18 results about "Biosensor array" patented technology

The invention provides methods for associating volatile compounds or mixtures of volatile compounds with combinatorial response patterns of Olfactory Receptors (OlfRs), a biosensor array comprising a plurality of biosensors, the use of said biosensor and of the method of the invention to screen for diseases, a molecule identified by a method of the invention as well as the use se of said molecule to modify the behavior of an insect.

A semiconductor device includes a first gate electrode, an insulating part, a source electrode, a drain electrode, and a contact part. The insulating part is on one surface of the first gate electrode. The source electrode is connected to the insulating part. The drain electrode is connected to the insulating part. The contact part is between the source electrode and the drain electrode and on the insulating part. The contact part contains an atomic layered material. The contact part has a second part contactable with a sample. The second surface is opposite to a first surface facing the insulating part. A surface of the insulating part, the surface facing the contact part, has an uneven structure with respect to the first gate electrode.

This invention relates to the fields of medical device technology, biosignal processing, and artificial intelligence-assisted diagnostic technology. Specifically, it relates to a non-invasive dynamic monitoring system and method for gastric function based on quantum sensing and multimodal artificial intelligence. The system includes a quantum dot biosensor array, a multimodal signal fusion processor, an artificial intelligence dynamic evaluation algorithm, and a cloud-based collaborative diagnostic platform. The quantum sensor uses graphene quantum dot material and achieves 128-channel parallel acquisition through capacitive coupling. In this invention, by setting up the quantum dot biosensor, the sensitivity reaches the single-photon level, the resolution is 0.1μV, the frequency response range is 0.01-100Hz, and the anti-interference capability is 90dB common-mode rejection ratio, thus improving signal acquisition accuracy and anti-interference capability.

This invention relates to the field of metabolite monitoring technology and discloses a multimodal cross-calibrated method for continuous multi-metabolite monitoring. The method acquires multi-source metabolite signal sequences containing electrochemical, optical, and thermodynamic signals through an implanted biosensor array; it performs time-series segmentation of the multi-source metabolite signal sequences to generate metabolite concentration time-series segments; and it synchronously inputs these metabolite concentration time-series segments into a metabolite dynamic response model and a metabolite interaction feature model to generate a first metabolite concentration trajectory and a second metabolite concentration trajectory, respectively. This method, by leveraging multimodal signal fusion and cross-calibration, combined with time-series segmentation and multi-model analysis, can comprehensively capture the dynamic changes and interactions of metabolites, improving the completeness and accuracy of metabolite monitoring, and is suitable for continuous multi-metabolite monitoring scenarios.

This invention relates to the fields of digital therapy, intelligent medical devices, and applied artificial intelligence technologies, and more specifically to a closed-loop pain management method and system integrating diagnostic assistance and interference-resistant sensing. It includes: a wearable device comprising: a neuromodulation signal generator configured to generate neuromodulation stimulation pulses; a biosensor array physically integrated with the neuromodulation signal generator configured to sense physiological data indicating the user's autonomic nervous system activity; a controller configured to execute a time-division multiplexing protocol; a processor; and a non-transitory computer-readable storage medium associated with the processor for generating a diagnostic support report. This invention enables simultaneous effective neural stimulation and high-fidelity ANS signal acquisition on a single wearable device.

The invention relates to the technical field of pain state prediction, and discloses a method for constructing a patient pain state prediction model based on brain electric signals. The method comprises the following steps: acquiring an original data stream of brain electric signals of a patient through a multi-channel biosensor array, synchronously recording pain event labeling information, and generating a standardized signal sequence after preprocessing; extracting multi-modal features of time domain stability, frequency domain resonance and space domain connectivity, and constructing a feature-label mapping table; training the graph convolutional neural network model, and obtaining an initial prediction model in combination with a node embedding technology; dynamically adjusting parameters through an adaptive filtering algorithm to generate an optimization model; and in combination with the real-time electric signal flow and the physiological context information, outputting a pain level evaluation report, and automatically matching a clinical intervention protocol library to form a personalized scheme. According to the method, multi-modal features and a dynamic optimization mechanism are integrated, and prediction accuracy and real-time performance are improved.

The invention provides an emotion monitoring system based on a hybrid architecture, and the system comprises a multi-mode biosensor array which is used for collecting a physiological monitoring signal of a target person; the edge calculation unit is used for determining preliminary emotion classification information according to the physiological monitoring signals and a lightweight model; the cloud server is used for determining an emotion monitoring result according to the preliminary emotion classification information and a GNN-Transform mixed architecture model; wherein the GNN-Transform hybrid architecture model comprises a plurality of layers of GNN-Transform co-processing modules, and the GNN-Transform hybrid architecture model comprises a plurality of layers of GNN-Transform co-processing modules; each layer of GNN-Transform coprocessing module is used for completing feature optimization and determining an attention sampling focus of the next layer according to a feature optimization result of the current layer; and the lightweight model is obtained by the lightweight of the GNN-Transform hybrid architecture model. According to the method, a GNN-Transform hybrid architecture is provided, time-space association among all modals is modeled through a neural network, and meanwhile, multi-modal time sequence data is processed by utilizing a self-attention mechanism of Transform, so that deep fusion of cross-modal features is realized, and the emotion monitoring precision is improved.

A foot covering comprising an elongated fabric body. The foot covering includes a biosensor system that includes a biosensor array integrated into the elongated fabric body, and a microprocessor that is configured to removably electronically couple to the biosensor array and removably physically couple to the elongated fabric body.

The application discloses a groundwater pollution rapid detection device based on a biosensor array, and relates to the technical field of detection devices.The device comprises a crawling pipeline, a biosensing detection module, a sampling module and a liquid feeding module.The sampling module, the liquid feeding module and the biosensing detection module are independently assembled and are connected through standardized interfaces, which facilitates maintenance and cleaning, and simultaneously solves the problem of independent work and connection faults of traditional split-type equipment modules.The liquid feeding module of the application can automatically feed sample liquid to the detection module, and the whole process is seamlessly connected, thereby avoiding time loss caused by independent work of modules in traditional split-type devices, reducing redundant structures, and reducing the overall volume by arranging the modules along an axis instead of stacking them, which is more suitable for deployment in narrow spaces.

This invention discloses an intelligent monitoring contact lens system and its control method based on the integration of radio frequency wireless power supply and self-oxygen supply, belonging to the technical field of intelligent contact lenses and flexible wearable devices. The system includes a self-oxygenating contact lens, a radio frequency energy harvester, a microcontroller, a biosensor array, and an antenna; the accompanying control software is deployed on a smart terminal to realize device management, energy scheduling, and data interaction. The radio frequency energy harvester is used to capture ambient radio frequency energy and power the intelligent contact lens; the antenna is used for bidirectional data transmission with the terminal; and the biosensor array collects biological information from the cornea and tears in real time. This invention significantly simplifies the contact lens structure by offloading complex control logic to the smart terminal software, which is beneficial for the integrated design of multifunctional modules, while improving the system's wearing safety and ease of use.

The invention relates to the technical field of biosensors and wireless health monitoring, and particularly discloses a renal rejection monitoring system based on a wireless implantable biosensor. The system comprises a wireless implantable biosensor array, an in-vivo signal preprocessing unit, an in-vitro data receiving and synchronizing device, a multi-modal heterogeneous data fusion engine and a space-time diagram neural network prediction model, immune and metabolism multi-dimensional biomarkers are collected around a transplanted kidney in real time, a dynamic'immunity-metabolism 'knowledge graph is constructed, and accurate prediction and graded early warning of rejection risks are achieved by means of space cascading and time evolution laws of time-space diagram neural network modeling signals. By the adoption of the technical scheme, sub-clinical rejection signals can be recognized before conventional indexes such as creatinine are abnormal, a window is intervened in advance, meanwhile, long-term non-invasive monitoring is achieved through wireless low-power-consumption design, and the accuracy and the intelligent level of postoperative management are improved.

This invention relates to a quality testing method for low-odor wire harness materials, belonging to the field of quality testing technology. The method includes: placing a wire harness sample in an intelligent sealed chamber; dynamically adjusting temperature, humidity, and ventilation rate using the Internet of Things (IoT) to simulate the entire lifecycle of VOCs slow-release processes, generating a test gas that closely resembles real-world operating conditions; capturing ppb-level trace VOCs molecular signals using a chip equipped with specific biological olfactory receptor proteins, forming a high signal-to-noise ratio dataset; performing toxicity verification by matching a sensitive bacterial strain library with an AI-based microbial sensor array, outputting component-toxicity correlation assessment results; and outputting quantitative detection results after multi-channel filtering calibration and operating condition deviation correction. Based on the results, an N-BEATS model is constructed to achieve risk warning, matching remediation solutions, and forming a closed loop of detection-warning-control-verification. This invention clearly defines the concentration and toxicity contribution of trace components, captures synergistic / antagonistic toxicity effects between components, and significantly improves detection accuracy and data reliability.

This invention discloses an integrated light source biological cell culture sensing device. The device comprises a Micro-LED array and a biosensor array, which are bonded together. A DGHFET device and the Micro-LED device form a field-effect biosensor, with the DGHFET device located within the light spot coverage area of ​​the Micro-LED device within the same field-effect biosensor. This integrated light source biological cell culture sensing device successfully integrates efficient photostimulation, high-sensitivity biosensing, and high-reliability anti-interference capabilities onto a single platform, providing a powerful, reliable, and flexible integrated solution for optogenetic research. This invention has wide applications in the semiconductor technology field.

This invention relates to a biosensor array monitoring system and method for imaging the distribution of small organic molecules in plants. The system includes a biosensor array and a control module. The control module includes a signal output and input conditioning unit and a control and data processing unit. The biosensor array is used to couple with a live plant sample and acquire current signals caused by corresponding small organic molecules based on an applied linear scanning voltage signal. The signal output and input conditioning unit is used to apply the linear scanning voltage signal to the biosensor array and condition the current signals acquired by the biosensor array to obtain voltage signals. The control and data processing unit is used to calibrate the biosensor array and the signal output and input conditioning unit, generate linear scanning voltage signals, and analyze and process the obtained voltage signals to obtain in-situ distribution images of small organic molecules in the plant. This invention can be widely applied in the field of sensor technology in agriculture.

The invention discloses an apple planting greenhouse environment tracking management method and system based on the Internet of Things. The method comprises the steps of collecting multi-source data of apples in a greenhouse through a multispectral imaging sensor, a biosensor array and a wearable apple physiological sensor; the multi-source data is preliminarily integrated, verified and cached through a local gateway and then uploaded to a data processing system through a 4G / 5G network; the data processing system extracts spatial features of the multi-source data based on a neural network fusing CNN and RNN features, and compares and matches the spatial features of the multi-source data with pest and disease damage feature modes and apple pathophysiological knowledge in a knowledge base to obtain a matching result; and outputting an apple planting optimization scheme or a disease and pest diagnosis result based on a matching result. The method is used for solving the problems of how to accurately know the growth condition of apples and how to immediately optimize a cultivation scheme.

The invention provides a layered dressing comprising: a permeable wound contact layer for placement in contact with a wound; a gas permeable barrier layer; a biosensor sensor array configured to detect one or more markers in wound fluid; and a fluid collection layer disposed between the wound contact layer and the gas permeable barrier layer and configured to deliver wound fluid from a wound in contact with the wound contact layer to the biosensor sensor array by capillary action. In preferred embodiments, the layered dressing comprises aptamer-based electrodes for detecting TNF-a, IL-6, IL-8, TGF-b, and Staphylococcus aureus epitopes.

The invention belongs to the technical field of biological medicine, particularly relates to a drug targeted therapy effect verification system based on a transgenic mouse model, and discloses a drug therapeutic effect verification system integrating genetic engineering modeling, multi-modal sensing and AI data fusion. According to the system, transgenic mice modified by humanized immunization and controllable pathogenic genes are combined with an implantable multifunctional biosensor array, so that in-situ dynamic monitoring on drug concentration, target response and microenvironment change is realized, and physiological and behavior multi-dimensional data are synchronously acquired. Space-time alignment and fusion analysis are carried out through a deep learning engine, a quantitative evaluation matrix containing pharmacodynamics and off-target risks is generated, and closed-loop feedback regulation drug administration is supported. The problems that a traditional model is poor in predictability and static and single in detection means are solved, and the new drug research and development efficiency and clinical transformation accuracy are remarkably improved.

A biosensor pixel for measuring current that flows through the electrode surface in response to electrochemical interactions and a biosensor array architecture that includes such biosensor pixels. The biosensor pixel includes an electrode transducer configured to measure a current generated by electrochemical interactions occurring at a recognition layer placed directly on top of it in response to an electrical voltage placed across an electrode transducer-electrolyte interface. The biosensor pixel further includes a trans-impedance amplifier connected to the electrode transducer, where the trans-impedance amplifier is configured to convert the current into a voltage signal as the electrochemical interactions occur. Additionally, the biosensor pixel includes a 1-bit comparator coupled to the trans-impedance amplifier and a 1-bit digital-to-analog converter coupled to the 1-bit comparator, where the 1-bit digital-to-analog converter injects different levels of charge into an input of the trans-impedance amplifier at each cycle based on an output of the 1-bit comparator.