38 results about "Population screening" patented technology

The invention relates to the technical field of tumor markers, in particular to a pancreatic cancer early screening marker combination based on blood protein and application of the pancreatic cancer early screening marker combination. Through proteomics analysis and bioinformatics screening, 12 kinds of proteins such as NME3, HPGDS, CEACAM5, TNFSF12, SCG3, VTCN1, GFRA1, KRT19, TMPRSS12, NELL2, KAZALD and MMP12 are determined to serve as the potential early screening markers of the pancreatic cancer. Researches show that the combination of the proteins can effectively distinguish pancreatic cancer patients from non-pancreatic cancer patients, and has good sensitivity and specificity. A screening model constructed based on the protein combination can be applied to auxiliary diagnosis, risk assessment and population screening of pancreatic cancer, and has important clinical transformation value.

The invention discloses a lung cancer high-risk group screening questionnaire cognitive calculation correction method and system. The method comprises the following steps: acquiring questionnaire text data of a subject; constructing a BERT-QA model based on the clinical term knowledge graph, analyzing questionnaire text semantics, and extracting structured risk factor data; mapping the risk factor data to a topological space, and constructing a risk topological representation; calculating a dynamic weight coefficient of each risk factor based on an information entropy principle, and generating a risk assessment value of entropy weight optimization; carrying out calibration in combination with regional pollution map data; according to the method, deep learning, topology and fuzzy logic technologies are fused, intelligent analysis of questionnaire texts and dynamic weight calculation of risk factors are realized, environmental pollution factors are effectively integrated, the screening accuracy and individuation level are improved, and the method is suitable for large-scale popularization and application. The method provides technical support for accurate identification and early intervention of lung cancer high-risk groups, and has important clinical application value.

The invention discloses a related product of FT3 in depression risk assessment and application, and relates to the field of depression diagnosis. The marker provided by the invention comprises FT3 and shows a remarkable technical effect in the aspect of predicting depression, high-precision early recognition and risk prediction of depression are realized, a key window and an objective basis are provided for early active intervention and whole-course management of diseases, and the accuracy and reliability of screening and diagnosis are greatly improved. Meanwhile, the marker has excellent universality, is suitable for wide population screening, and lays a technical foundation for low-cost and high-efficiency large-scale depression screening. In conclusion, a brand new technical means is provided for effective diagnosis and risk early warning of depression, and the industrial application prospect is wide.

This invention discloses an early screening system and method for hyperuricemia based on fingertip blood uric acid detection. The system includes a fingertip blood collection module, a detection module, a data processing module, a storage module, an analysis module, an early warning module, a display module, a communication module, a calibration module, and a power supply module. It accurately collects fingertip blood samples from subjects, obtains blood uric acid concentration data using specific detection equipment, constructs a multi-dimensional analysis model based on clinical characteristic parameters, and determines the optimal diagnostic cutoff value based on ROC curves, achieving rapid and accurate screening for hyperuricemia. The method includes steps such as sample collection, detection, data processing, model analysis, result determination, and early warning. It is simple to operate, fast, and highly accurate, solving the problems of poor compliance and difficulty in grassroots implementation associated with traditional venous blood testing. It is suitable for large-scale population screening and early diagnosis of hyperuricemia in areas with scarce medical resources, and has significant clinical application value and social significance.

The invention relates to the technical field of biomedical detection, in particular to a screening method for triple negative breast cancer immunotherapy drug-resistant population, which comprises the following steps: acquiring gene expression data of triple negative breast cancer patients, calculating to generate an immune matrix score comparison data set, processing single cell sample mRNA sequence data, clustering annotation cells, and screening drug-resistant associated subgroups; extracting specific subgroup genes to establish a drug-resistant characteristic gene combination, detecting transcriptome gene expression states of to-be-screened patients to obtain a matching degree, and comparing the matching degree with a threshold value to generate an immunotherapy drug-resistant population screening result. The method comprises the following steps: calculating gene expression data of a triple negative breast cancer patient to generate a corresponding data set, processing single-cell mRNA sequence data, clustering annotation cells, screening drug-resistant associated subgroups, extracting specific subgroup genes to establish a feature combination, detecting a gene expression state of a to-be-screened patient to obtain a matching degree, and comparing a threshold value to generate a screening result. Drug resistance related cells and genes are positioned, and the screening accuracy is improved.

The invention discloses a high-throughput heart rate variability group detection device and detection method. The high-throughput heart rate variability group detection device comprises a physiological signal acquisition module, a storage cabin, a central management system, a data line and a charging power supply, according to the high-throughput heart rate variability group detection method, a data temporary storage and connection technology is adopted, staged collection and transmission of group heart rate variability test data are achieved, and the data temporary storage and connection technology is used in cooperation with a wireless distributed terminal of a heart rate variability group detection device. The subjective hysteresis of a psychological scale and the low-efficiency limitation of an objective method in physical and psychological health evaluation are broken through, and high-throughput rapid detection and convenient data management of the heart rate variability of large-scale crowds are realized, so that objective and accurate data support is provided for screening of physical and psychological health risk groups; the whole detection process is easier to organize and operate, and the accuracy and safety of the detection result are ensured.

The application discloses a multi-cancer early screening method based on fragmentomics and microbiomics characteristics and application thereof, and belongs to the technical field of biomedical detection. The method comprises the following steps: extracting fragmentomics characteristics of human cfDNA and microbiomics characteristics of plasma microbial DNA based on sequencing data; inputting the two types of characteristics into a first-stage classification model after fusion, and judging whether a subject is cancer positive; and further predicting the cancer type through a second-stage classification model for the positive result. The application combines fragmentomics and microbiomics characteristics, and is used for multi-cancer early screening. Through single non-invasive blood sampling, early screening and organization tracing of eight high-incidence cancers such as lung cancer, liver cancer and colorectal cancer can be realized. The method has high sensitivity and high specificity under low sequencing depth, is low in cost, wide in coverage, and suitable for large-scale population screening.

The invention provides a permanent resident population determination method and system based on communication big data, and the method comprises the steps: carrying out the efficient organization and quick retrieval of massive signaling position data through a spatial index technology, and screening out candidate users in any polygonal region from hundred million-level data in a second level; work parameter data such as a base station antenna azimuth angle and a coverage angle are introduced, a base station coverage space is accurately calculated, geometrical relationship analysis is carried out on the base station coverage space and a target area, effective base stations are intelligently screened by using a threshold value, population misjudgment caused by directional coverage and a boundary effect of the base stations is fundamentally corrected, and the accuracy of population estimation is improved. And the accuracy of small-scale region population screening is obviously improved. The whole scheme is constructed on a big data parallel computing framework, and quick response to ten-thousand-level region query in a high-concurrency environment is realized through data fragmentation, task dynamic splitting and parallel processing.

The invention discloses a three-gene combination, a primer probe set and a system for qPCR (quantitative polymerase chain reaction) rapid screening of tuberculosis progression risk and application of the three-gene combination, the primer probe set and the system. The method comprises the following steps: firstly, screening out candidate genes APOL1, C1QC and SOCS1 which are stable across time windows and have high prediction performance; then a three-gene combination scoring you model (PRI3) composed of APOL1, C1QC and SOCS1 is constructed and used for predicting the risk that a latent tuberculosis infected individual progresses into active tuberculosis within a set time window; the method comprises the following steps of: firstly, selecting a reference model (RISK6, Sweeney3, Gliddon4 and BATF2), then performing performance verification in an independent verification queue, and comparing with the existing reference model (RISK6, Sweeney3, Gliddon4 and BATF2), and the result shows that the scoring model disclosed by the invention can keep stable performance in adult population and multiple time windows (such as 0-6 months, 0-12 months and 0-24 months). The scheme of the invention is suitable for crowd screening, disease early warning and individualized intervention decision.

The invention provides a metabolic marker composition for distinguishing health from breast cancer. Based on metabonomics data of blood plasma, a blood plasma metabolism marker combination for breast cancer diagnosis is screened, and a breast cancer diagnosis model with relatively high sensitivity and specificity is constructed in combination with a machine learning algorithm. The method has high sensitivity and specificity, samples are convenient to obtain, non-invasive performance is achieved, accurate screening of the breast cancer can be achieved, important help is provided for prevention of the breast cancer and reduction of the morbidity, and the method is suitable for large-scale population screening and long-term tracking in areas with shortage of medical resources.

The invention discloses a voice anomaly detection method for pre-driving period screening of Parkinson's disease, which is mainly used for performing non-invasive pre-driving period screening of the Parkinson's disease on voice information of a subject, analyzing by using daily voice, and discovering early signs of the Parkinson's disease without invasive examination such as blood drawing, imaging and the like. The method provides a new way for large-scale population screening, reduces the detection cost, is good in patient compliance, is high in result interpretation, is combined with a knowledge graph or feature importance analysis, provides a basis for an anomaly detection result, enables a clinician to understand the correlation between voice anomaly and Parkinson pathology, and improves the detection accuracy. And the credibility of the AI result is improved.

The invention provides a metabolic marker composition for distinguishing benign thyroid diseases from thyroid cancer and application of the metabolic marker composition. Based on blood metabonomics data, a diagnosis model capable of accurately distinguishing benign thyroid diseases and thyroid cancer is constructed by screening a specific metabolic marker combination and combining a machine learning algorithm, and the model has relatively high sensitivity and specificity. Based on the constructed diagnosis model, whether the patient suffers from the thyroid cancer or the benign thyroid disease can be judged at the same time only through one-time detection, excessive examination of the patient suffering from the benign thyroid disease can be avoided, excessive diagnosis and treatment are reduced, and excessive panic of the patient is avoided. The thyroid cancer diagnosis model method provided by the invention has the advantages of convenience, economy and convenience in sample acquisition, and is more suitable for large-scale population screening and regular tracking in regions with shortage of medical resources.

The present invention belongs to the technical field of molecular breeding, and specifically relates to an SNP locus combination associated with mastitis resistance traits in dairy cows, as well as probes, a chip, and applications thereof. The SNP locus combination comprises 348 SNP loci located at specific chromosomal positions, the physical positions of which are determined based on the ARS-UCD2.0 reference genome. The present invention further provides probes and a liquid-phase chip for detecting the SNP locus combination, which can be used for early selection and breeding of mastitis resistance in dairy cows, identification of germplasm resources, kinship analysis, and pedigree correction. By means of high-throughput genotyping technology, the present invention enables rapid and accurate evaluation of innate resistance in dairy cows, and has the advantages of high detection efficiency, low cost, and suitability for large-scale population screening, thereby having important significance for improving disease-resistant breeding efficiency in dairy cows and ensuring the safe production of dairy products.

The application discloses a method for constructing a hyperlipidemia-resistant rat model, and belongs to the technical field of experimental animal model construction. S1, basic population screening: abnormal lipid metabolism rats are selected, and the rats are fed with high-fat feed at the age of 4 weeks, and the feeding is continuously performed for 4 weeks; S2, blood lipid detection and preliminary screening: the animals are fasted for 12h, blood is taken from the submandibular vein, serum is separated by centrifugation at 3000r / min for 10min, and the total cholesterol, triglyceride, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol contents are detected by using an automatic biochemical analyzer; S3, paired reproduction and subculture: 30 male rats and 30 female rats are selected, and the rats are paired for reproduction at a ratio of 1:1 to obtain F1 generation; S4, inbreeding and stabilization: the F2-F4 generation rats are repeatedly fed, detected, screened and paired for reproduction according to steps S1-S3, and the F4 generation hyperlipidemia-resistant rat model is obtained through inbreeding, and the technical effect of obtaining the hyperlipidemia-resistant rat model which is stable in phenotype and can be inherited and subcultured is achieved through the combination of hybridization screening and inbreeding.

The invention discloses a multi-cancer-species early screening method based on fragment omics and microbiological omics characteristics and application thereof, and belongs to the technical field of biomedical detection. The method comprises the following steps: extracting fragment omics characteristics of human cfDNA and microbiome characteristics of plasma microorganism DNA based on sequencing data; fusing the two types of features, inputting the fused features into a first-stage classification model, and judging whether the subject is cancer positive or not; and further predicting the cancer type of the positive result through a second-stage classification model. The fragment omics and microbiome characteristics are combined to be used for early screening of multiple cancer species, early screening and tissue tracing of eight high-incidence cancers such as lung cancer, liver cancer and colorectal cancer can be achieved through single-time non-invasive blood sampling, and the method still has high sensitivity and high specificity under the low sequencing depth, is low in cost and wide in coverage and is suitable for large-scale population screening.

The invention provides an artificial-intelligence–based platform for automated analysis of Pap smear slides. The system integrates preprocessing, dual deep learning models (CNN-Transformer and EfficientNet-LSTM), and reporting modules to classify cytological images into diagnostic categories including NILM, ASC-US, LSIL, HSIL, and SCC. The platform includes interpretability outputs, secure data management, and active learning capabilities, enabling scalable, accurate, and transparent cervical-cancer screening across diverse laboratory settings. The platform is suitable for implementation in clinical pathology laboratories, telemedicine networks, and population-level screening programs. It allows rapid, reproducible, and objective evaluation of cytology slides, improving throughput while reducing reliance on manual slide examination. The invention is deployable on local servers, private clouds, or public cloud infrastructures and is adaptable to various cytology specimens. Adoption of this system enhances early detection of precancerous lesions, reduces inter-observer variability, and facilitates integration with laboratory information systems. It also supports continuous learning and adaptation to evolving imaging protocols. The invention provides multiple advantages over existing methods: (1) Dual-model architecture combining CNN-Transformer and EfficientNet- LSTM ensures robust classification across both dense and sparse cellular regions. (2)Preprocessing and adaptive tiling improve feature extraction from variable-quality images. (3)Interpretability modules enhance clinician trust and facilitate regulatory compliance. (4) Active learning allows continuous improvement based on expert feedback. (5) Modular deployment supports both local laboratory and cloud-based operation. (6) Data security features meet international standards for privacy and encryption. (7) High scalability enables screening of large slide volumes without compromising accuracy. The invention provides a clinically relevant, efficient, and technologically advanced solution for automated cervical-cancer screening, applicable in diverse healthcare environments and adaptable to future cytology domains.

The invention discloses application of plasma CXCL16 as a marker in evaluation of pulmonary arterial hypertension, and the application comprises the following specific steps: S1, treating a plasma sample; s2, detecting the concentration of CXCL16 in the blood plasma by using an ELISA (enzyme-linked immunosorbent assay) method; s3, comparing the CXCL16 concentration with a healthy control group, a CTEPD non-pulmonary arterial hypertension group and other types of pulmonary arterial hypertension groups; s4, the CXCL16 concentration is associated with hemodynamic parameter changes before and after BPA treatment, the detection operation is non-invasive, convenient and safe, a part of invasive examination can be replaced to serve as a preliminary screening tool, the method is suitable for grass-roots popularization and large-scale population screening, and the medical cost and the patient diagnosis and treatment risk are reduced.

Provided are a method and system for generating a screening model and screening a high-risk infection population of an infectious disease. The method for generating a screening model of a high-risk infection population of an infectious disease comprises: obtaining a training data set, wherein the training data set comprises user trajectory information, wherein the user trajectory information is obtained based on mobile terminal related data of a user; establishing a sample table, wherein each sample in the sample table comprises a user identifier and a sample label, and the sample label indicates that the user is a positive sample user who has been diagnosed as infected or suspected to be infected with a specified type of infectious disease or a negative sample user who is normal; based on the training data set, extracting features for each sample in the sample table, and incorporating the extracted features into the sample table; using a machine learning algorithm, performing machine learning model training based on the sample table incorporating the features, and generating a high-risk infection population screening model for the specified type of infectious disease.

The application discloses a metabolic marker composition for early cervical cancer diagnosis and a screening method and application thereof. The metabolic marker composition for early cervical cancer diagnosis comprises pyroglutamic acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2 and pyruvic acid. The metabolic marker composition provided by the application has high sensitivity and specificity when used for early cervical cancer diagnosis, can realize precise screening of early cervical cancer, and provides important help for prevention of cervical cancer and reduction of morbidity. Meanwhile, the metabolic marker composition is simple in operation, convenient in sample acquisition, low in cost, and non-invasive when used for early cervical cancer diagnosis, so that patients can more conveniently, quickly and safely receive detection, and is more suitable for large-scale population screening and regular tracking in areas with tight medical resources.

The application belongs to the technical field of proteomics and clinical disease screening, and relates to application of a combination of plasma proteins in screening of pancreatic cancer. The application is based on mass spectrometry quantitative proteomics technology, screens and determines a marker combination composed of four plasma proteins of GCLC, ALAD, HAGH and SORD, and comprehensively analyzes relative abundance of the marker combination through a binary Logistic regression model to distinguish pancreatic cancer patients from healthy people. Experimental results show that the protein combination has high sensitivity, specificity and area under curve (AUC) in a screening set and an independent verification set, and has good stability and repeatability. The method has the advantages of non-invasiveness, feasible operation and applicability to population screening by taking plasma as a detection sample, and can provide a new technical means for early screening of pancreatic cancer.

The invention provides a metabolic marker composition for distinguishing healthy kidney cancer from early kidney cancer. According to an analysis method based on urine metabonomics, a diagnosis model capable of accurately distinguishing healthy people from early-stage kidney cancer patients is constructed by detecting a specific metabolic marker combination and combining a machine learning algorithm. The model can noninvasively discriminate the early-stage kidney cancer in a high-sensitivity and high-specificity manner, and breaks through the limitation of an existing clinical detection method. The method disclosed by the invention has the advantages of noninvasive property, simple and convenient sampling and the like, is suitable for large-scale population screening, is particularly beneficial to popularization of early-stage kidney cancer risk screening in remote areas deficient in medical resources, and provides support for early-stage noninvasive diagnosis and effective prevention and control of kidney cancer.

The application provides a method and system for determining permanent population based on communication big data, utilizes spatial index technology to efficiently organize and quickly search massive signaling location data, realizes second-level screening of candidate users in any polygonal area from hundred-million-level data, introduces base station antenna azimuth, covering angle and other working parameter data, accurately calculates base station covering space and carries out geometric relation analysis with a target area, utilizes threshold intelligent screening of "effective base stations", fundamentally corrects population misjudgment caused by base station directional covering and boundary effect, and significantly improves the accuracy of small-scale regional population screening. The whole scheme is constructed on a big data parallel computing framework, realizes quick response to ten-thousand-level regional query in a high-concurrency environment through data fragmentation, task dynamic splitting and parallel processing.

The present application belongs to the technical field of biological detection, and particularly relates to a heterogeneous composite material for capturing exosomes, a preparation method of the heterogeneous composite material, a method for specifically enriching exosomes and detecting exosome metabolites by using the heterogeneous composite material, and application of the method in disease screening and diagnosis. The heterogeneous composite material prepared by the present application has good magnetic response, excellent ultraviolet absorption capacity and electron-hole separation efficiency, can be successfully used for continuous capture of urine exosomes of healthy controls and bladder cancer patients and mass spectrum analysis of metabolites, and can screen 5 exosome metabolites as potential bladder cancer biomarkers, successfully realizes accurate discrimination of bladder cancer patients, and has great application prospect in large-scale population screening, early diagnosis and prognosis monitoring of bladder cancer.

This invention discloses a liver fibrosis grading system based on serum index data. The invention relates to the field of liver fibrosis grading, and includes a standardized time-series acquisition module for liver fibrosis-related serum physiological signals, a gold-standard paired liver fibrosis grading pattern library construction and pattern clustering module, a subject physiological signal pattern matching and grading module, and a diagnostic result verification and pattern library dynamic optimization module. This liver fibrosis grading system based on serum index data utilizes a completely non-invasive serum detection method. Through standardized process design and intelligent module linkage, it achieves full automation from signal acquisition, preprocessing, feature extraction to judgment output, significantly shortening the diagnostic cycle and reducing reliance on the professional skills of clinical operators. It is suitable for large-scale population screening, dynamic follow-up, and promotion in primary healthcare institutions, possessing extremely high clinical application value and socio-economic benefits.

This invention proposes a method for screening and optimizing enzyme catalytic efficiency and stability based on protein language models and domain knowledge. The method includes: defining a set of allowable mutation intervals through multidimensional analysis; constructing a dual-source mutant library using a dual-source complementarity strategy based on protein language models and co-evolutionary models; using the dual-source mutant library as the initial population, employing a multi-model fusion catalytic efficiency prediction strategy as the population's overall fitness calculation method, and a dual-index verification strategy based on the SPIRED-Stab model for structural stability as the population screening method; iteratively obtaining the optimal mutant enzyme sequence through a semantically guided genetic algorithm; and verifying the structure and dynamic stability of the optimal mutant enzyme sequence based on three-dimensional structure prediction and molecular dynamics simulation to obtain an experimentally feasible final mutant enzyme sequence. This invention achieves improved optimization efficiency while ensuring the structural feasibility of key enzymes.

The application discloses a population screening method and system, a terminal device and a computer readable storage medium. The population screening method comprises the following steps: determining a resident population whose staying time in a to-be-screened area exceeds a preset time threshold, and obtaining a feature vector of a travel trajectory of the resident population; clustering the travel trajectory of the resident population based on the feature vector to obtain a student population in the resident population, so as to determine a graduate group from the student population. The application can realize accurate screening of the graduate group.

The invention provides a metabolic marker composition for distinguishing benign thyroid diseases from early thyroid cancer, and provides an auxiliary diagnosis means independent of invasive biopsy by constructing a diagnosis model based on the metabolic marker composition. The metabolic marker combination can realize functional early warning and diagnosis in an earlier stage of thyroid cancer occurrence, solves the problems of insufficient specificity and high false positive rate in distinguishing benign and malignant thyroid lesions in existing imaging examination, has high sensitivity and specificity, can provide a basis for clinical early intervention, and has a good application prospect. The method is suitable for large-scale population screening and repetitive dynamic monitoring of high-risk individuals.

The present application relates to anti-SMPDL3A monoclonal antibodies Sm-1 and Sm-4, and a diagnostic kit for early colorectal cancer prepared therefrom. The present application successfully prepared and screened a plurality of high-affinity anti-SMPDL3A monoclonal antibodies, which laid a material foundation for subsequent establishment of a high-sensitivity immunodetection method. Through systematic double antibody pairing screening, the present application found that Sm-1 and Sm-4 can recognize different epitopes of SMPDL3A, and the spatial distance between them is far and they do not interfere with each other, so as to form a stable "sandwich" sandwich structure; and the remaining antibody combinations have poor pairing effect due to adjacent epitopes or spatial steric hindrance. The preferred combination significantly improves the signal-to-noise ratio and stability of the double antibody sandwich ELISA. The detection kit provided by the present application can be directly applied to serum samples, is simple to operate, and has controllable cost, thereby providing a new serological marker and effective technical means for early non-invasive diagnosis of colorectal cancer, and being suitable for large-scale population screening, and having a good clinical application prospect.

The embodiment of the application discloses a data query method and system based on a large language model, which comprises the following steps: receiving natural language data for describing a target population, performing semantic analysis on the natural language data, extracting semantic elements related to population screening, and forming an initial condition element set; inputting the initial condition element set into a preset large language model, and converting the initial condition element set into a structured label condition group; based on pre-constructed automobile label metadata and corresponding mapping rules, performing field mapping and value conversion processing on condition elements in the structured label condition group to obtain standardized label condition data matched with a target database; logically combining the standardized label condition data to construct a hierarchical condition structure; generating a query statement executable for the target database based on the hierarchical condition structure, and screening user data in the database by using the query statement to obtain target user data.

This invention provides a combination of metabolic biomarkers for distinguishing between healthy individuals and gynecological tumors. Based on metabolomics research, this invention analyzes and compares metabolites in the plasma of gynecological tumor patients and healthy individuals to screen for a set of metabolic biomarker combinations with significant differences. Based on the characteristics of plasma metabolites, this invention constructs a diagnostic model with high sensitivity and specificity. This method can be used for risk screening of three types of gynecological tumors: ovarian cancer, cervical cancer, and endometrial cancer. It is non-invasive, easy to obtain samples, and has high subject compliance, making it suitable for large-scale population screening, especially in remote areas with limited medical resources. It also avoids over-testing and reduces testing costs.