Multi-mode sensor chip detection method and system, storage medium and terminal

Abstract

The invention provides a multi-modal sensor organ chip detection method and system, a storage medium and a terminal. The system comprises an organ chip, an optical signal acquisition module, an electrophysiological signal acquisition module, a signal preprocessing module and a multi-modal data processing module. According to the multi-modal sensor organ chip detection method and system, the storage medium and the terminal, automatic and intelligent detection of the organ chip can be conveniently achieved based on the optical signals, the electrophysiological signals and other multi-modal signals.

Description

Technical Field

[0001] This invention relates to the field of organ-on-a-chip technology, and in particular to a multimodal sensor organ-on-a-chip detection method, system, storage medium and terminal. Background Technology

[0002] Organ-on-a-chip is a biomimetic system that integrates microfluidics, biomaterials, and cell engineering. By constructing physiologically functional human tissue models on a microscale chip, it can simulate the in vivo microenvironment, mechanical stress, and dynamic physiological processes. This technology enables long-term maintenance and functional reproduction of cells and tissues under in vitro conditions, providing a new experimental platform for drug screening, toxicity evaluation, and disease model research.

[0003] Currently, organ-on-a-chip detection and analysis mainly rely on single-modality measurement methods. Common detection methods include endpoint biochemical analysis, fluorescence staining, microscopic imaging, and electrophysiological signal recording. Endpoint analysis methods can only obtain specific indicators at the end of the experiment and are difficult to reflect dynamic changes. Although microscopic imaging methods can provide information on cell morphology and spatial distribution, they are limited by illumination, focal length, and field of view, making it difficult to achieve long-term stable continuous observation. Electrophysiological detection can reflect the electrical activity of cells or tissues in real time, but it usually has a limited number of channels, a low signal-to-noise ratio, and lacks a time synchronization mechanism with optical detection.

[0004] Due to the limitations of each of these single-modal detection methods, current organ-on-a-chip systems still have shortcomings in dynamic monitoring and comprehensive evaluation. Relying solely on optical or electrical signals cannot fully reflect the physiological state and functional changes of cell populations, and it is difficult to reveal the correlation between cellular behavior and electrical activity. In addition, the temporal inconsistency between different modal signals also limits the quantitative analysis of the overall function of the on-a-chip system. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a multimodal sensor organ-on-a-chip detection method, system, storage medium and terminal, which realizes automated and intelligent organ-on-a-chip detection based on multimodal signals such as optical signals and electrophysiological signals.

[0006] In a first aspect, the present invention provides a multimodal organ-on-a-chip detection system, the system comprising: an organ-on-a-chip; an optical signal acquisition module connected to the organ-on-a-chip for acquiring optical image signals of cells or tissues in the organ-on-a-chip; an electrophysiological signal acquisition module connected to the organ-on-a-chip for acquiring electrophysiological signals of the organ-on-a-chip; a signal preprocessing module connected to the optical signal acquisition module and the electrophysiological signal acquisition module for preprocessing the optical image signals and the electrophysiological signals to obtain multimodal feature parameters; and a multimodal data processing module connected to the signal preprocessing module for generating performance evaluation results of the organ-on-a-chip based on the multimodal feature parameters.

[0007] In one implementation of the first aspect, the organ-on-a-chip includes an organoid, a microelectrode array, and an optical glass substrate; the organoid is an organoid with electrophysiological signals or a functional multicellular structure; the optical glass substrate serves as the substrate of the microelectrode array and provides optical transmission conditions; the microelectrode array is used to acquire electrical signals from the organoid or cells so that the electrophysiological signal acquisition module generates electrophysiological signals.

[0008] In one implementation of the first aspect, the thickness of the optical glass substrate is 170–500 μm; the diameter of the electrode points in the microelectrode array is 10–1000 μm, used to record the electrophysiological signals of cells or tissues in the culture chamber; the number of channels in the microelectrode array is set according to requirements, and it is connected to the electrophysiological signal acquisition module through a conductive connection to realize real-time monitoring of the electrical activity of organoids or cell populations within the organ-on-a-chip.

[0009] In one implementation of the first aspect, the electrophysiological signal acquisition module includes an electrode input unit, a low-pass filter module, an analog-to-digital converter module, a microprocessor, and a regulated power supply;

[0010] The electrode input unit is used to acquire electrical signals provided by the organ chip;

[0011] The low-pass filter module is used to perform low-pass filtering on the electrical signal;

[0012] The analog-to-digital converter module is used to convert the low-pass filtered electrical signal into a digital signal.

[0013] The microprocessor is used to process the acquired digital signals as the electrophysiological signals;

[0014] The regulated power supply is used to provide the power supply voltage.

[0015] In one implementation of the first aspect, the signal preprocessing module includes a time alignment module, an optical signal denoising module, an electrical signal filtering module, an optical signal correction module, an electrical signal denoising module, an optical signal feature extraction module, and an electrical signal feature extraction module;

[0016] The time alignment module is used to perform time alignment on the optical image signal and the electrophysiological signal;

[0017] The optical signal denoising module is used to denoise time-aligned optical image signals;

[0018] The optical signal correction module is used to perform flat field correction on the filtered optical image signal;

[0019] The optical signal feature extraction module is used to extract features from the flat-field corrected optical image signal and obtain optical feature parameters;

[0020] The electrical signal filtering module is used to filter time-aligned electrophysiological signals;

[0021] The electrical signal denoising module is used to denoise the filtered electrophysiological signals;

[0022] The electrical signal feature extraction module is used to extract features from the denoised electrophysiological signal and obtain electrophysiological feature parameters.

[0023] In one implementation of the first aspect, the multimodal data processing module includes a feature fusion module and an analysis module;

[0024] The feature fusion module is used to register and normalize the optical and electrophysiological feature parameters in the multimodal feature parameters on the time axis to generate a multimodal feature matrix.

[0025] The analysis module is used to generate performance evaluation results of the organ-on-a-chip based on the multimodal feature matrix.

[0026] In one implementation of the first aspect, the analysis module employs a long short-term memory neural network, and the performance evaluation results include changes in cell activity, organoid or cell functional state, and changes after external stimulation.

[0027] Secondly, the present invention provides a multimodal sensor chip detection system, the method comprising the following steps:

[0028] The optical signal acquisition module acquires optical image signals of cells or tissues in the organ chip;

[0029] The organ-on-a-chip acquires electrophysiological signals based on the electrophysiological signal acquisition module;

[0030] The optical image signal and the electrophysiological signal are preprocessed by the signal preprocessing module to obtain multimodal feature parameters;

[0031] The multimodal data processing module generates the performance evaluation results of the organ-on-a-chip based on the multimodal feature parameters.

[0032] Thirdly, the present invention provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described multimodal sensor chip detection method.

[0033] Fourthly, the present invention provides a terminal, comprising: a processor and a memory;

[0034] The memory is used to store computer programs;

[0035] The processor is used to execute the computer program stored in the memory, so that the terminal performs the above-described multimodal sensor chip detection method.

[0036] As described above, the multimodal sensor chip detection method, system, storage medium, and terminal of the present invention have the following beneficial effects:

[0037] (1) Based on multimodal signals such as optical signals and electrophysiological signals, it is beneficial to realize the automated and intelligent detection of organ-on-a-chip;

[0038] (2) By integrating optical signal acquisition and electrophysiological signal acquisition functions on the same platform, a micro-array electrode structure with an optical glass substrate is adopted. The substrate thickness is 170-500μm, which has both optical transmittance and electrical signal detection capabilities. Signal alignment and feature fusion are achieved in the time dimension, enabling simultaneous observation of cell morphology, metabolic activity and electrical activity in the chip system, thereby improving detection accuracy and physiological relevance.

[0039] (3) It overcomes the shortcomings of existing technologies such as single detection modality and asynchronous acquisition of biological information; it is especially suitable for real-time and non-invasive monitoring of single organ-on-a-chip or multi-organ-on-a-chip. Attached Figure Description

[0040] Figure 1 The diagram shown is a schematic representation of the structure of the multimodal sensor chip detection system of the present invention in one embodiment.

[0041] Figure 2 The diagram shown is a schematic representation of the structure of the organ-on-a-chip according to one embodiment of the present invention;

[0042] Figure 3 The diagram shown is a schematic representation of the electrophysiological signal acquisition module of the present invention in one embodiment.

[0043] Figure 4 The diagram shown is a schematic representation of the structure of the signal preprocessing module of the present invention in one embodiment.

[0044] Figure 5 The diagram shown is a schematic representation of the structure of the multimodal data processing module of the present invention in one embodiment.

[0045] Figure 6 The flowchart shown is a process for detecting a multimodal sensor chip according to an embodiment of the present invention;

[0046] Figure 7 The diagram shown is a structural schematic of the terminal of the present invention in one embodiment. Detailed Implementation

[0047] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0048] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0049] The technical solutions of the present invention will now be described in detail with reference to the accompanying drawings.

[0050] like Figure 1 As shown, in one embodiment, the multimodal sensor organ-on-a-chip detection system of the present invention includes an organ-on-a-chip 1, an optical signal acquisition module 2, an electrophysiological signal acquisition module 3, a signal preprocessing module 4, and a multimodal data processing module 5.

[0051] The organ-on-a-chip 1 is a biomimetic culture platform with a microfluidic network structure, used to maintain the long-term culture and physiological function of cells or tissues in a microscale environment, thereby constructing an in vitro multi-organ coupled biomimetic environment. In one embodiment, as... Figure 2As shown, the organ-on-a-chip 1 includes an organoid module 11, a microelectrode array 12, and an optical glass substrate 13. The optical glass substrate 13 is disposed at the bottom of the organoid module 11, and the microelectrode array 12 is disposed on the optical glass substrate 13. The organoid module 11 is a single organoid structure or a multi-organoid structure, and its interior may contain one or more independent or interconnected culture chambers and fluid channels to simulate the fluid circulation and metabolic environment in vivo. Preferably, the bottom of the organ-on-a-chip 1 is provided with an optical glass substrate with a microelectrode array (MEA). The thickness of the optical glass substrate is 170-500 μm, which has good optical transmittance, facilitating the generation of optical image signals by the optical signal acquisition module 2, and also provides a stable electrical signal detection interface so that the electrophysiological signal acquisition module 3 can generate electrophysiological signals, thereby realizing the synchronous acquisition of optical and electrical signals. The diameter of the electrode points in the microelectrode array can be 10-1000 μm, used to record the electrophysiological signals of cells or tissues in the culture chamber. The number of channels in the microelectrode array is set according to experimental requirements. It is connected to the electrophysiological signal acquisition module 3 by welding, wire bonding or other equivalent conductive connection methods to realize real-time monitoring of the electrical activity of cell populations in the organ-on-a-chip 1.

[0052] The optical signal acquisition module 2 is connected to the organ-on-a-chip 1 and is used to acquire optical image signals of cells or tissues in the organ-on-a-chip to reflect cell morphology and metabolic state. In one embodiment, the optical signal acquisition module 2 uses an inverted microscope or an equivalent imaging device without modifying the optical system structure. The inverted microscope is seeded below the organ-on-a-chip body and achieves transmission imaging through a glass substrate to observe the morphology, distribution, and fluorescence changes of cells in the culture area.

[0053] In one embodiment, before acquiring optical image signals, the focal length, light intensity, and exposure time of the inverted microscope are manually adjusted and fixed to ensure consistent imaging parameters throughout the acquisition process, avoiding signal errors caused by focal plane drift or brightness changes. The field of view of the inverted microscope is aligned with the target culture area within the organ-on-a-chip and remains stable throughout the observation process. During acquisition, the inverted microscope continuously captures images for approximately 2-48 hours, recording dynamic changes in cell morphology and activity. Image data can be saved as a video stream or as an image sequence acquired at a set frame rate for subsequent image processing and analysis. Constant environmental conditions, including temperature, humidity, and light stability, are maintained during acquisition to ensure image clarity and data comparability.

[0054] The electrophysiological signal acquisition module 3 is connected to the organ-on-a-chip 2 and is used to acquire the electrophysiological signals from the organ-on-a-chip. The electrophysiological signal acquisition module 3 has multi-channel synchronization and low-noise characteristics, and can continuously record long-term electrophysiological signals. Figure 3 As shown, in one embodiment, the electrophysiological signal acquisition module 3 includes an electrode input unit 31, a low-pass filter module 32, an analog-to-digital converter module 33, a microprocessor 34, and a regulated power supply 35. The electrode input unit 31 is connected to the microelectrode array at the bottom of the organ-on-a-chip 1 and is used to acquire the electrical signals provided by the organ-on-a-chip 1. The low-pass filter module 32 is connected to the electrode input unit 31 and is used to perform low-pass filtering on the electrical signals, thereby filtering out high-frequency noise and environmental interference, and retaining the electrical signals in the physiologically effective frequency band. The analog-to-digital converter module 33 is connected to the low-pass filter module 32 and is used to convert the low-pass filtered electrical signals into digital signals. Preferably, the analog-to-digital converter module 33 uses two ADS1299 chips to form a master-slave high-precision multi-channel acquisition structure. The ADS1299 chip has a built-in 24-bit Σ-Δ analog-to-digital converter and a programmable gain amplifier, which can amplify and digitize the input signal at high resolution. The master and slave chips are connected via a daisy chain to achieve multi-channel synchronous sampling. The microprocessor 34 is connected to the analog-to-digital converter 33 and is used to convert the acquired digital signal into the electrophysiological signal. Preferably, the microprocessor 34 is an STM32 microcontroller, used to control the sampling timing, data buffering, and communication management. The STM32 microcontroller communicates with the ADS1299 chip via the SPI bus to complete data reading and status control. The regulated power supply 35 is used to provide the power supply voltage, thereby providing a stable power supply voltage for each unit and ensuring electrical stability and signal-to-noise ratio during the electrophysiological signal acquisition process.

[0055] In one embodiment, both the electrophysiological signals and the optical image signals are continuously acquired and stored, with an acquisition duration of approximately 2-48 hours. Upon initiation of acquisition, the operator simultaneously activates the electrophysiological signal acquisition system and the inverted microscope recording function, achieving synchronization of the start time manually. After acquisition, the two types of data are time-registered based on the acquisition time information, and nodes are selected at 0.5-hour intervals. From each node, a 3-minute electrophysiological signal segment and the corresponding time-segmented inverted microscope image sequence are extracted as the electrophysiological signal and the optical image signal, respectively, for subsequent analysis and processing.

[0056] The signal preprocessing module 4 is connected to the optical signal acquisition module 2 and the electrophysiological signal acquisition module 3, and is used to preprocess the optical image signal and the electrophysiological signal to obtain multimodal feature parameters. In one embodiment, such as Figure 4As shown, the signal preprocessing module 4 includes a time alignment module 41, an optical signal denoising module 42, an electrical signal filtering module 43, an optical signal correction module 44, an electrical signal denoising module 45, an optical signal feature extraction module 46, and an electrical signal feature extraction module 47. The time alignment module 41 is used to align the optical image signal and the electrophysiological signal in time, ensuring that data from different modalities are comparable in the time dimension. The optical signal denoising module 42 is connected to the time alignment module 41 and is used to denoise the time-aligned optical image signal, where denoising can employ Gaussian filtering or median filtering. The optical signal correction module 44 is connected to the optical signal denoising module 42 and is used to perform flat-field correction on the filtered optical image signal to eliminate uneven illumination. The optical signal feature extraction module 46 is connected to the optical signal correction module 44 and is used to extract features from the flat-field corrected optical image signal to obtain optical feature parameters. Specifically, after threshold segmentation and edge detection, optical feature parameters such as cell or tissue area, roundness, deformation rate, and brightness changes are extracted. The electrical signal filtering module 43 is connected to the time alignment module 41 and is used to filter the time-aligned electrophysiological signals. The filtering process can employ a bandpass filter of 0.1–300Hz to remove DC drift and high-frequency noise, and a notch filter is placed at 50Hz to suppress power frequency interference. The electrical signal denoising module 45 is connected to the electrical signal filtering module 43 and is used to denoise the filtered electrophysiological signals. Denoising can employ moving average or wavelet thresholding methods to improve the signal-to-noise ratio. The electrical signal feature extraction module 47 is connected to the electrical signal denoising module 45 and is used to extract features from the denoised electrophysiological signals to obtain electrophysiological feature parameters. Peak detection algorithms are used to extract electrophysiological feature parameters such as spike events, firing rate, amplitude, spectral energy, and synchronicity to characterize the electrical activity of cells or tissues. The optical feature parameters and the electrophysiological feature parameters serve as the multimodal feature parameters.

[0057] The multimodal data processing module 5 is connected to the signal preprocessing module 4 and is used to generate performance evaluation results of the organ-on-a-chip based on the multimodal feature parameters. The performance evaluation results include changes in cell activity, organoid or cell functional state, and changes after external stimuli, including but not limited to acoustic, optical, electrical, and magnetic stimulation, and drug effects. The multimodal data processing module 5 identifies the synergistic change patterns of multiple organ systems and determines cell activity, organ functional state, and drug response effects through feature fusion and correlation analysis. The analysis results can be output in numerical, curve, or graphical form through the system interface, achieving automated detection and intelligent judgment of the organ-on-a-chip. In one embodiment, as... Figure 5As shown, the multimodal data processing module 5 includes a feature fusion module 51 and an analysis module 52. The feature fusion module 51 is used to register and normalize the optical and electrophysiological feature parameters in the multimodal feature parameters along the time axis, generating a multimodal feature matrix that can be used for joint analysis. The analysis module 52 is connected to the feature fusion module 51 and is used to generate performance evaluation results of the organ-on-a-chip based on the multimodal feature matrix. Specifically, the analysis module 52 evaluates the cell activity, functional stability, and drug effects of the organ-on-a-chip by establishing a time-series correlation model or a neural network-based multimodal learning model. The model can be constructed using Long Short-Term Memory (LSTM) networks or other equivalent time-series learning algorithms to mine the correlation and dynamic changes between optical and electrical signals. The LSTM model, by inputting the multimodal feature matrix, outputs trend curves of cell activity, functional stability, and drug effects for the corresponding time period, thereby realizing the intelligent and automated analysis and prediction functions of the organ-on-a-chip system. Cellular activity can be defined as a comprehensive indicator reflecting the overall functional state of cells, calculated based on a weighted fusion of optical characteristics (area, roundness, deformation rate) and electrical signal characteristics (peak firing rate, amplitude, synchronicity, etc.). This indicator is used to characterize the physiological integrity and activity level of organoids. The drug effect can be defined as the relative rate of change of multimodal characteristics of organoids before and after drug treatment, reflecting the intensity and direction of cellular responses to drug stimulation. By comparing the changes in multimodal characteristics between the baseline window and the drug treatment window, the drug effect coefficient is calculated, and combined with the time trend output by the LSTM model, the prediction and evaluation of drug response are achieved.

[0058] The protection scope of the multimodal sensor chip detection system described in this embodiment is not limited to the components listed in this embodiment. Any solution implemented by adding, removing, or replacing components in the prior art based on the principles of this invention is included within the protection scope of this invention.

[0059] like Figure 6 As shown, in one embodiment, the multimodal sensor chip detection method of the present invention includes steps S1-S4.

[0060] Step S1: Acquire optical image signals of cells or tissues in the organ-on-a-chip based on the optical signal acquisition module.

[0061] Step S2: Acquire the electrophysiological signals of the organ-on-a-chip based on the electrophysiological signal acquisition module.

[0062] Step S3: Based on the signal preprocessing module, preprocess the optical image signal and the electrophysiological signal to obtain multimodal feature parameters.

[0063] Step S4: The multimodal data processing module generates the performance evaluation results of the organ-on-a-chip based on the multimodal feature parameters.

[0064] It should be noted that the functional modules involved in the multimodal sensor chip detection method of the present invention are consistent with the modules in the above-mentioned multimodal sensor chip detection system, and therefore will not be described again here.

[0065] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0066] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of the present invention, depending on actual needs. For example, the functional modules / units in the various embodiments of the present invention may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0067] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0068] This invention also provides a computer-readable storage medium. Those skilled in the art will understand that all or part of the steps in the multimodal sensor chip detection method of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. This available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0069] This invention also provides a terminal. The terminal includes a processor and a memory.

[0070] The memory is used to store computer programs.

[0071] The memory includes various media capable of storing program code, such as ROM, RAM, magnetic disk, USB flash drive, memory card, or optical disk.

[0072] The processor is connected to the memory and is used to execute the computer program stored in the memory so that the terminal performs the above-described multimodal sensor chip detection method.

[0073] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0074] like Figure 7As shown, the terminal of the present invention is presented in the form of a general-purpose computing device. The components of the terminal may include, but are not limited to: one or more processors or processing units 71, a memory 72, and a bus 73 connecting different system components (including the memory 72 and the processing unit 71).

[0075] Bus 73 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0076] Terminals typically include various computer system-readable media. These media can be any available media that can be accessed by the terminal, including volatile and non-volatile media, and removable and non-removable media.

[0077] Memory 72 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 721 and / or cache memory 722. The terminal may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 723 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 7 Not shown; usually referred to as a "hard drive"). Although Figure 7 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 73 via one or more data media interfaces. Memory 72 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0078] A program / utility 724 having a set (at least one) of program modules 7241 may be stored, for example, in memory 72. Such program modules 7241 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 7241 typically perform the functions and / or methods described in the embodiments of the present invention.

[0079] The terminal can also communicate with one or more external devices (e.g., keyboard, pointing device, display, etc.), one or more devices that enable user interaction with the terminal, and / or any device that enables the terminal to communicate with one or more other computing devices (e.g., network interface card, modem, etc.). This communication can be performed through input / output (I / O) interface 74. Furthermore, the terminal can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 75. Figure 7 As shown, network adapter 75 communicates with other modules of the terminal via bus 73. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the terminal, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0080] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A multimodal sensor chip detection system, characterized in that, The system includes: Organ-on-a-chip; An optical signal acquisition module, connected to the organ-on-a-chip, is used to acquire optical image signals of cells or tissues in the organ-on-a-chip. An electrophysiological signal acquisition module, connected to the organ-on-a-chip, is used to acquire the electrophysiological signals of the organ-on-a-chip; The signal preprocessing module, connected to the optical signal acquisition module and the electrophysiological signal acquisition module, is used to preprocess the optical image signal and the electrophysiological signal to obtain multimodal feature parameters; A multimodal data processing module, connected to the signal preprocessing module, is used to generate performance evaluation results of the organ-on-a-chip based on the multimodal feature parameters.

2. The multimodal sensor chip detection system according to claim 1, characterized in that, The organ-on-a-chip includes organoids, a microelectrode array, and an optical glass substrate; the organoids are organoids with electrophysiological signals or functional multicellular structures; the optical glass substrate serves as the substrate for the microelectrode array and provides optical transmission conditions; the microelectrode array is used to acquire electrical signals from organoids or cells, so that the electrophysiological signal acquisition module generates electrophysiological signals.

3. The multimodal sensor chip detection system according to claim 1, characterized in that, The optical glass substrate has a thickness of 170–500 μm; the electrode point diameter in the microelectrode array is 10–1000 μm, used to record the electrophysiological signals of cells or tissues in the culture chamber; the number of channels in the microelectrode array is set according to requirements, and it is connected to the electrophysiological signal acquisition module through a conductive connection to realize real-time monitoring of the electrical activity of organoids or cell populations within the organ-on-a-chip.

4. The multimodal sensor chip detection system according to claim 1, characterized in that, The electrophysiological signal acquisition module includes an electrode input unit, a low-pass filter module, an analog-to-digital converter module, a microprocessor, and a regulated power supply; The electrode input unit is used to acquire electrical signals provided by the organ chip; The low-pass filter module is used to perform low-pass filtering on the electrical signal; The analog-to-digital converter module is used to convert the low-pass filtered electrical signal into a digital signal. The microprocessor is used to process the acquired digital signals as the electrophysiological signals; The regulated power supply is used to provide the power supply voltage.

5. The multimodal sensor chip detection system according to claim 1, characterized in that, The signal preprocessing module includes a time alignment module, an optical signal denoising module, an electrical signal filtering module, an optical signal correction module, an electrical signal denoising module, an optical signal feature extraction module, and an electrical signal feature extraction module. The time alignment module is used to perform time alignment on the optical image signal and the electrophysiological signal; The optical signal denoising module is used to denoise time-aligned optical image signals; The optical signal correction module is used to perform flat field correction on the filtered optical image signal; The optical signal feature extraction module is used to extract features from the flat-field corrected optical image signal and obtain optical feature parameters; The electrical signal filtering module is used to filter time-aligned electrophysiological signals; The electrical signal denoising module is used to denoise the filtered electrophysiological signals; The electrical signal feature extraction module is used to extract features from the denoised electrophysiological signal and obtain electrophysiological feature parameters.

6. The multimodal sensor chip detection system according to claim 1, characterized in that, The multimodal data processing module includes a feature fusion module and an analysis module; The feature fusion module is used to register and normalize the optical and electrophysiological feature parameters in the multimodal feature parameters on the time axis to generate a multimodal feature matrix. The analysis module is used to generate performance evaluation results of the organ-on-a-chip based on the multimodal feature matrix.

7. The multimodal sensor chip detection system according to claim 6, characterized in that, The analysis module employs a long short-term memory neural network, and the performance evaluation results include changes in cell activity, organoid or cell functional status, and after external stimulation.

8. A multimodal sensor chip detection system, characterized in that, The method includes the following steps: The optical signal acquisition module acquires optical image signals of cells or tissues in the organ chip; The organ-on-a-chip acquires electrophysiological signals based on the electrophysiological signal acquisition module; The optical image signal and the electrophysiological signal are preprocessed by the signal preprocessing module to obtain multimodal feature parameters; The multimodal data processing module generates the performance evaluation results of the organ-on-a-chip based on the multimodal feature parameters.

9. A storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the multimodal sensor chip detection method as described in claim 8.

10. A terminal, characterized in that, include: Processor and memory; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory, so that the terminal performs the multimodal sensor chip detection method described in 8.

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