8523 results about "Biomedicine" patented technology

A scalable platform for orchestrating networks of specialized AI multi-agent networks that enables secure collaboration through token-based protocols and real-time result streaming with advanced dynamic chain-of-thought pruning. The central orchestration engine manages domain-specific agents, implementing sophisticated multi-branch reasoning with contribution-estimation layers that evaluate each agent's utility using Shapley value-inspired metrics. The system employs information-theoretic and gradient-based surprise metric to guide memory updates and dynamic reasoning expansion, preventing local minima stagnation while preserving valuable insights through adaptive forgetting mechanisms. The platform unifies Monte Carlo tree search with contribution-aware estimation to detect high-synergy expert combinations while maintaining privacy through partial data approaches. It scales across distributed computing environments, enabling complex collaborative tasks like materials discovery, product engineering and manufacturing process design, biomedical research, and drug development. The system supports multi-party economic rewards through systematic contribution effort, cost and importance tracking, while standardized interfaces manage security, privacy, and policy constraints across heterogeneous agents.

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

The invention relates to a drug-disease relationship prediction method and system based on a dual-channel fusion knowledge graph. The method comprises the following steps: S1, constructing a biomedical fusion knowledge graph; s2, constructing a drug-disease sub-graph based on the biomedicine fusion knowledge graph; s3, constructing a dual-channel adaptive fusion feature module, and embedding a drug-disease sub-graph; s4, performing enhanced splicing on the drug-disease sub-graphs in the dual-channel fusion knowledge graph pre-embedded network, importing an edge-node iterative updating learning mechanism, training a spliced sub-graph relation perception learning network, and obtaining enhanced sub-graph feature embedding; and S5, utilizing the trained enhanced subgraph features to embed, calculate and output the prediction probability of the drug-disease relationship. According to the method, the semantic representation capability of the knowledge graph and the topological modeling advantages of the graph neural network are integrated, and efficient and accurate prediction of the drug-disease relationship is realized through multi-modal feature interaction, sub-graph enhancement and a dynamic sub-graph learning mechanism.

The invention relates to the technical field of biomedical engineering, in particular to a multi-mode brain anomaly detection method and system based on machine learning. The method comprises the following steps: acquiring brain medical image data of different modalities, and realizing spatial registration and alignment through a multi-modal registration algorithm based on mutual information; a multi-branch feature extraction model including a convolutional neural network, a converter and a state space model is utilized to perform feature embedding on the original image of each modal; performing frequency decoupling on the features of each mode through adaptive approximate wavelet transform, and decomposing the features into high-frequency detail information and low-frequency global information; a frequency band fusion strategy based on an attention mechanism is implemented on high and low frequency features of different modal images, and fused frequency sub-band features are input into a space-frequency Mama module. Through the adaptive frequency domain decomposition and cross-modal fusion mechanism, the multi-modal brain image information is effectively integrated, and the accuracy and robustness of brain anomaly detection are remarkably improved.

A recombinant humanized collagen and a preparation method therefor. The recombinant humanized type III collagen can be efficiently and stably expressed in three expression systems of yeast, Escherichia coli, and CHO cells, and has the biological activity superior to that of a commercially available collagen product. The recombinant humanized type III collagen has structural characteristics and functional activity similar to those of a natural collagen, and thus has wide application prospects in biomedical materials, tissue engineering products, cosmetics, foods, health care products or drugs, medical instruments, medical cosmetology and the like.

The invention relates to the technical field of medical information, in particular to a medical data conjoint analysis system based on medical knowledge graph driving. The system comprises a medical knowledge graph construction module, a medical data quality evaluation module, a medical feature engineering module and a medical knowledge driven analysis module, and heterogeneous knowledge graph modeling can be performed by integrating medical clinical guidelines, biomedical literatures, a drug database and historical medical data so as to generate a medical heterogeneous knowledge graph; obtaining new medical clinical test information and carrying out delayed contradictory learning update to generate a medical dynamic update knowledge graph; the method comprises the following steps: obtaining multi-modal medical data, carrying out quality verification evaluation and medical feature engineering analysis, carrying out knowledge path joint driving analysis at the same time, generating a decision support reasoning path corresponding to medical clinical knowledge, and outputting a corresponding medical knowledge path confidence coefficient. According to the method, fusion reasoning among cross-source data can be realized by constructing the multi-modal medical knowledge graph.

The invention relates to the technical field of biomedical engineering and neuroscience, and discloses an in-ear vagus nerve stimulation control method, system and device based on scalp electroencephalography, and the method comprises the steps: collecting a scalp electroencephalogram signal, a body movement signal and a pulse signal of a to-be-detected user; performing preprocessing and frequency domain separation on the signal; calculating a sleep coupling score, and dividing a physiological state according to the sleep coupling score; dynamically adjusting the intensity and frequency of intra-ear vagus nerve stimulation of the user according to the physiological state, and generating feedback data; in-ear vagus nerve stimulation is performed and the dynamic adjustment parameters are updated based on the feedback data. According to the method, accurate sleep recognition is realized through multi-source signal parallel processing and SCS modeling, and the classification precision is remarkably improved; lSTM prediction and biphase modulation are combined to optimize stimulation parameters, sleep time is shortened, and side effects are reduced; a GPU acceleration and low-power-consumption framework is adopted, and efficient self-adaptive closed-loop regulation and control are achieved.

The invention provides a protein methylation site prediction method fusing quantum and region perception, which comprises the following steps: step 1, acquiring a protein sequence as a data source, and respectively constructing a training set and an independent test set; 2, constructing a multi-modal feature for each protein sequence by adopting a three-way nested scattering network, and fusing the multi-modal features to obtain an optimized fusion feature tensor; and step 3, inputting the optimized fusion feature tensor into a RaQMeNet network model, and performing a methylation site prediction task. The performance indexes of the method are greatly superior to those of the prior art, and the method has higher adaptability, stability and interpretability, can be widely applied to a plurality of bioinformatics and biological medicine related fields such as protein function annotation, disease mechanism research and drug target discovery, and has good application prospects and commercial values.

An AI-driven, cloud-based system (100) for real-time biomedical and pharmaceutical compliance and risk management, including: (a) a compliance knowledge module configured to ingest, interpret and structure regulatory data using natural language processing (NLP) and generate machine-readable compliance rules; (b) a real-time monitoring and event recording module configured to collect and normalise operational data from distributed biomedical and pharmaceutical systems, including laboratory information management systems (LIMS), manufacturing execution systems (MES) and IoT-enabled devices; (c) an intelligent risk assessment and prediction module configured to correlate operational data with compliance rules, calculate dynamic risk scores and predict potential compliance violations using machine learning models; (d) an automated policy and workflow enforcement module configured to initiate remedial actions, assign tasks and log activities based on predefined standard operating procedures (SOPs); (e) an audit readiness and reporting module configured to generate compliance logs, audit trails and standardised regulatory reports in real time; and (f) an adaptive learning and feedback optimization module configured to refine rule sets and predictive models based on feedback, historical data and regulatory updates; g) the modules are integrated into a cloud infrastructure to enable real-time, scalable and predictive compliance and risk management across biomedical and pharmaceutical processes.

The invention provides humanized recombinant XVII collagen for promoting hair follicle stem cell repair and resisting aging as well as a preparation method and application thereof, and belongs to the technical field of biological medicines. The humanized recombinant XVII type collagen comprises polypeptide derived from the XVII type collagen or repeated protein formed by N times of repeated series connection of the polypeptide serving as a unit, n is an integer from 2 to 4; the polypeptide derived from the XVII type collagen comprises a polypeptide A and / or a polypeptide B; the amino acid sequence of the A polypeptide is as shown in SEQ ID NO: 1; and the amino acid sequence of the B polypeptide is as shown in SEQ ID NO: 2. The humanized recombinant XVII type collagen not only can be massively and stably recombined and expressed, but also has cell adhesion activity, cell proliferation promotion activity, cell migration promotion activity and excellent hair follicle repair and regeneration promotion activity, and has no sensitization side effect. The invention provides a new way for preparing collagen products with rich functions.

The invention discloses a multi-omics pathological analysis system and method for colorectal cancer liver metastasis risk prediction, and relates to the technical field of biomedicine and cancer diagnosis, the system comprises a data acquisition module, a data preprocessing module, a cell analysis module, a score construction module, a model construction and training module and a prediction and evaluation module; according to the method, multiple omics data including the single cell data set, the space transcriptome data, the batch RNA-seq data, the clinical data and the full slice image WSIs are integrated, comprehensive and accurate prediction of the colorectal cancer liver metastasis risk is achieved, cross-dimensional data fusion and analysis improve the prediction accuracy and reliability, and the risk of the colorectal cancer liver metastasis is reduced. The invention also provides a powerful tool for deeply exploring the molecular mechanism of tumorigenesis, development and metastasis, and particularly in a cell analysis module, cell subtypes and molecular characteristics closely related to liver metastasis can be disclosed by identifying and analyzing malignant cell subgroups LMTMCs triggering liver metastasis, so that the liver metastasis can be discovered. And a scientific basis is provided for formulating a personalized treatment scheme.

The invention discloses a PPG-based blood pressure detection method, and particularly relates to the technical field of biomedical signal detection, and the method specifically comprises the following steps: 1, collecting a PPG original waveform through a PPG sensor; step 2, preprocessing the collected PPG signals; step 3, carrying out feature extraction on the preprocessed PPG signal; 4, the extracted PPG features serve as input, reference blood pressure parameters serve as output, an LSTM network model is constructed, the network weight is trained, and the mapping relation between the features and the blood pressure is established; step 5, obtaining a personalized model; and 6, collecting a large-scale multi-source heterogeneous data sample, and optimizing the model by adopting a transfer learning technology and an incremental learning technology. By processing and modeling PPG signals, accurate detection of blood pressure parameters is achieved, non-invasive continuous blood pressure monitoring can be achieved, and the long-term monitoring requirements of old people and patients with cardiovascular diseases are met.

The invention relates to the technical field of biomedicine, and discloses a female private rehabilitation training system integrating a breathing method and neural motor control, and the system comprises a data collection module which is used for collecting physiological data of a user; the personalized training scheme generation module is used for generating a personalized nerve-muscle-fascia training scheme; the nerve-muscle synchronous resonance training module is used for constructing a time sequence matching model and generating an adaptive training dynamic control instruction; the space-time adaptability training module is used for adjusting training duration, intensity and stimulation frequency; the multi-dimensional nerve-muscle feedback fusion module is used for constructing a feedback response model; and the fascia synchronous activation and relaxation module is used for coordinating the tension relationship between the fascia and the muscle system. According to the method, feature matching is carried out on an electric activation heat map matrix through the convolutional neural network, analysis of a myoelectricity response state transition probability is carried out in combination with a hidden Markov model, and stimulation parameters and respiratory rhythm are optimized in real time according to a training process.

The invention relates to a recombinant A type humanized XVII type collagen as well as construction and application thereof, the protein is the humanized XVII type collagen which is composed of an intracellular region, a transmembrane region and an extracellular region, and the amino acid sequence is as shown in SEQ No. 1. The hair follicle repairing agent has the advantages of hair follicle repairing, hair regeneration, good biological activities of cell proliferation, cell adhesion and the like, and can be widely applied to the fields of medicines, medical instruments, biological materials, tissue engineering, cosmetics and the like. The gene sequence of the recombinant A type XVII collagen is 100% identical to the corresponding part of the gene sequence of the recombinant A type XVII collagen, so that the recombinant A type XVII collagen has good hydrophilicity and high activity, and has application potential in the fields of biomedical materials, beauty cosmetics, skin care products and the like.

The invention relates to the technical field of biomedical signal processing and intelligent health monitoring, and particularly discloses an intelligent heart rate monitoring method and system based on heart sound and electrocardiosignals, heart sound signals and electrocardiosignals of a user are collected in real time, quality evaluation is conducted on the collected signals through signal quality indexes, and accurate signals are extracted; extracting time domain and frequency domain features of the first heart sound and the second heart sound of the heart sound signal, fusing multiple features by adopting wavelet transform and a main frequency analysis algorithm, and calculating a heart sound abnormal coefficient to evaluate the abnormal degree of the heart sound signal; the vibration amplitude and frequency characteristics of P waves in the electrocardiosignals are extracted, and an electrocardiograph abnormal coefficient is calculated through a vibration amplitude abnormal coefficient and a vibration frequency fluctuation coefficient and used for evaluating the stability of the electrocardiosignals; based on the heart sound abnormal coefficient and the electrocardiogram abnormal coefficient, a decision tree model is constructed, a heart rate abnormal index is dynamically calculated, dynamic monitoring of the heart rate is achieved, a dynamic early warning mechanism is conducted, and heart rate abnormal information is output.

The invention belongs to the technical field of biological medicine, and particularly relates to a video content quality analysis and knowledge recommendation method and system based on a large model, and the method comprises the steps: carrying out the video obtaining through an input keyword based on a short video platform, and obtaining a video ID, a video content text and video metadata; based on the video content text, multi-dimensional text information extraction and problem diagnosis are carried out, and a structured video text is generated; performing multi-dimensional scoring on the structured video text, calculating to obtain a video quality score, and constructing an initial high-quality video candidate set; based on the initial high-quality video candidate set and the user input question, calculating a similarity score of the user input question and the initial high-quality video candidate set, and constructing a high-quality video candidate set; and in combination with the video quality score and the similarity score, carrying out weighted calculation on a comprehensive recommendation score of each video in the high-quality video candidate set, and outputting the first three recommended videos and recommendation reasons to realize precise recommendation.

The invention discloses a polyrotaxane covalent organic framework material capable of regulating interlayer spacing through cyclodextrin and application of the polyrotaxane covalent organic framework material, and belongs to the technical field of biological medicine. The polyrotaalkylated covalent organic framework material is obtained by taking a cyclodextrin aromatic multi-amino inclusion compound and a (multi-aldehyde bipyridine) ruthenium (II) complex as structural units and carrying out copolymerization; the cyclodextrin aromatic polyamine inclusion compound is a beta-cyclodextrin p-phenylenediamine inclusion compound with a hydroxyl group or without a hydroxyl group; the (multi-aldehyde dipyridyl) ruthenium (II) complex is tris (4, 4 '-diformaldehyde-2, 2'-dipyridyl) ruthenium (II). The interlayer spacing of the polyrotaxane covalent organic framework material is greatly increased by utilizing a cavity of gamma-cyclodextrin, and meanwhile, the material has excellent photo-thermal and photodynamic activity, is positively charged, and can better adsorb negatively charged bacteria; and the hydrogel is good in biocompatibility and free of toxic and side effects, and has an excellent treatment effect on wounds infected with staphylococcus aureus.

This disclosure provides a novel method and system for characterizing morphodynamic profiles of objects, such as biological entities. This disclosure provides a shape, appearance, and motion (SAM) phenotype Observation Tool (SPOT). SPOT establishes a standardized SAM “phenome,” image descriptors resembling single-cell transcriptomes, to comprehensively quantify a cell's instantaneous state without prior knowledge. SPOT also establishes a standardized workflow for temporal analysis. SPOT is a generalist tool, applicable to any live-cell imaging and advances biomedical discovery through its standardized, unbiased, streamlined workflow to quantify phenotypic heterogeneity and predict phenotype-genotype-function coupling.

The invention discloses a dynamic blood glucose monitoring and intelligent pump speed adjusting system and method, and relates to the technical field of biomedical engineering.The dynamic blood glucose monitoring and intelligent pump speed adjusting method comprises the steps that precise monitoring is conducted through a multi-mode sensing module, an enzyme electrode sensor adopts an anti-interference coating, a Raman spectrum dynamic correction algorithm is combined, and motion artifacts and temperature drift are restrained; the metabolic feature fusion calibration module is integrated with a time convolution network, time sequence modeling is carried out on diet, exercise and historical blood glucose data, an individualized metabolic parameter database is constructed, and self-adaptive signal drift compensation is achieved; the self-adaptive pump control module innovatively designs a double-layer fuzzy PID controller, a base layer generates an infusion rate according to a real-time blood glucose state and insulin activity, an optimization layer introduces an LSTM metabolism prediction model, and the slope of an infusion curve is dynamically adjusted in combination with future behavior pre-judgment; in the edge-cloud collaborative architecture, a lightweight TCN model is locally deployed to guarantee real-time control, and a cloud end optimizes a global digital twinborn model through federal learning and generates a multi-dimensional metabolic risk early warning report.

The invention belongs to the technical field of biomedical engineering, and particularly relates to a biological rotator cuff patch with a double-gradient structure and a preparation method of the biological rotator cuff patch. The preparation method comprises the following steps: carrying out electrostatic spinning on a spinning solution, controlling the rotating speed of a receiving roller to increase at a constant speed in the electrostatic spinning process, and obtaining a nanofiber membrane after electrostatic spinning; l-polylactic acid and D-polylactic acid are simultaneously dissolved in the spinning solution; the minimum value of the rotating speed of the receiving roller is 100-500 rpm, and the maximum value of the rotating speed of the receiving roller is 3000-4000 rpm. The finally prepared biological rotator cuff patch with the double-gradient structure presents an orientation degree gradient and a piezoelectric effect gradient at the same time on the thickness scale, and has mechanical supporting capacity and biocompatibility, and bionic healing promotion of the patch with the gradient structure is achieved.

The invention relates to the technical field of artificial intelligence and biomedical sensing, and discloses a cognitive disorder risk identification device and method integrating multi-modal physiological data, and the device comprises an EEG module, an eye movement module, an fNIRS module, an edge calculation module and an acceleration sensor; the EEG module collects EEG data, the eye movement module collects eye movement eye-tracing data, and the fNIRS module collects near infrared spectrum fNIRS data and inputs the data to the edge calculation module; the edge calculation module runs the multi-modal space-time attention fusion model to output a cognitive impairment risk assessment result by using the multi-modal space-time attention fusion model; the acceleration sensor operates based on an operation dynamic filtering algorithm of a motion accelerometer to inhibit signal drift caused by head motion. According to the invention, through the modularized head-mounted multi-mode edge device, the physiological data is collected and edge end processing and cognitive disorder recognition and screening are carried out in combination with the edge calculation module, so that early recognition and screening of neurodegenerative diseases can be conveniently and effectively carried out at low cost.

The invention discloses a biomedical data analysis method based on adaptive multi-modal data fusion, and belongs to the technical field of multi-modal feature data processing. The method comprises the following steps: acquiring multi-modal data, and preprocessing the multi-modal data; extracting the preprocessed multi-modal data by adopting a modal specific neural network model; performing primary fusion on each extracted modal feature to generate a primary fusion feature; introducing an adaptive fusion module based on an attention mechanism to obtain final fusion features; and performing classification prediction on the final fusion features by using an FCNN model to complete classification of the multi-modal data. According to the invention, through dual mechanisms of primary average fusion and adaptive fusion, high-precision identification is maintained under the condition of multi-modal data missing, and performance reduction caused by incomplete data is avoided; meanwhile, the modal weight is dynamically adjusted through an attention mechanism, the fusion process can be optimized according to the quality and integrity of each modal data, and the feature expression ability is remarkably improved.

The invention relates to the technical field of biomedical engineering, and discloses an acute respiratory distress syndrome mechanical ventilation adaptive adjustment control system, which comprises a data acquisition module, a signal preprocessing module, a physiological parameter calculation module, a prediction module, a decision and control logic module and the like. Wherein the prediction module predicts a dynamic lung compliance change trend by using a long short-term memory neural network model, and the decision and control logic module generates a ventilation parameter adjustment instruction according to a prediction result and a clinical safety rule. By adopting the technical scheme, the system overcomes the hysteresis of traditional feedback regulation, reduces the risk of breathing machine related lung injury, optimizes oxygenation and carbon dioxide removal efficiency, and provides an accurate, safe and individualized mechanical ventilation treatment scheme.

The invention discloses a postoperative drainage device for clinical nursing and a monitoring system, and belongs to the technical field of biomedical engineering. The problems that an existing device manually monitors drainage liquid data, consequently, the monitoring precision is insufficient, and the complication probability cannot be predicted are solved, drainage liquid and patient vital sign data are collected in real time through the micro spectrograph, the character multi-parameter sensor, the micro pressure sensor and the intelligent bracelet and transmitted to the remote monitoring platform, and the monitoring accuracy is improved. Medical staff can check real-time data of a patient at any time and adjust a treatment scheme in time, so that the nursing efficiency and quality are improved; multi-parameter data such as color, metering and property of drainage liquid and vital sign data are analyzed through a risk prediction model, intervention measures are dynamically adjusted according to different complication types and probabilities in combination with patient condition changes and real-time monitoring data, accurate intervention on the complication is achieved, and the occurrence rate and severity of the complication are reduced.

The invention relates to the technical field of biomedical detection, and discloses a non-invasive sperm cell death and viability detection method, which utilizes a bright field image and a fluorescence image to train a fluorescence-assisted bright field attention network, introduces a fluorescence attention guidance module to provide guidance for training of a Transformer encoder, and realizes non-invasive sperm cell death and viability detection through priori knowledge of the fluorescence image. By means of the method, the model can be quickly focused on key features displayed in the fluorescent highlight area, the situation that training resources are consumed on a large amount of irrelevant information is avoided, convergence is accelerated, the model training time is shortened, meanwhile, the detection precision is improved, and then a more accurate and efficient sperm cell death and activity detection model is obtained. And during actual detection, the sperm cell death and viability detection can be realized only by collecting a microscopic image, pre-processing the microscopic image and inputting the preprocessed microscopic image into the model without dyeing a sperm sample, so that the damage of a traditional detection method to sperms is avoided, the non-invasive detection of the sperm cell death and viability is realized, and the method is more suitable for clinical detection.

The invention discloses a tooth intelligent segmentation and parameterized bionic reconstruction method fusing multi-modal data, and belongs to the field of artificial intelligence and computer-aided biomedical engineering. The method comprises a CBCT intelligent segmentation module, an intraoral scanning intelligent segmentation module, an internal tissue segmentation module, a point cloud generation registration module and a parameterized bionic reconstruction module. The point cloud generation registration module further comprises a point cloud generation sub-module and a point cloud registration sub-module; the parameterized bionic reconstruction module further comprises a dentin reconstruction sub-module, a cancellous bone reconstruction sub-module and a gingival reconstruction sub-module. The invention aims to realize tooth semantic segmentation, point cloud reconstruction and parameterized bionic modeling of an organization structure under cross-modal data fusion, and provides high-precision bionic internal structure information for teeth in combination with an artificial intelligence model and parameterized modeling.

The invention relates to the technical field of biomedical engineering and computer vision, in particular to a non-contact physiological signal extraction method and system based on frequency self-adaption and illumination noise perception.The method comprises the following steps of multi-mode video stream collection and spatio-temporal data preprocessing, illumination-noise perception mask generation and feature filtering, multi-mode video stream collection and spatio-temporal data preprocessing, illumination-noise perception mask generation and feature filtering, and non-contact physiological signal extraction. Frequency adaptive gating and frequency domain feature enhancement, depth time attention feature re-calibration, physiological signal regression and closed loop optimization; the method has the beneficial effects that a lightweight end-to-end deep learning network architecture is constructed by systematically fusing three core modules of illumination-noise perception mask, frequency adaptive gating and depth time attention, and the defects that a traditional physical model depends on artificial prior and is poor in anti-interference performance and high in reliability are overcome. And the one-sidedness caused by high calculation complexity and difficulty in distinguishing the signal and noise of the existing deep learning model is avoided, and the weak physiological signal can be recovered from the face video more accurately and robustly.

The invention relates to the technical field of biomedicine, in particular to a hepatic cell differentiation degree evaluation method based on multi-omics data, which comprises the steps of sample collection and preprocessing, transcriptomics, proteomics and metabonomics data analysis, multi-omics data integration and modeling and result output. By integrating multi-level biological information, a multi-dimensional scoring model is constructed, the liver cell differentiation state is quantitatively evaluated, and a standardized grading system is provided for clinic. The method can solve the limitation of single omics analysis, improves the evaluation accuracy and reliability, has universality, can be popularized to other malignant tumor research, and assists precise medical development.

The invention relates to an infectious bacterium detection method and system based on double effects of a reflection spectrum and autofluorescence, and belongs to the field of biomedical detection. The method comprises the following steps: acquiring hyperspectral images of different samples under different light sources; performing data preprocessing on the collected sample hyperspectral image, realizing pixel-level alignment of the dual-effect image based on feature matching and image registration, and constructing a data set; the method comprises the following steps: constructing a wound infection bacterium detection model based on double effects of a reflection spectrum and autofluorescence, and comprising a double-branch feature extraction module which comprises a reflection branch and a fluorescence branch which are respectively used for extracting reflection hyperspectral image features and fluorescence hyperspectral image features; the redundant feature screening module screens the features extracted by the reflection branch and the fluorescence branch through a channel attention mechanism; and the feature fusion module adopts a cross-branch cross attention mechanism to fuse the features of the double branches. According to the method, learning is carried out from two physicochemical characteristics, and the effectiveness and adaptability of the technology are improved.

The invention discloses a method for extracting extracellular vesicles from dried Sanguisorba officinalis rhizomes and application of the extracellular vesicles, and relates to the technical field of molecular biology and biomedicine.The method comprises the following steps that S1, rehydration is conducted, specifically, the dried Sanguisorba officinalis rhizomes are subjected to liquid nitrogen quick freezing and then ground into powder at the low temperature, the powder and hydration liquid are mixed according to the proportion of 1: 10 (w / v), the mixture is slowly stirred for 2-4 h at the temperature of 4 DEG C, and the extracellular vesicles are obtained; removing large-particle impurities by using sterile gauze or a filter screen, and collecting filtrate; according to the method, through the synergistic effect of multiple enzymolysis and in combination with cellulase, pectinase and the like, the degradation efficiency is remarkably improved, meanwhile, EDTA is chelated with metal ions, and the enzymolysis effect is enhanced. Short-time alternating ultraviolet light is introduced to activate'dormant 'vesicles, so that the yield and the biological activity are improved. Secondly, multiple protection mechanisms, such as vitamin C, EDTA (Ethylene Diamine Tetraacetic Acid), cane sugar, BSA (Bovine Serum Albumin) and an ultraviolet protective agent are adopted, so that the integrity and the biological activity of the vesicles are ensured.