49 results about "Thyroid disease" patented technology

The invention discloses a thyroid disease full-period management system and method, and the system comprises a medical service module which is used for medical registration appointment; the data management module is used for collecting and storing human body life health indications of the patient in the hospital treatment period and in the remote re-treatment period; the follow-up diagnosis monitoring module is used for storing dose change data and setting regular review reminding; the health risk early warning module is used for performing correlation analysis on the disease health risk of the patient according to the human body life health indication and the dosage use data, and giving out doctor seeing guidance according to an analysis result; and the daily health management module is used for giving daily diet suggestions and exercise suggestions to the patient according to the analysis result of the health risk early warning module. The thyroid disease full-period management system shows obvious advantages in the aspects of optimizing the re-consultation process, improving the medical treatment efficiency, enhancing remote management, improving the medical compliance, providing personalized health guidance and the like, and provides more comprehensive, convenient and efficient health management services for patients.

The invention relates to the technical field of machine learning, and discloses a thyroid disease risk prediction method and system based on machine learning. The method comprises the steps of collecting medical record data of a thyroid disease patient, establishing a classification model based on the medical record data, and outputting a thyroid disease type corresponding to the medical record data; establishing a word vector model and a machine learning model, extracting a first feature quantity and a second feature quantity from the medical record data, screening out a first key feature and a second key feature of each thyroid disease from the first feature quantity and the second feature quantity, fusing the first key feature and the second key feature to obtain a comprehensive feature vector, and establishing a risk prediction model; acquiring medical record data of the current observation object, inputting the medical record data into the risk prediction model, and predicting and generating a thyroid disease risk level and a disease type of the current observation object based on the medical record data; if the risk level is a medium risk or a high risk, an optimal adjustment vector is generated, health adjustment suggestions are obtained based on the optimal adjustment vector, and the prediction accuracy of the thyroid disease risk is improved.

Disclosed are compounds that are thyroid stimulating hormone receptor antagonists, and methods useful for preventing or treating a thyroid disease.

The present invention provides methods of treating autoimmune thyroid disease using an antibody, or antigen-binding fragment thereof, capable of specifically binding to TSHR.

The invention relates to the technical field of disease risk prediction, and discloses a risk prediction method for thyroid diseases, which comprises the following steps: acquiring thyroid ultrasound image data and preprocessing the thyroid ultrasound image data, the preprocessing comprising denoising, standardization and image enhancement; extracting feature information in the preprocessed thyroid image data, and performing feature extraction by using a convolutional neural network; obtaining gene data related to the thyroid disease, standardizing the gene data, removing redundant gene data, and selecting gene information related to the thyroid disease; and fusing the image data and the gene data. According to the method, the technical scheme of fusing the image data and the gene data is adopted, two types of data characteristics are effectively combined through mutual information maximization and weighted summation, and the technical effect of remarkably improving the disease risk prediction accuracy is achieved. According to the method, the defect that information of a single data source is incomplete is overcome, and a more comprehensive decision basis is provided.

The invention provides a thyroid disease multi-omics mass spectrum construction analysis method and system.The thyroid disease multi-omics mass spectrum construction analysis method comprises the steps that a thyroid ultrasound image and multi-omics mass spectrum data are obtained, and a cross-modal incidence relation is generated based on anatomical structure characteristics of the thyroid ultrasound image and biomolecule space distribution characteristics of the multi-omics mass spectrum data; in combination with the thyroid anatomical structure deformation field and a multi-task optimization target, performing nonlinear coupling calculation on a back propagation error in a multi-task learning iteration process to obtain a gradient contribution weight between tasks; and generating a target multi-omics mass spectrum based on the cross-modal correlation features, the anatomical structure deformation field and the gradient contribution weight, and performing thyroid pathological feature analysis on the target multi-omics mass spectrum to obtain a three-dimensional positioning coordinate and a malignancy probability prediction value of the thyroid nodule. The thyroid disease diagnosis comprehensiveness and accuracy are improved.

The application discloses a thyroid disease prediction method based on machine learning, which comprises the following steps: S1, acquiring relevant sample data of a patient; S2, preprocessing the sample data to obtain processed sample data; S3, constructing a thyroid disease prediction model by using a LightGBM algorithm according to the processed sample data; S4, gradually reducing indexes and evaluating them on a test set to obtain a minimum index set; S5, reconstructing a model according to the index set; S6, analyzing test set data by using the model to obtain an analysis result; S7, judging whether the analysis result meets a preset condition, if yes, proceeding to step S8, otherwise, returning to step S1; S8, analyzing data of a to-be-detected person by using the model to obtain a prediction result; and S9, constructing a three-dimensional reference system of FT3, FT4 and TSH. The application can solve the problem of low accuracy of an existing thyroid disease prediction model and improve the three-dimensional reference system of FT3, FT4 and TSH.

Compositions, kits, systems, and methods for determining a total TSH concentration in a biological sample and / or determining the presence, severity, and / or predisposition of thyroid disease and / or pituitary disorder in an individual are disclosed. The compositions, kits, systems, and methods utilize at least three antibodies or antigen-binding fragments thereof that specifically bind to TSH, wherein at least one of the three antibodies or antigen-binding fragments thereof specifically binds to a TSH variant so that at least antibody-TSH-antibody sandwich immunocomplexes and antibody-TSH variant-antibody sandwich immunocomplexes can be formed. The methods include determining an amount of total TSH present in the biological sample based on the amount of the two sandwich immunocomplexes formed.

The present application provides a novel thyroid hormone β receptor agonist of formula (I) having better activity, selectivity or safety and the use thereof for preventing or treating diseases related to the action of β receptor agonists, including, for example, obesity, hyperlipidemia, hypercholesterolemia, diabetes, liver diseases (fatty liver, NASH, NAFLD, etc.), cardiovascular diseases (atherosclerosis, etc.), thyroid diseases (hypothyroidism, thyroid cancer, etc.), and the like.

The present application relates to the technical field of traditional Chinese medicine, and in particular, to a traditional Chinese medicine composition, a method for preparing same, and use thereof in preparing a medicament for preventing and treating a thyroid disease. The traditional Chinese medicine composition consists of Ranunculi Ternati radix, Taraxaci herba, Cyperi rhizoma, and Semiaquilegiae radix. The method comprises: mixing and crushing formulation amounts of Ranunculi Ternati radix, Taraxaci herba, Cyperi rhizoma, and Semiaquilegiae radix; or extracting formulation amounts of Ranunculi Ternati radix, Taraxaci herba, Cyperi rhizoma, and Semiaquilegiae radix. The compounding of the four traditional Chinese medicines, Ranunculi Ternati radix, Taraxaci herba, Cyperi rhizoma, and Semiaquilegiae radix, can significantly treat hypothyroidism, thyroid nodules, and the like caused by insufficient thyroid hormones. Compared with the prior art, the traditional Chinese medicine composition features clear ingredients, good safety, and better therapeutic effects, providing technical support for the treatment of thyroid diseases with traditional Chinese medicines.

Prenylated tetrahydroquinolines and quinolines and pharmaceutical compositions comprising the same. Prenylated tetrahydroquinoline and quinolines and pharmaceutical compositions comprising the same, for use in the prevention and / or treatment of peroxisome proliferator-activated receptor (PPAR)-mediated diseases such as metabolic syndrome, type 2 diabetes mellitus, dyslipidemia, hyperlipidemia, hypertriglyceridemia, hypercholesterolemia, obesity, dyslipidemic atherosclerosis, metabolic dysfunction- associated fatty liver disease (MAFLD), cardiovascular disease, cardiometabolic disease, neurodegenerative disease, Friedreich's ataxia, Parkinson's disease, multiple sclerosis, Alzheimer's disease, autoimmune disease, rheumatoid arthritis, autoimmune thyroid disease, dermatological disease, and cancer.

The invention discloses an ultrasonic thyroid nodule detection method and device based on deep learning feature fusion. The method comprises the following steps: firstly, acquiring an ultrasonic video; extracting an image sequence of the ultrasonic video; then training a BDA-YOLO thyroid nodule prediction model, and predicting a target frame and a focus area of the thyroid nodule; and finally, the thyroid nodules, the shapes, the sizes, the positions and the area contours of the nodules are visualized, a doctor is helped to diagnose thyroid diseases more easily through a visual visualization result, and support is provided for follow-up disease treatment of a patient. According to the method, the thyroid nodule positioning and segmentation precision of the model can be improved.

The present invention relates to the field of molecular biology, and more particularly to nucleic acid aptamers, derivatives, and applications thereof for detecting TSHR protein. The present invention provides a nucleic acid aptamer for detecting TSHR protein, the nucleotide sequence of which is shown in SEQ NO. 1. The present invention provides a DNA aptamer YC6 with high affinity for TSHR protein, which has been validated at the molecular, cellular, and tissue levels. The present invention discovers that the DNA aptamer YC6 has the ability to recognize and target cells expressing thyrotropin receptors, providing new insights into the diagnosis and treatment of thyroid diseases and thyroid-related eye diseases.

Disclosed are compounds that are thyroid stimulating hormone receptor antagonists, and methods useful for preventing or treating a thyroid disease.

The present application provides a method and system for constructing and analyzing multi-omics mass spectrometry maps of thyroid diseases, wherein thyroid ultrasound images and multi-omics mass spectrometry data are obtained, and cross-modal correlation relationships are generated based on the anatomical structural features of the thyroid ultrasound images and the spatial distribution features of the biomolecules of the multi-omics mass spectrometry data. In combination with the thyroid anatomical deformation field and multi-task optimization objectives, nonlinear coupling calculations are performed on the back-propagation error during the multi-task learning iteration process to obtain the gradient contribution weights between tasks; based on the cross-modal correlation features, the anatomical deformation field, and the gradient contribution weights, a target multi-omics mass spectrometry map is generated, and the thyroid pathological characteristics are analyzed to obtain the three-dimensional positioning coordinates of the thyroid nodules and the predicted value of the malignancy probability. The present application improves the comprehensiveness and accuracy of thyroid disease diagnosis.

This invention relates to the field of image analysis technology, specifically to a method for prognostic assessment of breast and thyroid diseases combined with AI analysis of pathological images. The method includes the following steps: acquiring pathological slide images and performing grayscale statistics to generate tissue coordinate data; calculating the edge gradient and contour features of pixel neighborhoods; quantifying the morphological aberration features of cell nuclei and constructing a nuclear population potential field; analyzing the potential difference and gradient inversion regions; correcting directional consistency based on the gradient magnitude distribution direction; and finally generating a disease prognostic assessment result. In this invention, stable tissues are screened and coordinate data is generated through grayscale statistics and threshold segmentation of pathological slide images; edge gradients are calculated and closed contours are extracted; cell nucleus axis ratios and contour roughness are quantified; a regular grid is established and distance attenuation is introduced to form a nuclear population potential field; and the potential difference and gradient inversion regions are statistically analyzed and directional consistency is corrected, thereby improving the stability and consistency of the prognostic assessment.

The application relates to the technical field of medical data processing, and discloses a thyroid disease management and health data statistical analysis system. A physiological kinetics model containing a drug metabolism and endogenous secretion mechanism is constructed, discrete detection data are filled in time sequence blanks by using an unscented Kalman filtering algorithm, a continuous physiological panorama conforming to a human mass conservation law and a neural humoral regulation mechanism is generated, a time lag causal tensor construction mechanism with an adaptive causal window is introduced into the system, nonlinear lagging effects of hormone fluctuations on nodule morphological evolution are accurately captured, and deep causal chains of pathological causes and morphological results are restored, finally, an entropy-driven dynamic scheduling module based on information thermodynamics replaces a traditional fixed follow-up mode, a Lyapunov cognitive failure critical point is calculated as an optimal review time by quantizing an uncertainty accumulation process of a disease condition prediction and combining a risk adaptive mechanism.

The present invention provides a nucleic acid aptamer targeting TSHR protein, a nucleic acid derivative and its application, and belongs to the field of molecular biology technology. The nucleic acid aptamer targeting TSHR protein in the present invention comprises a nucleotide sequence as shown in SEQ ID NO.1, and one or more modifications including radioactive labeling, therapeutic drug connection, fluorescent labeling or biotin labeling are performed on both ends or one end of the nucleic acid aptamer targeting TSHR protein to obtain a nucleic acid aptamer targeting TSHR protein and its derivatives. The nucleic acid aptamer and its derivatives in the present invention can identify and target cells expressing thyrotropin receptors, have high affinity, strong stability, low cost, and have the advantages of in vitro and in vivo imaging labeling functions, providing new molecular tools for the diagnosis and treatment of thyroid diseases and thyroid-related eye diseases.

The invention relates to a microwave-induced thermoacoustic transcranial quantitative imaging method based on a REAGU-Net network, and the method achieves the simultaneous quantitative reconstruction of dielectric constant, conductivity and sound velocity in brain tissue through the MITAT technology, and provides a new methodology for the characteristic analysis of the brain tissue. A REAGU-Net novel network is developed, an attention guiding block and a residual error inducing block are combined to process the influence of acoustic non-uniformity on reconstruction quality, and effective application of deep learning in quantitative reconstruction is shown; the technology not only shows a good result in cerebral hemorrhage detection, but also can be potentially applied to detection and diagnosis of various diseases including breast cancer, liver diseases and thyroid diseases, and a new quantitative analysis tool is provided for clinic; the DL-MITAT method provided by the invention can provide quantized physical characteristic data, and the data not only is helpful for imaging, but also can provide support for physiological information prediction and the like in subsequent electromagnetic treatment; compared with a traditional TCD or ultrasonic imaging method, the transcranial MITAT has the remarkable advantages that due to the fact that ultrasonic waves of the transcranial MITAT only need to be transmitted with the skull for one time, the possibility of image attenuation and degeneration is reduced, and the imaging quality is improved.

This application designs a method for implementing multi-dimensional Q&A for thyroid diseases, including: S1. After the patient inputs a question, the intention recognition model in the first layer of the multi-dimensional Q&A system determines whether the question is a concern or consultation intention. If the concern intention is hit, enter the concern Q&A; otherwise, enter the consultation intention. S2. Enter the consultation intention, establish a multi-round Q&A model based on the semantic slot template, and conduct the second-layer Q&A. S3. When the intention not summarized in the semantic slot template or the knowledge queried in the knowledge graph is empty, transfer to the FAQ retrieval Q&A, and let the retrieval robot be the fallback reply for the last link of intelligent consultation. S4. Through multiple fixed template Q&A interactions, the multi-dimensional Q&A system conducts logical judgments to achieve the patient's self-disease assessment and self-diagnosis. Through the above design, this application can achieve multi-dimensional Q&A in the field of intelligent consultation for thyroid diseases, thereby improving the intelligent Q&A coverage rate and accuracy.

Disclosed herein are immunoassays for detecting anti-thyroid peroxidase antibodies in a biological sample from a subject and / or diagnosing a thyroid disease in a subject. The disclosed immunoassays employ recombinant thyroid peroxidase (rTPO) and anti-TPO monoclonal antibodies to assess the level of anti-thyroid peroxidase antibody-induced formation or disruption of complexes comprising a solid support and the rTPO. Also disclosed are methods and kits thereof.

The invention belongs to the technical field of collagen, and particularly relates to a preparation method of deer-horn glue micromolecular collagen and application of the deer-horn glue micromolecular collagen in treating thyroid nodules. According to the preparation method of the deer-horn glue small-molecule collagen, the deer-horn glue small-molecule collagen is prepared from deer flower disc through specific steps of decoction, concentration, enzymolysis and the like, the deer-horn glue small-molecule collagen has an obvious effect on thyroid diseases, and a new scheme is provided for treatment of the thyroid diseases.

The invention provides a metabolic marker composition for distinguishing benign thyroid diseases from thyroid cancer and application of the metabolic marker composition. Based on blood metabonomics data, a diagnosis model capable of accurately distinguishing benign thyroid diseases and thyroid cancer is constructed by screening a specific metabolic marker combination and combining a machine learning algorithm, and the model has relatively high sensitivity and specificity. Based on the constructed diagnosis model, whether the patient suffers from the thyroid cancer or the benign thyroid disease can be judged at the same time only through one-time detection, excessive examination of the patient suffering from the benign thyroid disease can be avoided, excessive diagnosis and treatment are reduced, and excessive panic of the patient is avoided. The thyroid cancer diagnosis model method provided by the invention has the advantages of convenience, economy and convenience in sample acquisition, and is more suitable for large-scale population screening and regular tracking in regions with shortage of medical resources.

The invention belongs to the technical field of knowledge maps and large models, and discloses a breast and thyroid disease preoperative decision support evaluation method based on a large model and a knowledge map, and the method comprises the steps: obtaining the preoperative diagnosis and treatment data of a target patient, and obtaining the preoperative diagnosis and treatment data of the target patient based on a breast and thyroid disease diagnosis and treatment path knowledge map containing specific nodes and relationships; generating a path state vector containing a path identifier, a missing type and a stage code; generating a structured constraint prompt according to a knowledge graph crossing forbidding condition; inputting the two schemes into a trained large model to obtain decision candidate schemes and corresponding path sufficiency arguments; and finally, by calculating the path dependence intensity and the risk stability, screening out a final decision suggestion. According to the method, the problem of incomplete diagnosis and treatment information can be accurately solved, the accuracy, interpretability and risk controllability of preoperative decision making are improved, and clinical actual requirements are met.

The invention discloses a daily iodine content statistical system, which comprises a database module connected with a calculation module, a user interaction interface module, a data chart generation module, a reminding module and a doctor side login module and used for recording initial data and storing the recorded initial data in a database; the user interaction interface module is connected with the database module, the calculation module, the reminding module and the data chart generation module and is used for inputting food types and quantity and generating feedback information; the calculation module is connected with the user interaction interface module, the database module, the data chart generation module and the reminding module and is used for calculating the daily iodine intake of the user; the data chart generation module is connected with the calculation module, the database module and the user interaction interface module and is used for generating a visual data chart according to the iodine intake historical record of the user; the iodine intake can be accurately controlled, accurate data can be provided to assist diagnosis and treatment, and thyroid disease research can be promoted.

The invention discloses a method for establishing a reference interval of thyroid stimulating hormone in the early pregnancy stage of a pregnant woman. The method comprises the following steps: S1, collecting data and establishing a database; s2, performing data processing on the database obtained in the step S1, determining a data distribution type, and determining a reference interval; the interval combination obtained in the invention is convenient for assisting doctors in subsequent diagnosis and treatment, and in combination with the TSH reference interval, the pregnant women with thyroid diseases can be prevented from being neglected through the judgment standard.

The invention relates to the technical field of thyroid treatment, in particular to an intelligent thyroid inquiry system which comprises an inquiry system and is used for intelligent auxiliary diagnosis of thyroid diseases, and the inquiry system comprises a heterogeneous data integration unit which is used for synchronously accessing electronic medical record data, ultrasonic DICOM images, patient chief complaint voice and wearable equipment monitoring data; the multi-dimensional feature extraction unit is used for processing the ultrasonic image by adopting a deep convolutional network to extract nodule features, and analyzing a medical history text through a BiLSTM network; the dynamic decision center unit integrates a GNN-based knowledge graph inference engine and an interpretability algorithm, and outputs a differential diagnosis list; the diagnosis accuracy is improved through multi-modal data fusion, and comprehensive analysis is realized by integrating image, examination and medical history data; the diagnosis and treatment process is optimized by adopting an intelligent inquiry path, the problem sequence is dynamically adjusted, critical cases are preferentially processed, and the inquiry efficiency is remarkably improved.

The invention relates to the technical field of intelligent medical prediction, in particular to a thyroid disease prediction system based on big data. The thyroid disease prediction system comprises a difference coding module, a mode mining module, a habit feature extraction module, a state matching analysis module and a risk prediction module. According to the thyroid function detection method, the evolution direction and the internal change rule of the thyroid health state are deeply revealed by capturing and symbolizing the dynamic difference of thyroid function detection data of adjacent years instead of only depending on static numerical values; the method comprises the following steps of: performing symbolization processing of an offset direction on living habits such as sleep duration and motion frequency of a subject from a median value of personal quarterly data, and establishing a direct correlation between dynamic change of clinical indexes and individual living behavior offset; and screening out a specific difference combination mode with high frequency and high importance in the group through statistical analysis, and carrying out synchronous change matching on the specific difference combination mode and a living habit offset symbol, so that risk assessment is no longer isolated clinical data interpretation.

The invention discloses a THR-beta receptor agonist as well as a preparation method and a use method thereof. The invention provides a compound as shown in a formula (I), and pharmaceutically acceptable salt or stereoisomer thereof. The agonist provided by the invention has relatively good THR-beta agonistic activity and selectivity and strong liver targeting, and can be applied to metabolic diseases (such as non-alcoholic steatohepatitis (NASH), hepatic steatosis, hyperlipidemia and obesity), cardiovascular diseases (such as atherosclerosis) and thyroid diseases (such as hypothyroidism and thyroid cancer).

The invention provides a metabolic marker composition for distinguishing benign thyroid diseases from early thyroid cancer, and provides an auxiliary diagnosis means independent of invasive biopsy by constructing a diagnosis model based on the metabolic marker composition. The metabolic marker combination can realize functional early warning and diagnosis in an earlier stage of thyroid cancer occurrence, solves the problems of insufficient specificity and high false positive rate in distinguishing benign and malignant thyroid lesions in existing imaging examination, has high sensitivity and specificity, can provide a basis for clinical early intervention, and has a good application prospect. The method is suitable for large-scale population screening and repetitive dynamic monitoring of high-risk individuals.