6 results about "Healthy population" patented technology

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.

The embodiment of the present application relates to a kind of method and device based on SFC feature prediction early Alzheimer's disease, the method comprises: setting first brain region set and its corresponding first brain atlas, design first prediction model;The brain multi-modal MRI image of early Alzheimer's disease population and healthy population is carried out big data acquisition to obtain original sample set;According to first brain atlas and original sample set, generate first data set;First prediction model is trained based on first data set;After training, the brain multi-modal MRI image of any subject is received, and first SFC feature vector is calculated according to first brain atlas and current multi-modal image, and first SFC feature vector is input into first prediction model and is predicted to obtain current prediction result.The present application can mine SFC feature, and can be based on SFC feature early Alzheimer's disease and be classified and predicted.

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 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 application relates to the technical field of artificial intelligence, and discloses a pet health abnormality intelligent recognition method based on multi-dimensional behavior characteristic analysis. The method comprises the following steps: collecting multi-source behavior time sequence data of a target pet; constructing an individual behavior baseline model; extracting species-level behavior common characteristic features of a healthy population of the same species; dynamically coupling the two to generate individualized behavior representation subject to species prior constraints; calculating the multi-dimensional deviation degree of the current behavior from the representation; and determining that the pet is in a health abnormality state when the deviation degree is greater than an adaptive threshold. The system comprises six units, namely, multi-source data collection, individual baseline modeling, species commonness extraction, individualized representation generation, deviation degree calculation, and abnormality determination. The application improves the accuracy, robustness and individualization level of abnormality recognition by fusing individual habits and species prior, combining multi-dimensional behavior data and an adaptive discrimination mechanism.

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.