128 results about "Healthy individuals" patented technology

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.

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 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 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 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.

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 application of a gene inhibitor in preparation of a medicine for treating ovulation dysfunction, and belongs to the field of biomedicine.The FST content in serum of a healthy person and an OD patient is compared, it is found that the FST content in serum of the OD patient is far higher than that in serum of the healthy person, and then it is obtained that the FST content can be used as the basis for judging that the patient suffers from ovulation dysfunction. Therefore, early intervention and treatment can be carried out on patients. According to the invention, the FST high-expression monkey model is successfully constructed, and the pathogenic effect of FST high expression on the OD pathogenesis process is obtained through research of the FST high-expression monkey model; the invention further designs specific shRNA (short hairpin ribonucleic acid) to successfully inhibit the expression of FST, and after the expression of FST is inhibited, the ovarian structure of the test monkey tends to be healthy, the follicular development is normal, and the ovulation regularity is recovered, and the results show that the inhibition of the FST gene has a remarkable promotion effect on the ovarian function.

This invention relates to a method and system for cardiovascular disease risk assessment based on machine learning models. By utilizing machine learning models to focus on predicting the risk of heart failure, it accurately distinguishes between healthy individuals and those with cardiovascular disease, emphasizing the differentiation of heart failure and other common cardiovascular diseases, and deeply exploring diagnostic indicators, thus opening new avenues for cardiovascular diagnosis and treatment. It hierarchically utilizes features closely related to blood cells, blood lipids, and blood glucose, and constructs predictive models using multiple machine learning models, laying a solid foundation for further in-depth research. This invention fully utilizes diagnostic indicators and multiple machine learning models, making full use of the predictive results and differentiated characteristics of different machine learning models to achieve efficient and accurate 5-category risk assessment.

This application discloses a sheep milk probiotic formula milk powder that can regulate intestinal flora and its preparation method. By weight, it contains the following raw material components: 600-700 parts sheep milk base, 5-15 parts compound probiotic powder, 30-60 parts prebiotic composition, 10-30 parts functional additives, and 5-20 parts nutritional fortifiers. This compound formula combines multiple probiotics and optimizes the strain ratio to achieve a synergistic colonization effect. The probiotics are prepared using a freeze-drying process, effectively improving problems caused by flora imbalance. Simultaneously, multiple compound prebiotics provide nutritional support for the proliferation of beneficial bacteria, synergistically enhancing the intestinal regulatory effect. Added functional components such as lactoferrin, chitosan, β-glucan, and poria cocos polysaccharide make it suitable for long-term conditioning consumption by sub-healthy individuals and those with weak digestive systems. The raw sheep milk undergoes vacuum distillation to remove the muttony odor. A process of wet powdering followed by aseptic dry mixing of probiotics is used, resulting in a more stable overall quality of the finished product.

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 the cosmetic and non-therapeutic use of a composition consisting essentially of a compound of formula I below: (I) or its enantiomers, isomers or diastereoisomers, or a mixture thereof, or a salt or a solvate thereof, wherein R1 and R2, independently of each other, are a C3-C6 sugar, a uronic acid, or a glucoside, for bleaching or whitening the skin and skin appendages of a healthy individual.

The present application relates to a kind of automatic sleep staging method, its steps include: the EEG original data is processed to obtain EEG feature map, while the EOG original data is processed to obtain EOG feature map;EEG feature map and EOG feature map are multiplied and fused, and highlight feature map is obtained;Highlight feature map is directly added with EEG feature map and EOG feature map and fused to obtain initial fusion feature map;Initial fusion feature map is processed to obtain the most weight feature map by weight adjustment;Initial fusion feature map and the most weight feature map are multiplied and fused to obtain the final fusion feature map with weight information;According to the weight information of final fusion feature map, each sleep stage prediction probability is output.The automatic sleep staging method described in the present application is further trained and optimized, does not need too much artificial intervention, and can be simply and effectively applied in sleep staging of healthy individuals and patients with consciousness disorders.

The application belongs to the field of biotechnology, and particularly relates to application of UBQLN1 protein in a cirrhosis diagnosis product, a kit and an in vitro detection method. The application comprises: using serum UBQLN1 protein as a marker, and applying the marker in preparation of a product for diagnosing or assisting in diagnosing cirrhosis; or applying the marker in preparation of a product for distinguishing cirrhosis from healthy individuals; or applying the marker in preparation of a product for evaluating progression of cirrhosis or a degree of liver fibrosis; and applying the marker in screening or evaluating a drug for preventing and / or treating cirrhosis.

A molecular biology method, by which relative (normalized to an internal control) N6-methyladenine (6mA) level at a selected specific genomic site in a tissue sample containing numerous individual cellular genomes is accurately determined, and this level is projected to a reference “6mA level-age” curve determined earlier. The relative 6mA level of the examined individual assigns her / his (biological) age on the curve. The reference curve was previously established by determining 6mA levels of a large (>1000) number of healthy individuals with known age. On the reference curve, the relative 6mA level correlates with age; the higher the 6mA level at the tested genomic site, the higher the age of the individual analyzed. The accuracy of the measurement depends on the accuracy of the method by which 6mA level is determined.

This application provides a disease intelligent diagnostic device based on brain structural connectivity identifiers and its application. The device includes (1) a data acquisition module; (2) a data processing module: reconstructing the spin distribution function of the data and distributing the spin distribution function in a standard space; (3) a data projection module: projecting the spin distribution functions of patients and healthy individuals into the standard space to obtain the Z-value of the maximum direction within the voxel; and (4) a Connectome Identifier extraction module: reducing the dimensionality of the Z-value to form a 1-dimensional feature value. The device of this application can be used for the diagnosis of various brain diseases and the determination of lesion location and severity.

This invention discloses a method for identifying unipolar and bipolar depression based on deep learning-based multi-state EEG fusion, comprising: Step 1, performing continuous wavelet transform on EEG signals in open and closed states respectively to obtain open-eye time-frequency maps and closed-eye time-frequency maps; Step 2, using a deep learning model to extract features from the open-eye and closed-eye time-frequency maps obtained in Step 1 to obtain feature vectors for open-eye and closed-eye states; then, fusing the feature vectors in open-eye and closed-eye states to obtain a multi-state fused feature vector; finally, using a deep learning classification network to classify and identify the multi-state fused feature vector to obtain the identification result, thus completing the identification. This invention uses a deep learning model based on EEG signals to perform three-class classification identification of unipolar depression, bipolar disorder, and healthy individuals, improving classification performance.

The application relates to the technical field of medical information, and specifically discloses a multi-modal Alzheimer's disease early screening algorithm based on a large language model. The algorithm first collects speech data and demographic information of participants. The speech data is processed through noise suppression, voice enhancement and speaker separation, and is transcribed into descriptive text. Then, the eGeMAPS feature set is extracted from the speech data and time-series spliced, and combined with the demographic information to form a multi-modal feature matrix. Key features are obtained using an attention pooling module, and feature classification is performed using a support vector machine to distinguish potential Alzheimer's disease patients from healthy individuals. The classification results, descriptive text and demographic information are input into the large language model, and zero-shot thinking chains are used to analyze four language barriers: reduced information content, lexical semantic defects, poor referential coherence and language loss of psychological state, and a comprehensive screening report is finally generated. The algorithm improves the accuracy of early diagnosis by combining audio and semantic features.

The application discloses a DNA methylation site marker for detecting hepatocellular carcinoma, a multiplex dPCR kit and a diagnostic model construction method. The DNA methylation site marker comprises cg02829688, cg13080379, cg03760839, cg10703826, cg12664119, cg16990168 and cg23371746 sites. The application designs a primer probe group for the above sites, constructs a multiplex dPCR kit of a double reaction system, realizes quantification of target DNA methylation sites, takes the methylation levels of the 7 sites as characteristic variables, adopts an XGBoost algorithm and / or an LR algorithm to construct a diagnostic model. Experimental verification shows that the model can effectively distinguish hepatocellular carcinoma patients from liver cirrhosis, chronic hepatitis B, metabolic dysfunction-related fatty liver disease patients and healthy individuals, especially shows good efficiency in auxiliary diagnosis of early hepatocellular carcinoma, and provides a new technical scheme for clinical diagnosis and screening of hepatocellular carcinoma.

Embodiments of the present disclosure pertain to methods and systems for assessing a subject's vulnerability to developing at least one cardiometabolic-related condition. Such methods and systems generally include the following steps or instructions: (1) receiving a plurality of health-related data of the subject; (2) calculating a risk score from the plurality of health-related data; (3) correlating the risk score to the subject's vulnerability to the cardiometabolic-related condition; and (4) making a treatment decision based on the subject's vulnerability to the cardiometabolicrelated condition.

This application relates to the field of speech signal processing technology, providing an acoustic feature processing method, apparatus, computer device, readable storage medium, and program product. The method includes: extracting acoustic features from speech data of healthy individuals and Huntington's disease patients to obtain acoustic feature sets; evaluating the stability of the acoustic features in the acoustic feature sets to obtain candidate acoustic feature sets; evaluating the significant independent contribution of the candidate acoustic features in the candidate acoustic feature sets to obtain multiple target acoustic features and their corresponding weights; performing weighted and scaled processing on the multiple target acoustic features based on their weights to obtain speech index features; and using the speech index features to characterize the severity of Huntington's disease. This method can integrate the multidimensional language impairments of Huntington's disease into clinically interpretable speech index features that are significantly correlated with the severity of Huntington's disease, improving the robustness of tracking Huntington's disease progression.

The application discloses a primer group, a kit and application for screening autism spectrum disorder patients, and belongs to the field of disease diagnosis. The technical problem solved by the application is how to screen autism spectrum disorder patients in Chinese Han population. The primer group for screening autism spectrum disorder patients disclosed by the application is directed to 132 autism susceptible gene sites, and is composed of 264 single-stranded DNAs shown in SEQ ID No. 1 to SEQ ID No. 264. According to the 132 autism susceptible gene sites, a data model is established by SVM, autism spectrum disorder patients can be screened, and the data model has good accuracy, precision, recall rate, F1 score and AUC value, can provide stable and efficient prediction performance between balanced precision and recall rate, and has strong ability in distinguishing autism spectrum disorder patients and healthy individuals.

This invention relates to the field of medical data processing technology, and discloses an Alzheimer's disease prediction method, system, device, and medium, comprising: obtaining spectral data of a target group in a non-invasive manner; training an Alzheimer's disease prediction model based on the spectral data; the target group includes healthy individuals and individuals with Alzheimer's disease. This invention non-invasively obtains samples from the target group, utilizing the advantages of laser-induced breakdown spectroscopy (LAS) for simultaneous detection and analysis of all elements at high speed. It achieves qualitative or quantitative analysis of the chemical composition of material surfaces by measuring the emission spectrum of micro-plasma, accurately and rapidly obtaining spectral data of the target group. The Alzheimer's disease prediction model trained based on the spectral data can effectively assist doctors in early screening of individuals, enabling early intervention for patients with early-stage Alzheimer's disease, and has broad application prospects.

This invention relates to the application of PWWP2B as a target in the preparation of drugs for the treatment of leukemia, belonging to the field of biomedical technology. To address the problem that traditional AML treatment targets lack tumor specificity and fail to meet clinical needs, this invention provides the application of PWWP2B as a target in the preparation of drugs for the treatment of leukemia. This invention demonstrates that PWWP2B is highly expressed in acute myeloid leukemia (AML) cells, and as a molecular marker, it can accurately distinguish AML from healthy individuals or non-leukemia patients. In vivo and in vitro experiments demonstrate that knocking out PWWP2B can significantly inhibit the proliferation and colony formation of AML cells, promote their differentiation and apoptosis, and inhibit the activity of leukemia cells in patients. This invention also reveals for the first time a novel use of the small molecule compound EZM0414 in the treatment of AML and its synergistic effect when used in combination with methyltransferase inhibitors.

1. Name of the product in this design: Phototherapy Chamber. 2. Purpose of this design: for physical therapy of sub-healthy individuals. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

Objective discrimination of a disease label of a depressive symptom with respect to an active state of a brain is achieved. One means for solving the problems of the present invention is to provide a discriminating device for assisting in determination of whether a subject has a depressive symptom. The discriminating device includes a storage device for storing information for identifying a classifier generated by classifier generation processing based on a signal obtained by using a brain activity detecting apparatus to measure, in advance and time-sequentially, a signal indicating a brain activity of a plurality of predetermined regions of each brain of a plurality of participants in a resting state, the plurality of participants including healthy individuals and patients with depression. The classifier is generated so as to discriminate a disease label of a depressive symptom based on a weighted sum of a plurality of functional connectivities selected by feature selection as being relevant to the disease label of the depressive symptom through machine learning from among functional connectivities of the plurality of predetermined regions. The discriminating device further includes a processor configured to execute discriminating processing of generating a classification result for the depressive symptom of the subject by using the classifier.

The invention provides a biological age prediction correction method and system based on individual differences, relates to the field of artificial intelligence, and solves the technical problems of systematic prediction deviation, missing of individual difference modeling and insufficient clinical interpretability caused by the fact that a biological age prediction model only depends on a time age label and a statistical loss function in the prior art. The method comprises the following steps: acquiring multi-modal physiological data and corresponding time and age of a plurality of healthy individual samples; predicting an initial biological age prediction value of the individual sample based on the physiological data; and based on the time age of the individual sample and the corresponding initial biological age prediction value, fitting to obtain an average biological age prediction function, calculating an aging difference value of the individual sample relative to the same-age group, further constructing a composite loss function, and training the biological age prediction model by minimizing the composite loss function to obtain a biological age prediction result. And obtaining a biological age prediction correction model. The method is used in the biological age prediction correction process.

The invention provides a metabolic marker combination for diagnosis or auxiliary diagnosis of kidney cancer and application of the metabolic marker combination, and belongs to the technical field of in-vitro diagnosis. The invention provides a metabolic marker combination for diagnosis or auxiliary diagnosis of kidney cancer. The metabolic marker combination comprises L-glucan and aspartic acid, the metabolism marker comprises a blood metabolism marker or a urine metabolism marker. The diagnosis model for distinguishing healthy individuals and kidney cancer patients is constructed by adopting L-glucan and aspartic acid, so that the diagnosis efficiency is high, and the clinical diagnosis significance is realized. In addition, 12 metabolic markers, 10 metabolic markers, 8 metabolic markers, 6 metabolic markers and 4 metabolic markers containing L-glucan and aspartic acid are respectively subjected to construction of diagnosis models for distinguishing healthy individuals from kidney cancer patients, and the constructed diagnosis models have relatively high diagnosis efficiency; the clinical diagnosis significance is realized.

Only healthy individuals with sufficient arm and finger strength to handle the belt's elasticity can pull the end of the belt from the anterior superior iliac spine to the greater trochanter of the femur with both hands and then secure it with hook-and-loop fasteners to support the pelvis. Therefore, providing a pelvic belt that can be easily attached with one hand is a challenge. [Solution] The pelvic support belt is characterized by comprising a metal or resin square ring 1 positioned at one end of the pelvic support belt, a metal or resin tube 2 positioned to surround the long side of the metal or resin square ring 1, a main belt 3, ventilation holes 4 provided in the main belt 3, a permanent fastening hook-and-loop fastener 5 positioned on the central flat part of the main belt 3, temporary fastening hook-and-loop fasteners 6 and 7 positioned on the side of the pelvic support belt opposite to the metal or resin square ring 1, and a hook-and-loop fastener 8 positioned at the end of the pelvic support belt opposite to the metal or resin square ring 1.

This invention discloses the application of urinary microvesicle proteins as diagnostic markers for renal cell carcinoma. The protein markers derived from urinary extracellular vesicles associated with clear cell renal carcinoma are characterized by being selected from one or more of the following proteins: CSPG4, AQP1, PLIN2, CA9, and HAVCR1. The invention also describes the application of reagents for detecting these protein markers in the preparation of auxiliary diagnostic reagents for clear cell renal carcinoma. The inventors, using flow cytometry, analyzed 200 patients and found that five proteins derived from urinary extracellular vesicles could effectively distinguish patients with clear cell renal carcinoma from controls (including healthy individuals, patients with benign renal tumors, patients with benign renal cysts, and patients with chronic kidney disease).