control the biological system, for example, if the biological system contains cells that are responsive to electrical stimulation (e.g., pancreatic cells). For example, certain cells may be induced to mature more quickly, and / or the functionality of the biological system may be enhanced (or inhibited) by using such models to apply suitable electrical stimuli to the biological structure, and / or portions of the biological system. For many embodiments, the device (e.g., a mesh comprising nanoelectrodes) is integrated within tissues or organoids and is configured to transmit and receive electrical signals (e.g., direct currents, impedance). Certain cells, such as stem cells, are known to respond to electrical stimulation, but how those cells respond and how those cells can be controlled or induced into specific cell types is ambiguous and depends on the particular type of cell. For example, a liver cell may respond much differently to electrical stimulation than a brain cell. However, the Inventors have recognized and appreciated that by monitoring electrical inputs and outputs from the cell, control of cell differentiation may be controlled. In some cases, the cells may grow in response to the specific type of electrical stimulation (e.g. electrical inputs and outputs) such that the cells grow along with the device to form a “cyborg” or tissue comprising cells and electrical components (e.g., nanowires; nanoarrays) integrally connected to one another. In one set of embodiments, electrical stimuli are applied using one or more of the electrodes within the scaffold. The electrical stimuli may be generated, for example, using a predictive model programmed by electrically stimulating the tissue using one or more of the electrodes and determining a tissue response based on the electrical stimulation. The predictive model may include Gaussian and / or Bayesian processes, and / or reinforcement learning models. The scaffold may be in contact with cells, such as any of those described herein. For instance, the cells may include human induced pluripotent stem cell-derived cardiomyocytes, or any other type of cell, such as those described herein. In some cases, the cells may be part of a tissue, an organoid, or the like. For example, in some embodiments, a computer-readable medium comprises executable instructions to determine input and / or output signals using a Gaussian and / or Bayesian process. In some embodiments, a computer-readable medium comprises a predictive model comprising a reinforcement learning model. Additional details regarding certain systems and methods are provided below. Various embodiments described herein include a device able to interact with (e.g., electrically stimulate) one or more biological entities, such as cells, tissues, organoids, and / or organs. The device may be or comprise nanowires and / or nanoelectrodes, along with nano- sized interconnects that electrically connect various components of the device. However, it should be understood that not every component of the device is of the nanoscale and some components may be larger or smaller, as desired, for the fabrication or design of the device. In some embodiments, the device is or comprises a mesh, the mesh having an array of nanowires. In some such embodiments, the mesh is integrally connected with portions of the biological system (e.g., tissues, organoids) such that the mesh can receive and / or send electrical signals to portions of the biological system. Details regarding electrical signals sent and / or received from the biological system are described elsewhere herein. Accordingly, a component within the device may comprise an electrode (e.g., a nanoelectrode). The electrode may comprise any suitable material, for example, carbon, or metals such as gold, platinum, silver, or the like. In some cases, the electrode may be used to determine a property of a biological system (e.g., an electrical property, a chemical property, a mechanical property, etc.), and / or to apply a stimulus (e.g., an electrical stimulus) to the biological system. In some cases, a conductive polymer may also be used with the electrode. Non-limiting examples of conductive polymers include poly(3,4-ethylenedioxythiophene) (PEDOT), polyacetylene, polyphenylene vinylene, polypyrrole, polythiophene (for example poly(3,4-ethylenedioxythiophene)), polyphenylene sulfide, or other conductive polymers such as those described herein. In some embodiments, a device comprises one or more components such as nanowires, nanoelectrodes and / or interconnects of the nanoscale. In some embodiments, a component has a dimension (e.g., a length, a width, a diameter) of less than or equal to 1,000 nm, less than or equal to 500 nm, less than or equal to 250 nm, less than or equal to 100 nm, less than or equal to 75 nm, less than or equal to 50 nm, less than or equal to 25 nm, or less than or equal to 10 nm. In some embodiments, a component has a dimension of greater than or equal to 10 nm, greater than or equal to 25 nm, greater than or equal to 50 nm, greater than or equal to 75 nm, greater than or equal to 100 nm, greater than or equal to 250 nm, greater than or equal to 500 nm, or greater than or equal to 1,000 nm. Combinations of the above- referenced ranges are also possible (e.g., greater than or equal to 10 nm and less than or equal to 1,000 nm). Other ranges are possible. In some embodiments, a nanowire or nanoelectrode may be used to assist in determining a property of a biological system, e.g., when it is embedded within a biological structure. For example, one or more locations within the biological system (for example, an electrode or a nanoscale wire, etc.) may be determined to determine a property, such as a chemical property, an electrical property, a mechanical property, or the like. Other examples include sensing Ca2+spikes, voltage changes, cell signaling pathways, ion concentrations, pH changes, sensing of biomolecules or reaction entities, etc. In some cases, the locations are defined as one or more nodes within the biological system, some or all of which may be individually addressable. For example, a node within a cell scaffold may comprise a nanoscale wire, such as those described in more detail elsewhere herein. Devices (e.g., meshes, nanowire arrays) described herein may be integrally a part of biological systems. Accordingly, devices described herein may have a degree of flexibility and / or stretchability to move with the biological system during motion of biological system while not damaging components of the device (e.g., nanowires, interconnects). Accordingly, one or more materials within device are stretchable and / or flexible. For example, in some cases, the device may comprise a mesh or portions thereof (e.g., interconnects) that can be stretchable and / or flexible, or can be manipulated or distorted in some fashion. It should be understood that the flexibility of a material is not purely an intrinsic material propriety; a thinner piece of material may offer more flexibility than a comparably thicker piece of the same material. In addition, in some cases, the flexibility of the material may also be a function of its shape, e.g., as described elsewhere herein. Some embodiments are generally directed to flexible or stretchable devices, such as meshes or networks, that can be used as cell scaffolds to grow structures, such as organoids, tissues, or even whole organisms in some cases. In general, cell scaffolds are structures that cells can attach to and grow on, e.g., to form biological tissues, organoids, and other biological structures. For example, the biological structure may be the heart of an organism. The cell scaffold may comprise biocompatible and / or biodegradable materials, and may in some embodiments also contain growth factors such as growth hormones, extracellular matrix proteins, specific metabolites or nutrients, or the like. The cell scaffold typically is porous, e.g., to facilitate cell seeding therein, and / or diffusion into and out of the cell scaffold, for example, of nutrients, waste products, etc. In some embodiments, a device, or a component of the device (e.g., a nanowire, a mesh), is flexible and / or stretchable such that the device, or the component of the device, has an effective bending stiffness of greater than or equal to 0.01 n·Nm, greater than or equal to 0.02 n·Nm, greater than or equal to 0.03 n·Nm, greater than or equal to 0.05 n·Nm, greater than or equal to 0.1 n·Nm, greater than or equal to 0.3 n·Nm, greater than or equal to 0.5 n·Nm, greater than or equal to 1 n·Nm, greater than or equal to 2 n·Nm, greater than or equal to 3 n·Nm, or greater than or equal to 5 n·Nm. In some embodiments, the device, or a component of the device, has an effective bending stiffness of less than or equal to 5 n·Nm, less than or equal to 3 n·Nm, less than or equal to 2 n·Nm, less than or equal to 1 n·Nm, less than or equal to 0.5 n·Nm, less than or equal to 0.3 n·Nm, less than or equal to 0.1 n·Nm, less than or equal to 0.05, less than or equal to 0.03 n·Nm, less than or equal to 0.02 n·Nm, or less than or equal to 0.01 n·Nm. Combinations of foregoing ranges are also possible (e.g., greater than or equal to 0.01 n·Nm and less than or equal to n·Nm). Other ranges are possible as this disclosure is not so limited. In one set of embodiments, the device (e.g., nanowires, a mesh) may have a shape and / or may be formed from one or more materials that allow the device to be flexible and / or stretchable. For example, the scaffold may be formed of shapes, such as serpentine shapes, that can be extended. In some cases, the device can be formed of components that are not straight, and can be extended, e.g., when pulled on. For instance, the cell scaffold may comprise one or more nodes that are connected by various interconnects, e.g., forming a mesh or a network. The nodes may be evenly or nonevenly distributed within the cell scaffold, and the interconnects may connect them in a regular pattern (for example, in rectangular or triangular arrays of nodes), or in an irregular pattern. As a non-limiting example, Fig.7, panel I, shows a mesh of nodes (dots) in a square array connected by a plurality of interconnects between pairs of nodes (shown as wiggly lines). The nodes may represent points of connectivity, or there may be one or more electronic components at some or all of the nodes, such as conductive pathways, nanoscale wires, sensors, or the like. The same or different electronic components may independently be present at different nodes within a mesh or network. Interconnects connecting two (or more) nodes together may have the same or different shapes or structure within a device (e.g., a mesh or network), and different interconnects within the device may independently have the same or different shapes. In some cases, an interconnect may have a shape that is extendible. For example, an interconnect may have a straight-line or linear shape, or have shapes that are non-linear, such as S shapes, serpentine shapes (e.g., having two, three, four, or more bends or inflection points), zigzag shapes (e.g., having two, three, four, or more vertices), coiled shapes, or the like. Such interconnect shapes may allow various manipulations to occur without disrupting the connection of the interconnect to the nodes, e.g., during stretching, compression, folding, etc. In one set of embodiments, an interconnect (or some other component of the device) may comprise one or metal leads and one or more polymers, such as those discussed below. The polymers can include photoresist polymers (such as SU-8), and / or biocompatible polymers (such as MatrigelTM). Other examples of photoresist polymers include, but are not limited to, those described. In addition, in some embodiments, the device may have components, such as interconnects, that are sufficiently flexible or stretchable such that the device (or a component thereof, such as an interconnect) is foldable by at least 30o, at least 45o, at least 90o, at least 135o, at least 150o, at least 180o, etc. from an initial planar structure. In some instances, a device may have components, such as interconnects, that are sufficiently flexible or stretchable such that the device (or a component thereof, such as an interconnect) may be stretchable in a linear direction by at least 10%, at least 20%, at least 30%, at least 50%, at least 75%, at least 100%, at least 150%, at least 200%, at least 250%, at least 300%, at least 350%, at least 400%, at least 450%, at least 500%, etc., for example, before catastrophic failure of the cell scaffold, breakage, disruption of the connection of the interconnect to the nodes, loss of electrical connections, or the like. In addition, in certain cases, the cell scaffold may also exhibit some degree of elasticity, e.g., such that the cell scaffold may return (at least partially) to its original structure prior to stretching. For instance, the device (or a component thereof, such as an interconnect) may return at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or 100% (perfectly elastic) back to its original structure, measured from when stretching of the material is stopped. For example, a 1 cm material stretched to 2 cm experiences a 100% stretch in a linear direction, and if it afterwards contracts to 1.5 cm, it exhibits a 50% recovery to its original structure (returning 0.5 cm from its stretch of 1 cm). However, it should be understood that in some embodiments, the device is not elastic. In some cases, the device may have an overall filling ratio or area of less than or equal to 50%, less than or equal to 40%, less than or equal to 30%, less than or equal to 25%, less than or equal to 20%, less than or equal to 15%, less than or equal to 13%, less than or equal to 12%, less than or equal to 11%, less than or equal to 10%, less than or equal to 9%, less than or equal to 8%, less than or equal to 7%, less than or equal to 6%, less than or equal to 5%, etc. The filling ratio or area is the area of the physical components of the device, compared to the overall area of the device or biological system (including void spaces). Thus, this is a measure of the “porosity” in two dimensions. For example, in some cases, the device or the biological system may have a mesh structure or layout as described above, where the mesh is relatively open. Devices with smaller filling ratios thus would have greater “open space,” for example, to allow cells to penetrate. As was just mentioned, in some cases, the device or biological system can be defined by one or more pores. Pores that are too small can hinder or restrict cell access. Thus, in some embodiments, the device or the biological system may have an average pore size of at least 100 micrometers, at least 200 micrometers, at least 300 micrometers, at least 400 micrometers, at least 500 micrometers, at least 600 micrometers, at least 700 micrometers, at least 800 micrometers, at least 900 micrometers, or at least 1 mm. However, in other embodiments, pores that are too big may prevent cells from being able to satisfactorily use or even access the pore volume. Thus, in some cases, the device may have an average pore size of no more than 1.5 mm, no more than 1.4 mm, no more than 1.3 mm, no more than 1.2 mm, no more than 1.1 mm, no more than 1 mm, no more than 900 micrometers, no more than 800 micrometers, no more than 700 micrometers, no more than 600 micrometers, or no more than 500 micrometers. Combinations of these are also possible, e.g., in one embodiment, the average pore size is at least 100 micrometers and no more than 1.5 mm. In addition, larger or smaller pores than these can also be used in a cell scaffold in certain cases. Pore sizes may be determined using any suitable technique, e.g., through BET measurements. Devices (e.g., nanowires, meshes) described herein may comprise a variety of materials in different embodiments. For example, the device may comprise one or more polymers, such as photoresists, that define interconnects or other components within the device. In some cases, one or more portions of the device may comprise components, such as nanoelectric components, that may form electrical circuits within the device. For example, the device may contain metal or other conductive pathways, e.g., which define an electrical circuit, and / or can be connected to an external electrical device. In one set of embodiments, the device may contain metal or other conductive pathways, e.g., within interconnects or nodes within the device (e.g., a cell scaffold). Examples of metals for metal leads or pathways that can be used include, but are not limited to platinum, aluminum, gold, silver, copper, molybdenum, tantalum, titanium, nickel, tungsten, chromium, palladium, or the like, as well as any combinations of these and / or other metals. Other examples include conductive polymers such as poly(3,4- ethylenedioxythiophene) (PEDOT), polyacetylene, polyphenylene vinylene, polypyrrole, polythiophene (for example poly(3,4-ethylenedioxythiophene)), polyphenylene sulfide, etc. In certain embodiments, the device includes one or more polymers, e.g., photoresists, biocompatible polymers, biodegradable polymers, etc., as is described herein. For example, in various embodiments, one or more of the polymers may be a photoresist. Photoresists are typically used in lithographic techniques, which can be used as described herein. For example, the photoresist may be chosen for its ability to react to light to become substantially insoluble (or substantially soluble, in some cases) to a photoresist developer. Photoresists that can be used include, but are not limited to, SU-8, S1805, LOR 3A, poly(methyl methacrylate), poly(methyl glutarimide), phenol formaldehyde resin (diazonaphthoquinone / novolac), diazonaphthoquinone (DNQ), Hoechst AZ 4620, Hoechst AZ 4562, Shipley 1400-17, Shipley 1400-27, Shipley 1400-37, or the like. These and many other photoresists are available commercially. Other examples of photoresist polymers include, but are not limited to, those described below, and those described in Int. Pat. Apl. Pub. No. WO 2019 / 084498, incorporated herein by reference. In some cases, the photoresist may be a soft material, for example, a hydrogel. In some embodiments, the photoresist comprises a polymer formed by photo-curing a fluorinated monomer including cross-linkable function groups using a photoinitiator. One example of such a polymer is perfluoropolyether dimethacrylate (PFPE-DMA). Other examples are discussed in more detail below. In some cases, one or more of the polymers may be biocompatible and / or biodegradable. Examples of such biocompatible and / or biodegradable polymers include, but are not limited to, poly(lactic-co-glycolic acid), polylactic acid, polyglycolic acid, poly(methyl methacrylate), poly(trimethylene carbonate), collagen, fibrin, polysaccharidic materials such as chitosan or glycosaminoglycans, hyaluronic acid, polycaprolactone, and the like. Certain photoresists are also biocompatible and / or biodegradable in some cases. Typically, a biocompatible material is one that does not illicit an immune response, or elicits a relatively low immune response, e.g., one that does not impair the cell scaffold or the cells therein from continuing to function for its intended use. In some embodiments, the biocompatible material is able to perform its desired function without eliciting any undesirable local or systemic effects in a subject, e.g., when present within a subject. In some cases, the material is present without eliciting any undesirable local or systemic effects, or such that any biological response by the subject does not substantially affect the ability of the material from continuing to function for its intended use. For example, in a device that is a cell scaffold, the cell scaffold may be able to support appropriate cellular or tissue activity when implanted within a subject, e.g., including the facilitation of molecular and / or mechanical signaling systems, without substantially eliciting undesirable effects in those cells, or undesirable local or systemic responses, or without eliciting a response that causes the cell scaffold to cease functioning for its intended use. A biodegradable material typically degrades over time when exposed to a biological system, e.g., through oxidation, hydrolysis, enzymatic attack, phagocytosis, or the like. For example, a biodegradable material can degrade over time when exposed to water (e.g., hydrolysis) or enzymes. In some cases, a biodegradable material is one that exhibits degradation (e.g., loss of mass and / or structure) when exposed to physiological conditions for at least about a month, at least about 6 months, or at least about a year. For example, the biodegradable material may exhibit a loss of mass of at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, or at least 90%. In certain cases, some or all of the degradation products may be resorbed or metabolized, e.g., into cells or tissues. For example, certain biodegradable materials, during degradation, release substances that can be metabolized by cells or tissues. For many embodiments describes herein, the device comprises a stimulator. A stimulator provides an electrical signal to a portion of a biological system (e.g., a cell, a portion of tissue, an organoid). In some embodiments, the stimulator is a nanowire or nanoelectrode of the device configured to provide electrical current. Details describing nanowires and nanoelectrodes are provided elsewhere herein. In some embodiments, a device includes a recording sensor. The recording sensor may comprise one or more nanowires or nanoelectrodes and is configured to receive an electrical signal (e.g., an electrical impedance). In some embodiments, the recording sensor is operatively associated with a computer-readable medium, and the compute readable-medium is configured to use the received electrical signal as data for a machine-learning and / or an AI process. As a non-limiting example, electrical signals received by the recording sensor can be used to weight how a subsequent signal should be provided to the biological system. Details regarding machine learning and / or AI processes (e.g., predictive models) are provided elsewhere herein. As mentioned above and elsewhere herein, the device (e.g., a stimulator of the device, a recording sensor of the device) may be configured to receive and / or transmit electrical signals within the biological system. For example, in some embodiments, a first current is applied to an organoid and / or a second signal is applied to the organoid. In some embodiments, a first signal and / or a second signal is received by the recording signal, for example, as an impedance signal. Of course, additional signals (e.g., a third signal, a fourth signal, a fifth signal, and so forth) may be transmitted and / or received. In some embodiments, a signal received and or transmitted may have a particular peak shape. That is to say, when a signal is transmitted and / or received, an intensity of the signal may vary over time such that a distribution of values is received and / or transmitted. In some such embodiments, a peak shape has a relatively Gaussian distribution. Of course, other distributions are possible, as this disclosure is not so limited. Devices (e.g., a mesh comprises nanowires and / or interconnects) described herein may be a part of a biological system. The biological system may comprise a cell, cells, tissue, and / or organoid. As described elsewhere herein, the device may be integrated into the biological system and may also move or flex with the biological system. In some embodiments, the device is a part of an in vivo biological system. However, in other embodiments, the device is a part of in vitro biological system. In addition, certain embodiments are generally related to nanoscale wires and nanoelectronics, which, in some aspects, may be embedded in biological systems, such as cells, tissues, organoids, organs, organisms, and the like. In one embodiment, the biological structure is a cardiomyocyte, which may develop to form a heart of the growing organism. The devices and method described herein may promote, or otherwise induce, cell maturation and / or cell differentiation. By way of illustration and not limitation, a device may provide electrical signals to a biological system (e.g., an organoid) and the provided electrical signals may promote cell maturation and / or differentiation. In some embodiments, electrical signals provided to the biological system are based, at least in part, on previous electrical signals or previous data provided from the biological system. As described elsewhere herein, a computer-readable medium may receive input signals and based, at least in part, on those input signals, may determine output signals to provide to or within the biological system. A power source and related circuitry may also be associated with the device to provide appropriate electrical impulses to and from the device. In some embodiments, the device may contains additional materials, for example, to promote growth and / or maturation of cells, tissues, etc. Non-limiting examples include polymers, growth hormones, extracellular matrix protein, specific metabolites or nutrients, or the like. For example, in one of embodiments, one or more agents able to promote cell growth can be added to a cell scaffold, e.g., hormones such as growth hormones, extracellular matrix protein, pharmaceutical agents, vitamins, or the like. Many such growth hormones are commercially available, and may be readily selected by those of ordinary skill in the art based on the specific type of cell or tissue used or desired, and in view of the present disclosure. Similarly, non-limiting examples of extracellular matrix proteins include gelatin, laminin, fibronectin, heparan sulfate, proteoglycans, entactin, hyaluronic acid, collagen, elastin, chondroitin sulfate, keratan sulfate, MatrigelTM, or the like. Many such extracellular matrix proteins are available commercially, and also can be readily identified by those of ordinary skill in the art based on the specific type of cell or tissue used or desired, and in view of the present disclosure. In some embodiments, scaffold materials can be added to the device (or biological system), e.g., to control the size of pores within the device, to promote cell adhesion or growth within the device, to increase the structural stability of the device, to control the flexibility of the device, etc. For instance, additional fibers or other suitable polymers may be added to the cell scaffold, e.g., electrospun fibers can be used as a secondary scaffold. The additional scaffold materials can be formed from any of the materials described herein in reference to the device, e.g., photoresists or biocompatible and / or biodegradable polymers, or other polymers described herein. As another non-limiting example, a glue such as a silicone elastomer glue can be used to control the shape of the device. In various aspects, cells may be cultured on or within a device, such that the device is a cell scaffold. The cells may be allowed to grow to become a biological structure or system, such as an organoid or a tissue, or the cell scaffold may be exposed to a pre-existing biological structure, such as an embryo. The cell scaffold can become partially or completely embedded within the structure, e.g., during growth of structure. The biological structure may be, for example, tissues, organoids, organs, organisms, and the like. A variety of cells may be used with the scaffold. The cell may be an isolated cell, a cell aggregate, in a tissue construct containing cells, or the like. Examples of cells include, but are not limited to, a bacterium or other single-cell organism, or a eukaryotic cell, such as a plant cell, or an animal cell. If the cell is from a multicellular organism, the cell may be from any part of the organism. For instance, if the cell is from an animal, the cell may be a cardiac cell, a fibroblast, a keratinocyte, a hepatocyte, a chondrocyte, a neural cell, an osteocyte, an osteoblast, a muscle cell, a blood cell, an endothelial cell, an immune cell (e.g., a T-cell, a B-cell, a macrophage, a neutrophil, a basophil, a mast cell, an eosinophil), etc. In some cases, the cells may be cancer cells. Examples of cells able to form suitable organoid or organs include, but are not limited to brain cells, cardiac (heart) cells, nephron (kidney) cells, or the like. In some cases, the cells are cancer cells, e.g., that can grow to form a tumor. In certain embodiments, the cells are stem cells, e.g., pluripotent stem cells. In some cases, the cells may also be exposed to other compounds, such as drugs, to determine their effects on the growth of the cells into organoids, organs, or organisms. This may be useful, for example, for drug testing. For example, in one set of embodiments, cells such as stem cells may be seeded on a cell scaffold, and allowed to grow or self-assemble into a biological structure, such as an organoid, an organ, or even an organism. In some cases, the cells may form a “ball” shape as it forms a biological structure, and the flexibility of the scaffold may be such that the cells are able to cause the cell scaffold to stay embedded within the biological structure, thereby resulting in a cell scaffold embedded within the biological structure. For example, the cell scaffold may be sufficiently flexible such that the cells within the biological structure are able to manipulate or distort the cell scaffold as it forms a biological structure. For instance, the cells may be able to distort the cell scaffold by at least 20% in a linear direction, or other distortions as described herein. In contrast, many other scaffolds lack this flexibility, and thus cannot become embedded within the biological structure as it forms. Accordingly, in certain embodiments, the cell scaffold may be embedded within a biological structure without being inserting or injected into the biological structure, e.g., after it has been formed. In some cases, the cell scaffold may contain at least part of the biological structure. For example, the cell scaffold may be manipulated or distorted, e.g., by the cells, to form a 3- dimensional structure defining an internal volume (e.g., such that the cell scaffold is not a 2- dimensional planar structure), where the cell scaffold is embedded at least partially within the biological structure. The cell scaffold may be partially or completely embedded within the biological structure. For example, the cell scaffold may be completely embedded inside of the biological structure, such that no portion of the cell scaffold is exposed externally of the biological structure. In another set of embodiments, the cell scaffold is only partially embedded within the biological structure, and at least a portion of the cell scaffold is exposed externally of the biological structure. For instance, an external portion of the cell scaffold may be used to electrically connect the cell scaffold to an external electrical device, such as a computer. For instance, a suitable connector may be connected to the exposed portion of the cell scaffold, e.g., to form a connection between an electrical circuit within a cell scaffold, and the external device. In some cases, a device contains a relatively large part of the biological structure or biological system. For instance, the device may contain at least 30 vol%, at least 40 vol%, at least 50 vol%, at least 60 vol%, at least 70 vol%, at least 80 vol%, at least 90 vol%, or at least 95 vol% of the biological structure or biological system. In addition, in some cases, the device may be distributed within a relatively large part of the biological structure or system. For example, the device may be manipulated or distorted such that at least 10 vol% of the biological structure or biological system is no more than 5, 10, or 30 micrometers from the device or cell scaffold embedded therein, and in some cases, at least 20 vol%, at least 30 vol%, at least 40 vol%, at least 50 vol%, at least 60 vol%, at least 70 vol%, at least 80 vol%, at least 90 vol%, or at least 95 vol% of the biological structure is no more than 5, 10, or 30 micrometers from the cell scaffold. In some cases, a cell scaffold may be manipulated or distorted, e.g., by the cells, to stretch the cell scaffold. For example, the biological structure may grow and or expand, and the embedded cell scaffold may be stretched along with the biological structure as it expands. In certain embodiments, the cell scaffold may exhibit a lower filling ratio as it is expanded by the biological structure. For instance, the cell scaffold may have an first, initial filling ratio (e.g., prior to adding cells) of less than 50%, less than 40%, less than 30%, less than 25%, less than 20%, less than 15%, less than 13%, less than 12%, less than 11%, less than 10%, less than 9%, less than 8%, less than 7%, less than 6%, less than 5%, etc., and a second filling ratio, after expansion, that is less than the initial filling ratio. For example, the second filling ratio may be less than 90%, less than 80%, less than 70%, less than 60%, less than 50%, less than 40%, less than 30%, less than 20%, or less than 10% of the initial filing ratio. In some cases, the second filing ratio may be less than 10%, less than 9%, less than 8%, less than 7%, less than 6%, less than 5%, etc. In addition, in some cases, the cells may case strain to the cell scaffold during expansion. For example, at least a portion of the cell scaffold may exhibit a tensile strain of at least 10% or at least 20%. A variety of biological structures are contemplated, such as tissues, organoids, organs, whole organisms, or the like. Non-limiting examples of tissues or organs include brain, heart, a kidney, etc. In some cases, the tissue or organ may also arise from cancerous or tumor cells. In some embodiments, the biological structure may be an organoid. An organoid, in some cases, is a miniaturized and simplified version of an organ produced in vitro. They can be derived from various sources, such as one or a few cells from a tissue, embryonic stem cells, induced pluripotent stem cells, or the like. In some cases, such cells are able to self- organize in three-dimensional culture, e.g., owing to their self-renewal and differentiation capacities. Thus, in certain embodiments, such cells may be added to a cell scaffold, and the cells may form an organoid that embeds the cell scaffold. In addition, in some cases, the biological structure may be an organism, i.e., an entire organism. The organism may be any suitable organism, including plants or animals, such as invertebrate or vertebrate organisms. The organism may be, for example, an invertebrate (e.g., a fruit fly), a fish (e.g., a zebrafish), an amphibian (e.g., a frog), a reptile, a bird, or a human or non-human mammal, such as a monkey, a cow, a sheep, a goat, a horse, a rabbit, a pig, a rodent such as a mouse or a rat, a dog, or a cat. In some cases, the cell scaffold may be added to a partially-formed biological structure, such as an embryo, which can grow into an organism. Non-limiting examples include plant cells, animal cells, or the like. For example, as the embryo develops into an organism, the growing cells may manipulate and embed the cell scaffold such that the cell scaffold becomes embedded, partially or completely, within the organism. For example, the cell scaffold may be embedded within an embryo, in accordance with certain embodiments. In addition, in one set of embodiments, a cell scaffold (with or without a biological structure) may be implanted into an organism. For example, an organoid or an organ containing a cell scaffold can be implanted within an organism. The organism may be a human or non-human mammal, such as a monkey, cow, sheep, goat, horse, rabbit, pig, mouse, rat, dog, or cat. The organoid or organ may be from the same or different species as the organism, and may be from the same individual or a different one. Certain aspects described herein are directed to systems and methods for preparing cell scaffolds. The cell scaffolds can be fabricated, for example, using well-known lithographic techniques such as those discussed below. In various embodiments, a cell scaffold is constructed by assembling various polymers, metals, and other components (for example, nanoscale wires) together on a substrate. For example, lithographic techniques such as e-beam lithography, photolithography, X-ray lithography, extreme ultraviolet lithography, ion projection lithography, etc. may be used to pattern polymers, metals, etc. on the substrate. After assembly, at least a portion of the substrate (e.g., a sacrificial material) may be removed, allowing the scaffold to be partially or completely removed from the substrate. Other materials may also be added to the scaffold, e.g., to help stabilize the structure, to add additional agents to enhance its biocompatibility, etc. The scaffold can be used in vivo, e.g., by implanting it in a subject, and / or in vitro, e.g., by seeding cells, etc. on the scaffold. In addition, in some cases, cells may initially be grown or cultured on the scaffold, e.g., to form a biological structure, such as tissues, organoids, organs, organisms, and the like. In some cases, as discussed, the cell scaffold may be sufficiently flexible such that the cell scaffold becomes embedded within the biological structures as it forms. For example, in one set of embodiments, a cell scaffold may be constructed by providing a substrate, depositing a sacrificial layer on the substrate, then patterning a first photoresist on the sacrificial layer, a conductive pathway on the first photoresist, and a second photoresist on the conductive pathway, and removing the sacrificial layer to produce the cell scaffold. See, e.g., Fig.9. The first and second photoresists may comprise the same or different materials. Optionally, other components can also be added to the cell scaffold, before or during formation, such as electrode components, nanoscale wires, connectors such as cables, or the like. The substrate may be chosen to be one that can be used for lithographic techniques such as e-beam lithography or photolithography, or other lithographic techniques including those discussed herein. For example, the substrate may comprise or consist essentially of a semiconductor material such as silicon, although other substrate materials (e.g., a metal) can also be used. Typically, the substrate is one that is substantially planar, e.g., so that polymers, metals, and the like can be patterned on the substrate. In some cases, a portion of the substrate can be oxidized, e.g., forming SiO2and / or Si3N4on a portion of the substrate, which may facilitate subsequent addition of materials (metals, polymers, etc.) to the substrate. In certain embodiments, one or more polymers can also be deposited or otherwise formed prior to depositing the sacrificial material. In some cases, the polymers may be deposited or otherwise formed as a layer of material on the substrate. Deposition may be performed using any suitable technique, e.g., using lithographic techniques such as e-beam lithography, photolithography, X-ray lithography, extreme ultraviolet lithography, ion projection lithography, etc. In some cases, some or all of the polymers may be biocompatible and / or biodegradable. The polymers that are deposited may also comprise methyl methacrylate and / or poly(methyl methacrylate), in some embodiments. Next, a sacrificial material may be deposited. The sacrificial material can be chosen to be one that can be removed without substantially altering other materials (e.g., polymers, other metals, nanoscale wires, etc.) deposited thereon. For example, in one embodiment, the sacrificial material may be a metal, e.g., one that is easily etchable. For instance, the sacrificial material can comprise germanium or nickel, which can be etched or otherwise removed, for example, using a peroxide (e.g., H2O2) or a nickel etchant (many of which are readily available commercially). In some cases, the sacrificial material may be deposited on oxidized portions or polymers previously deposited on the substrate. In some cases, the sacrificial material is deposited as a layer. The layer can have a thickness of less than about 5 micrometers, less than about 4 micrometers, less than about 3 micrometers, less than about 2 micrometers, less than about 1 micrometer, less than about 900 nm, less than about 800 nm, less than about 700 nm, less than about 600 nm, less than about 500 nm, less than about 400 nm, less than about 300 nm, less than about 200 nm, less than about 100 nm, etc. In some embodiments, a first photoresist can be deposited, e.g., on the sacrificial material. The photoresist may include one or more polymers, which may be deposited as one or more layers. Examples of photoresist include, but are not limited to, SU-8, S1805, LOR 3A, poly(methyl methacrylate), poly(methyl glutarimide), phenol formaldehyde resin (diazonaphthoquinone / novolac), diazonaphthoquinone (DNQ), Hoechst AZ 4620, Hoechst AZ 4562, Shipley 1400-17, Shipley 1400-27, Shipley 1400-37, etc., as well as any others discussed herein. The photoresist can be used to at least partially define a cell scaffold. In one set of embodiments, the photoresist may be deposited as a layer of material, such that portions of the photoresist may be subsequently removed. For example, the photoresist can be deposited using lithographic techniques such as e-beam lithography, photolithography, X-ray lithography, extreme ultraviolet lithography, ion projection lithography, etc., or using other techniques for removing polymer that are known to those of ordinary skill in the art. In some cases, more than one photoresist is used, e.g., deposited as more than one layer (e.g., sequentially), and each layer may independently have a thickness of less than about 5 micrometers, less than about 4 micrometers, less than about 3 micrometers, less than about 2 micrometers, less than about 1 micrometer, less than about 900 nm, less than about 800 nm, less than about 700 nm, less than about 600 nm, less than about 500 nm, less than about 400 nm, less than about 300 nm, less than about 200 nm, less than about 100 nm, etc. For example, in some embodiments, portions of the photoresist may be exposed to light (visible, UV, etc.), electrons, ions, X-rays, etc. (e.g., projected onto the photoresist), and the exposed portions can be etched away (e.g., using suitable etchants, plasma, etc.) to produce the pattern. Accordingly, the photoresist may be formed into a particular pattern, e.g., in a grid or a mesh, e.g., as discussed herein. For instance, the pattern may include a mesh and interconnects that have a shape that allow the interconnects to be manipulated or distorted without disrupting their connections, e.g., during stretching, compression, folding, or the like. The pattern can be regular or irregular. Next, a metal or other conductive material can be deposited e.g., on one of the previous materials, to form conductive pathways within the cell scaffold. More than one metal can be used, which may be deposited as one or more layers. For example, a first metal may be deposited, and a second metal may be deposited on at least a portion of the first metal. Optionally, more metals can be used, e.g., a third metal may be deposited on at least a portion of the second metal, and the third metal may be the same or different from the first metal. In some cases, each metal may independently have a thickness of less than about 5 micrometers, less than about 4 micrometers, less than about 3 micrometers, less than about 2 micrometers, less than about 1 micrometer, less than about 900 nm, less than about 800 nm, less than about 700 nm, less than about 600 nm, less than about 500 nm, less than about 400 nm, less than about 300 nm, less than about 200 nm, less than about 100 nm, less than about 80 nm, less than about 60 nm, less than about 40 nm, less than about 30 nm, less than about 20 nm, less than about 10 nm, less than about 8 nm, less than about 6 nm, less than about 4 nm, or less than about 2 nm, etc., and the layers may be of the same or different thicknesses. Any suitable technique can be used for depositing metals, and if more than one metal is used, the techniques for depositing each of the metals may independently be the same or different. For example, in one set of embodiments, deposition techniques such as sputtering can be used. Other examples include, but are not limited to, physical vapor deposition, vacuum deposition, chemical vapor deposition, cathodic arc deposition, evaporative deposition, e-beam PVD, pulsed laser deposition, ion-beam sputtering, reactive sputtering, ion-assisted deposition, high-target-utilization sputtering, high-power impulse magnetron sputtering, gas flow sputtering, or the like. The metals can be chosen in some cases such that the deposition process yields a pre- stressed arrangement, e.g., due to atomic lattice mismatch, which causes the subsequent metal leads to warp or bend, for example, once released from the substrate. Although such processes were typically undesired in the prior art, in certain embodiments of the present invention, such pre-stressed arrangements may be used to cause the resulting cell scaffold to form a 3-dimensional structure, in some cases spontaneously, upon release from the substrate. See, e.g., U.S. Pat. Apl. Pub. Nos.2014 / 0073063, 2014 / 0074253, 2017 / 0069858, 2017 / 0072109, each of which is incorporated herein by reference in its entirety. However, it should be understood that in other embodiments, the metals may not necessary be deposited in a pre-stressed arrangement. Examples of metals that can be deposited (stressed or unstressed) include, but are not limited to, aluminum, gold, silver, copper, molybdenum, tantalum, titanium, nickel, tungsten, chromium, palladium, as well as any combinations of these and / or other metals. For example, a chromium / gold / chromium deposition process can be used, as is shown in Fig.9. In certain embodiments, a second photoresist can be deposited on the previous materials. The second photoresist may be the same or different from the first photoresist, and may include any of the photoresist materials discussed herein, including any of those described with reference to the first photoresist. The second photoresist may include one or more polymers, which may be deposited as one or more layers. In some embodiments, the second photoresist may be deposited on one or more portions of a substrate, e.g., as a layer of material such that portions of the second photoresist can be subsequently removed, e.g., using lithographic techniques such as e-beam lithography, photolithography, X-ray lithography, extreme ultraviolet lithography, ion projection lithography, etc., or using other techniques for removing photoresist that are known to those of ordinary skill in the art. In some cases, more than one photoresist may be used, e.g., deposited as more than one layer (e.g., sequentially), and each layer may independently have a thickness of less than about 5 micrometers, less than about 4 micrometers, less than about 3 micrometers, less than about 2 micrometers, less than about 1 micrometer, less than about 900 nm, less than about 800 nm, less than about 700 nm, less than about 600 nm, less than about 500 nm, less than about 400 nm, less than about 300 nm, less than about 200 nm, less than about 100 nm, etc. After formation of the cell scaffold, some or all of the sacrificial material may then be removed in some cases. In one set of embodiments, for example, at least a portion of the sacrificial material is exposed to an etchant able to remove the sacrificial material. For example, if the sacrificial material is a metal such as nickel, a suitable etchant (for example, a metal etchant such as a nickel etchant, acetone, etc.) can be used to remove the sacrificial metal. Many such etchants may be readily obtained commercially. In addition, in some embodiments, the cell scaffold can also be dried, e.g., in air (e.g., passively), by using a heat source, by using a critical point dryer, etc. Other materials may be also added to the cell scaffold, e.g., before or after it forms a 3-dimensional structure, for example, to help stabilize the structure, to add additional agents to enhance its biocompatibility (e.g., growth hormones, extracellular matrix protein, MatrigelTM, etc.), to cause it to form a suitable 3-dimension structure, to control pore sizes, etc. Non-limiting examples of such materials have been previously discussed above, and include other polymers, growth hormones, extracellular matrix protein, specific metabolites or nutrients, additional scaffold materials, or the like. In addition, in some cases, the cell scaffold is exposed to cells, which can be cultured or allowed to grow, e.g., to form a biological structure. In some cases, the cells are plated or seeded as individual cells, although in certain cases, larger cell assemblies (tissues, embryos, etc.) may be used. In one set embodiments, the cell scaffold may be exposed to cells in vitro, and / or the cell scaffold may be exposed or even submerged within a suitable cell growth medium. Such media are widely available commercially. In some embodiments, the cell scaffold can be subsequently implanted in vivo into a subject, e.g., upon the growth of tissue, an organ, an organoid, etc. However, it should be understood that implantation is not required in all embodiments, for example, in cases where an entire organism develops from the cells. In addition, it should be understood that exposure to cells is not necessarily required in all embodiments. For instance, in one set of embodiments, the cell scaffold may be prepared without the presence of cells. For example, the cell scaffold may be sold as part of a kit, and the user may expose the cell scaffold to cells (or use it for other purposes). In addition, the cell scaffold can be interfaced in some embodiments with one or more electronics, e.g., an external electrical system such as a computer or a transmitter (for instance, a radio transmitter, a wireless transmitter, etc.), e.g., as discussed herein. The interfacing may occur at any suitable time, e.g., before or after exposure to cells, before or after a biological structure (e.g., an organoid or an organism) has formed, before or after sale to a user, or the like. For instance, in some cases, electronic testing of the cell scaffold may be performed. The cell scaffold, or a portion thereof, can be connected to an external electrical circuit, e.g., to electronically interrogate or otherwise determine the electronic state of the cell scaffold. For example, the cell scaffold may comprise one or more nanoscale wires, or other nanoelectronic components, that can be used as sensors. Such determinations may be performed quantitatively and / or qualitatively, depending on the application, and can involve all, or only a portion, of the cell scaffold, e.g., as discussed herein. Thus, as mentioned, in some aspects, the cell scaffold can comprise one or more nanoscale wires. For instance, one or more nodes may contain nanoscale wires, and / or nanoscale wires may be contained within interconnects, or the like. In some cases, the cell scaffold within the organoids, organs, or organisms may include one or more sensors or stimulators, interconnected with stretchable mesh interconnects, to form a network, e.g., as is shown in Figs.7 and 8. The sensors or stimulators may, in some embodiments, comprise nanoscale wires, such as those described herein. Such sensors may be monitored, e.g., individually or collectively. Non-limiting examples of suitable nanoscale wires include carbon nanotubes, nanorods, nanowires, organic and inorganic conductive and semiconducting polymers, metal nanoscale wires, semiconductor nanoscale wires (for example, formed from silicon), and the like. If carbon nanotubes are used, they may be single-walled and / or multi-walled, and may be metallic and / or semiconducting in nature. Other conductive or semiconducting elements that may not be nanoscale wires, but are of various small nanoscopic-scale dimension, also can be used within the cell scaffold. In general, a “nanoscale wire” (also known herein as a “nanoscopic-scale wire” or “nanoscopic wire”) generally is a wire or other nanoscale object, that at any point along its length, has at least one cross-sectional dimension and, in some embodiments, two orthogonal cross-sectional dimensions (e.g., a diameter) of less than 1 micrometer, less than about 500 nm, less than about 200 nm, less than about 150 nm, less than about 100 nm, less than about 70, less than about 50 nm, less than about 20 nm, less than about 10 nm, less than about 5 nm, than about 2 nm, or less than about 1 nm. In some embodiments, the nanoscale wire is generally cylindrical. In other embodiments, however, other shapes are possible; for example, the nanoscale wire can be faceted, i.e., the nanoscale wire may have a polygonal cross-section. The cross-section of a nanoscale wire can be of any arbitrary shape, including, but not limited to, circular, square, rectangular, annular, polygonal, or elliptical, and may be a regular or an irregular shape. The nanoscale wire can also be solid or hollow. In some cases, the nanoscale wire has one dimension that is substantially longer than the other dimensions of the nanoscale wire. For example, the nanoscale wire may have a longest dimension that is at least about 1 micrometer, at least about 3 micrometers, at least about 5 micrometers, or at least about 10 micrometers or about 20 micrometers in length, and / or the nanoscale wire may have an aspect ratio (longest dimension to shortest orthogonal dimension) of greater than about 2:1, greater than about 3:1, greater than about 4:1, greater than about 5:1, greater than about 10:1, greater than about 25:1, greater than about 50:1, greater than about 75:1, greater than about 100:1, greater than about 150:1, greater than about 250:1, greater than about 500:1, greater than about 750:1, or greater than about 1000:1 or more in some cases. In some embodiments, a nanoscale wire is substantially uniform, or the nanowire may have a variation in average diameter of the nanoscale wire of less than about 30%, less than about 25%, less than about 20%, less than about 15%, less than about 10%, or less than about 5%. For example, the nanoscale wires may be grown from substantially uniform nanoclusters or particles, e.g., colloid particles. See, e.g., U.S. Patent No.7,301,199, issued November 27, 2007, entitled “Nanoscale Wires and Related Devices,” by Lieber, et al., incorporated herein by reference in its entirety. In some cases, the nanoscale wire may be one of a population of nanoscale wires having an average variation in diameter, of the population of nanowires, of less than about 30%, less than about 25%, less than about 20%, less than about 15%, less than about 10%, or less than about 5%. In some embodiments, a nanoscale wire has a conductivity of or of similar magnitude to any semiconductor or any metal. The nanoscale wire can be formed of suitable materials, e.g., semiconductors, metals, etc., as well as any suitable combinations thereof. In some cases, the nanoscale wire will have the ability to pass electrical charge, for example, being electrically conductive. For example, the nanoscale wire may have a relatively low resistivity, e.g., less than about 10-3Ohm m, less than about 10-4Ohm m, less than about 10-6Ohm m, or less than about 10-7Ohm m. The nanoscale wire can, in some embodiments, have a conductance of at least about 1 microsiemens, at least about 3 microsiemens, at least about 10 microsiemens, at least about 30 microsiemens, or at least about 100 microsiemens. The nanoscale wire can be solid or hollow, in various embodiments. As used herein, a “nanotube” is a nanoscale wire that is hollow, or that has a hollowed-out core, including those nanotubes known to those of ordinary skill in the art. As another example, a nanotube may be created by creating a core / shell nanowire, then etching away at least a portion of the core to leave behind a hollow shell. Accordingly, in one set of embodiments, the nanoscale wire is a non-carbon nanotube. In contrast, a “nanowire” is a nanoscale wire that is typically solid (i.e., not hollow). Thus, in one set of embodiments, the nanoscale wire may be a semiconductor nanowire, such as a silicon nanowire. For example, in one embodiment, a nanoscale wire may comprise or consist essentially of a metal. Non-limiting examples of potentially suitable metals include aluminum, gold, silver, copper, molybdenum, tantalum, titanium, nickel, tungsten, chromium, or palladium. In another set of embodiments, a nanoscale wire comprises or consists essentially of a semiconductor. Typically, a semiconductor is an element having semiconductive or semi-metallic properties (i.e., between metallic and non-metallic properties). An example of a semiconductor is silicon. Other non-limiting examples include elemental semiconductors, such as gallium, germanium, diamond (carbon), tin, selenium, tellurium, boron, or phosphorous. In other embodiments, more than one element may be present in the nanoscale wire as the semiconductor, for example, gallium arsenide, gallium nitride, indium phosphide, cadmium selenide, etc. Still other examples include a Group II-VI material (which includes at least one member from Group II of the Periodic Table and at least one member from Group VI, for example, ZnS, ZnSe, ZnSSe, ZnCdS, CdS, or CdSe), or a Group III-V material (which includes at least one member from Group III and at least one member from Group V, for example GaAs, GaP, GaAsP, InAs, InP, AlGaAs, or InAsP). In certain embodiments, the semiconductor can be undoped or doped (e.g., p-type or n-type). For example, in one set of embodiments, a nanoscale wire may be a p-type semiconductor nanoscale wire or an n-type semiconductor nanoscale wire, and can be used as a component of a transistor such as a field effect transistor (“FET”). For instance, the nanoscale wire may act as the “gate” of a source-gate-drain arrangement of a FET, while metal leads or other conductive pathways (as discussed herein) are used as the source and drain electrodes. In some embodiments, a dopant or a semiconductor may include mixtures of Group IV elements, for example, a mixture of silicon and carbon, or a mixture of silicon and germanium. In other embodiments, the dopant or the semiconductor may include a mixture of a Group III and a Group V element, for example, BN, BP, BAs, AlN, AlP, AlAs, AlSb, GaN, GaP, GaAs, GaSb, InN, InP, InAs, or InSb. Mixtures of these may also be used, for example, a mixture of BN / BP / BAs, or BN / AlP. In other embodiments, the dopants may include alloys of Group III and Group V elements. For example, the alloys may include a mixture of AlGaN, GaPAs, InPAs, GaInN, AlGaInN, GaInAsP, or the like. In other embodiments, the dopants may also include a mixture of Group II and Group VI semiconductors. For example, the semiconductor may include ZnO, ZnS, ZnSe, ZnTe, CdS, CdSe, CdTe, HgS, HgSe, HgTe, BeS, BeSe, BeTe, MgS, MgSe, or the like. Alloys or mixtures of these dopants are also be possible, for example, (ZnCd)Se, or Zn(SSe), or the like. Additionally, alloys of different groups of semiconductors may also be possible, for example, a combination of a Group II-Group VI and a Group III-Group V semiconductor, for example, (GaAs)x(ZnS)1-x. Other examples of dopants may include combinations of Group IV and Group VI elemnts, such as GeS, GeSe, GeTe, SnS, SnSe, SnTe, PbO, PbS, PbSe, or PbTe. Other semiconductor mixtures may include a combination of a Group I and a Group VII, such as CuF, CuCl, CuBr, CuI, AgF, AgCl, AgBr, AgI, or the like. Other dopant compounds may include different mixtures of these elements, such as BeSiN2, CaCN2, ZnGeP2, CdSnAs2, ZnSnSb2, CuGeP3, CuSi2P3, Si3N4, Ge3N4, Al2O3, (Al, Ga, In)2(S, Se, Te)3, Al2CO, (Cu, Ag)(Al, Ga, In, Tl, Fe)(S, Se, Te)2and the like. The doping of the semiconductor to produce a p-type or n-type semiconductor may be achieved via bulk-doping in certain embodiments, although in other embodiments, other doping techniques (such as ion implantation) can be used. Many such doping techniques that can be used will be familiar to those of ordinary skill in the art, including both bulk doping and surface doping techniques. A bulk-doped article (e.g. an article, or a section or region of an article) is an article for which a dopant is incorporated substantially throughout the crystalline lattice of the article, as opposed to an article in which a dopant is only incorporated in particular regions of the crystal lattice at the atomic scale, for example, only on the surface or exterior. For example, some articles are typically doped after the base material is grown, and thus the dopant only extends a finite distance from the surface or exterior into the interior of the crystalline lattice. It should be understood that “bulk-doped” does not define or reflect a concentration or amount of doping in a semiconductor, nor does it necessarily indicate that the doping is uniform. “Heavily doped” and “lightly doped” are terms the meanings of which are clearly understood by those of ordinary skill in the art. In some embodiments, one or more regions comprise a single monolayer of atoms (“delta- doping”). In certain cases, the region may be less than a single monolayer thick (for example, if some of the atoms within the monolayer are absent). As a specific example, the regions may be arranged in a layered structure within the nanoscale wire, and one or more of the regions can be delta-doped or partially delta-doped. Accordingly, in one set of embodiments, the nanoscale wires may include a heterojunction, e.g., of two regions with dissimilar materials or elements, and / or the same materials or elements but at different ratios or concentrations. The regions of the nanoscale wire may be distinct from each other with minimal cross-contamination, or the composition of the nanoscale wire can vary gradually from one region to the next. The regions may be both longitudinally arranged relative to each other, or radially arranged (e.g., as in a core / shell arrangement) on the nanoscale wire. Each region may be of any size or shape within the wire. The junctions may be, for example, a p / n junction, a p / p junction, an n / n junction, a p / i junction (where i refers to an intrinsic semiconductor), an n / i junction, an i / i junction, or the like. The junction can also be a Schottky junction in some embodiments. The junction may also be, for example, a semiconductor / semiconductor junction, a semiconductor / metal junction, a semiconductor / insulator junction, a metal / metal junction, a metal / insulator junction, an insulator / insulator junction, or the like. The junction may also be a junction of two materials, a doped semiconductor to a doped or an undoped semiconductor, or a junction between regions having different dopant concentrations. The junction can also be a defected region to a perfect single crystal, an amorphous region to a crystal, a crystal to another crystal, an amorphous region to another amorphous region, a defected region to another defected region, an amorphous region to a defected region, or the like. More than two regions may be present, and these regions may have unique compositions or may comprise the same compositions. As one example, a wire can have a first region having a first composition, a second region having a second composition, and a third region having a third composition or the same composition as the first composition. Non-limiting examples of nanoscale wires comprising heterojunctions (including core / shell heterojunctions, longitudinal heterojunctions, etc., as well as combinations thereof) are discussed in U.S. Patent No.7,301,199, issued November 27, 2007, entitled “Nanoscale Wires and Related Devices,” by Lieber, et al., incorporated herein by reference in its entirety. In some embodiments, a nanoscale wire is a bent or a kinked nanoscale wire. A kink is typically a relatively sharp transition or turning between a first substantially straight portion of a wire and a second substantially straight portion of a wire. For example, a nanoscale wire may have 1, 2, 3, 4, or 5 or more kinks. In some cases, the nanoscale wire is formed from a single crystal and / or comprises or consists essentially of a single crystallographic orientation, for example, a <110> crystallographic orientation, a <112> crystallographic orientation, or a crystallographic orientation. It should be noted that the kinked region need not have the same crystallographic orientation as the rest of the semiconductor nanoscale wire. In some embodiments, a kink in the semiconductor nanoscale wire may be at an angle of about 120oor a multiple thereof. The kinks can be intentionally positioned along the nanoscale wire in some cases. For example, a nanoscale wire may be grown from a catalyst particle by exposing the catalyst particle to various gaseous reactants to cause the formation of one or more kinks within the nanoscale wire. Non-limiting examples of kinked nanoscale wires, and suitable techniques for making such wires, are disclosed in International Patent Application No. PCT / US2010 / 050199, filed September 24, 2010, entitled “Bent Nanowires and Related Probing of Species,” by Tian, et al., published as WO 2011 / 038228 on March 31, 2011, incorporated herein by reference in its entirety. In one set of embodiments, the nanoscale wire is formed from a single crystal, for example, a single crystal nanoscale wire comprising a semiconductor. A single crystal item may be formed via covalent bonding, ionic bonding, or the like, and / or combinations thereof. While such a single crystal item may include defects in the crystal in some cases, the single crystal item is distinguished from an item that includes one or more crystals, not ionically or covalently bonded, but merely in close proximity to one another. In some embodiments, the nanoscale wires used herein are individual or free-standing nanoscale wires. For example, an “individual” or a “free-standing” nanoscale wire may, at some point in its life, not be attached to another article, for example, with another nanoscale wire, or the free-standing nanoscale wire may be in solution. This is in contrast to nanoscale features etched onto the surface of a substrate, e.g., a silicon wafer, in which the nanoscale features are never removed from the surface of the substrate as a free-standing article. This is also in contrast to conductive portions of articles which differ from surrounding material only by having been altered chemically or physically, in situ, i.e., where a portion of a uniform article is made different from its surroundings by selective doping, etching, etc. An “individual” or a “free-standing” nanoscale wire is one that can be (but need not be) removed from the location where it is made, as an individual article, and transported to a different location and combined with different components to make a functional device such as those described herein and those that would be contemplated by those of ordinary skill in the art upon reading this disclosure. In various embodiments, more than one nanoscale wire may be present within the cell scaffold. The nanoscale wires may each independently be the same or different. For example, the cell scaffold can comprise at least 5 nanoscale wires, at least about 10 nanoscale wires, at least about 30 nanoscale wires, at least about 50 nanoscale wires, at least about 100 nanoscale wires, at least about 300 nanoscale wires, at least about 1000 nanoscale wires, etc. The nanoscale wires may be distributed uniformly or non-uniformly throughout the cell scaffold. In some cases, the nanoscale wires may be distributed at an average density of at least about 10 nanoscale wires / mm3, at least about 30 nanoscale wires / mm3, at least about 50 nanoscale wires / mm3, at least about 75 nanoscale wires / mm3, or at least about 100 nanoscale wires / mm3. In certain embodiments, the nanoscale wires are distributed within the cell scaffold such that the average separation between a nanoscale wire and its nearest neighboring nanoscale wire is less than about 2 mm, less than about 1 mm, less than about 500 micrometers, less than about 300 micrometers, less than about 100 micrometers, less than about 50 micrometers, less than about 30 micrometers, or less than about 10 micrometers. Within the cell scaffold, some or all of the nanoscale wires may be individually electronically addressable. For instance, in some cases, at least about 10%, at least about 20%, at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70%, at least about 80%, at least about 90%, or substantially all of the nanoscale wires within the cell scaffold may be individually electronically addressable. In some embodiments, an electrical property of a nanoscale wire can be individually determinable (e.g., being partially or fully resolvable without also including the electrical properties of other nanoscale wires), and / or such that the electrical property of a nanoscale wire may be individually controlled (e.g., by applying a desired voltage or current to the nanoscale wire, for instance, without simultaneously applying the voltage or current to other nanoscale wires). In other embodiments, however, at least some of the nanoscale wires can be controlled within the same electronic circuit (e.g., by incorporating the nanoscale wires in series and / or in parallel), such that the nanoscale wires can still be electronically controlled and / or determined. The nanoscale wire, in some embodiments, may be responsive to a property external of the nanoscale wire, e.g., a chemical property, an electrical property, a physical property, etc. Such determination may be qualitative and / or quantitative. For example, in one set of embodiments, the nanoscale wire may be responsive to voltage. For instance, the nanoscale wire may exhibits a voltage sensitivity of at least about 5 microsiemens / V; by determining the conductivity of a nanoscale wire, the voltage surrounding the nanoscale wire may thus be determined. In other embodiments, the voltage sensitivity can be at least about 10 microsiemens / V, at least about 30 microsiemens / V, at least about 50 microsiemens / V, or at least about 100 microsiemens / V. Other examples of electrical properties that can be determined include resistance, resistivity, conductance, conductivity, impendence, or the like. As another example, a nanoscale wire may be responsive to a chemical property of the environment surrounding the nanoscale wire. For example, an electrical property of the nanoscale wire can be affected by a chemical environment surrounding the nanoscale wire, and the electrical property can be thereby determined to determine the chemical environment surrounding the nanoscale wire. As a specific non-limiting example, the nanoscale wires may be sensitive to pH or hydrogen ions. Further non-limiting examples of such nanoscale wires are discussed in U.S. Patent No.7,129,554, filed October 31, 2006, entitled “Nanosensors,” by Lieber, et al., incorporated herein by reference in its entirety. As an example, the nanoscale wire may have the ability to bind to an analyte indicative of a chemical property of the environment surrounding the nanoscale wire (e.g., hydrogen ions for pH, or concentration for an analyte of interest), and / or the nanoscale wire may be partially or fully functionalized, i.e. comprising surface functional moieties, to which an analyte is able to bind, thereby causing a determinable property change to the nanoscale wire, e.g., a change to the resistivity or impedance of the nanoscale wire. The binding of the analyte can be specific or non-specific. Functional moieties may include simple groups, selected from the groups including, but not limited to, –OH, –CHO, –COOH, –SO3H, –CN, – NH2, –SH, –COSH, –COOR, halide; biomolecular entities including, but not limited to, amino acids, proteins, sugars, DNA, antibodies, antigens, and enzymes; grafted polymer chains with chain length less than the diameter of the nanowire core, selected from a group of polymers including, but not limited to, polyamide, polyester, polyimide, polyacrylic; a shell of material comprising, for example, metals, semiconductors, and insulators, which may be a metallic element, an oxide, an sulfide, a nitride, a selenide, a polymer and a polymer gel. In some embodiments, a reaction entity may be bound to a surface of the nanoscale wire, and / or positioned in relation to the nanoscale wire such that the analyte can be determined by determining a change in a property of the nanoscale wire. The “determination” may be quantitative and / or qualitative, depending on the application. The term “reaction entity” refers to any entity that can interact with an analyte in such a manner to cause a detectable change in a property (such as an electrical property) of a nanoscale wire. The reaction entity may enhance the interaction between the nanowire and the analyte, or generate a new chemical species that has a higher affinity to the nanowire, or to enrich the analyte around the nanowire. The reaction entity can comprise a binding partner to which the analyte binds. The reaction entity, when a binding partner, can comprise a specific binding partner of the analyte. For example, the reaction entity may be a nucleic acid, an antibody, a sugar, a carbohydrate or a protein. Alternatively, the reaction entity may be a polymer, catalyst, or a quantum dot. A reaction entity that is a catalyst can catalyze a reaction involving the analyte, resulting in a product that causes a detectable change in the nanowire, e.g. via binding to an auxiliary binding partner of the product electrically coupled to the nanowire. Another exemplary reaction entity is a reactant that reacts with the analyte, producing a product that can cause a detectable change in the nanowire. The reaction entity can comprise a shell on the nanowire, e.g. a shell of a polymer that recognizes molecules in, e.g., a gaseous sample, causing a change in conductivity of the polymer which, in turn, causes a detectable change in the nanowire. The term “binding partner” refers to a molecule that can undergo binding with a particular analyte, or “binding partner” thereof, and includes specific, semi-specific, and non- specific binding partners as known to those of ordinary skill in the art. The term “specifically binds,” when referring to a binding partner (e.g., protein, nucleic acid, antibody, etc.), refers to a reaction that is determinative of the presence and / or identity of one or other member of the binding pair in a mixture of heterogeneous molecules (e.g., proteins and other biologics). Thus, for example, in the case of a receptor / ligand binding pair the ligand would specifically and / or preferentially select its receptor from a complex mixture of molecules, or vice versa. An enzyme would specifically bind to its substrate, a nucleic acid would specifically bind to its complement, an antibody would specifically bind to its antigen. Other examples include, nucleic acids that specifically bind (hybridize) to their complement, antibodies specifically bind to their antigen, and the like. The binding may be by one or more of a variety of mechanisms including, but not limited to ionic interactions, and / or covalent interactions, and / or hydrophobic interactions, and / or van der Waals interactions, etc. Additionally, as discussed, a cell scaffold in some aspects may include a photoresist, such as a soft photoresist. For example, in some embodiments, the photoresist may comprise a polymer formed by photo-curing a fluorinated monomer including cross-linkable function groups using a photoinitiator. This may, for examlpe, facilitate stretchability of the cell scaffold. One example of such a polymer is perfluoropolyether dimethacrylate (PFPE- DMA). In addition, in some cases, the photoresist may be a photo-curable composition. In some embodiments, a photo-curable composition includes: a fluorinated monomer including cross-linkable functional groups; and a photoinitiator. Additional non-limiting examples of photoresist may be found in Int. Pat. Apl. Pub. No. WO 2019 / 084498, incorporated herein by reference in its entirety. Some embodiments of this disclosure are directed to a photo-curable composition that can be cured to form an elastomer exhibiting high stretchability and that is chemically orthogonal to various development solvents used in photolithography and, hence, compatible with photolithography. Further, the elastomer can be patterned with fine feature resolution, and can be used as a photoresist for patterning various materials, including electrically (or electronically) active materials. Examples of applications of such photo-patternable composition include forming stretchable and transparent substrates, stretchable and transparent dielectric / passivation / encapsulation films or layers for elastic or stretchable microelectronics, and photoresists for patterning of materials, such as in the context of implantable medical devices, wearable electronic devices, and soft electronic devices; other biomedical devices; cosmetics; prosthetics; and other applications involving an interface with a human body, an animal body, or other biological tissue where matching of mechanical properties with the biological tissue is desired. In some embodiments, a kit may be provided, e.g., comprising a cell scaffold as is discussed herein. Cells may or may not be provided with the kit. The kit may include a package or an assembly including the cell scaffold, and optionally other components associated with the cell scaffold, such as cells. Examples of other components include, but are not limited to, solvents, surfactants, diluents, salts, buffers, emulsifiers, chelating agents, fillers, antioxidants, binding agents, bulking agents, preservatives, drying agents, antimicrobials, needles, syringes, packaging materials, tubes, bottles, flasks, beakers, dishes, frits, filters, rings, clamps, wraps, patches, containers, and the like, for example, for using, administering, modifying, assembling, storing, packaging, preparing, mixing, diluting, and / or preserving the cell scaffold. A kit may include instructions in any form that are provided in connection with the components of the kit in such a manner that one of ordinary skill in the art would recognize that the instructions are to be associated with those components. For instance, the instructions may include instructions for the use, modification, mixing, diluting, preserving, administering, assembly, storage, packaging, and / or preparation of the cell scaffold. The instructions may be provided in any form recognizable by one of ordinary skill in the art as a suitable vehicle for containing such instructions, for example, written or published, verbal, audible (e.g., telephonic), digital, optical, visual (e.g., videotape, DVD, etc.) or electronic communications (including Internet or web-based communications), provided in any manner. The following documents are incorporated herein by reference in their entireties: U.S. Provisional Patent Application Serial No.62 / 865,648, filed June 24, 2019, entitled “Organoids Containing Electronics, and Methods Thereof,” by Liu, et al.; U.S. Provisional Patent Application Serial No.62 / 872,031, filed July 9, 2019, entitled “Organoids Containing Electronics, and Methods Thereof,” by Liu, et al.; U.S. Patent No.7,211,464, issued May 1, 2007, entitled “Doped Elongated Semiconductors, Growing Such Semiconductors, Devices Including Such Semiconductors, and Fabricating Such Devices”; U.S. Patent No.7,301,199, issued November 27, 2007, entitled “Nanoscale Wires and Related Devices”; and International Patent Application No. PCT / US2010 / 050199, filed September 24, 2010, entitled “Bent Nanowires and Related Probing of Species,” published as WO 2011 / 038228 on March 31, 2011. In addition, the following are each incorporated herein by reference in their entireties: U.S. Pat. Nos.9,786,850 and 9,457,128; U.S. Pat. Apl. Pub. Nos.2017 / 0069858, 2014 / 0073063, 2017 / 0072109, and 2014 / 0074253; and Int. Pat. Apl. Pub. No. WO 2019 / 084498. Also incorporated herein by reference in their entireties are U.S. Pat. Apl. Pub. No. 2022-0213425 and Int. Pat. Apl. Pub. No. WO 2020 / 263772. In some cases, a material of a device can be chosen to be one that is readily introduced into the device, e.g., using techniques compatible with lithographic techniques. For example, in one set of embodiments, lithographic techniques such as e-beam lithography, photolithography, X-ray lithography, extreme ultraviolet lithography, ion projection lithography, etc. may be used to layer or deposit one or more metals on a substrate. Additional processing steps can also be used to define or register the pathways in some cases. In some embodiments, more than one metal can be used within a pathway. For example, two, three, or more metals may be used within a pathway. The metals may be deposited in different regions or alloyed together, or in some cases, the metals may be layered on top of each other, e.g., layered on top of each other using various lithographic techniques. If dissimilar metals are layered on top of each other, they may be layered in some embodiments in a “stressed” configuration (although in other embodiments, they may not necessarily be stressed). For example, a chromium / palladium / chromium deposition process, in some embodiments, may form a pre-stressed arrangement that is able to spontaneously form a 3-dimensional structure after release from the substrate. See, e.g., U.S. Pat. Nos. 9,457,128 or 9,786,850, each incorporated herein by reference in its entirety. In some embodiments, the conductive pathway may be relatively narrow. For example, the conductive pathway may have a smallest dimension or a largest cross-sectional dimension of less than about 5 micrometers, less than about 4 micrometers, less than about 3 micrometers, less than about 2 micrometers, less than about 1 micrometer, less than about 700 nm, less than about 600 nm, less than about 500 nm, less than about 300 nm, less than about 200 nm, less than about 100 nm, less than about 80 nm, less than about 50 nm, less than about 30 nm, less than about 10 nm, less than about 5 nm, less than about 2 nm, etc. The conductive pathway may have any suitable cross-sectional shape, e.g., circular, square, rectangular, polygonal, elliptical, regular, irregular, etc. As is discussed in detail below, such conductive pathways may be achieved using lithographic or other techniques. In some cases, the conductive pathways may define an electrical circuit that is internally contained within the device, and / or that extends externally of the device, e.g., such that the electrical circuit is in electrical communication with an external electrical system, such as a computer or a transmitter (for instance, a radio transmitter, a wireless transmitter, an Internet connection, etc.). The device, in some embodiments, may contain components such as nanoelectric components. Non-limiting examples of such components include nanoscale wires, sensors such as nanosensors, transistors such as field effect transistors, resistors, capacitors, inductors, diodes, integrated circuits, batteries, power sources, RFID tags, antennae, transmitter, or the like, which may be present in one or more electrical circuit within the device. In certain embodiments, a conductive pathways may define an electrical circuit that is interfaceable or connectable with an external electrical device, such as a computer, using a suitable connector. For example, the cell scaffold may be directly connected to an external device (for instance, using an interface such as described in U.S. Pat. Apl. Pub. No. 2018 / 0328884, incorporated herein by reference in its entirety). In some cases, a suitable connector, such as a cable, may be used to make such a connection between an electrical circuit within a cell scaffold and the external device. Non-limiting examples of cables include those commercially available, such as ribbon cables, flexible flat cable, 8-pin cables, 16-pin cables, etc., or other electrical cables. However, an external connection is not always required, and in some cases, the scaffold may be a self-contained electrical circuit. For example, the circuit may be able to transmit information wirelessly to an external device, store information for later access (e.g., after sacrificing the organoids, organs, or organisms), or the like. In addition, in certain embodiments, the device may be able to communicate with an external device using wireless communications, e.g., in addition to and / or instead of an electrical connection. For example, the device may contain a transmitter (for instance, a radio transmitter, a wireless transmitter, an Internet connection, etc.) and / or a receiver, e.g., which may be in communication with a transmitter and / or a receiver on an external device. In some embodiments, more than one electrical circuit and / or more than one conductive pathway may be used within a device. For example, multiple conductive pathways or circuits can be used such that some or all of the nodes may be individually electronically addressable within the cell scaffold. However, in other embodiments, more than one node may be addressable by a particular conductive pathway. In some cases, the device may be connected to a computer system, e.g., a general or a special purpose computer system. The system may be used in some embodiments to apply electrical stimulation to one or more portions of the device, e.g., to be applied to one or more cells within the scaffold. For instance, one or more portions of the device may be individually addressable, e.g., via the computer system. In addition, in some embodiments, the system may be used to determine a property of the device, e.g., when it is embedded within a biological structure or system, such as described elsewhere herein. For instance, a property, such as a chemical property, an electrical property, a mechanical property, or the like may be determined and recorded by the computer system. In some cases, one or more electrodes may be used. In certain embodiments, the computer system may be used to apply electrical stimulation to the scaffold, determine one or more properties of the cell scaffold, and predict electrical stimulation to be applied based on properties of the cell scaffold. A non-limiting illustrative implementation of a special purpose computer system 300 that may be specially programmed to be used in connection with any of the embodiments of the disclosure provided herein is shown in Fig.10. The computer system 300 may include one or more processors 310 and one or more articles of manufacture that comprise non- transitory computer-readable storage media (e.g., memory 320 and one or more non-volatile storage media 330). The processor 310 may control writing data to and reading data from the memory 320 and the non-volatile storage device 330 in any suitable manner. To perform any of the functionality described herein (e.g., build predictive models, provide inputs to the predictive models, apply electrical stimuli to one or more portions of the scaffold, etc.), the processor 310 may execute one or more processor-executable instructions stored in one or more non-transitory computer-readable storage media (e.g., the memory 320), which may serve as non-transitory computer-readable storage media storing processor-executable instructions for execution by the processor 310. The terms “program” or “software” or “app” are used herein in a generic sense to refer to any type of computer code or set of processor-executable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the disclosure provided herein need not reside on a single computer or processor, but may be distributed in a modular fashion among different computers or processors to implement various aspects of the disclosure provided herein. Processor-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments. Also, data structures may be stored in one or more non-transitory computer-readable storage media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a non-transitory computer-readable medium that convey relationships between the fields. However, any suitable mechanism may be used to establish relationships among information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationships among data elements. Also, various concepts may be embodied as one or more processes, of which examples have been provided herein. The acts performed as part of each process may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments. As noted above and elsewhere herein, a computer-readable medium may be associated with a biological system to receive and / or transmit electrical signals. In some such embodiments, the computer-readable medium is configured to process a first signal based on a first current having a first peak shape to determine a second current having a second peak shape, different from the first peak shape, to apply to the biological system. In some embodiments, the computer-readable medium is configured to use machine learning to determine a set of subsequent outputs. For example, a biological system may provide a series of inputs (e.g., electrical impedance measurements), and the computer- readable medium is configured to determine a set of subsequent outputs. In some embodiments, the computer-readable medium is configured to train using a predictive model and at least one of a voltage supplied during electrical stimulation, a duration of electrical stimulation, a volume or density of the portions of the tissue undergoing electrical stimulation, type of cells comprising tissue undergoing electrical stimulation, an intensity or pattern of electrical stimulation, a frequency of electrical stimulation, periodicity of electrical stimulation, and, optionally, further trained on a resulting tissue response. U.S. Patent Application Serial No.63 / 681,627, filed August 9, 2025, entitled “AI- Driven Flexible Bioelectronics for Various Applications,” by Liu, et al., is incorporated herein by reference in its entirety. The following examples are intended to illustrate certain embodiments of the present disclosure, but do not exemplify the full scope of the disclosure. EXAMPLE 1 The following example describes the integration of a stretchable mesh comprising nanoelectronics (e.g., nanowires) with hiPSC-CMs (human induced pluripotent stem cell- derived cardiomyocytes). This example reveals the potential applications and benefits in biomedical applications of electrically stimulating an organoid of a biological system using an AI-driven process. By harnessing the power of AI-driven stimulation, the example illustrates enhanced electrical maturation of these cells. In particular, this example presents an AI-driven biological system designed for advanced applications in drug screening and stem cell therapy. The system uses flexible, soft, or stretchable nanoelectrode array equipped with multiple types of sensors and stimulators, ensuring long-term stable recording and closed-loop stimulation within biological systems. The bioelectronics of this system can be tailored with properties such as flexibility, softness, or stretchability, and are constructed using biocompatible materials like polyimide, parylene, hydrogel, etc. The system's conductive interconnects, made from materials like gold and platinum, ensure efficient recording and stimulation. This system also has the capability for 3D integration with various biological entities, from cardiomyocytes to cyborg organoids, allowing tissue-wide, single-cell activity recording without disrupting natural tissue processes. The AI-system employs a predictive model for accurate cellular activity prediction and a reinforcement learning-driven control system for precise tissue-level function guidance. The integration of Gaussian Processes and Bayesian Optimization within a reinforcement learning framework allows for optimal action identification based on the state of cardiac cell development. Fig.1 illustrates IPSC-CMs Maturation in AI-driven Cyborg Organoids through stretchable mesh nanoelectronics integration, enabling long-term stable recording and AI- driven Stimulation. This is a schematic of hiPSC-CMs integrating with stretchable mesh nanoelectronics for long-term stable electrical measurement under (1) no stimulation, (2) fixed stimulation and (3) AI-driven stimulation. Fig.2 illustrates the design, fabrication and characterization of stretchable mesh nanoelectronics in another embodiment. Fig.2A shows a mask design of a whole stretchable mesh nanoelectronics with 64 individual recording sensors and 4 stimulators. Fig.2B shows a mask design of the zoomed-in stretchable mesh nanoelectronics with 4 individual recording sensors, 2 stimulators, barcodes, and interconnects. Fig.2C shows a representative photograph of stretchable mesh nanoelectronics with cell culture chamber and flexible flat cables connected recording sensors and stimulators. Fig.2D shows a representative bright- field microscope image of the unreleased stretchable mesh nanoelectronics with 4 individual recording sensors, 2 stimulators, barcodes, and interconnects corresponding to the mask design in Fig.2B. Fig.2E shows a representative bright-field microscope image of the released stretchable mesh nanoelectronics. Fig.2F shows a representative bright-field microscope image of the released stretchable mesh nanoelectronics with 4 individual recording sensors, 2 stimulators, barcodes, and interconnects corresponding to the mask design in Fig.2B. Fig.2G shows an average electrochemical impedance of electrodes at 1 kHz (n = 64 electrodes for each sample device, values are mean + / - s.e.m.). Fig.2H shows an average impedance of electrodes at 1 kHz as a function of incubation time in PBS at 37oC (n = 10 samples, values are mean + / - s.d.). Fig.3 shows IPSC-CMs differentiation and integration with stretchable mesh nanoelectronics to form the AI-driven cyborg CMs organoids. Fig.3A shows schematic and representative bright-field microscope images of CM differentiation from hiPSCs. Fig.3B shows representative bright-field microscope images of (i) overview and (ii) zoomed-in view of the integrated stretchable mesh nanoelectronics with IPSC-CMs. Fig.3C shows representative 3D reconstructed fluorescence images showing the sarcomere structures in hiPSC-CMs (i) with AI-driven stimulation and (ii) without stimulation as control. Shadings indicate TNNT2, 4′,6-diamidino-2-phenylindole, and the stretchable mesh nanoelectronics. Fig.4 shows that long-term stable recording reveals hiPSC-ECs development and maturation trajectories. Fig.4A shows representative (i) raw voltage traces and (ii) zoom-in traces showing extracellular electrical activities across the 64 recording sensors. Fig.4B shows a UMAP plot showing all the pseudotime-ordered electrical waveform profiles of all channels in all three hiPSC-CMs groups (AI-driven stimulation, fixed stimulation, and control without stimulation) of samples recorded during days 21 to days 41 of differentiation. Each dot represents a channel averaged electrical waveform and shadings represent the inferred pseudotime. Fig.4C shows a density plot showing the distribution of the pseudotime value of hiPSC-CM during day 21-24, day 25-27, day 28-30, day 31-34, day 35-38 and day 39-41 of differentiation. Fig.4D shows a UMAP plot showing all the electrical waveform profiles of hiPSC-CMs during (i) day 21-24, (ii) day 25-27, (iii) day 28-30, (iv) day 31-34 and (v) day 35-38 of differentiation. Fig.4E is a projection of the extracted electrical features of (i) peak, (ii) max dv / dt, (iii) trough and (v) amplitude into the UMAP. Fig.5 shows pseudotime and electrical waveform features analysis of CMs’ signals reveals that AI-driven stimulation accelerated the electrical maturation of hiPSC-CMs compared to these two groups with fixed stimulation or without stimulation. Fig.5A shows a projection of the extracted electrical features from (i) AI-driven stimulation, (ii) no stimulation, (iii) fixed stimulation and (iv) all these three groups into the UMAP. Fig.5B is a density plot showing the distribution of the pseudotime value of hiPSC-CM from three representative groups of (i) no stimulation, (ii) fixed stimulation and (iii) AI-driven stimulation during day 24 to day 41 of differentiation. Fig.5C shows a comparison of (i) pseudotime and spike waveform features of (ii) amplitude, (iii) max dv / dt and (iv) full width at half maximum of hiPSC-CMs from three groups (AI-driven stimulation, fixed stimulation, and control without stimulation) during day 24 to day 41 of differentiation. Each dot represents a channel that has electrical signals from 17 samples. Fig.6 shows an AI model based on Gaussian processes and Bayesian optimization approaches. This shows that IPSC-CMs with AI-driven stimulation tend to reach higher mature stage faster than control groups without stimulation or with fixed stimulation. Fig.6A shows an increase in the distribution of pseudotime across different frequency and amplitude of stimulation. Fig.6B shows that the uncertainty surrounding the next optimal pseudotime decreases as the training sample size increases, first from 6 to 12, and subsequently to 17. Fig.6C shows stimulation amplitude and frequency for given pseudotime. Fig.6D shows that the standard deviation of various pseudotime values decreases as the training sample size increases, progressing from 6 to 12 and eventually 17. EXAMPLE 2 The design of bioelectronics that seamlessly integrate with biological systems for recording and controlling tissue properties is crucial for advancing biological studies and clinical applications. As the capabilities of bioelectronics increase, integrating numerous sensors and stimulators has become more common. While passive recording of biological signals is well-established, interpreting these signals to generate control policies for stimulators presents significant challenges. The numerous tunable parameters for stimulators require continuous updates based on recording results, making conventional control methods impractical. Recent developments in artificial intelligence (AI) provide promising solutions. Ideally, an AI system should be given a final goal, such as achieving specific physiological states in cells and cellular networks through stimulation. The AI would then learn the optimal parameters based on continuous measurements of cell properties, self-exploring to achieve the set goal. It is hypothesized in this example that integrating bioelectronics, AI and biological systems into an AI system can achieve this objective. In this system, bioelectronics with sensor-actuator capabilities are controlled by AI, enabling real-time, bidirectional, and long- term stable interrogation and intervention of cellular activities across the three-dimensional (3D) tissue networks at cellular resolution. Major challenges in such a system include: (i) a bioelectronic sensing and actuating system that can seamlessly integrate in 3D with biological systems, capable of long-term stable recording and control of tissue-wide, single-cell activities without interrupting natural tissue development, differentiation, and function; (ii) an inference model that can continuously and accurately infer underlying cellular activities from continuous sensing data, and (iii) a reinforcement learning (RL)-driven control system that can make decisions based on sensing data and subsequently provide optimal feedback stimuli to cells through tissue- embedded actuators, precisely guiding, promoting, and ameliorating tissue-level functions and dysfunctions. This example shows an AI-driven flexible bioelectronics system to apply the most effective modulation conditions throughout the biological system during specific events. As a demonstration, this system was integrated with hPSC-CM organoids to mediate their functional maturation. hPSC-derived organoids hold significant potential for applications such as drug screening and cell therapies, but their clinical applications are often hindered by the immature state of the cells. Immature cells have a higher risk of uncontrolled proliferation, leading to potential tumor formation post-transplantation or issues with drug efficiency. Despite many protocols aimed at enhancing cardiac functional maturation, their effectiveness is limited due to the variability in cell status changes, complicating the consistent application of optimal stimulation parameters. The AI system disclosed herein addresses these issues by continuously providing adaptive and optimized stimulation parameters based on stable cell activity recording. With real-time, closed-loop feedback from tissue-embedded electronics, optimal maturation conditions throughout cardiac development are ensured, enhancing the maturation process of hPSC-derived organoids. The enhanced functional maturation of hPSC-CMs was verified through cellular and tissue level analysis. The approach disclosed herein (Fig.11A) involves stepwise integration of AI-driven flexible electronics with sensors and stimulators into hPSC-organoids, forming AI-driven organoids: (i) Flexible and stretchable mesh electronics with sensing and stimulation electrodes are embedded into the 3D intact organoids. (ii) Electrical activities of hPSC-CMs in the cardiac organoid are recorded, and signal features are extracted. Pseudotime values are calculated to infer cell developmental states and assess cell states along the developmental trajectory. (iii) Pseudotime values are input into the AI agent to interpret the recording data and generate control policies aimed at increasing the maturation states of hPSC-CMs in the organoid. (iv) Optimal stimulation conditions are delivered back to the organoids through embedded stimulation electrodes. Figs.11A-11E show an overview of the cardiac organoids, in certain embodiments. Fig.11A shows schematics showing the workflow of AI-driven cardiac organoids, according to one embodiment. In particular, stretchable mesh electronics with sensing and actuating electrodes are integrated with human induced pluripotent stem cell (hiPSC)-derived cardiac organoids. Cardiac electrical signals are continuously recorded, which are analyzed to determine the developmental state of cardiomyocytes. The developmental states are input into an AI agent that uses Bayesian optimization to learn how to accelerate the developmental state transition along the developmental trajectory and then generate a control policy to stimulate the cardiac organoids. Subsequently, the AI-generated optimal stimulation is delivered to the cardiac organoids through the tissue-embedded actuators. This closed-loop process can be iterated to yield optimized control policies that accelerate the functional maturation of cardiac organoids. The maturation of cardiac organoids can be characterized by their functional and molecular phenotypes. Fig.11B is a schematic showing the structure of stretchable mesh nanoelectronics, including polymeric passivation layers, Cr / Au interconnects, Pt electrodes, and electronic fluorescence barcodes. Fig.11C illustrates representative bright-field (BF) microscopic images of stretchable mesh nanoelectronics showing freestanding stretchable mesh nanoelectronics with recording and stimulation electrodes in a culture chamber (i), zoomed-in view of the recording and stimulation electrodes (ii) highlighted the black dashed box-highlighted region (i), and representative recording and stimulation electrodes on the substrate. Fig.11D is BF images of mesh electronics embedded in hiPSC-derived cardiac organoids. Fig.11E shows a fluorescence image of cleared, immunostained 3D cardiac organoids with embedded nanoelectronics showing the sarcomere structures of hiPSC-CMs, including Troponin T2 (TNNT2); Wheat Germ Agglutinin (WGA); 4′,6-diamidino-2-phenylindole (DAPI); device (SU8 of nanoelectronics were labeled by R6G). The stretchable mesh electronics are designed with similar structures as previously reported, including an SU-8 bottom passivation layer, 2-micrometer-wide Au interconnects, 25- micrometer-diameter Pt sensing electrodes, and SU-8 top passivation, along with electronic fluorescence barcodes for sensor location registration (Fig.11B). Previous results demonstrated that these designs allow for 3D integration with stem cell-derived organoids for long-term stable recording throughout development with minimal interruptions. To enable closed-loop control of cell activities, individually addressable 100- micrometer-diameter Pt stimulation electrodes were incorporated into the mesh electronics (Figs.11B-11C and Figs.16A-16C). A representative mesh electronics contains 64 recording electrodes and four stimulation electrodes (Fig.11C and Figs.16A-16C, see Methods). Pt Black is electroplated to reduce electrode impedance and increase recording signal-to-noise ratio (SNR) (Fig.16D; See, i-ii). The electrode performance is stable across different samples (Fig.16D; See, iii) and for over 50 days in physiological solution, ensuring long-term electrical recording and stimulation (Fig.16D; See, iv). Figs.16A-16D illustrates fabrication of flexible stretchable mesh nanoelectronics with sensing and stimulation electrodes for human cardiac organoids. Fig.16A is a schematic illustration of the fabrication process for stretchable mesh nanoelectronics. (i), Deposition of the Ni sacrificial layer on a SiO2 / Si wafer; (ii), Patterning of the bottom SU8 passivation layer; (iii), Deposition and patterning of Au interconnects; (iv), Deposition and patterning of Pt electrodes; (v), Patterning of the top SU8 passivation layer; (vi), Integration of electronic barcodes for electrode identification. Fig.16B shows bright field (BF)_optical microscope images showing the fabricated flexible stretchable mesh nanoelectronics. (i), Overview of the 64-channel flexible stretchable mesh nanoelectronics; (ii-v), Zoomed-in view showing the stimulators, recording electrodes, barcodes and interconnects at a higher magnification. Fig. 16C shows (i), Images of the packaged flexible stretchable mesh nanoelectronics with I / O connections and culture chamber; (ii), Overview of the 64-channel flexible stretchable mesh nanoelectronics after releasing into saline water; (iii), Detailed view showing interconnects and recording electrodes alongside the stimulator after releasing into saline water. Fig.16D illustrates electrical characterization of mesh nanoelectronics. (i), Electrochemical impedance at 1 kHz before and after Pt black deposition; (ii), Electrochemical impedance spectroscopy and phase response across frequencies for mesh nanoelectronics with and without PI black; (iii), Statistics of impedance measurements at 1 kHz across different samples; (iv), Stability of impedance at 1 kHz over 50 days of incubation times. Data are presented as mean + / - error. Stretchable mesh electronics were integrated with hiPSC-CMs at 14 days of differentiation on a Matrigel layer to form 3D organoids (Fig.11D and Figs.17A-17C). Immunostaining and fluorescence imaging images (Fig.11E and Extended Data Fig.17D) confirm the tissue-wide integration of bioelectronics with cellular networks. Recording and stimulation of this system were characterized using a customized setup with a stimulation controller and Intan RHD recording system. The stimulation controller can maintain continuous stimulation for 24 hours per day over the entire period of hPSC-CM development (21 days), under tailored and programmable stimulation policies. To benchmark simultaneous stimulation and recording performance, stimulation was applied through paired stimulators embedded in the cardiac organoids with variable amplitudes from 10mV to 1.5V at a frequency of 1.25 Hz. Simultaneous recording was conducted across the 3D organoids under these conditions (Fig.12A, Fig.18E). Representative voltage traces show both stimulation artifacts and cardiac electrophysiological signals, demonstrating the ability of the system to simultaneously record and stimulate hPSC-CMs (Fig.12A and Figs.18A-18C). For stimulation amplitudes below 1.3V, the organoids maintained a ∼0.7 Hz beating frequency. When the amplitude increased to 1.5V, the cardiac organoid signals were paced and locked at 1.25 Hz, matching the stimulation frequency. Stimulation across different trials demonstrate that organoid beating frequency can be stable synchronized and locked at 1.25 Hz when the stimulation amplitude reach 1.5V (Figs.18D-18E). This highlights the ability of the system to effectively modulate cardiac activity. Figs.17A-17D illustrate integration of flexible stretchable mesh nanoelectronics with IPSC-CMs for human cardiac organoids. Fig.17A shows timeline and phase contrast images of hiPSC-CM differentiation and development. The differentiation protocol includes treatment with B27-insulin and CHIR at specified days. Day 0: Culture hiPSC-CM in1640 medium with B27-insulin (1:50), and CHIR (12 micromolar); Day 1: Culture hiPSC-CM in 1640 medium with B27-insulin (1:50); Day 3: Culture hiPSC-CM in 1640 medium with B27- insulin (1:50), and IWR1 (12 ); Day 5: Culture hiPSC-CM in 1640 medium and B27-insulin (1:50); Day 7 / 9 / 11: Culture hiPSC-CM in 1640 medium and B27-insulin (1:50). Fig.17B show schematics illustrating the integration process of hiPSC-CMs with the stretchable mesh nanoelectronics. Left: Integration of hiPSC-CMs with flexible stretchable mesh nanoelectronics on day 13 of differentiation. Middle: Stimulation starts on hiPSC-CMs at day 20 of differentiation; Right: Stimulations of hiPSC-CMs finishes on day 41 of differentiation. Fig.17C shows BF microscopic images showing the integration of hiPSC-CMs with the mesh nanoelectronics: Left: Overview of the integrated system; Middle: Zoomed-in view highlighting the position of recording and stimulation electrodes in relation to hiPSC-CMs; Right: High magnification view showing the detailed integration of recording electrodes and stimulators with hiPSC-CMs. Fig.17D shows fluorescent images showing the structure and integration of the mesh nanoelectronics with hiPSC-CMs: Left: DAPI staining indicating cell nuclei. Middle Left: TNNT2 staining indicating cardiac troponin T in hiPSC-CMs. Middle Right: Mesh nanoelectronics showing the distribution of the electronic mesh. Right: Overlay of DAPI, TNNT2, and mesh nanoelectronics indicating the successful integration and spatial relationship between the cells and the nanoelectronics. By registering the distribution of sensors across the tissue through fluorescence barcode signals, organoid electrical activities and their propagation were mapped (Fig.12B). Electrical activities of hPSC-CM organoids were recorded twice per week during the development (Fig.12C). In a representative sample, electrical signals were recorded at days 20, 24, 27, 30, 34, 37 and 41 of hPSC-CM differentiation, equivalent to days 7, 11, 14, 17, 20 and 24 following reseeding onto stretchable mesh electronics (Fig.12C). The recorded spike signals show clear evolution during in vitro cardiac maturation, progressing from slow waveforms to repolarization and ultimately to rapid depolarization. Figs.12A-12M illustrate long-term electrical recording and stimulation of cardiac activity. Fig.12A shows representative raw voltage traces showing the capability of tissue- embedded sensing and stimulation electronics for the long-term tracking and stimulating electrical activity of cardiac organoids. Fig.12B shows representative raw voltage traces showing electrical activities of a cardiac organoid recorded by a tissue-embedded 8x8 electrode array. Fig.12C shows representative extracellular spike waveforms recorded from a cardiac organoid at days 20, 24, 27, 31, 34, 37, and 41 of cardiac differentiation (days 7, 11, 14, 17, 20 and 24 following integration with stretchable mesh nanoelectronics). Fig.12D is a UMAP plot showing all the pseudotime-ordered electrical waveform profiles of hiPSC-CMs from all channels in the representative sample recorded at days 20, 24, 27, 31, 34, 37, and 41 of differentiation. Each dot represents a channel averaged electrical waveform and shadings represent the inferred pseudotime from Slingshot. Fig.12E is a density plot showing the distribution of the pseudotime value of hiPSC-CM at days 20, 24, 27, 31, 34, 37, and 41 of differentiation. Inset: UMAP plot showing the projection on pseudotemporal trajectory of each channel of averaged electrical waveform at days 20, 24, 27, 31, 34, 37, and 41 of differentiation, shadings represent different development time from day 20 to day 41, highlighting the variation in cardiac activity across the development. Fig.12F shows bar plots showing the pseudotime value of the hiPSC-cardiac organoids at days 20, 24, 27, 31, 34, 37, and 41 of differentiation. Fig.12G shows schematics showing the definition of electrical features from spike waveforms for analysis. Figs.12H-12J show bar plots showing the amplitude of the spike waveform (Fig.12H), max dv / dt of the spike waveform (Fig.12I), and half width of the spike waveform (Fig.12J) of the hiPSC-cardiac organoids at days 20, 24, 27, 31, 34, 37, and 41 of differentiation. Figs.12K-12M are scatter plots showing the relationship between pseudotime and extracted electrical features: amplitude of the spike waveform (Fig.12K), maximum dv / dt of the spike waveform (Fig.12L), and half width of the spike waveform (Fig.12M). The data points are coded based on pseudotime, indicating a trend of increasing amplitude and maximum dV / dt with progression in pseudotime, and decreasing half-width with increasing pseudotime. Data are mean + / -SEM. Then, development of an effective cyber-control policy that uses continuous electrical recording data to control organoids through embedded stimulation electrodes was explored, aiming for faster functional maturation. Designing the cyber-control component involves two steps: (i) specifying the states, actions and rewards in the control problem, and (ii) designing an appropriate learning algorithm for the problem. In a control / RL framework, the agent (the organoid) may be in different states over time, and need to be actuated by selecting different actions at the current state. An action taken in a state results in a reward and a transition to a new state at the next timestep. Optimal control requires selecting a policy—a mapping from states to actions—that maximizes rewards over a trajectory The reward function should be designed such that the corresponding optimal policy effectively achieves the real-world objective, which in the present case is the accelerated maturation of the organoid. The first challenge in developing the cyber-control policy is determining the states, actions and rewards to model the organoid system. The actions are relatively straightforward: electrical impulse provided through the embedded stimulation electrodes, represented as a 2D vector of frequency and amplitude parameters. However, determining the state from electrical recording data is challenging due to the noisy and high-dimensional nature of time-series electrical data, which may not directly correlate with cell states. To address this, pseudotime trajectory inference, an ML-based dimensional reduction approach previously used for single-cell gene expression analysis, was applied to analyze the high-dimensional electrical waveforms recorded from 3D hPSC-CM organoids. This method reconstructs and illustrates the continuous phenotypic evolution path of hPSC-CMs during functional maturation based on the spike waveform. Previous multimodal paired characterization including both electrical and transcriptional measurements confirmed that the pseudotime trajectory from the spike waveform corresponds to that from gene expression analysis, allowing for the inference of cell developmental states. Using this ML-based pseudotemporal trajectory inference method, cardiac electrical activities were mapped to a pseudotime value between 0 and 1, capturing the maturation level of the organoid and providing a compact one-dimensional state representation. Specifically, the Palantir algorithm was applied on continuously recorded spike waveforms and used Uniform Manifold Approximation and Projection (UMAP) to project the waveforms into a 2D space, shaded by inferred pseudotime values. This approach allowed for the projection of long-term electrical recording data from hiPSC cardiac organoids, constructing an inferred pseudotemporal trajectory that maps electrical phenotypic state transitions during cardiac development (Figs.12D-F, Figs.18A-18E). Spike waveforms from each channel were extracted, showing a clear temporal evolution of electrical activity (Figs.12G-12J, Figs.18A-18E). Statistical analysis demonstrated a significant increase in voltage amplitude and maximum dV / dt, a significantly decreasing of the spike duration during the hPSC-CM development (Figs.12H-12J). Scatter plots illustrate the relationship between pseudotime and key electrical features, revealing trends of increasing spike amplitude (Fig.12K) and maximum dv / dt (Fig.12L), and decreasing half-width (Fig.12M) as pseudotime increases. These results confirm that pseudotime value can explain functional state development throughout cardiac maturation. Figs.18A-18E illustrate stimulation and recording of human cardiac organoids. Fig. 18A show representative recording and stimulation raw traces. Each trace represents the electrical activity recorded from an electrode channel. Fig.18B show a zoomed-in view of three consecutive stimulation events and paced cardiac activity following the stimulation signals. Fig.18C shows a zoomed-in view showing detailed stimulation-induced cardiac spike characteristics for a single stimulation event across different channels. Fig.18D show raster plots showing the cardiac activities under stimulation amplitude (10, 100, 500, 1000, and 1500 mV) over time (10 seconds). Each plot corresponds to a different stimulation amplitude, illustrating the pacing pattern of electrical responses at 1500 mV of stimulation. Fig.18E shows a box plot of firing rates under different stimulation amplitude. The firing rate fixed at 1.25 Hz under 1500mV stimulation. Data points represent individual measurements. Data are presented as means ± SEM. Figs.19A-19B illustrate a recording of human cardiac organoids. Fig.19A shows an expanded view of continuous recording raw trace. Fig.19B shows a detailed view of cardiac activities recorded from multiple electrodes, illustrating the time delay of the cardiac activities at different locations. Figs.20A-20C show immunofluorescence images of human cardiac organoids with no stimulation, fixed stimulation policy, and AI-driven stimulation policy, in another embodiment. Immunofluorescence staining images of cardiac organoids, with no stimulation (Fig.20A), fixed stimulation policy (Fig.20B), and AI-driven stimulation policy (Fig.20C): Top row: Immunofluorescence staining for cardiac troponin T (TNNT2) indicating the structure of cardiomyocytes. Middle row: Combined staining for TNNT2 and DAPI highlighting cell nuclei. Bottom row: Triple staining for TNNT2, wheat germ agglutinin (WGA), and DAPI, showing cardiomyocytes, cell membranes, and nuclei, respectively. Figs.21A-21C illustrate comparative analysis of electrical activity and conduction velocity in human cardiac organoids under different stimulation conditions. Fig.21A shows representative (i) raw voltage traces and (ii) raw voltage traces with activation time detection showing electrical activity in human cardiac organoids under different conditions across various days of differentiation at days 20, 24, 27, 31, 34, 37, and 41. Fig.21B show heatmaps illustrating the propagation delay of electrical signals across different channels of (i) raw map and (ii) commutated map of human cardiac organoids at various days of differentiation of days 20, 24, 27, 31, 34, 37, and 41. The heatmaps use a scale between longer delays and shorter delays, showing the efficiency of signal propagation across the cardiac organoid tissue at days 20, 24, 27, 31, 34, 37, and 41. Fig.21C show bar plots comparing the putative conduction velocity (micrometers / ms) across different days of differentiation for control, fixed stimulation, and AI-driven stimulation groups. The plots show significant differences in conduction velocity, with the AI-driven group displaying consistently higher values compared to control and fixed stimulation groups. Statistical significance is indicated by asterisks (**** p<0.0001, n.s. not significant). Data are presented as mean + / - SEM. Figs.22A-22C illustrate representative groups of human cardiac organoid whole- tissue maturation through embedded AI system. Fig.22A shows representative raw voltage traces showing the electrical activity of cardiac organoids under different conditions (control, fixed, and AI-driven stimulation) across various days of differentiation (days 22, 24, 27, 30, 34, 37, and 41). Fig.22B show heatmaps illustrating the propagation delay of electrical signals across different channels for control, fixed, and AI-driven stimulation groups at the same time points. Fig.22C show box plots comparing the propagation rate micrometers / ms) across different days of differentiation for control, fixed stimulation, and AI-driven stimulation groups. The plots show significant differences in conduction velocity, with the AI-driven group displaying consistently higher values compared to control and fixed stimulation groups. Statistical significance is indicated by asterisks (**** p<0.0001, *** p<0.001, ** p<0.01, n.s. not significant). Data are presented as mean + / - SEM. Finally, the reward of taking an action at a given state (pseudotime) was defined as the difference between the next state (pseudotime) and the current state (pseudotime), aiming to achieve faster maturation. Actions that transition the organoid to a more mature state (i.e. higher pseudotime) will receive higher rewards. Having specified the states, actions and rewards in this control problem, next is the design of the RL algorithm. Traditional RL faces challenges in optimizing cardiac tissue maturation, primarily due to the data efficiency requirement. Traditional RL methods need extensive interactions with the environment, often requiring tens of thousands to millions of data samples, typically facilitated by accurate simulators. However, no comprehensive simulators exist for most biological systems, including cardiac organoid development, necessitating real biological experiments that yield limited samples. This raises the question of designing RL algorithms that learn efficiently from limited biological data. To address this, efficient and tailored RL methods were developed that harness known biological principles and leveraged available experimental results. First, utilizing the unidirectional maturation property of the organoid, it was assumed that an action leading to higher maturation at the next measurement time will also lead to higher maturation at subsequent timesteps, assuming following an optimal policy subsequently. This allows for the evaluation of actions based on the maturation level (pseudotime) they achieve at the next transition, reducing the problem to finding an optimal stimulation action at each pseudotime, making the process more sample-efficient than generic RL approaches. A Bayesian Optimization (BO)-inspired approach was adopted to learn good stimulation actions for each pseudotime. The change in pseudotime was modeled as a function of the current pseudotime and stimulation action using a Gaussian Process (GP) model. The GP was updated with new data, and the maxima of the function was identified by selecting new points in regions with high function values. The system disclosed herein allows simultaneous recording and stimulation of three cardiac tissues per batch, so a parallel BO approach was used to choose new stimulation conditions for the three tissues collectively, maximizing information gain about optimal actions to accelerate tissue maturation. Collectively, by applying pseudotemporal trajectory analysis to continuous electrical recording data, the states, actions and rewards were specified. Using GP and parallel BO, an appropriate learning algorithm was established for generating cyber-control policies with limited biological data. The cyber-control algorithm was integrated with the organoid as an AI system. Stimulation of the AI organoid begins one week after organoid-device integration. The stimulation controller maintains continuous stimulation for 24 hours per day over 21-day hPSC-CM development period. The AI organoids were recorded at intervals of three or four days during the three weeks of development. On each recording day, cardiac electrical activities of each sample were measured to calculate the pseudotime value. A parallel BO sampling strategy was used to learn the pseudotime value and develop the stimulation parameters, which were applied though the embedded stimulators. Fig.13B illustrates the changes in the probabilistic GP model for three different fixed starting pseudotime levels as a function of n, the number of datapoints. Each datapoint included an input-output pair, with the input comprising the pseudotime s on a measurement day and the action a taken starting on that day (lasting until the next measurement day), and the output being the change in pseudotime measured on the next measurement day. In each plot, the central surface indicates the expected change in pseudotime (resulting from choosing a stimulation action for a given starting pseudotime) predicted by the GP given the current data. The translucent surfaces above and below reflect the uncertainty of the GP in its predictions. Fig.13B shows that the uncertainty in the GP model is reduced as more data is collected for each starting pseudotime. Moreover, the mean function of the GP gradually converges for each starting pseudotime as more data is gathered. Fig.13C plots the GP model’s estimate of the optimal stimulation action (frequency and amplitude) at different starting pseudotimes. Initially, the estimated optimal stimulation amplitude fluctuates significantly, reflecting the uncertainty of the learning process in the early stages when data is limited. However, as more data is collected, the estimated optimal stimulation amplitude began to converge to a distinct level for each starting pseudotime. A similar trend was observed for the estimated optimal frequency. Notably, the model suggested that higher stimulation amplitudes are optimal for larger starting pseudotimes, while lower stimulation frequencies were optimal for lower starting pseudotimes. Figs.13A-13C illustrate Bayesian optimization for cardiac modulation control policy generation. Fig.13A shows schematics of Bayesian Optimization (BO) process demonstrating the update of the Gaussian Process (GP) model and selection of new batches of stimulation conditions. (i) On the left, the top subpanel shows the true (simulated) function (surface) and prior observations (dots) on the function values of an existing set of two- dimensional stimulation parameters (voltage and frequency) space. The bottom subpanel shows the true function (surface) and prior observations as well as new observations (dots) on the function values of a new set of two-dimensional stimulation parameters. On the right, the top subpanel shows the GP model mean (solid plane) and uncertainty bounds (shaded regions) for the function values of the two stimulation parameters. This subpanel also shows the initial GP functional belief with prior observations, illustrating the predicted GP mean and uncertainty bounds. The bottom subpanel then shows the updated GP functional belief (with updated GP mean and uncertainty bounds) after adding new observations (red dots). (ii) 2D cross-sections of the prior and posterior belief for fixed functional values of stimulation parameters as indicated by the dashed boxes in (i). Top plot shows the cross- section at stimulation parameter 1 = 0.4, while the bottom plot shows the cross-section at stimulation parameter 2 = 0.6. Shaded regions indicate the uncertainty bounds, with the true function (dashed line) and new observations (dots) overlaid. Fig.13B illustrates the effect of the number of datapoints (n) and pseudotime on the prediction (solid plane, mean functional values, and region between the two translucent planes, uncertainty) of function value changes across different amplitudes and frequencies. Each subplot shows the predicted change in function value as a function of amplitude (mV) and frequency (Hz) for different numbers of datapoints (n = 10, 30, 60, 100) and pseudotime values (top: 0.3, middle: 0.5, bottom: 0.7). The scale represents the magnitude of the predicted change in function value. Fig.13C illustrates predicted optimal amplitude and frequency as a function of the number of datapoints for different pseudotime values. Top and bottom plot shows predicted optimal amplitude and frequency versus the number of datapoints for different pseudotime values, respectively. Lines of different shadings correspond to different pseudotime values, showing the trend in optimal parameters as more data is collected. The efficiency of the AI system was evaluated through the following experiments. The organoids were divided into three distinct groups. The first group received the AI-driven stimulation as described above. The second group received fixed stimulation parameters optimized for cardiac organoid maturation from previous reports, which are updated but do not adapt dynamically to changes in cardiac electrical signals. The third group served as the control, integrated with flexible electronics for chronic recording but without any stimulation. Fig.14B shows the clustering of cardiac electrical activities under these three different conditions, presented using UMAP. The distinct clusters for control, fixed stimulation, and AI-driven stimulation suggest that different stimulation approaches have distinct impacts on the electrical properties of the cardiac tissue. The pseudotime trajectory calculated from these clusters provides a continuous metric reflecting the maturation process. The AI-driven stimulation group shows a significant extension of pseudotime trajectory compared to the other two groups. Fig.14C outlines the key electrical features extracted from the waveform including amplitude, maximum dV / dt, and half-width of the action potential. These features are critical for assessing the maturation and functional state of the cardiac organoids. Fig.14C also presents the spatial distribution of these electrical features within the UMAP embedding, demonstrating how different stimulation conditions influence these metrics across the developmental trajectory. Fig.14D shows the density plots of pseudotime values for control, fixed stimulation, and AI-driven stimulation groups at various developmental stages. The AI-driven approach results in a more advanced and uniform progression along the pseudotime trajectory compared to control and fixed stimulation, indicating accelerated and enhanced maturation. Boxplots (Fig.14E) compare the amplitude, maximum dV / dt, and half-width across the three conditions, with statistical analysis highlighting significant differences. The AI-driven stimulation group consistently shows statistically significantly higher amplitude and maximum dV / dt, along with a shorter half-width, suggesting more mature electrical activity. Fig.14F displays representative electrophysiological recordings over time for these three groups. The AI-driven stimulation group demonstrates more consistent and mature electrical activity patterns. Statistical summaries (Fig.14G) of the progression of pseudotime values over various time points illustrates that the AI-driven stimulation significantly accelerates the maturation process compared to control and fixed stimulation. Figs.14A-14G illustrate advancing human cardiac organoid functional maturation through embedded AI system. Fig.14A is a schematic of three stimulation strategies: no stimulation (control), stimulation, and AI-driven stimulation. Fig.14B illustrates joint UMAP plots illustrating the distribution of pseudotime values for all six samples (two samples for each condition) across the entire recording-stimulation period. Shadings represent inferred pseudotime values. Inset: the plots show data points for each condition on the joint UMAP. Fig.14C is a diagram of a representative electrophysiological waveform highlighting key features including peak, trough, maximum dv / dt, amplitude, and half-width for statistical analysis. Fig.14D shows dot plots showing the distribution of electrical waveform characteristics, including amplitude, maximum dv / dt, and half-width, for each stimulation condition and timepoints projected on the joint UMAP in Fig.14B. Shadings represent the value of each characteristic. Fig.14E shows density plots showing the distribution of pseudotime values across different days of differentiation for three stimulation conditions. Shadings represent the differentiation days. Fig.14F shows box plots comparing the electrical properties of cardiac organoids under different stimulation conditions: (Left) Amplitude of the spike waveform. (Middle) Maximum dv / dt of the spike waveform. (Right) Half-width of the spike waveform. Statistical significance is indicated by asterisks (**** p<0.0001, * p<0.05, n.s. not significant). Fig.14G shows representative raw voltage spike waveforms recorded at different differentiation days for control, fixed stimulation, and AI-driven stimulation groups. Fig.14H shows box plots showing the pseudotime values for control, fixed stimulation, and AI-driven stimulation groups across different days of differentiation. Statistical significance is indicated by asterisks (**** p<0.0001, * p<0.05, n.s. not significant). Data are presented as median + / - 1.5x interquartile range (IQR). In addition to waveform comparison, the tissue-embedded devices can chronically map 2D / 3D electrical activity propagation across the tissue network. Fig.15A shows longitudinal recordings of electrical activities in cardiac organoids over time. Using the temporal alignment of spikes across different cells to benchmark the maturation stage, the control group exhibited relatively stable but less mature electrical patterns over time, evidenced by the large temporal delay among channels. The fixed stimulation group showed reduced time delay, and AI-driven stimulation group demonstrated substantial reduced time delays among channels, indicating more mature and synchronized cardiac functions for the intact cardiac tissue. The calculated heatmaps (Fig.15B) of the propagation delay across the cardiac tissue show that the AI-driven stimulation group exhibited a consistent reduction in propagation delay comparing to the other two groups. Statistical analysis of the conduction velocity calculated from the heatmap shows that the AI-driven stimulation group achieved the highest increases over time, further confirming the improved functional maturation of the cardiac tissue at the tissue. The alignment of sarcomeres is essential for effective muscle contraction and a key indicator of cardiac tissue maturation. To further verify the accelerated functional maturation of cardiac tissues, the organization and alignment of sarcomeres was examined by staining and imaging the cardiac muscle troponin T (TNNT). The control group shows less organized sarcomere structures, while the fixed stimulation group demonstrates moderate improvements in sarcomere organization. The AI-driven stimulation group, however, exhibits well-aligned and highly organized sarcomere structures. Fig.15E shows the analysis of sarcomere organization using Haralick correlation at various offset distances. The AI-driven stimulation group maintains higher correlation values across distances, indicating better sarcomere alignment and organization compared to the control and fixed stimulation groups. The statistical summary of the sarcomere organization scores demonstrates that AI-driven group showing significantly higher scores than both the control and fixed stimulation groups, highlighting the effectiveness of AI-driven dynamic stimulation in enhancing structural maturation. Figs.15A-15F illustrate functional maturation characterizations in cardiac organoids. Fig.15A shows representative raw voltage traces showing 32-channel electrical activity mapping of cardiac organoids under different conditions at different days of differentiation (days 20, 24, 27, 31, 34, 37, and 41). Fig.15B shows heatmaps illustrating the calculated propagation delay of electrical signals from the electrical mapping projected to the recording electrode arrangement for three stimulation groups from the same batch of cells at the same time points. Fig.15C is a line graph showing the statistical changes in putative conduction velocity over channels for three stimulation groups across different differentiation days. Data points represent the mean values + / - SEM. Fig.15D shows confocal fluorescence images of immunostained cardiac organoids at 65 days of differentiation from three conditions: Red, TNNT2 and blue, DAPI. Fig.15E shows plots showing the haralick correlation as a function of offset distance for representative cells of the three stimulation groups. Shadings represent different angles, with the scale indicating the degree of correlation. Fig.15F shows violin plots comparing the sarcomere organization score for three stimulation groups. unpaired two tailed test. Statistical significance is indicated by asterisks (*** p<0.001, ** p<0.01). Data are presented as mean + / -SEM. As presented in this example, tissue-like bioelectronics were integrated with AI algorithms to create an AI-driven bioelectronics system. This system allows a long-term bidirectional bioelectronic interface, capable of continuously adapting and optimizing control policies based on stable cell state mapping. It allows AI-driven real-time, closed-loop feedback through tissue-embedded flexible electrode arrays, identifying the most effective stimulation conditions throughout the 3D volume of biological system during development. The policy learned by the AI algorithms disclosed herein is data-driven and adaptively, operating with limited prior knowledge of the underlying biophysical dynamics. As a demonstration, integration of AI-driven stretchable mesh electronics with organoids as an AI system provides a powerful tool for optimizing the development and maturation of hiPSC- derived cardiac organoids. By continuously adapting stimulation parameters based on real- time data, the AI-driven stimulation enhanced the maturation process, validated by enhanced pseudotime, electrical features, reduced propagation delay heatmaps, and enhanced conduction velocity. Immunofluorescence imaging and sarcomere organization analysis further confirmed that AI-driven stimulation promotes superior structural alignment and organization compared to groups with fixed stimulation and no stimulation. These findings demonstrated the potential of the AI system to be used to promote the functional maturation of stem cell-derived organoids for advanced therapeutic applications and more accurate disease models. Natural organ development involves nerve innervation providing closed-loop sensing and stimulation throughout in vivo development. This nerve innervation and continuous bidirectional sensing and modulation is lacking in in vitro organoid systems. The system disclosed herein mimics nerve innervation by continuously monitoring and controling the organoid system. Compared to no or fixed stimulation environments, this approach significantly enhances the maturation and functionality of organoids, making them more physiologically relevant for research and therapeutic applications. By providing a dynamic environment that can adapt to the evolving needs of tissue, the system promotes more accurate cell differentiation, tissue organization, and overall development. This AI system can be applied to various types of organoids and tissue models for understanding complex diseases, developing new treatments, and even applying in vivo biological systems. The adaptive learning capabilities of the AI system ensure that it can evolve and improve over time, continually enhancing its performance and effectiveness. Fabrications of soft, stretchable mesh nanoelectronics and packaging Fabrications of the ultra-flexible, stretchable mesh nanoelectronics include steps that are described as follows: 4-inch glass wafers (Soda lime Glass, Double Side Polished (DSP)) were used as an optical-transparent and electrical-insulative substrate for the mesh nanoelectronics. The glass wafers were cleaned by piranha solution (3:1 mixture of sulfuric acid and 30% hydrogen peroxide), followed by rinsing with deionized (DI) water and by blowing and drying with the N2 gun. HMDS (hexamethyldisilazane, MicroChem) was spin- coated at 4000 rpm and was used as the tackifier for increasing the adhesion of photoresist with surfaces. Then, LOR 3A (300nm, MicroChem) / S1805 (500nm, MicroChem) was spin- coated at 4000 rpm / 4000 rpm, followed by baking at 180 °C for 5 min and at 115 °C for 1 min, respectively. Ni patterns were exposed by using a Karl Suss MA6 mask aligner with 365 nm Ultraviolet (UV) light for 40 mJ / cm2and developed using CD-26 developer (MICROPOSIT) for 70 s to define Ni patterns. O2 plasma (Anatech Barrel Plasma System) was used for descum of the photoresist residues in the pattern at 50 W for 30 s. Sharon Thermal Evaporator was used for the deposition of 100 nm Ni and followed by a standard lift-off procedure in remover PG (MicroChem) for 2 hours to define the Ni pattern as a sacrificial layer. Next, SU-8 precursor (SU-82000.5, MicroChem) was spin-coated at 4000 rpm, and pre-baked at 65 °C / 95 °C for 2 min each, exposed to 365 nm UV for 200 mJ / cm2, post-baked at 65 °C / 95 °C for 2 min each, developed using SU-8 developer (MicroChem) for 60 s, rinsed by isopropyl alcohol (IPA) for 30s, blow for drying by N2gun, and hard-baked at 180 °C for 40 min to define mesh SU-8 patterns (400-nm thickness) for bottom encapsulation. Then, photo-lithographical patterns of interconnects were defined using HMDS / LOR3A / S1805 bilayer photoresists as described above, followed by depositing 5 / 40 / 5-nm-thick chromium / gold / chromium (Cr / Au / Cr) by electron-beam evaporator (Denton), followed by a standard lift-off procedure in remover PG (MicroChem) overnight to define the Au interconnects. Next, photo-lithographical patterns of electrode arrays were defined using HMDS / LOR3A / S1805 bilayer photoresists as described above, followed by depositing 5 / 50-nm-thick chromium / platinum (Cr / Pt) by electron-beam evaporator (Denton), followed by a standard lift-off procedure in remover PG (MicroChem) for 10 min to define the electrode array. Then, photo-lithographical patterns of top SU-8 encapsulating layer were defined using the method for fabricating the bottom SU-8 as described above, followed by patterning the fluorescence barcodes with adding 0.004 wt% of Rhodamin 6G powder (Sigma-Aldrich) into SU-8 precursor. Next, the flexible flat cable (Molex) was soldered onto the input / output pads using a flip-chip bonder (Finetech Fineplacer), followed by gluing a chamber onto the substrate wafer to completely enclose the mesh part of the device using a bio-compatible adhesive (Kwik-Sil, WPI). Then, Pt black (PtB) was electroplated on the Pt electrode array using a precursor of 0.08 wt% Chloroplatinic acid (H2PtCl6) solution (Sigma- Aldrich) in H2O. The precursor was drop-casted onto the device, followed by passage of a 1 mA / cm2DC electric current density for 3 mins using device electrodes as the anode and an external Pt wire as the cathode. The device was then rinsed with DI water for 30 s and dried by gaseous N2. Finally, the surface of the device was treated with light oxygen plasma (Anatech 106 oxygen plasma barrel asher), followed by adding 1 mL of Ni etchant (type TFB, Transene) into the chamber for 2 to 4 hours to completely release the mesh electronics from the glass substrate. The device was then ready for subsequent sterilization steps before cell culture. Electrochemical measurements The electrochemical impedance spectra (EIS) of the electrodes of the ultra-flexible, stretchable mesh nanoelectronics were based methods described previously. The three- electrodes setup was used to measure the EIS of the electrodes from each mesh nanoelectronics device. A standard silver / silver chloride (Ag / AgCl) electrode and platinum wire (300 micrometers in diameter, 1.5 cm in length immersed) were used as reference electrode and counter electrode, respectively. The device was immersed in 1 x PBS solution (Thermofisher) during measurement. The SP-150 potentiostat (Bio-logic) along with its commercial software EC-lab was used to perform the measurements. For each measurement, at least three frequency sweeps were measured from 1 MHz down to 1Hz to obtain statistical results. A sinusoidal voltage of 100 mV peak-to-peak was applied. For each data point, the response to 10 consecutive sinusoids (spaced out by 10% of the period duration) was accumulated and averaged. Differentiation of iPSCs-derived cardiomyocytes HiPSCs (hiPSC-line GSB-L88, Greenstone) were seeded into 10 cm tissue culture plates then allowed to grow for ~4 days until the cells became ~70% confluent. Cardiomyocytes were differentiated from iPSCs as previously described. Media was changed to RPMI 1640 + B27- insulin (RPMI / B27-insulin) containing GSK3 inhibitor CHIR99021 (Selleck Chemicals) (6 micromolar) on day 0 of cardiomyocyte differentiation. On day 1 of differentiation, media was changed to RPMI / B27-insulin. On day 3 of differentiation, media was changed to RPMI / B27-insulin containing IWR1 (5 micromolar). On day 5 of differentiation, media was changed to fresh RPMI / B27-insulin, and on day 7 of differentiation, basal media was changed to RPMI / B27 (10 micrograms / ml), with media subsequently changed every 2-3 days with RPMI / B27. Beating of cardiomyocytes was usually observed between days 7-9 of differentiation. 3D cardiac tissue culture hiPSC-derived cardiomyocytes were integrated with stretchable mesh nanoelectronics to form the 3D cardiac tissue. The 3D cardiac tissue in a customized cell culture chamber was maintained at 37 °C, 5% CO2 in the incubator. The medium was changed daily. Briefly, the released stretchable mesh nanoelectronics was rinsed with deionized water and then decontaminated by 70% ethanol. Then the device was incubated with Poly-D-lysine hydrobromide (0.01% w / v) overnight followed by coated with Matrigel solution (10 mg / mL) for about 1 hour at 37 °C. Then the device is ready for cell integration. The hiPSC-derived cardiomyocytes will be seeded onto the stretchable mesh nanoelectronics which was place on the surface of the Matrigel hydrogel layer to form the 3D cardiac tissue. Detailed steps were as follows: 1). The device was pre-cold on an ice bag in the biosafety hood and then 60 microliters Matrigel solution (10 mg / mL) was added to the cell culture chamber from the device-free side on ice. Make sure that the Matrigel will cover the whole bottom of the cell culture chamber and also float the soft stretchable mesh nanoelectronics onto the surface of the Matrigel solution. Finally, transfer the device into the incubator for at least 30 mins at 37oC to solidify the Matrigel solution into the Matrigel hydrogel layer.2). hiPSC-derived cardiomyocytes at 2D surface were incubated with 0.05% Trypsin-EDTA solution (Biosciences) for 5 mins and then dissociated into single cells. About 3~4 million cells were suspended in 1 mL RPMI 1640 medium plus 1% B27 and then transferred onto the cured Matrigel hydrogel in the cell culture chamber and maintained at 37oC, 5% CO2.5 micromolar rock inhibitor (Y27632) was added to the medium in the first day to improve the cell viability. The cardiomyocytes will form a continuous cell patch with the soft stretchable mesh nanoelectronics embedded (i.e., 3D cardiac tissue) within 24-48 hours. The 3D cardiac tissues are ready for electrophysiological recording. Make sure to decontaminate all the electrodes and cables that will be connected to the cell culture chamber. The electrophysiological signal was collected every three days during the 3D cardiac tissue maturation process. Electrophysiological measurement Recording / stimulation of the human cardiac organoids. Electrical activity was recorded using a RHD 64-channel headstage (Intan technologies) connected to the Intan 1024 ch recording controller (Intan technologies). The headstage-to-device connector was homemade printed circuit board with integrated Omnetics connector (Omnetics) to connect with RHD 64-channel headstage and FFC connector (Digi- Key) to connect with the FFC (Digi-Key). The organoid culture media was grounded to earth and a reference electrode was also inserted in the media, far from the device and cells. Platinum wires were used as both ground and reference electrodes. The hiPSCs-derived cardiac organoid was taken out from incubator and placed on a battery powered warming plate with maintaining thermostatic 37oC during the whole recoding process. The setup was placed into a Faraday cage to block electromagnetic fields causing noise. A sampling rate of 20,000 samples per second was used for the electrophysiological recording. After the cardiomyocytes were planted and integrated with the mesh electronics, electrophysiological activities were recorded twice per week. For stimulation samples were maintained under the electrical stimulation using Master-8 (AMPI) with wire connected with the samples into the incubator. Electrical data analysis A customized Python (version 3.8) pipeline was used to extract electrical waveforms and to conduct the downstream feature analyses. Before detecting the spikes, the raw recording traces are first smoothed with “gaussian_filter” and then spike candidates are identified by “find_peaks” from the Scipy library. A threshold of 5 multiplying the standard deviation from the mean of the smoothed traces was applied in the peak detection process. Then, all the spike candidates from the same recording channels were averaged to obtain the spike templates, which were manually curated to reject noises. Next, the spike templates are used to compute the waveform features. Peak measures the maximum recorded voltage, trough measures the minimum recorded voltage and amplitude measures the voltage difference between peak and trough. Then, the derivatives of the spike templates were computed and the maximum dV / dt denotes the maximum absolute derivative value. The half width was calculated as the time difference between the time points where half of the trough value was first and last reached. The pseudotime scores from the spike templates were inferred with Scanpy using the Palantir algorithm. The extracted spike templates were combined from all samples across all experiment days and used them as the input to the “palantir” function to infer the pseudotime scores. UMAP was then used to project all the spike templates onto the same 2D space to visualize the pseudotime distribution. Sarcomere alignment quantification The Sarcomere Organization Texture Analysis algorithm was applied where Haralick texture structures are used to quantify sarcomere alignment as previously introduced. Immunostaining and imaging The staining was performed as previously reported. Briefly, samples were first placed in an X-CLARITY hydrogel polymerization device for 4 hours at 37 °C with -90 kPa vacuum and then placed in the X-CLARITY electrophoretic tissue clearing (ETC) chamber to extract electrophoretic lipids. For the staining, the primary antibodies, TNNT2 and WAG were incubated at 4 °C for 4 days and the secondary antibodies were incubated at 4 °C for 2 days. The samples were submerged in optical clearing solution and embedded in 1% agarose gel before imaging using Leica TCS SP8 confocal microscopy. Reinforcement Learning Model Overview It is hypothesized that appropriately stimulating the cardiac tissue with electrical impulses can accelerate the maturation process of the tissue. To be more precise, good electrical stimulation policy was identified, where a different stimulation was provided depending on the maturation level of the tissue. To achieve this goal, a Bayesian Optimization (BO) based Reinforcement Learning (RL) approach was adopted. The pseudotime measurement (which is normalized between 0 and 1) of a tissue was used to represent its maturation state. The action is an electrical stimulation impulse, which has two components: its amplitude (which was set to be between 150mV to 1000mV) and its frequency (which was set to be between 1hZ and 6hZ). The aim was to find a stimulation policy that speeds up the stimulation process, i.e., gets the pseudotime to as close to 1 (highest value) as quickly as possible. Utilizing the unidirectional maturation assumption, the quality of an action was evaluated by how fast it increases growth at the next round of measurement. Thus, an optimal policy may be found by learning, for each state, the action that increases its pseudotime the most at the next measurement. The change is modeled in pseudotime by taking an action at a pseudotime as a function plus noise , i.e. if is the pseudotime at the next measurement, then it is assumed that the change in pseudotime takes the form , where denotes a noise term. Since the function was expected to vary smoothly across nearby states and actions, a Gaussian Process (GP) was used to model the function . As more data is collected, the probabilistic GP model of the function will become progressively more accurate, allowing, for each pseudotime , for the identification of the action that leads to the highest pseudotime change. Since data collection is time and cost expensive (as to collect data, experiments are run on actual cardiac tissues, starting from stem cells), a batch Bayesian Optimization (BO) approach was use to guide the sampling of actions to increase data efficiency. In the batch BO framework, at each round, a new batch of actions was choosen (one action for each tissue grown; typically, up to three tissues can be grown simultaneously) such that the actions strike a balance between likelihood to lead to higher pseudotime increase (based on the current GP belief of the function) and diversification of the actions (to prevent redundancy). After each round of measurements, the actual change in pseudotime is then observed, and the GP model of the function is updated given the new (and all previous) data. As the data collection progresses, a more refined GP model of is obtained, allowing for understanding of what actions may be good at different states, yielding, ultimately, a good stimulation policy to accelerate the maturation of cardiac tissues. A more detailed problem setup is provided below. Problem setup and preliminaries Let denote the state-space representing the pseudotime. For the action space, the following constraints are imposed on any valid physical action : any such should satisfy (i) hZ lies between 1hZ and 6hZ, (ii) mV lies between 150mV to 1000mV. It is noted that these constraints have been imposed due to prior knowledge of the physical dynamics. For simplicity, any valid action is mapped to an action in a two-dimensional action space which is the product of two unit intervals, via the map Thus, the action space is represented as . Let denote the pseudotime of a cardiac device measured at a day . Let be the action applied to the cardiac device starting on day . Let be the next day where a measurement is taken; the action is continuously applied to the device until day . Typically but occasionally ; for simplicity of exposition, suppose always holds, i.e. always measured at an interval of 3 days. Let be the pseudotime of the same cardiac device when measured on day . It is assumed that there exists some map such that In other words, the change in pseudotime between day and day (day of next measurement) can be explained (up to some noise term ) by a function that takes into account the pseudotime on day , as well as the action taken between day and day . Then, for any pseudotime , an action is learned such that will be the action that advances the next pseudotime the most (in expectation). To learn across the different , the function is represented by a Gaussian Process (GP), and then optimizing the function (for each ) via Bayesian Optimization (BO). Concretely, it is assumed that is drawn from a GP prior, i.e. denoted as, for convenience, and for another state-action pair and denotes an initial kernel which can be picked. It is noted that a GP is an appropriate choice to model the function since the function is expected to vary smoothly in and . Moreover, by using a GP, the belief about can be adaptively updated each time new data is obtained. It is also noted that in each experiment, up to three cardiac devices can be maintained in parallel. Let be the total number of devices being maintained in an experiment; as mentioned, in the experiments, . Thus, on each measurement day , the pseudotimes can be measured for each of the devices, and also pick three different actions Unlike the standard sequential BO setting where the measurement may be observed before picking (so on and so forth), in this case, the measurements are only available simultaneously at the next measurement day. This places it in the parallel BO setting. While in general, learning under parallel BO is not as effective as learning under sequential BO with the same number of total evaluations, parallel BO with rounds of interaction and devices can be significantly better than standard BO with rounds of interaction (due to the increase in data). Thus, by applying techniques from parallel BO, the sample-efficiency of the algorithm may be improved. Some helpful notation for describing the algorithm is introduced in the next section. For ease of notation, any state-action tuple is referenced as a point, and the actual measured is referenced at the next measurement as an evaluation of the point . Let denote the points available after rounds where points (1 point per device) were evaluated per round, denoting the -th point evaluated at the -th batch; for notational convenience, the dependence on the batch number is omitted and refer to as througout the paper. Let denotes recalling that and it is noted that where denotes the empirical kernel matrix, . In particular, for any , where the posterior variance satisfies Given any sigma-algebra comprising any data of points and corresponding measurements for notational convenience, if referred to herein as and as . For any set of points is also useful to introduce the following notation of posterior variance , where which can equivalently be expressed as Next, the algorithm that used to learn for each is described in detail. Algorithm The crux of any parallel Bayesian Optimization (BO) algorithm is the sampling strategy at each round. In this case, in each round is given, which represent the pseudotimes of the devices used. The sampling strategy in this setting is thus the choice of actions one action for each device. Ideally, collectively, the evaluations of the actions would provide information about , i.e. the optimal actions at the states Intuitively, considering for instance the case when the ’s are the same across the devices, this means collectively, the actions should be actions where the existing GP belief suggests could potentially be high value, whilst minimizing redundancy across the actions (i.e. do not simply pick the same action for each device). Compared to a standard sequential BO setting (with the same number of rounds), the parallel BO setting presents both challenges (coordinating intelligently across different agents to minimize redundancy) and opportunities (ability to learn more about the function by sampling more actions). To design the sampling strategy, the Entropy Search (ES) framework was adapted to this setting. ES is a popular BO framework based on information-theoretic principles, and has formed the basis for many empirically successful BO algorithms. At a high level, in this setting, an ES-based sampling strategy would pick where denotes the measured outcome (which is a random variable), and for any random variables , and sigma-algebra , denotes the mutual information between and given . Intuitively, such a sampling strategy chooses a collection of actions such that, given the existing information , the potential information gain about the optimal actions at the states in the current round (which are random variables given ) is maximized. As the sampling function for standard ES can be computationally expensive to optimize, recent works have proposed the max-value ES, which instead seeks to maximize the mutual information with the maximal value (rather than maximizer, i.e. max rather than argmax) of the function. Adapted to this setting, the objective would be denotes the maximal value of holding fixed. However, in the parallel setting, even max-value ES can be computationally expensive. Thus, samples were evaluated according to the following objective: a confidence parameter. may be thought of as the action that maximizes an upper-confidence-bound (UCB), holding fixed, of the function given current knowledge . To parse the objective above, the term (which appears in max-value ES) is replaced by an estimate which is exactly the pointwise Gaussian . In other words, , i.e. is evaluated at an UCB-action (holding fixed) as the proxy Returning to the objective in (3), it is noted that it has where the first equality follows from the property of mutual information (for any , where denotes differential entropy, the second equality uses the definition of in (4), and the third equality uses the definition of , as well as the fact that the posterior standard deviation is independent of the measurement outcomes Since is independent of the choice of , the objective in (3) is equivalent to This has the nice intuitive interpretation that the actions are being picked such that they reduce uncertainty about the potential high-value points, i.e. (5) may be simplified further. Let and denote Recall that for any pair, by (2), it has Hence, the objective in (5) can be further simplified to where Putting everything together, the algorithm can be described as follows. 1. Let denote the number of devices. 2. Pick an initial kernel and a noise variance estimate . 3. Choose a confidence parameter sequence . 4. Let denote the initial data (no data). 5. For round a. Let b. Measure the pseudotimes of the devices currently being maintained: c. Compute for each d. (Entropy Search) Choose e. At the end of the round, measure the pseudotimes . f. Set each . g. Add data to and form 6. If the algorithm stops at any round , estimate the optimal action at any state as In the actual implementation, the objective in the Entropy Search section of the algorithm is optimized by gradient descent. While several embodiments of the present disclosure have been described and illustrated herein, those of ordinary skill in the art will readily envision a variety of other means and / or structures for performing the functions and / or obtaining the results and / or one or more of the advantages described herein, and each of such variations and / or modifications is deemed to be within the scope of the present disclosure. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and / or configurations will depend upon the specific application or applications for which the teachings of the present disclosure is / are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the disclosure described herein. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, the disclosure may be practiced otherwise than as specifically described and claimed. The present disclosure is directed to each individual feature, system, article, material, kit, and / or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and / or methods, if such features, systems, articles, materials, kits, and / or methods are not mutually inconsistent, is included within the scope of the present disclosure. In cases where the present specification and a document incorporated by reference include conflicting and / or inconsistent disclosure, the present specification shall control. If two or more documents incorporated by reference include conflicting and / or inconsistent disclosure with respect to each other, then the document having the later effective date shall control. All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and / or ordinary meanings of the defined terms. The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.” The phrase “and / or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and / or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and / or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and / or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc. As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and / or” as defined above. For example, when separating items in a list, “or” or “and / or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of” or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.” As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and / or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc. When the word “about” is used herein in reference to a number, it should be understood that still another embodiment of the disclosure includes that number not modified by the presence of the word “about.” It should also be understood that, unless clearly indicated to the contrary, in any methods claimed herein that include more than one step or act, the order of the steps or acts of the method is not necessarily limited to the order in which the steps or acts of the method are recited. In the claims, as well as in the specification above, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” “composed of,” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of” and “consisting essentially of” shall be closed or semi-closed transitional phrases, respectively, as set forth in the United States Patent Office Manual of Patent Examining Procedures, Section 2111.03. What is claimed is:

Claims

CLAIMS 1. A method, comprising: providing a tissue containing a scaffold defining at least a portion of an electrical circuit and comprising a plurality of electrodes; and applying electrical stimuli to portions of the tissue using one or more of the electrodes within the scaffold, wherein the electrical stimuli are generated using a predictive model programmed by electrically stimulating the tissue using one or more of the electrodes and determining a tissue response based on the electrical stimulation.

2. The method of claim 1, wherein the predictive model comprises a Gaussian Process.

3. The method of any one of claims 1 or 2, wherein the predictive model comprises Bayesian Optimization.

4. The method of any one of claims 1-3, wherein the predictive model comprises reinforcement learning model.

5. The method of any one of claims 1-4, wherein the scaffold is in electrical communication with a power source and circuitry to control the application of electrical stimuli to portions of the tissue.

6. The method of claim 5, wherein the circuitry is configured to independently apply an electrical stimulus to different portions of the tissue within the tissue scaffold.

7. The method of any one of claims 1-6, wherein at least a portion of the scaffold is stretchable by at least 20% in a linear direction under a force of no more than 1 micronewton.

8. The method of any one of claims 1-7, wherein the scaffold comprises a mesh comprising plurality of nodes, at least some of which nodes are connected by interconnects to form the mesh.

9. The method of claim 8, wherein at least some of the interconnects are serpentine.

10. The method of any one of claims 1-9, further comprising an act of training a predictive model on at least one or more or any combination of: voltage supplied during electrical stimulation, duration of electrical stimulation, volume or density of the portions of the tissue undergoing electrical stimulation, type of cells comprising the tissue undergoing electrical stimulation, intensity or pattern of electrical stimulation, frequency of electrical stimulation, periodicity of electrical stimulation, and further trained on a resulting tissue response.

11. The method of claim 10, wherein the tissue response includes data associated with characteristics of maturing or mature cells.

12. The method of any one of claims 10 or 11, wherein the method further comprises training respective predictive models based on training data associated with a type of cell that comprises the portions of the tissue undergoing electrical stimulation.

13. The method of any one of claims 10-12, wherein the method further comprises an act of generating a prediction of tissue response based on input to the predictive model of at least one of or any combination of voltage supplied during electrical stimulation, duration of electrical stimulation, volume or density of the portions of the tissue undergoing electrical stimulation, type of cells comprising the tissue undergoing electrical stimulation, intensity or pattern of electrical stimulation, frequency of electrical stimulation, and periodicity of electrical stimulation.

14. A method, comprising: providing human induced pluripotent stem cell-derived cardiomyocytes contained within a cell scaffold defining at least a portion of an electrical circuit and comprising a plurality of electrodes; and applying electrical stimuli to the cardiomyocytes using one or more of the electrodes within the cell scaffold to accelerate maturation of the cardiomyocytes, wherein the electrical stimuli are generated using a predictive model programmed by repeatedly stimulating the tissue using one or more of the electrodes and determining a tissue response based on the stimulation.

15. A method, comprising: providing electroactive cells contained within a cell scaffold defining at least a portion of an electrical circuit and comprising a plurality of electrodes; and applying electrical stimuli to the electroactive cells using one or more of the electrodes within the cell scaffold, wherein the electrical stimuli are generated using a predictive model programmed by repeatedly stimulating the tissue using one or more of the electrodes and determining a tissue response based on the stimulation.

16. The method of claim 15, wherein the electroactive cells comprise cardiomyocytes.

17. The method of any one of claims 15 or 16, wherein the electroactive cells comprise stem cells.

18. The method of any one of claims 15-17, wherein the electroactive cells comprise pluripotent stem cell-derived cardiomyocytes.

19. The method of any one of claims 15-18, wherein the electroactive cells are present in an organoid.

20. The method of any one of claims 15-19, wherein the electroactive cells are present in a cardiac organoid.

21. The method of any one of claims 15-20, wherein the electroactive cells are present in a neural organoid.

22. A method, comprising: providing human induced pluripotent stem cell-derived cardiomyocytes contained within a cell scaffold defining at least a portion of an electrical circuit and comprising a plurality of electrodes; and applying electrical stimuli to the cardiomyocytes using one or more of the electrodes within the cell scaffold to accelerate maturation of the cardiomyocytes, wherein the electrical stimuli are generated using a predictive model programmed by repeatedly stimulating the tissue using one or more of the electrodes and determining a tissue response based on the stimulation.

23. A method, comprising: providing electroactive cells contained within a cell scaffold defining at least a portion of an electrical circuit and comprising a plurality of electrodes; and applying electrical stimuli to the electroactive cells using one or more of the electrodes within the cell scaffold, wherein the electrical stimuli are generated using a predictive model programmed by repeatedly stimulating the tissue using one or more of the electrodes and determining a tissue response based on the stimulation.

24. The method of claim 23, wherein the electroactive cells comprise cardiomyocytes.

25. The method of any one of claims 23 or 24, wherein the electroactive cells comprise stem cells.

26. The method of any one of claims 23-25, wherein the electroactive cells comprise pluripotent stem cell-derived cardiomyocytes.

27. The method of any one of claims 23-26, wherein the electroactive cells are present in an organoid.

28. The method of any one of claims 23-27, wherein the electroactive cells are present in a cardiac organoid.

29. The method of any one of claims 23-28, wherein the electroactive cells are present in a neural organoid.

30. A method for stimulating a biological system comprising an organoid, the method comprising: to a mesh comprising a plurality of nanoelectrodes comprising a stimulator and a recording sensor, wherein at least one nanoelectrode of the plurality of nanoelectrodes is electrically coupled to a cell of the organoid: applying a first current to the organoid having a first peak shape; recording a first signal with the recording sensor; processing the first signal within a computer-readable medium; andstimulating the organoid with the stimulator, wherein stimulating comprises applying a second current to the organoid having a second peak shape different from the first peak shape, wherein the second peak shape is determined, at least in part, based on processing the first signal.

31. The method of claim 30, wherein the stimulator is electrically coupled to the cell of the organoid.

32. The method of any one of claims 30 or 31, wherein the organoid comprises pancreatic cells.

33. The method of any one of claims 30-32, wherein the organoid comprises pluripotent stem cells.

34. The method of any one of claims 30-33, wherein the organoid comprises less mature cells and, after stimulating, the method further comprises culturing more mature cells within the organoid.

35. The method of any one of claims 30-34, wherein the organoid comprises less mature cells and, after stimulating, the method further comprises growing more mature cells within the organoid.

36. The method of any one of claims 30-35, wherein each nanoelectrode of the plurality of nanoelectrodes is coupled to a barcode.

37. The method of any one of claims 30-36, further comprising measuring an impedance of the first signal.

38. A system, comprising: an organoid; a mesh comprising a plurality of nanoelectrodes comprising a stimulator and a recording sensor, wherein at least one nanoelectrode of the plurality of nanoelectrodes is configured to electrically couple to a cell of the organoid; anda computer-readable medium configured to process a first signal based on a first current having a first peak shape to determine a second current having a second peak shape, different from the first peak shape, to apply to the organoid.

39. A system, comprising: an organoid comprising less mature cells and more mature cells; a mesh comprising a plurality of nanoelectrodes comprising a stimulator and a recording sensor, wherein at least one nanoelectrode of the plurality of nanoelectrodes is electrically coupled to the less mature cells and at least one nanoelectrode of the plurality of nanoelectrodes is electrically coupled to the more mature cells; and a computer-readable medium configured to process a first signal based on a first current having a first peak shape from the less mature cells and to provide a second current having a second peak shape, different from the first peak shape, to the more mature cells.

40. The method of any one of claims 38 or 39, wherein the less mature cells and / or the more mature cells comprise pancreatic cells.

41. The method of any one of claims 38-40, wherein the less mature cells and / or the more mature cells comprise pluripotent stem cells.

42. The method of any one of claims 38-41, wherein each nanoelectrode of the plurality of nanoelectrodes is coupled to a barcode.

43. The method of any one of claims 38-42, further comprising one or more interconnects between two or more nanoelectrodes within the mesh.

44. A method for stimulating a biological system comprising an organoid, the method comprising: to a device comprising a plurality of nanoelectrodes comprising a stimulator and a recording sensor, wherein at least one nanoelectrode of the plurality of nanoelectrodes is electrically coupled to a cell of the organoid: applying a first current to the organoid having a first peak shape; recording a first signal with the recording sensor;processing the first signal within a computer-readable medium; and stimulating the organoid with the stimulator, wherein stimulating comprises applying a second current to the organoid having a second peak shape different from the first peak shape, wherein the second peak shape is determined, at least in part, based on processing the first signal.

45. The method of claim 44, wherein the biological system comprises tissue and wherein the device is configured to apply electrical stimuli to different portions of the tissue.

46. The method of any one of claims 44-45, wherein the device comprises a mesh.

47. The method of any one of claims 44-46, wherein the biological system is an in vivo biological system.

48. The method of any one of claims 44-47, wherein at least a portion of the biological system is in vitro.

49. The method of any one of claims 44-48, wherein the biological system comprises cardiomyocytes, neurons, pancreatic cells, cultured tissues, implanted tissues, organoids, and / or cyborg organoids.

50. The method of any one of claims 44-49, wherein the stimulator is electrically coupled to the cell of the organoid.

51. The method of any one of claims 44-50, wherein the organoid comprises pancreatic cells.

52. The method of any one of claims 44-51, wherein the organoid comprises pluripotent stem cells.

53. The method of any one of claims 44-52, wherein the organoid comprises less mature cells and, after stimulating, the method further comprises culturing more mature cells within the organoid.

54. The method of any one of claims 44-53, wherein the organoid comprises less mature cells and, after stimulating, the method further comprises growing more mature cells within the organoid.

55. The method of any one of claims 44-54, wherein each nanoelectrode of the plurality of nanoelectrodes is coupled to a barcode.

56. The method of any one of claims 44-55, further comprising measuring an impedance of the first signal.

57. The method of any one of claims 44-56, further comprising a power source.

58. The method of any one of claims 44-57, wherein at least a portion of the device is stretchable by at least 20% in a linear direction under a force of no more than 1 micronewton.

59. The method of any one of claims 44-58, wherein the device comprises polyimide, Parylene N, Parylene C, SU8 epoxy, perylene, hydrogel, SEBS, and / or PDMS.

60. The method of any one of claims 44-59, wherein the device comprises nanoelectrodes comprising gold, platinum, and / or titanium.

61. A system, comprising: an organoid; a device comprising a plurality of nanoelectrodes comprising a stimulator and a recording sensor, wherein at least one nanoelectrode of the plurality of nanoelectrodes is configured to electrically couple to a cell of the organoid; and a computer-readable medium configured to process a first signal based on a first current having a first peak shape to determine a second current having a second peak shape, different from the first peak shape, to apply to the organoid.

62. A system, comprising: an organoid comprising less mature cells and more mature cells;a mesh comprising a plurality of nanoelectrodes comprising a stimulator and a recording sensor, wherein at least one nanoelectrode of the plurality of nanoelectrodes is electrically coupled to the less mature cells and at least one nanoelectrode of the plurality of nanoelectrodes is electrically coupled to the more mature cells; and a computer-readable medium configured to process a first signal based on a first current having a first peak shape from the less mature cells and to provide a second current having a second peak shape, different from the first peak shape, to the more mature cells.

63. The method of claim 62, wherein the computer-readable medium comprises a predictive model using a Gaussian process and / or a Bayesian process.

64. The method of any one of claims 62 or 63, wherein the computer-readable medium is configured to train using a predictive model and at least one of a voltage supplied during electrical stimulation, a duration of electrical stimulation, a volume or density of the portions of the tissue undergoing electrical stimulation, type of cells comprising tissue undergoing electrical stimulation, an intensity or pattern of electrical stimulation, a frequency of electrical stimulation, periodicity of electrical stimulation, and, optionally, further trained on a resulting tissue response 65. The method of any one of claims 62-64, wherein the less mature cells and / or the more mature cells comprise pancreatic cells.

66. The method of any one of claims 62-65, wherein the less mature cells and / or the more mature cells comprise pluripotent stem cells.

67. The method of any one of claims 62-66, wherein each nanoelectrode of the plurality of nanoelectrodes is coupled to a barcode.

68. The method of any one of claims 62-67, further comprising one or more interconnects between two or more nanoelectrodes within the mesh.

Images