21 results about "Healthy population" patented technology

The invention relates to the technical field of health evaluation, in particular to a group image health evaluation method and system based on intestinal flora, and the method comprises the steps: collecting intestinal flora samples of a target group through a standardized process, and obtaining microbial sequence information through a high-throughput sequencing technology; microflora diversity characteristics and core flora characteristics are extracted through strain identification and relative abundance analysis, individual flora characteristics are compared with a healthy population reference database, health state scores are calculated, health grades are divided, population health grade distribution is counted, and the population health grade distribution is calculated. And generating a group health evaluation report containing a visual chart and text analysis. According to the method, a complete technical system from sample collection to health strategy making is established, systematic evaluation of the group health state from the perspective of intestinal flora is achieved, and the limitation of a traditional method in group health early evaluation is overcome. The method is suitable for health monitoring of different scales of groups, and provides effective technical support for public health management and health service.

The application discloses a Bifidobacterium longum subsp.longum strain for relieving pathological characteristics of mice with chronic kidney disease induced by adenine, and belongs to the technical fields of microorganisms and medicines.The application provides a biomarker combination related to end-stage renal disease of peritoneal dialysis, wherein the corresponding species abundance in the biomarker combination presents significant difference in matched healthy control population and peritoneal dialysis population, can effectively distinguish the end-stage renal disease population of peritoneal dialysis and the healthy population, and has high sensitivity and specificity.The application also provides a Bifidobacterium longum subsp.longum strain CCFM1375 capable of delaying the progression of renal failure, which can be used for preparing products for improving chronic kidney disease.

A multi-modal pan-cancer early prediction method is disclosed, comprising: collecting cfDNA samples of i cancer populations and healthy populations respectively and extracting methylation data; merging CpG sites based on the methylation data to obtain multiple methylation intervals, screening multiple candidate marker intervals from the multiple methylation intervals, and further screening the multiple candidate marker intervals; training a first prediction model using the multiple candidate marker intervals, training a second prediction model based on the fragmentation characteristics of the chromosomes extracted from the reference genome, and training a third prediction model by extracting the methylation entropy characteristics of the chromosomes; training a multi-modal pan-cancer early prediction model through the cancer population prediction values of the first prediction model, the second prediction model and the third prediction model, which can provide precise joint early screening for lung cancer, intestinal cancer, gastric cancer, liver cancer, esophageal cancer, thyroid cancer and ovarian cancer.

The invention provides a voice voiceprint characteristic value clustering method and system for representing a human health state. The method comprises the following steps: acquiring voice print data of different crowds, wherein the different crowds comprise healthy crowds, specific crowds or sick crowds; the obtained voice voiceprint data are preprocessed; extracting a voiceprint feature value from the preprocessed voice voiceprint data so as to characterize the voice voiceprint; constructing a healthy crowd reference model by using the voiceprint feature values of the healthy crowd; carrying out difference comparison on the voiceprint characteristic value of the specific system or disease population and a healthy population reference model, and marking characteristic data related to the human physiological system or disease according to a comparison result; and drawing a physiological voiceprint block graph based on the marked feature data, and performing clustering analysis on the feature data in the physiological voiceprint block graph to form a dominant and high-stability feature data group associated with the human physiological system or disease.

The invention provides a wireless respiration monitoring system and method for clinical treatment, and relates to the technical field of respiration test medical treatment, and the method comprises the following steps: collecting respiratory muscle function physiological parameters of a clinical patient; establishing a mathematical evaluation model and a large sample healthy population database, and mapping and outputting a recovery evaluation initial value of the respiratory muscle function; interference item parameters of a clinical patient are collected, and an evaluation correction coefficient is obtained; and adjusting a recovery evaluation initial value of the respiratory muscle function output by the mathematical evaluation model to generate a recovery evaluation correction value. The breathing state quality of the patient after the clinical treatment stage can be predicted in advance through the mathematical evaluation model. In addition, through the collected interference item parameters, the initial value of the recovery evaluation can be calibrated, so that the finally output correction value of the recovery evaluation can more accurately predict the breathing state quality of the patient after the clinical treatment stage. And the accuracy is higher.

The application discloses polypeptide markers based on autoantibodies and application thereof in early lung cancer diagnosis, and relates to the technical field of biological detection. The application finds that there are 143 polypeptide probes with significant signal differences between a lung adenocarcinoma group in early stage and a healthy population group through research and analysis of plasma samples of the two groups, and further screens 14 polypeptide probes that can produce more stable signal differences between the two groups. Through attempts of various machine learning methods, it is found that a classification model can be established by taking the signal value accumulation of the 14 polypeptide probes as an index for model construction, thereby providing a path for effective diagnosis of early lung adenocarcinoma.

The invention belongs to the technical field of biological medicines, and particularly relates to an oral microbial gene marker for noninvasive diagnosis of esophageal cancer patients and application of the oral microbial gene marker. The invention provides an oral microbial gene marker for distinguishing esophageal cancer patients from healthy people, which consists of six microbial genes as shown in SEQ ID NO: 1-6, and the microbial genes are enriched in the oral cavity of a human body. The microbial gene distinguishing model disclosed by the invention has good distinguishing ability in esophageal cancer patients and healthy control people, and the feasibility, applicability and universality of the microbial gene distinguishing model in the esophageal cancer patients are proved.

The embodiment of the invention relates to a method and a device for predicting an early Alzheimer's disease based on dynamic and static SFC characteristics. The method comprises the following steps: setting a brain region set and a prediction model; performing big data acquisition on the dynamic and static SFC characteristics of the early Alzheimer patient group and the healthy people to obtain a data set; training a prediction model according to the data set; after the model training is finished, receiving a brain fMRI image and a brain DTI image of any testee input by a user, and performing dynamic and static SFC feature extraction processing based on the testee image to obtain a static SFC feature vector A and a dynamic SFC feature matrix B; and inputting the feature vector A and the feature matrix B into a prediction model for prediction to obtain a corresponding prediction vector which is fed back to the current user. According to the method, the defect that a conventional model cannot be combined with dynamic SFC features is overcome, and the prediction accuracy and generalization of the model can be improved.

The embodiment of the invention relates to a method and device for predicting an early Alzheimer's disease based on SFC and ALPS features. The method comprises the following steps: constructing a neural network model for performing early Alzheimer's disease dichotomy prediction based on the SFC and ALPS features; the method comprises the following steps: performing big data acquisition on brain fMRI + DTI images of an early Alzheimer patient group and a healthy crowd, analyzing SFC + ALPS features corresponding to individual acquired images, and constructing a model training data set based on feature analysis results and health states of all individuals; training a prediction model according to the data set; and after training is finished, analyzing corresponding SFC + ALPS features according to the brain fMRI + DTI image of any testee, and inputting an analysis result into the prediction model for prediction. Based on the scheme of the invention, the screening cost of the early Alzheimer's disease can be effectively reduced, and the screening popularity rate is improved.

The embodiment of the present application relates to a kind of method and device for predicting early Alzheimer's disease based on dynamic and static SFC characteristics, the method comprises: setting brain region set, prediction model;The dynamic and static SFC characteristics of early Alzheimer's disease population and healthy population are carried out data set by big data acquisition;According to data set, prediction model is trained;After model training ends, the brain fMRI image and brain DTI image of arbitrary subject input by user are received, and dynamic and static SFC characteristics extraction processing is carried out based on subject image to obtain static SFC characteristic vector A, dynamic SFC characteristic matrix B;Characteristic vector A, feature matrix B are input into prediction model to obtain corresponding prediction vector, and the current user is fed back to feedback. The present application makes up the defect that conventional model cannot combine dynamic SFC characteristics, and can improve model prediction accuracy and generalization.

The application belongs to the field of health management and nutrition, and discloses a method for constructing a health database and a nutrient requirement database of different populations, which comprises the following steps: constructing a health index and nutrient requirement database of different healthy populations and sub-healthy populations; constructing a health index and nutrient requirement correlation database; formulating a health status evaluation scheme for different populations; and formulating a precise personalized nutrient intervention scheme for specific populations. Through the construction of the health database and the nutrient requirement database of different populations, the application realizes personalized health evaluation and nutrient intervention based on data analysis, provides precise health management and nutrient intervention suggestions for different populations, and thus improves the overall health level of different populations.

The embodiment of the present application relates to a kind of method and device for predicting early Alzheimer disease based on SFC and ALPS feature, the method comprises: the neural network model for early Alzheimer disease binary classification prediction based on SFC and ALPS feature is constructed;Brain fMRI+DTI image of early Alzheimer disease population and healthy population is collected in large data, and the SFC+ALPS feature corresponding to the image of individual is analyzed, and model training data set is constructed based on the feature analysis result of all individuals and health status;Predictive model is trained according to data set;After training, the SFC+ALPS feature corresponding to the brain fMRI+DTI image of any subject is analyzed, and the analysis result is input into predictive model for prediction.Based on the scheme of the present application, the screening cost of early Alzheimer disease can be effectively reduced, and the screening popularity rate is improved.

The present application belongs to the field of bioinformatics, and particularly relates to a method and device for detecting a thalassemia patient's copy number variation type, the method comprising the following steps: constructing a thalassemia copy number variation database, constructing a thalassemia-related gene copy number baseline database of a healthy population, using a region range of a copy number change of a to-be-tested sample and the copy number of the region, and combining the self-constructed thalassemia copy number variation database to determine a thalassemia copy number variation type to which the sample belongs; the present application can clearly detect a known structural variation carried by a sample and related to thalassemia, and can accurately find out an atypical thalassemia-related genomic structural variation; it has been verified that the method can be used for detecting known thalassemia copy number variation types and discovering new thalassemia copy number variation types according to a standard.

The invention discloses a MAFLD related biomolecular marker and application, the biomarker is human interleukin-24, and the amino acid sequence of the biomarker is as shown in SEQ ID No.1. The invention reveals that the expression level of human interleukin-24 (IL-24) in the plasma of a patient with the metabolism-related fatty liver disease (MAFLD) is obviously different from that of healthy people for the first time, and large-scale clinical sample verification proves that the plasma IL-24 level of the patient with the MAFLD is obviously reduced. The discovery provides a new molecular marker for clinical diagnosis of MAFLD.

The invention discloses a biomarker for prognosis evaluation of extrahepatic cholangiocarcinoma, a prognosis risk evaluation system and application, and belongs to the technical field of bioinformatics. According to the present invention, the combination of miR-34c-5p, miR-100-5p, miR-193b-5p, miR-122-5p, miR-5588-5p and miR-122-3p is adopted as the biomarker, such that the accuracy of the extrahepatic cholangiocarcinoma differential diagnosis can be effectively improved, and the extrahepatic cholangiocarcinoma patients, the healthy population and the benign biliary tract disease patients can be distinguished with the high precision; the limitation that a traditional tumor marker, such as CA19-9, is easy to have a false positive result in benign biliary tract diseases is overcome; meanwhile, the change of the expression level of the biomarker combination can dynamically reflect the change of the postoperative tumor load, so that the accurate evaluation of the surgical curative effect is facilitated.

The application discloses a diagnosis marker for early chronic obstructive pulmonary disease (COPD) and application thereof, and relates to the technical field of biomedicine. The application is based on the fact that the immunoglobulin glycosylation modification index in plasma / serum can be used as a marker when being combined with the lung function detection index FEV1 / FVC, so as to distinguish the high-risk population of early COPD from the healthy population. The method has the advantages of high prediction accuracy, low cost, high throughput and easy operation.

The application discloses an Alzheimer's disease early diagnosis method and device based on a diffusion model, and the method comprises the following steps: acquiring and preprocessing a plurality of brain MRI images of a diagnosis object at different periods; obtaining MRI compression features corresponding to each brain MRI image by using an encoder in a trained auto-encoder network; taking the MRI compression features as a conditional variable, and performing prediction by using a trained diffusion model to obtain MRI prediction features; acquiring a brain MRI image of the diagnosis object obtained by performing MRI scanning again, obtaining MRI real features by using a trained encoder, taking an error between the MRI real features and the MRI prediction features as an abnormal score, and determining that the diagnosis object is positive for Alzheimer's disease if the abnormal score exceeds a predetermined threshold. The method has the advantages of high calculation efficiency, strong interpretability and the like by combining the generation capability of the diffusion model and the time series modeling advantage, and can realize early diagnosis of Alzheimer's disease only by using healthy population data.

The invention provides a single-foot pressure distribution prediction method and device and a multi-channel pressure shoe. The single-foot pressure distribution prediction method comprises the steps that a pressure distribution prediction model is obtained through healthy crowd sample training; the pressure distribution prediction model divides single-foot pressure distribution into two types through an obtained pressure regression function; inputting the physical sign information of the patient into a pressure distribution prediction model to obtain a predicted pressure distribution value of a single foot on the healthy side of the patient; and acquiring a single-foot actually-measured pressure distribution value of the affected side of the patient, and comparing the predicted pressure distribution value with the actually-measured pressure distribution value to obtain the type of single-foot pressure distribution. According to the embodiment of the invention, a pressure distribution prediction model is obtained by training a healthy crowd training sample, a predicted pressure distribution value of a single foot on the healthy side of a patient is output after physical sign information of the patient is input, and then the actually measured pressure distribution value of the single foot on the affected side of the patient is compared with the predicted pressure distribution value to obtain the type of pressure distribution of the single foot. And research data of single-foot pressure distribution is realized.

The present application relates to the biomedical field, and specifically provides a medication recommendation method and device based on multi-omics analysis and electronic equipment. The method comprises: obtaining index data of each candidate metabolite of a user; in the case that the index data of each candidate metabolite indicates that the user has posterior circulation ischemic stroke, calculating the contribution degree of each candidate metabolite according to the target mean and target standard deviation of each candidate metabolite of a healthy population; sorting the plurality of candidate metabolites according to the contribution degree, and determining the first k candidate metabolites as target metabolites; performing an intersection operation on the expression genes corresponding to the target metabolites and the expression genes in the differential expression gene set to obtain target regulatory genes; and screening target recommended drugs matching the target regulatory genes in a drug feature database. The technical scheme provided by the present application improves the accuracy of medication recommendation for posterior circulation ischemic stroke to some extent.