1379 results about "Diagnosis laboratory" patented technology

The invention provides a management method and system for hazardous chemicals in a laboratory, and relates to the technical field of hazardous articles, and the method comprises the steps: obtaining hazardous article operation video data of an operator through an image collection device, extracting key node coordinates and skeleton movement track information, and mapping the information into standardized operation behavior sequence data; and a time sequence behavior evaluation model constructed based on a long and short term memory network is used to identify the non-standard operation fragments, a risk level coefficient is calculated in combination with a dangerous goods attribute information base, and a multi-dimensional evaluation report is generated. And according to the evaluation report, calling a correction strategy library to generate an improvement suggestion containing characters, videos and three-dimensional models, and pushing the improvement suggestion to an operator. By monitoring the behavior of the operator after executing the improvement suggestion and dynamically updating the risk score and the time sequence behavior evaluation model, the dynamic optimization of the evaluation standard is realized, and the safety of the laboratory hazardous chemical substance operation is effectively improved.

The invention discloses a blood tumor patient clinical data analysis method and system based on a knowledge graph, and relates to the technical field of medical information processing. The method comprises the following steps: analyzing an unstructured text by utilizing a natural language processing model, and constructing a standardized entity set; aggregating laboratory indexes through a dynamic window, and constructing a time axis data set for marking a treatment stage; a standardized entity is integrated based on a blood tumor ontology model, a dynamic global knowledge graph is constructed in combination with a clinical causal relationship and a time sequence path, and chain reaction early warning is realized; based on the real-time patient sub-atlas, successful cases with the same molecular features are retrieved in the knowledge atlas to generate a candidate treatment scheme set, a graph neural network trained through an optimization algorithm is utilized to predict the complication risk of each scheme, and finally the optimal treatment scheme is evaluated and selected; according to the method, a clinical data barrier is broken through, dynamic reasoning, advanced risk early warning and personalized scheme optimization of the whole treatment cycle are realized, and the diagnosis and treatment accuracy, safety and medical resource utilization efficiency are remarkably improved.

A deep learning-based virtual HER2 IHC staining method uses a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled / label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this staining framework was demonstrated by quantitative analysis of blindly graded HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs). A second quantitative blinded study revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory, and can be extended to other types of biomarkers to accelerate the IHC tissue staining and biomedical workflow.

The present invention relates to an early monitoring system for pancreatic cancer based on big data, including a data acquisition module, a feature extraction module, a risk assessment module and an early warning trigger module. The data acquisition module is used to obtain the user's laboratory test data, imaging test data and clinical symptom data. The feature extraction module is connected to the data acquisition module, and constructs a comprehensive feature vector by extracting biochemical indicator features, imaging features and symptom features. The risk assessment module uses a pre-trained deep learning model to analyze the feature vector and generate a pancreatic cancer risk probability. The early warning trigger module compares the risk probability with a preset threshold, generates an early warning signal when the risk probability exceeds the threshold, and pushes it to a medical service terminal to suggest further diagnosis. The present invention combines multimodal data and deep learning technology to achieve accurate early monitoring of pancreatic cancer and improve diagnostic efficiency and accuracy.

The invention discloses a laboratory analytical instrument automatic calibration system based on artificial intelligence, and belongs to the technical field of instrument automatic calibration. Comprising the following modules: an intelligent hardware interaction module for realizing plug-and-play of different instruments and accurate conveying of standard substances; the instrument digital twinning module automatically identifies the characteristics of the instrument and simulates the behaviors of the instrument; the multi-mode AI calibration module is used for optimizing calibration parameters and adjusting the calibration process in real time; the environment intelligent compensation module calculates and generates a compensation coefficient in real time, and eliminates the influence of environmental factors on a calibration result; the instrument health management module is used for evaluating the health state of the instrument and predicting the service life of a core component, and realizing conversion from passive maintenance to predictive maintenance; the calibration knowledge graph module is used for realizing automatic extraction, reasoning and application of calibration knowledge through a graph neural network and continuously optimizing a calibration strategy; and the intelligent scheduling and management module is used for generating an optimal calibration plan, coordinating the work of each module and realizing reasonable distribution and efficient utilization of calibration resources.

The invention discloses a laboratory automatic process management and multi-source data fusion system based on the Internet of Things, and belongs to the technical field of laboratory management. The system comprises a sensing layer, an analysis layer, an intelligent management layer and an application layer. The sensing layer carries out global data acquisition and real-time analysis; the analysis layer integrates data, mines an association relationship and optimizes task matching; the intelligent management layer performs full-process automatic management and control on a laboratory, and comprises an equipment cooperation module, a dynamic detection module, a dynamic configuration module, a real-time simulation module, a preset trigger module and an intelligent auditing module; and the application layer performs multi-terminal collaborative interaction and intelligent assistant assistance processing. According to the system, intelligent collaboration of laboratory equipment, automatic detection of samples, dynamic optimization of equipment parameters and automatic early warning and processing of abnormal conditions are realized through technical means such as knowledge graph construction, a digital twinborn technology and multi-mode biological recognition, and the laboratory management efficiency and the data processing accuracy are effectively improved.

The invention relates to the technical field of diagnosis, in particular to a diagnosis and treatment auxiliary system and method for the obstetrics and gynecology department, and the system comprises a parameter collection module, a feature offset recognition module, a feature reconstruction module, a collaborative trend recognition module and a diagnosis and treatment suggestion generation module. According to the system and the method, the body temperature, the heart rate, the pulse, the blood pressure and the image edge density are synchronously acquired and the timestamps are bound, so that a structured diagnosis and treatment data unit is formed, the time sequence comparison capability of multi-source information is effectively improved, and potential risk points can be positioned and feature tags can be constructed based on a numerical value offset identification and synchronous index combined judgment mechanism; according to the method, physical signs, images and laboratory data are normalized and fused into three-dimensional vectors, cross-dimensional feature linkage expression is achieved, the dynamic insight ability to pathological state changes is improved by screening features with large variation amplitude and long duration time and judging trend consistency, and the accurate response to the fetal risk state is enhanced by combining a specific trend combination matching rule.

The invention discloses an intelligent remote follow-up visit method and system for tumor patients, and relates to the technical field of medical intellectualization, and the method comprises the steps: collecting follow-up visit data through a remote medical terminal, carrying out the processing of the follow-up visit data, obtaining a physical sign index sequence and a symptom feature vector, and generating a dynamic follow-up visit evaluation score through a time sequence prediction model; performing semantic analysis on the electronic medical record data of the patient based on the medical knowledge map, extracting tumor stage classification data, chemoradiotherapy measurement data, weight nutrition indexes and laboratory inspection data, determining a basic risk coefficient, a cumulative dose ratio and a body state score, and generating a baseline follow-up assessment score through a prediction model; and according to the dynamic follow-up evaluation score and the baseline follow-up evaluation score, calculating a comprehensive follow-up grade and generating a next follow-up plan. According to the invention, the accuracy of patient state evaluation is improved, accurate configuration of medical resources is realized, and the quality and efficiency of remote follow-up visit are effectively improved.

The invention discloses a medical intelligent dialogue method based on deep learning, relates to the technical field of intelligent medical treatment, constructs a rare disease knowledge graph, and deduces implicit relations between diseases and symptoms through association relations in the graph even under the condition of insufficient training data. The ability of processing complex cases such as rare diseases is greatly enhanced, a more accurate rare disease list can be provided through a map reasoning mechanism, related diagnosis schemes are reasonably and preferentially displayed to a user, the misjudgment risk is reduced, and multi-source data such as medical record texts, medical images and laboratory detection results are effectively integrated through a multi-mode Transform model, so that the accuracy of diagnosis is improved. According to the method, a unified comprehensive feature representation is generated, the ability of comprehensively analyzing the health condition of the patient from different angles is deeply fused, the diagnosis accuracy is improved, the inquiry sequence and the problem depth can be adjusted according to the specific illness conditions of different patients based on the preliminary diagnosis result, and more accurate and personalized inquiry experience is provided.

The invention relates to the technical field of intelligent medical treatment, and discloses an AI-based severe patient sepsis early warning method and system. According to the method, a multi-organ interaction mechanism is deeply analyzed by dynamically constructing an organ-level causal network, and early-stage accurate early warning of sepsis is realized: high-frequency physiological waveforms, asynchronous laboratory indexes and treatment intervention data are fused, and the capture ability of microcirculation failure and autonomic nerve decline is improved; the early warning threshold value is dynamically adjusted based on the treatment coverage degree, and the delay and false alarm defects of a fixed threshold value mechanism are effectively overcome; a pathogen targeted therapy map and an organ function support scheme are automatically generated by a graded triggered clinical action chain, and the clinical decision response time is shortened; according to the method, the risk of missed diagnosis is reduced while the early warning sensitivity is improved, and an earlier and more reliable intervention window is provided for critical patients.

The invention relates to the technical field of medical diagnosis processing, and discloses an intelligent disease diagnosis and differential diagnosis system based on a knowledge graph, and the system comprises a data input module which is used for receiving and standardizing clinical symptoms, signs, laboratory examination data and historical medical record data of a patient; the knowledge graph construction and updating module is used for constructing and updating a knowledge graph of diseases and symptoms according to the medical literature and the clinical data, and the knowledge graph automatically extracts an incidence relation between the symptoms and the diseases from the medical literature through a natural language processing technology; and the reasoning and diagnosis module is used for performing intelligent disease diagnosis and differential diagnosis. According to the method, by optimizing reasoning path selection and information gain calculation, the path with the most information content is selected from multiple reasoning paths for diagnosis reasoning, and the most representative path is selected by calculating the correlation degree between each symptom and the disease.

The invention discloses a multi-agent large-model disease diagnosis knowledge reasoning system based on data dual drive, and relates to the technical field of artificial intelligence assisted medical diagnosis. The system collects patient symptom follow-up records, laboratory test results, observation diagnosis probabilities and expert diagnosis recommendation results in a multi-source manner; time sequence evolution characteristics are extracted, a time sequence diagnosis sensitivity coefficient is calculated, and early recognition of disease risks is achieved; in combination with anti-fact simulation and statistical reasoning, a causal consistency coefficient is obtained and is used for verifying causal reasonability of observation diagnosis and contrast results; based on agent group consensus analysis, calculating a game consistency coefficient for judging the credibility of a diagnosis conclusion; positioning and multi-level verification are carried out on abnormal reasoning steps and knowledge fragments, so that the reliability and safety of a result are guaranteed; continuous optimization of the diagnosis model is realized through a log analysis and knowledge backflow mechanism; according to the invention, the accuracy, interpretability and safety of disease diagnosis can be obviously improved.

The invention, which relates to the technical field of the medical platform, discloses a medical aid decision-making system based on a multi-modal large model, comprising an electronic medical record, a medical image and a laboratory examination result. Carrying out the preprocessing of the collected multi-modal medical data, and obtaining the preprocessed multi-modal medical data; the data processing module is further used for preprocessing the preprocessed multi-modal medical data, comparing the preprocessed multi-modal medical data with an existing medical knowledge base, verifying the reasonability of the data, evaluating the data, and deleting the non-conforming multi-modal medical data to obtain the evaluated multi-modal medical data; the problem that the generalization ability of a multi-modal large model in the medical field is limited is solved through the decision reasoning module, rich background knowledge is provided for the model by means of construction and distillation of a medical knowledge graph, the understanding and processing ability of the model for different tasks is improved, and the system has good application prospects. And the problem of insufficient reliability of the multi-modal large model in actual clinical application is solved through the verification module.

The invention relates to the technical field of medical monitoring and early warning, in particular to an acute kidney injury risk assessment and early warning system which comprises a urine bioelectrical impedance analysis module, a data acquisition module, a data preprocessing module, a model construction module, a risk assessment module and an early warning module. Wherein the urine bioelectrical impedance analysis module is used for acquiring urine electrical impedance spectroscopy data; the data acquisition module is used for acquiring physiological parameters of a patient and laboratory inspection indexes; the data preprocessing module is used for extracting urine electrical impedance characteristic parameters, physiological characteristic parameters and laboratory characteristic parameters; the model construction module is used for constructing an acute kidney injury risk assessment model; the risk assessment module is used for calculating the risk score and the risk grade of the acute kidney injury of the patient. According to the system and the method, early-stage accurate evaluation and graded early warning of the acute kidney injury are realized through multi-source data fusion and intelligent analysis technologies, timely intervention of medical staff is facilitated, and the accuracy and response efficiency of risk detection are remarkably improved.

The invention relates to the field of medical treatment, and discloses a full-process quality control management system for neonatal disease screening, which comprises a data real-time acquisition module for realizing data intercommunication and dynamic acquisition with a laboratory information system, detection equipment and a sample management system through a standardized API (Application Program Interface) and a data transmission protocol; the indoor quality control module is used for performing real-time monitoring and rule management on laboratory internal detection quality by applying a Westgard multi-rule control algorithm and a real-time data monitoring technology; and the interroom quality evaluation module is used for carrying out multi-laboratory detection result comparison and quality evaluation by adopting a data encryption and anonymization processing technology. Through cooperation of a cross-system docking unit and a dynamic data analysis unit of the data real-time acquisition module, data islands of a laboratory information system, detection equipment and a sample management system are broken through, and real-time intercommunication and standardized processing of sample information, detection data and process nodes are realized.

The invention belongs to the technical field of medicine, and discloses a method and system for predicting anastomotic fistula occurring after colorectal cancer operation, and the method comprises the steps: obtaining clinical pathology data and laboratory analysis data of a patient; filling missing data in the clinical pathology data and the laboratory analysis data; screening key indexes by using a single-factor regression analysis technology; performing multi-factor regression analysis on the key indexes, and screening to obtain independent prediction factors for predicting whether the colorectal cancer patient has the anastomotic fistula or not after operation; based on the independent prediction factors obtained through screening, an anastomotic fistula prediction model is constructed and trained, and the trained anastomotic fistula prediction model is used for predicting the postoperative anastomotic fistula occurrence probability of the colorectal cancer patient. The method can more accurately predict the probability of occurrence of anastomotic fistula of a colorectal cancer patient after an operation, has better accuracy and reliability, can facilitate preoperative prediction of the probability of occurrence of anastomotic fistula for clinical doctors, and assists in guiding treatment decision.

The invention discloses an in-vitro diagnosis liquid level detection method based on a 60GHz on-chip antenna millimeter wave radar, and relates to the technical field of liquid level detection in the IVD field, and the method comprises the steps: receiving a reflection signal through a sensor, and carrying out the preprocessing; extracting liquid level position data from the reflected signal through a machine learning algorithm; a temperature compensation and calibration mechanism is used for monitoring the temperature in real time and compensating and calibrating a measurement result; a friendly user interface is constructed and developed to feed back the system operation state in real time. The device can be accurate to a micron level, can accurately detect the liquid level height change in a micro cup or a reagent bottle, provides an extremely accurate reference for reagent addition, is greatly enhanced in reliability, is not influenced by factors such as liquid color, transparency, container material and shape, and can be used in a complex and changeable laboratory environment. The device can work stably and accurately under the conditions of temperature, humidity, electromagnetic interference and the like, and effectively avoids measurement errors caused by interference of external factors.

The invention provides an ultrasonic-guided critical patient enteral nutrition nursing decision support method and device, and relates to the field of intelligent nursing, and the method comprises the steps: firstly, carrying out nutrition evaluation on a critical patient to obtain nutrition evaluation data including medical history data and laboratory examination data, and meanwhile, collecting an abdominal ultrasonic image of the critical patient; and performing image enhancement processing on the abdominal ultrasound image to obtain an enhanced abdominal ultrasound image, and then inputting the enhanced abdominal ultrasound image and nutrition evaluation data into a nutrition nursing decision support engine based on a large model together to obtain a nutrition nursing decision support suggestion. Therefore, by monitoring and analyzing the health change and the intestinal function state of the patient in real time, personalized, accurate and timely nutrition nursing suggestions can be provided.

The invention relates to a renal transplantation post-operation rejection risk monitoring and early warning method and a renal transplantation post-operation rejection risk monitoring and early warning system. The method comprises the steps of collecting recipient nursing monitoring data, laboratory indexes and transplanted kidney ultrasonic blood flow parameters in a follow-up visit period, performing timestamp alignment, deletion processing and standardization on multi-source data, extracting features to construct a time sequence feature sequence, inputting the time sequence feature sequence into a pre-training risk prediction model, outputting the rejection reaction occurrence probability of the next period, and forming a risk trend. Calculating a nursing sensitive index contribution weight based on the model contribution information, and screening a target nursing monitoring index; and establishing an individualized baseline model to obtain a baseline value and an allowable fluctuation interval, extracting characteristics such as deviation amplitude, direction, rate, fluctuation and continuous deviation duration and the like, and performing individualized calibration on the occurrence probability to obtain a calibration risk score. When the threshold value is not reached and the trend is not triggered, generating a nursing monitoring suggestion of the next period; and pushing early warning and generating grading intervention suggestions when a threshold value is reached or a trend is triggered.

The invention relates to the field of medical informatization, in particular to a pathology laboratory ISO15189 intelligent face-to-face inspection auxiliary system based on a knowledge graph, and is applied to the quality management and standard authentication process of a pathology laboratory. According to the method, automatic mapping of the ISO15189 standard terms and pathological quality inspection key points is realized by constructing the knowledge graph in the pathology field and combining a natural language processing technology, so that the inspection efficiency is greatly improved; the system automatically generates a meeting inspection report meeting the standard, and shortens the document arrangement time from several days to several hours; meanwhile, the risk monitoring module collects service flow data, carries out risk assessment and early warning, and ensures that the inspection process is compliant. The application of the knowledge graph technology effectively overcomes the limitation of the traditional method in understanding the terminology semantics, and enhances the quality management and inspection preparation work of the pathology laboratory.

The invention discloses a full-automatic milk somatic cell detection system and method based on fluorescence analysis. According to the full-automatic fluorescence microscopic examination system, automatic sample introduction and automatic mixing are completed through the automatic sample introduction module, automatic focusing of dyed samples is completed through the automatic focusing module, automatic photographing processing and automatic counting are completed through the automatic photographing and counting module, and therefore full-automatic fluorescence microscopic examination is achieved. According to the method, the automatic detection level is remarkably improved, the consistency of the detection process is ensured, and manual operation errors are reduced, so that the experiment efficiency, the data accuracy and the reproducibility are improved; compared with traditional off-line detection or laboratory detection, the method and system provided by the invention have better real-time performance and accuracy, and also have higher automation degree.

The invention relates to the technical field of medical recommendation, in particular to a personalized nutrition treatment knowledge base construction method and system for critical patients. The system is used for acquiring basic information, medical record information, laboratory examination data and period diet intake information corresponding to critical patients, performing critical case entity construction, and generating case entity descriptors of the critical patients; constructing a corresponding personalized nutrition treatment initial knowledge base for each critical patient case entity descriptor in combination with a semantic knowledge association ratio between related scientific research literatures; obtaining clinical pathological characteristics and nutrition treatment knowledge entities, performing nutrition demand analysis and administration opportunity determination, and performing nutrition administration effect conflict evaluation to obtain a clinical nutrition administration-diet effect conflict ratio; and performing nutrition treatment knowledge updating optimization on the personalized nutrition treatment initial knowledge base to generate a personalized nutrition treatment knowledge optimization base. Accurate nutrition support can be provided for critical patients.

The invention belongs to the technical field of medical data processing and health assessment, and discloses a multi-source medical data-based risk prediction method, which comprises the steps of multi-source medical data acquisition, data preprocessing, feature selection, model training, risk prediction and model explanation. According to the method, by adopting a systematic multi-stage feature selection strategy, a specific key feature combination highly related to a specific medical event or disease can be screened out from multi-source heterogeneous medical data including clinical data, laboratory data, heart MRI (Magnetic Resonance Imaging) and the like; the screened feature subsets can be used for constructing an interpretable machine learning model, and through combination with SHAP and other model interpretation technologies, clinicians are helped to understand prediction logic, the credibility of results is enhanced, and more valuable reference information is provided for individualized clinical decision and intervention.

The invention provides a therapeutic drug monitoring kit and method and system, a magnetic bead extraction method is combined with LC-MS / MS detection to be applied to human blood drug concentration monitoring, a mixed magnetic bead solution containing phospholipid removal magnetic beads and to-be-detected drug adsorption magnetic beads is utilized to perform adsorption and extraction pretreatment on a to-be-detected drug in a sample, automatic treatment equipment is matched, and the concentration of the to-be-detected drug in the to-be-detected drug in the to-be-detected drug in the to-be-detected drug in the to-be-detected drug in the to-be-detected drug in the to-be-detected drug is detected. The pretreatment efficiency is high, the phospholipid content in the supernate is low, and the matrix effect and the chromatographic column washing pressure are greatly reduced. According to the therapeutic drug monitoring method provided by the invention, a unified pretreatment process is used, simultaneous extraction of multiple drugs is realized, and through a magnetic bead automatic extraction method, the pretreatment step of a sample can be rapidly completed, a therapeutic drug monitoring result can be rapidly obtained, the overall detection time is shortened, and the detection efficiency is improved. Therefore, a reliable laboratory examination basis is provided for individualized treatment.

The invention discloses an internal medicine laboratory data sharing and querying system, and relates to the technical field of laboratory data security sharing. A special management and control terminal is arranged to match internal medicine laboratory data of a corresponding patient according to identity information of a pre-queried patient; according to the method, the preset security conversion rule is used for converting the internal medicine laboratory data to obtain the corresponding shared query data, so that the dependence on a key is eliminated, the management cost of the key is saved, and the management difficulty is reduced; in the process of converting the internal medicine laboratory data, the internal medicine laboratory data is intercepted through the preset intercepted character quantity, whether each character in the intercepted data is 8 or 9 or not is judged, a plurality of grouped word strings are intercepted and selected based on judgment, and then the corresponding shared conversion sequence is obtained, so that the security of the data is improved, and the conversion efficiency of the internal medicine laboratory data is improved. And a safer and more reliable guarantee is provided for sharing and querying of internal medicine laboratory data, and the privacy of patients and the safety of medical data are ensured.

The invention discloses a measurement laboratory anomaly detection and diagnosis method, system and device based on deep learning and a medium, and relates to the technical field of anomaly detection and diagnos.The method comprises the steps that multi-source real-time data are collected and preprocessed; performing alignment processing based on sampling inconsistency among the data sources, and constructing unified data representation; generating a corresponding prediction result by using the prediction model; calculating a comprehensive abnormal score based on the aligned data and the prediction result; comparing the comprehensive abnormal score with a threshold value, and judging whether a comprehensive abnormal state exists or not; if the judgment result is abnormal, performing abnormal cause decoupling processing and causal inference to obtain a candidate root cause set; and inputting the candidate root cause set into a deep learning causal inference model to obtain an anomaly diagnosis result. A physical perception residual scoring mechanism is introduced, a comprehensive anomaly score is combined on the basis of anomaly detection, a weighted calculation method is adopted, and the contribution degree of each data source to an abnormal state can be accurately evaluated.

The invention discloses an ultrasonic anesthesia positioning method and system based on AI preoperative evaluation and a storage medium, and relates to the technical field of medical assistance. The method comprises the following steps: comprehensively collecting body health data of a patient, including a physical examination result, a laboratory examination result and a medical history, reasoning the body health data of the patient based on an operation risk knowledge graph model, and determining an operation risk level; if the operation risk level is higher than a preset risk level value, the operation risk is evaluated again in combination with past operation case information, and if the operation condition is met, the crowd is judged to be the high-risk crowd of the operation; an ultrasonic image of an in-vivo structure of an operation high-risk crowd is collected by utilizing an ultrasonic technology, the ultrasonic image is recognized by utilizing an image recognition AI algorithm, the relationship between an anatomical position and an adjacent part is determined, and then an anesthesia position is determined. The medical quality and the service level can be improved, and safer and more efficient surgical treatment experience can be brought to patients.

The invention discloses a pheromone binding protein derived peptide capable of effectively monitoring sex pheromones of fall webworms and a biosensor, and belongs to the field of biosensors. At present, methods such as sample plot survey and sex traps commonly used in fall webworm monitoring have the problems of poor timeliness, high labor cost, insufficient data continuity and the like. In order to solve the problem, the pheromone binding protein-derived peptide provided by the invention has an amino acid sequence as shown in SEQ ID NO: 2, the biosensor comprises a substrate and an interdigital electrode, a single-walled carbon nanotube is attached to the electrode, and the electrode is also connected with the pheromone binding protein-derived peptide. The sensor can specifically detect sex pheromones of fall webworms, has no response to 17 plant volatile matters, and can detect sex pheromones released by as low as five live female fall webworms under laboratory conditions. The method has application potential in the fields of early pest monitoring, prevention and control efficiency improvement, ecological safety guarantee and the like.

A specific antibody and an antigen binding fragment thereof against a CD228 protein, used for testing and treating multiple tumors, such as melanoma, mesothelioma, and colorectal cancer. An antibody binding fragment facilitates the diagnosis and treatment of cancer by specifically binding to CD228. The present invention further comprises antibody-drug conjugates (ADCs) in which drugs bind to antibodies by means of linkers, aiming to improve the treatment effect and reduce the impact on normal cells. Additionally, the present invention provides a nucleic acid encoding an antibody or an antigen binding fragment thereof, so as to produce a recombinant antibody or fragment. In addition, the present invention further provides a kit, used for laboratory research, clinical diagnosis and treatment effect monitoring, facilitating determination of whether tissues or cells express CD228, providing a new strategy for personalized and accurate treatment of cancer.