180 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.

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 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 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 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 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 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.

The present invention relates to the field of gas sensor technology, and specifically discloses a sensor drift correction method and system based on transfer learning. The method first collects sensor response data of healthy individuals and lung cancer patients at two different time periods, and uses the labeled early data as the source domain data set and the unlabeled later data as the target domain data set. The data is then preprocessed to extract features to obtain a source domain data feature set and a target domain data feature set. Finally, based on transfer learning, domain adaptation is performed on the target domain data feature set to perform drift correction. The present invention performs drift correction by minimizing the distance between the marginal distribution and the conditional distribution and forcing the maximization of cross-domain category information, and extracting a small number of representative features to perform high characterization of the original data. Experimental results show that the present invention can effectively correct sensor drift and achieve a higher classification accuracy in intelligent detection based on gas sensors, such as intelligent detection of lung cancer.

The invention relates to the field of behavior detection, in particular to a chronic pain protection behavior detection method based on a hierarchical weighted graph convolution model. The method comprises the following steps: acquiring a training set, wherein the training set comprises joint movement data of a plurality of healthy individuals and a plurality of chronic lower back pain individuals in the process of completing daily life tasks; preprocessing the data in the training set, and converting the data into three-dimensional time sequence skeleton data; training a detection model by using the preprocessed training set; the detection model converts the human skeleton image into a layered image structure, and features are extracted by using an image convolutional network; introducing a category-based weighting mechanism, and optimizing a classifier and a weighting network through iterative adversarial training; and after the joint movement data of an individual to be detected in the process of completing the daily life task is preprocessed, the trained detection model is input for detection. According to the method, the receptive field is expanded through a layered structure, the category imbalance problem is optimized through a reweighting mechanism, and the robustness and accuracy of detection are improved.

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 present invention provides a method, system, medium, and device for constructing a brain network based on mechanical parameters, comprising: step S1: acquiring a brain structural image and a mechanical parameter distribution image using magnetic resonance elastography, registering the brain structural image and the mechanical parameter distribution image, segmenting them using a standard spatial atlas, and extracting the mechanical modulus value of each pixel in each brain region; step S2: calculating the probability distribution function of each brain region and estimating the similarity of the probability distribution functions of each brain region using KL divergence; and step S3: using the calculated KL divergence values ​​as connections between different brain regions, constructing a mechanical network and performing graph theory analysis, comparing graph theory indicators between healthy individuals and patients with neurodegenerative diseases. The present invention can improve the efficiency of early diagnosis of neurodegenerative diseases.

The invention belongs to the technical field of biological analysis, and particularly relates to application of strand displacement reaction, rolling circle amplification, a CRISPR-Cas12a system and in-vitro transcription in miRNAs detection. The invention provides a method for sensitively and specifically detecting breast cancer miR-21 and miR-155 by driving NRCA and LRCA amplification through strand displacement reaction, realizing efficient amplification of an activator, activating trans-cleavage of Cas12a in combination with transcriptional crRNA, and realizing fluorescence signal amplification. The method is convenient, sensitive, low in background interference and high in specificity, can be applied to simultaneous detection of miR-21 and miR-155 in breast cancer serum, can effectively distinguish healthy individuals from breast cancer patients, expands methodology of CRISPR-Cas12a in the field of in-vitro diagnosis, and shows a wide application prospect.

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.

The present invention discloses a method, system, device, and medium for screening for depression based on fundus color photography. The method comprises the following steps: collecting a dataset of fundus color photography images of healthy individuals and patients with depression, measuring features of the image dataset, constructing a model based on a random forest algorithm, classifying the image data, validating and evaluating the model, and applying the model to a clinical workflow. The present invention uses fundus color photography images, depression diagnosis, and clinical risk factors to train and validate a model on confirmed patients with depression and healthy controls of the same age and gender. The trained random forest model is then used to distinguish between healthy individuals and patients with depression, providing a method and tool for early screening and identification of depression.