279 results about "Healthy individuals" patented technology

The invention relates to the technical field of medical information, and particularly provides a multi-modal Alzheimer's disease early screening algorithm based on a large language model. The algorithm first collects voice data and demographic information of participants. Voice data is processed through noise suppression, voice enhancement and speaker separation and is transcribed into a description text. An eGeMAPS feature set is extracted from the voice data and is subjected to time sequence splicing, and a multi-modal feature matrix is formed in combination with demographic information; the attention pooling module is used for obtaining key features, a support vector machine is used for feature classification, and potential Alzheimer's disease patients and healthy individuals are distinguished. And inputting the classification result, the description text and the demographic information into a large language model, deeply analyzing four language barriers by adopting a zero sample thinking chain: information content reduction, vocabulary semantic defects, poor reference continuity and psychological state language deficiency, and finally generating a comprehensive screening report. According to the algorithm, the accuracy of early diagnosis is improved by combining audio and semantic features.

Disclosed are aqueous formulations comprising urolithins particularly water-based formulations comprising urolithins and maltodextrin. Also disclosed are compositions for admixture with water, methods for the preparation of such aqueous formulations, and to the uses of such formulations for both the treatment of diseases, disorders and conditions, for example, diseases disorders or conditions characterized by inadequate mitochondrial function and for administration to generally healthy individuals for example, for improving health and improving muscle function and performance.

The invention discloses a bacterial polypeptide Gfo and application thereof in preparation of rheumatoid arthritis diagnosis products, and belongs to the technical field of rheumatoid arthritis diagnosis. The amino acid sequence of the bacterial polypeptide Gfo is LNSHGFLPETE. A diagnostic product prepared from the bacterial polypeptide Gfo can effectively detect that the level of an anti-bacterial polypeptide Gfo antibody in a biological sample of a rheumatoid arthritis patient is obviously higher than that of healthy people and other rheumatoid immune diseases similar to rheumatoid arthritis. In addition, in CCP antibody and RF negative patients, the increase is still significant. Therefore, the antibacterial polypeptide Gfo antibody is an important biomarker of rheumatoid arthritis, and provides an effective new method for diagnosis of RA, especially diagnosis of serum-negative RA.

The invention discloses an individualized pain prediction method and system based on a mixing effect. The method comprises the following steps: acquiring pain scores and corresponding central nervous system data when a plurality of healthy individuals receive pain stimulation; performing data processing on the obtained central nervous system data to obtain central nervous system physiological signals of pain stimulation received by corresponding individuals; combining the obtained physiological signals of the central nervous system and the corresponding pain scores to construct a mixed effect model; predicting the pain state of the individual by using the constructed mixed effect model; the system comprises a pain stimulation device, a central nervous detection system, a data acquisition module, a data analysis module, a prediction model construction module and a pain recognition module. The constructed mixed effect model gives consideration to the characteristics of an individualized model and a group model, not only can new individuals be migrated, but also individual differences are considered, the hierarchical characteristics of data are fully utilized, and the pain score prediction effectiveness can be effectively improved.

Provided are compositions comprising compounds or precursors to compounds which may be used for a variety of therapeutic applications including, for example, treating and / or preventing a disease or disorder related to reduced or inadequate mitochondrial activity, including aging or stress, diabetes, obesity, and neurodegenerative diseases. The compounds relate generally to urolithins and precursors thereof, including but not limited to ellagitannins and urolithin A. In certain embodiments the compositions are presented in or as food products or nutritional supplements. These same compounds and compositions can also be used advantageously in generally healthy individuals to increase or maintain metabolic rate, decrease percent body fat, increase or maintain muscle mass, manage body weight, improve or maintain mental performance (including memory), improve or maintain muscle performance, improve or maintain mood, and manage stress.

The invention belongs to the field of meat pigeon breeding, and particularly relates to miRNAs capable of serving as a trichomonad pigeon biomarker and application of the miRNAs. The invention provides application of miRNAs as a trichomonas pigeonae biomarker in preparation of a trichomonas pigeonae diagnostic reagent. According to the invention, high-throughput sequencing and bioinformatics analysis are simultaneously carried out on oral mucosa small RNA and transcriptome of a trichomonad infected positive individual and a healthy individual for the first time, so that an epigenetic molecular mechanism of oral mucosa tissue of a trichomonad infected regulation body is explained by mining a key significant difference miRNA-mRNA network.

The invention belongs to the technical field of biology, and relates to a plasma marker for colorectal cancer diagnosis or prediction and application of the plasma marker. The invention creatively provides the application of specific enhancer RNA (eRNA58712) in serum in early diagnosis or detection of colorectal cancer, which marks that important progress is achieved in the aspect of finding novel biomarkers. The expression quantity of the eRNA58712 in serum of a colorectal cancer patient is obviously higher than that of a healthy individual, and the expression level of the eRNA58712 has an obvious positive correlation relationship with occurrence and development of the colorectal cancer, which means that rapid and accurate early recognition of the colorectal cancer can be realized on a molecular level by measuring the concentration of the eRNA58712 in a blood sample.

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

This invention relates to the field of medical testing, specifically to a set of biomarkers, reagent kits, and their applications for diagnosing hypertension in children. This invention involves collecting tongue / intestinal samples from obese children with hypertension, obese children, and healthy individuals, performing metagenomic sequencing, and statistically analyzing the sequencing data using bioinformatics to identify disease-related tongue / intestinal flora. By integrating tongue / intestinal flora with disease information, a combination of flora biomarkers is obtained. A binary classification prediction model constructed using this combination can maximally detect hypertension in obese children.

The present invention discloses the use of extracellular vesicles derived from oligodendrocytes carrying FTH1 in differentiating between multiple system atrophy and Parkinson's disease, belonging to the field of biomedical technology. The present invention utilizes fluorescently labeled CNPase antibodies and FTH1 antibodies to accurately detect extracellular vesicles derived from oligodendrocytes carrying FTH1 in plasma. By detecting the concentration of extracellular vesicles derived from oligodendrocytes carrying FTH1 (FTH1-positive) in plasma, it is possible to accurately distinguish between MSA patients, PD patients, and healthy individuals. This provides a highly effective biomarker for MSA diagnosis, improves the ability to diagnose MSA early and differentiate between MSA and PD, and effectively addresses the complexity and low sensitivity issues faced by traditional diagnostic methods.

The invention relates to the field of disease prediction, in particular to a cognitive impairment prediction system based on synchronously acquired electroencephalogram-stomach electrical signals, comprising: a data acquisition module for acquiring data including electroencephalogram data and stomach and intestine electrical data; the first analysis module is used for obtaining an average electrode inconsistency index of the first channel and the second channel based on a first model according to the time sequence data; and the prediction module is used for comparing the average electrode inconsistency index of the subject with a preset evaluation threshold value so as to predict a cognitive impairment patient. The system provided by the invention can more accurately and efficiently distinguish patients with mild cognitive impairment from healthy people, and can be used as an auxiliary diagnosis tool to be applied to primary screening of mild cognitive impairment on relatively large people (such as communities and physical examination centers).

The present inventive concept provides for a method of generating picture description task images using AI. The method includes using AI to generate a story based on a user input prompt and / or a predetermined input. AI is used to generate an image based on the generated story. AI is used to generate written descriptions of the generated image simulating cohorts of healthy individuals and individuals with a predetermined condition. Diagnostic linguistic features are extracted from written descriptions of the cohorts of healthy individuals and individuals with the predetermined condition. The extracted diagnostic linguistic features of the written descriptions for the cohorts of healthy individuals and individuals with the predetermined condition are compared. The generated image is used in a picture description task when the compared extracted features of the written descriptions of the cohorts of healthy individuals and individuals with the predetermined condition exhibit a predetermined threshold of difference.

The invention discloses a method and a device for constructing a cancer early screening model based on cfDNA difference statistics. The construction method of the cancer early screening model comprises the following steps: performing difference statistics on tumor-derived cfDNA and healthy individual-derived cfDNA in a low-depth whole-genome sequencing mode, and establishing the cancer early screening model to realize non-invasive early screening of cancers; the difference statistics comprises cfDNA fragment size characteristic difference statistics, cfDNA upstream and downstream continuous three groups of 3bp base combination category distribution frequency change trend difference statistics at a fracture position and sequencing data coverage depth mode difference statistics of the cfDNA near TSS. The method has the advantages of being minimally invasive, high in early diagnosis sensitivity, capable of achieving dynamic monitoring and the like, the limitation of cfDNA gene mutation detection in the early stage of the tumor is overcome, and information lag or deletion caused by tumor heterogeneity is avoided.

The invention discloses an intelligent respiratory disease detection system based on lung function measurement indexes, and relates to the technical field of intelligent medical detection. The problems that diagnosis standards are not uniform and depend on experience, primary doctors are prone to missed diagnosis or misdiagnosis due to insufficient experience, and the efficiency of the detection process is low are solved. According to the method, a machine learning model is constructed based on lung function indexes, parameters are optimized through five-fold cross validation, automatic classification of asthma, COPD and ILD / DPLD is achieved, the overall diagnosis accuracy rate reaches 81.3%, the limitation that traditional diagnosis depends on doctor experience is broken through, and the basic medical diagnosis capacity is especially improved. Healthy people are rapidly eliminated through the FVC / FEV1 ratio, invalid calculation is reduced, meanwhile, multi-model weighted fusion is adopted for complex cases, diagnosis robustness is improved, it is ensured that diagnosis results conform to medical logic, the recognition degree of doctors is enhanced, a data basis is provided for clinical research and curative effect tracking, and respiratory disease diagnosis is promoted to develop towards the direction of automation and precision.

The invention belongs to the field of lung cancer detection, and particularly relates to a platelet marker for detecting lung cancer and a detection kit. The method comprises the following steps: firstly, carrying out transcriptome sequencing on platelets of a healthy (HC) individual and a lung cancer (Lung) individual, then, carrying out differential expression analysis to identify genes which are specifically and differentially expressed in the platelets of a lung cancer patient for the first time, and screening out ZYX, GMPR, RPL9 and RPL11 genes. A logistic regression model is constructed based on mRNA expression levels of the four genes, and in subject working characteristic curve analysis of a training set, the area under a curve is up to 0.97. The application of mRNA expression levels of platelet genes ZYX, GMPR, RPL9 and RPL11 in diagnosis of lung cancer is defined mainly in a liquid biopsy mode, the marker is used for predicting occurrence of the lung cancer, and the invention aims to identify a new marker of the lung cancer and prepare a lung cancer diagnosis kit based on the marker.

The invention relates to the technical field of gas sensors, and particularly discloses a sensor drift correction method and system based on transfer learning, and the method comprises the steps: firstly collecting sensor response data of a healthy individual and a lung cancer patient in two different periods, and taking the early-stage data with a label as a source domain data set; the method comprises the following steps of: taking post data without labels as a target domain data set, then preprocessing the data to extract features to obtain a source domain data feature set and a target domain data feature set, and finally performing domain self-adaption on the target domain data feature set based on transfer learning to perform drift correction. According to the method, drift correction is carried out by minimizing the distance between edge distribution and conditional distribution and forcibly maximizing cross-domain category information, and a small number of representative features are extracted to carry out high characterization on original data. Experimental results show that drifting of the sensor can be effectively corrected, and high classification accuracy is obtained on intelligent detection based on the gas sensor, such as lung cancer intelligent detection.

The invention discloses a brain abnormal development trajectory identification method and system, and belongs to the technical field of magnetic resonance image analysis, and the method comprises the following steps: obtaining T1 weighted magnetic resonance images of a plurality of healthy individuals, and carrying out the offset correction of the structure index of each brain region in the images, a generalized additive position-scale-shape model of the healthy crowd is constructed; generating a norm development trajectory curve and a normal change range thereof; obtaining a T1 weighted magnetic resonance image of a to-be-evaluated individual, and selecting a target brain region of the to-be-evaluated individual; obtaining various offset corrected structure indexes of the target brain region of the individual to be evaluated; obtaining a mean value and a standard deviation of each structure index at the position of the brain region corresponding to the same-age healthy population of the individual to be evaluated; and generating a recognition report of the abnormal brain development trajectory of the to-be-evaluated individual. The problem that it is difficult to accurately, clearly and visually reflect the abnormal recognition condition of the brain trajectory of the individual to be evaluated and the position of the corresponding abnormal brain region is solved.

The invention provides a non-small cell lung cancer biomarker and application thereof, and particularly relates to application of a reagent for detecting snoRNA SNORD78 or a fragment thereof in preparation of a kit, and the kit is used for diagnosing non-small cell lung cancer and / or screening non-small cell lung cancer tissues. Samples obtained from healthy paracancerous lung tissues and non-small cell lung cancer patients in different staging stages are screened, analyzed and verified to find that snoRNA SNORD78 or fragments thereof have significant expression difference in healthy lung tissue samples (paracancerous) and non-small cell lung cancer tissue samples; moreover, obvious expression difference also exists in healthy individual tissues of the non-small cell lung cancer and individual tissues of different stages of the non-small cell lung cancer, a new molecular marker is provided for indicating the non-small cell lung cancer and the disease course development thereof, and a new thought is provided for preventing and / or treating the non-small cell lung cancer.

The invention relates to a depression individualized TMS positioning method and system for a targeted dorsal-lateral prefrontal lobe maximum deviation brain region, and the method comprises the steps: calculating a brain region function connection strength value of a healthy individual based on the MRI data of the healthy individual; constructing a healthy individual brain region function connection strength value standard model based on the healthy individual brain region function connection strength value; on the basis of MRI data of the depression subject, calculating a brain region function connection strength value of the depression subject; based on the brain region function connection strength value of the depression subject, measuring a deviation value of the brain region function connection strength value of the depression subject in the healthy individual brain region function connection strength value standard model; and based on the obtained deviation value of the brain region function connection strength value of the depression subject, finding out the brain region with the maximum deviation in the left dorsal and outer prefrontal lobes, and taking the brain region as an individualized TMS target. The healthy individual standard model is used for measuring the deviation of the brain region function connection strength of the depression subject, and the targeted most abnormal brain region individualized TMS positioning method is achieved.

The invention discloses an MCI detection system based on dynamic adaptive fusion of physiological signals and cognitive data. Comprising a data acquisition module, a time sequence splitting and aligning module, a multi-modal data preprocessing module, a multi-scale feature extraction module, a dynamic adaptive feature selection module based on harmony search and a dynamic adaptive stacking classification module based on harmony search. According to the dynamic adaptive feature selection optimization algorithm based on harmony search, redundant information is reduced, and a feature set is optimized. And finally, by proposing a dynamic self-adaptive stacking classification optimization algorithm based on harmony search, accurate classification of MCI patients and healthy individuals is realized. According to the method, the conventional wearable device is combined with cognitive evaluation on the tablet computer, the method has the advantages of being easy and convenient to operate, suitable for the family environment and the like, and important technical support is provided for autonomously detecting the MCI.

The invention discloses a method, a system, equipment and a medium for screening depression based on fundus color photograph, and the method comprises the following steps: collecting a fundus color photograph image data set containing healthy individuals and depression patients, carrying out the feature measurement of the image data set, building a model based on a random forest, and classifying the image data. And verifying and evaluating the model, and applying the model to a clinical working process. According to the method, model training and verification can be carried out on patients diagnosed with depression and healthy people of the same age and gender through fundus color images, depression diagnosis and clinical risk factors, and a trained random forest model is obtained and is used for distinguishing the healthy people from the depression patients; and a method and a tool are provided for early screening and identification of depression.

A computer-implemented method of training a classifier for diagnosing predisposition to a disease comprises steps of: (a) obtaining a training dataset further comprising data relating to previously expertly diagnosed healthy individuals and individuals suffering from the disease; (b) obtaining features from obtained training dataset; (c) training a feature classifier algorithm on the obtained features. The step of obtaining training dataset comprises obtaining sets of multi-spectral and depth head images of each individual at predetermined angles. The step of obtaining features comprises extracting the features from each image of the set such that a multilayer descriptor is generated. The feature classifier algorithm is trained on the multilayer descriptors extracted from the sets of images relating to the expertly diagnosed individuals.

A method and system for obtaining a measurement of cognitive performance in an individual, provide for obtaining a measure of at least one of the following activity parameters for the individual: 1) spatial memory accuracy; 2) planning accuracy; 3) ability to carry out dual-task interactions while navigating to a goal, wherein omission of the dual-task interactions is measured; 4) perseverations of incorrect dual-task interactions while navigating to a goal; 5) total time required by the individual to complete a navigation route; 6) upper extremity neuro-motor parameters; 7) reaction time of dual-task interactions; and 8) reaction time of the idle state of the individual; receiving the measures obtained into an algorithm; and computing a functional impairment score for the individual on the basis of baseline measurements obtained from a population of healthy individuals. The measurements are obtained using an app on an electronic portable device. The system and method may include an application executable by a user device to generate a gamified environment, to receive a first user input or set of user inputs at a first time and to transform said first user input or set of user inputs into a first measurement or set of measurements, and to receive a second user input or set of user inputs at a second time and to transform said second user input or set of user inputs into a second measurement or set of measurements; configured to receive said first measurement or set of measurements and compute a first functional impairment score and to receive said second measurement or set of measurements and compute a second functional impairment score; and configured to compare said second functional impairment score with said first functional impairment score and determine a magnitude and / or a speed of change in said functional impairment scores.

The device (1) is used to determine insomnia in a patient (8). It has a sensor unit (2) for recording at least one EEG measurement signal of a brain wave of the patient (8) and a control and evaluation unit (3) for evaluating the recorded EEG measurement signal. The control and evaluation unit (3) is configured to carry out an objective assessment routine and in doing so, for the subsequent recording of the EEG measurement signal, to give the patient (8) a stipulation to open his eyes for the duration of an eyes-open period and to close his eyes for the duration of an eyes-closed period, and then to initiate the recording of the EEG measurement signal during at least one eyes-open period and during at least one eyes-closed period. The control and evaluation unit (3) is further configured to perform an evaluation of the EEG measurement signal thus recorded and in doing so to extract from the EEG measurement signal at least one first partial measurement signal from the at least one eyes-open period and at least one second partial measurement signal from the at least one eyes-closed period, and to decide on the presence of insomnia if the at least one second partial measurement signal from the at least one eyes-closed period has a lower proportion of α waves than in a healthy individual.

To provide a practical method for detecting repeat expansion diseases that can comprehensively test for repeat expansion diseases without the need for advanced specialized knowledge in repeat expansion diseases.SOLUTION: The method for detecting a repeat expansion disease according to the present invention includes a library preparation process, a sequencing process, a consensus sequence construction process, a ranking process, and a determination process. In the sequencing process, multiple disease-associated repeat regions and their neighboring genomic regions are designated as target regions, and nanopore sequencing is performed to obtain long-read sequence data for each target region in a subject's genome. In the ranking process, repeat number data for each disease-associated repeat region is compared between a subject and a group of normal healthy persons, and the disease-associated repeat regions in the subject's genome are ranked in the descending order of the possibility of pathological repeat expansion. In the determination process, evaluation is started from the disease-associated repeat region ranked first according to specific steps S1 to S7, and it is determined whether or not the subject has pathological repeat expansion.SELECTED DRAWING: Figure 1

The invention belongs to the technical field of medical detection, and particularly relates to a marker for smoke disease diagnosis and application thereof. The invention discloses a marker for smoke disease diagnosis. The marker for smoke disease diagnosis is miRNA (micro Ribonucleic Acid); the miRNA is hsa-miR-4433a-5p, and the nucleotide sequence of the miRNA is as shown in SEQ ID NO. 1 (sequence identifier number 1). Experiments prove that the expression level of hsa-miR-4433a-5p in smoke disease patients is obviously higher than that of hsa-miR-4433a-5p in healthy people, and hsa-miR-4433a-5p can be used as a marker for smoke disease diagnosis. Meanwhile, the marker for smoke disease diagnosis is applied to preparation of the kit, the content of hsa-miR-4433a-5p in serum can be detected through a real-time fluorescent quantitative PCR technology, and the kit has the advantages of being high in sensitivity and accuracy and wide in application prospect.

Therapeutic support device, systems, and methods are directed towards optimizing cerebrospinal fluid dynamics in patients and otherwise healthy individuals. In some embodiments, a therapeutic support device may comprise a base, a thigh rest frame supported by the base and a back frame supported by the base. A back support may be attached to the back frame. A headrest may be movable with respect to the back frame about a headrest access. The back support may define a reference plain. They headrest access may be oriented to a front side of the reference plane and the back frame oriented to a second side of the reference plane.

The invention relates to methylation markers of colorectal polyp / adenoma and colorectal cancer as well as a screening method and application of the methylation markers, and further relates to a construction method of a three-classification model for simultaneously screening colorectal polyp / adenoma and colorectal cancer. Related methylation markers are screened by adopting a methylation co-immunoprecipitation sequencing technology, and a multi-stage classification model is constructed through a characteristic methylation difference region analysis algorithm, so that classification prediction of normal healthy people, colorectal polyp / adenoma people and colorectal cancer people can be realized, high sensitivity is achieved, precancerous lesions can be found as early as possible, and the clinical application prospect is wide. And a new technical scheme is provided for early screening of colorectal cancer.

The subject invention pertains to a method that involves at least three steps: (1) acquisition of a NMR data set or spin relaxation curve for plasma, serum or whole blood samples, (2) analysis of the NMR data or relaxation curve to extract the T2 relaxation times for water (or surrogates thereof), and (3) conversion of the water T2 (or surrogates thereof) into a measure of someone's health status (referred to as a T2 health score depending on the T2 value associated with the score). The T2 health score utilizes a statistical database derived from previous studies of subjects of normal, healthy individuals to those having varying degrees of hidden or subclinical metabolic abnormalities, such as inflammation, insulin resistance, lipid abnormalities (dyslipidemia), oxidative stress or other abnormalities, and provides a measure of a subject's overall health status.

A novel framework to transform a T-cell receptor (TCR) repertoire sample into a fixed-length vector. Short peptide sequences with different lengths in each TCR may be encoded into a numeric vector with fixed dimensions. A large amount of existing TCRs from healthy individuals may be pooled to generate a distribution of the encoding vector in a high-dimensional Euclidean space. Unsupervised clustering may be performed on the “points” in this space (each point is a TCR) to group them into antigen-specific clusters. The centroid of each cluster may be defined as a repertoire functional unit (“RFU”). For a new TCR repertoire sample, each TCR may be assigned to its most similar RFU group, and the RFU counts may be normalized by the number of sequences in the repertoire. The output data may be a fixed-length RFU vector, with each number representing the relative abundance of the given RFU in the repertoire.