72 results about "Gestational diabetes" patented technology

The invention discloses a gestational diabetes risk intelligent prediction method based on multi-modal data fusion, and particularly relates to the technical field of data processing, and the method comprises the steps: S1, setting a prediction window, S2, obtaining risk detection data, S3, constructing a multi-modal time sequence matrix, S4, carrying out risk analysis, S5, constructing a gestational risk change curve, and S6, carrying out visual display. According to the method, detection is carried out through physiological risk indexes and biochemical risk indexes, multi-modal time sequence matrix construction is carried out based on risk detection data, the data acquisition accuracy is met, a matrix structure of time periods and multiple indexes is formed, correlation analysis is carried out on pregnancy risks and detection time periods, and a pregnancy risk change curve is constructed from the correlation analysis; the dynamic evolution of the risk along with pregnancy is visually displayed, the risk occurrence time can be accurately positioned through inflection point detection, key detection of the risk occurrence time is facilitated, and a structured foundation is laid for the follow-up risk change trend and fluctuation amplitude.

The invention provides a gestational diabetes risk prediction system and method based on multi-modal data fusion, and the system comprises a data collection module which is used for integrating clinical indexes and medical record text data; the data preprocessing module converts the multimode data into numerical values and text variables which can be used for modeling; the variable screening module is used for extracting data features by adopting LASSO regression in combination with a recursive feature elimination algorithm and a Clinical-BERT model; the data prediction module is used for constructing a GDM risk prediction model through a dual-channel calculation unit and a fusion unit, generating an accurate risk probability and providing an interpretable clinical index in combination with an SHAP value; and a prediction result is output through the output module in the forms of a dynamic column diagram, a webpage calculator and an API interface, so that clinical operation and application are facilitated. According to the method, the limitation of a traditional prediction method is broken through, the prediction precision and the real-time monitoring capability are improved, and the development of precise medical treatment is promoted.

The invention provides a gestational diabetes mellitus dynamic risk assessment method and a gestational diabetes mellitus dynamic risk assessment system, and relates to the technical field of gestational diabetes mellitus risk assessment. The collaborative fluctuation intensity of the three core characteristics is accurately quantified by using a matrix norm, a two-factor decision engine is generated by combining postprandial blood sugar daily mean value change direction marks, a long-term metabolism trend under a gestational week differentiation weight is quantified by using a risk coefficient, and a linkage factor is fused with multi-characteristic collaborative fluctuation and a blood sugar short-term change direction. A blood glucose threshold self-adaptive adjustment algorithm is driven in real time, and a traditional static threshold system is thoroughly broken through; an acute event hard interception network is synchronously established, risk assessment result forced coverage and minute-level monitoring activation are triggered through a linkage factor threshold value, and a mechanical response barrier for acute metabolism decompensation is formed; and finally, dynamic risk assessment of the gestational diabetes mellitus is realized.

The invention discloses a gestational diabetes mellitus early prediction method, a gestational diabetes mellitus early prediction system and a computer readable medium, and the method comprises the steps: obtaining pregnant woman sample data used for researching GDM early prediction, the pregnant woman sample data for researching the early prediction of the GDM comprises a plurality of basic clinical characteristics of each pregnant woman before 16 weeks of pregnancy, a plurality of biomarkers related to the pathogenesis of the GDM before 16 weeks of pregnancy, and a diagnosis result whether the biomarkers are GDM or not; screening a plurality of basic clinical features and a plurality of biomarkers in the obtained pregnant woman sample data to obtain basic clinical features and biomarkers with the highest GDM prediction value; and performing GDM prediction on the pregnant woman which is insufficient for 16 weeks of pregnancy by using the basic clinical features and the biomarkers with the highest GDM prediction value, so as to obtain a prediction result that GDM appears or does not appear in 24-28 weeks of pregnancy. According to the method, the GDM high-risk crowd can be predicted in the early stage of pregnancy, the GDM high-risk crowd is intervened, and the occurrence risk of bad outcomes is reduced.

The invention discloses a blood glucose data analysis system and method based on artificial intelligence, relates to the technical field of diabetes management, and aims to solve the problems of blood glucose data distortion and poor blood glucose monitoring suitability of gestational diabetes patients and children diabetes patients in a motion scene. The method comprises the following steps: acquiring blood glucose original data, exercise intensity data, skin impedance data, blood perfusion index data and special crowd exclusive physiological data, preprocessing, calculating three types of interference weight factors, substituting into a calibration formula to obtain corrected blood glucose data, generating a structured report, updating the calibration formula according to a period, and optimizing a special crowd model. By means of the method, motion multi-dimensional interference can be eliminated, the small sample modeling problem of special crowds is solved, and the blood glucose data precision and clinical practicability are improved.

The invention provides a method and system for predicting gestational diabetes risk in the early stage of pregnancy, and the method comprises the following steps: obtaining a multi-dimensional physiological index data sample of a source pregnant woman in the early stage of pregnancy; a pre-trained biomedical field large language model is adopted, the biomedical field large language model is finely adjusted through a parameter efficient fine adjustment technology, and a fine-adjusted large language model is generated; performing data enhancement processing on the multi-dimensional physiological index data sample by utilizing the fine-tuning large language model; training a machine learning prediction model by using a training data set containing the enhanced physiological data; receiving physiological index data of the target pregnant woman; inputting the physiological index data of the target pregnant woman into a machine learning prediction model; and outputting a prediction result indicating the risk of gestational diabetes mellitus of the target pregnant woman at the early stage of pregnancy. The risk of gestational diabetes mellitus can be evaluated in the early stage of pregnancy, the accuracy and early stage of prediction are improved, and high-risk pregnant women can be recognized as early as possible and effectively managed.

The invention discloses a self-management method for pregnant women with gestational diabetes mellitus and a related device. The method comprises the following steps: collecting comprehensive health information of the pregnant women in the gestational period according to a preset period; the comprehensive health information is input into a gestational diabetes health status assessment model, and the comprehensive health status assessment result of the pregnant woman gestational diabetes is predicted, and the comprehensive health status assessment result comprises the risk probability of hyperglycemia, hypoglycemia and gestation complications; according to the gestational diabetes mellitus medical knowledge base and the comprehensive health state evaluation result, a pregnant woman personalized intervention scheme is generated and uploaded to a medical platform for an attending physician to check and confirm, and the pregnant woman personalized intervention scheme comprises diet suggestions, exercise suggestions and medicine adjustment suggestions. The risk probability of the pregnant woman suffering from hyperglycemia, hypoglycemia and pregnancy complications is predicted through the gestational diabetes health status assessment model, personalized risk assessment is realized, and the risk of complications is reduced through a personalized intervention scheme.

The invention relates to the technical field of health risk assessment, in particular to a multimodal data fusion early warning method for eye complications related to gestational diabetes mellitus, and the method comprises the steps: obtaining feature vectors of a to-be-detected patient under different preset factors in each historical examination time period, blood glucose data and target eye images of the patient to be detected in each historical examination time period are obtained; determining a precursor risk characteristic value, a risk increase trend value and an eye complication risk value corresponding to each historical examination period; determining a target weight under each preset factor according to a correlation condition between the feature vector distribution and the eye complication risk value distribution; and performing multi-modal data fusion according to the target weights and the feature vectors under all the preset factors, and realizing risk early warning assistance through a risk early warning assistance model. According to the method, the weights of different factors are quantified in a self-adaptive manner, so that the multi-modal data fusion is relatively objectively realized, and the reasonability of the multi-modal data fusion is improved.

The present application relates to a blood sugar management system for pregnancy, comprising terminal devices carried by target users and reference users and a cloud server in data connection with the terminal devices, the cloud server being configured to: acquire physiological data and blood sugar data of the target users from the terminal devices, and calculate characteristic indexes of the target users based on the physiological data and the blood sugar data; acquire a plurality of blood sugar data of the reference users from the terminal devices and form an overall reference data set in a manner associated with gestational weeks, and assign the target users to a predetermined characteristic index value interval in the overall reference data set based on the characteristic indexes, so as to provide personalized health management suggestions for the target users based on factors identified to affect the risk of gestational diabetes mellitus of the target users.

The invention provides a food measuring device based on gestational diabetes mellitus of a pregnant woman, and relates to the technical field of diabetes mellitus medical equipment. Comprising a nutrition control mechanism and a blood sugar monitoring mechanism, and the nutrition control mechanism and the blood sugar monitoring mechanism are detachably connected; the nutrition control mechanism comprises a base, a limiting groove is formed in the base, a placement disc is arranged on the limiting groove, and a pressure sensor is arranged between the placement disc and the limiting groove; a telescopic rod is arranged on the base, and a camera is arranged on the telescopic rod; the blood glucose monitoring mechanism comprises a wearing main body, a blood glucose detector, a controller and a display screen are arranged on the wearing main body, and the controller is electrically connected with the blood glucose detector and the display screen; compared with the prior art, the device is simple in structure, convenient to use and carry and capable of effectively monitoring and controlling diet nutrition of the pregnant woman.

Provided are methods for treating a disorder characterized by elevated blood glucose levels in a subject in need thereof, comprising administering to the subject a therapeutically effective amount of a hyperimmunized egg product obtained from an egg-producing animal, wherein the hyperimmunized egg product comprises a therapeutically effective amount of one or more IgY antibodies to an extracellular glucose binding domain of a sodium-glucose co-transporter SGLT. Also provided are hyperimmunized eggs and egg products produced by an animal that has been hyperimmunized with an antigen that contains an extracellular glucose binding domain of a sodium-glucose co-transporter SGLT or fraction thereof, and use of the hyperimmunized eggs or hyperimmunized egg products to reduce blood glucose levels in a subject, such as to treat type 2 diabetes. Methods or preparing the hyperimmunized eggs and hyperimmunized egg products are also provided.

The invention belongs to the technical field of biological medicine, and particularly relates to application of LRRFIP1 serving as a bilberry extract to improvement of a target spot of insulin resistance of gestational diabetes mellitus. The insulin resistance of gestational diabetes mellitus is improved by specifically regulating and controlling a PI3K-AKT signal channel and a GLUT4 glucose transporter function in skeletal muscle based on the bilberry extract. The bilberry extract used in the invention is a natural plant source, is high in safety, and is suitable for pregnancy. PI3K-AKT signal channels can be activated at the same time, and insulin resistance and lipid metabolism disorder can be synergistically improved. The action mechanism is systematically illuminated through technical means such as proteomics, network pharmacology, molecular docking and the like. Glucose tolerance, insulin sensitivity and blood fat level of GDM rats can be remarkably improved, and no remarkable side effect exists.

The invention discloses a diet metering and recording system for gestational diabetes mellitus patients. The diet metering and recording system comprises a food containing structure; a food weighing structure; a food shooting structure; an information storage module; an analysis and control module; and a display structure. The information storage module is used for storing corresponding relations between food pictures and food categories; the signal receiving and transmitting module receives food category information and corresponding weight information of each meal. The analysis and control module is used for acquiring the pictures of the food in each food containing structure and the weight information of the weighed food one by one, and comparing the pictures and the weight information to obtain the names and the categories of the food; finally, comparing the obtained real food category and food weight information with the received food category and corresponding weight information, and outputting single ending information when the real food category and food weight information are matched with the received food category and corresponding weight information; when all matching is carried out, all ending information is output; the display structure receives the food category information and the corresponding weight information of each meal; and a timing and blood glucose monitoring module is arranged to obtain a 2h blood glucose value. The whole process is high in automation degree, and few events need to be memorized.

A group of marker sets capable of effectively predicting acute pain of a patient after cesarean section is screened out, and the marker sets comprise whether the patient has preoperative anxiety or not, whether the patient is gestational diabetes mellitus or not, the age of the patient, the Pittsburgh sleep quality index (PSQI) of the patient, the gestational week age of the patient and the abdominal circumference of the patient. On the basis, a visual and quantifiable column diagram prediction model is constructed, the method can be used for evaluating the risk probability of acute pain occurring after a cesarean section patient is operated clinically before an operation, targeted and individualized pain management is provided for the patient subsequently, the pregnancy safety of the patient is improved, and physical and psychological pain of the patient after the operation is relieved.

The present invention relates to a method for improving glucose homeostasis and / or lowering plasma glucose levels by administering a SIRT6 activator. The present invention also provides a method for treating hyperglycemia, prediabetes, and diabetes, including type I diabetes, type II diabetes, or gestational diabetes, by administering a SIRT6 activator.

The present application relates to the technical field of digital medical treatment, in particular to a pregnancy-induced diabetes risk early warning method based on multi-modal data fusion. Multi-modal data such as blood glucose time series data, exercise amount, body weight and placenta thickness of pregnant women during pregnancy are acquired, the peak value and back-off characteristics of blood glucose time series are analyzed, the exercise amount is combined, the blood glucose back-off trend degree is quantified, and the dynamic metabolic control capability is accurately reflected; further, on the basis of placenta thickness data, the body weight change condition, the exercise amount and the blood glucose back-off trend degree are combined to determine the placenta thickness estimation value time series data, and the physiological correlation characteristics between various data are improved; finally, the blood glucose back-off trend degree mean value and the placenta thickness sustained increasing trend characteristics are fused to reveal the correlation characteristics of blood glucose and abnormal placenta development, a neural network model is trained to capture multi-factor nonlinear relationships, and early and accurate risk prediction of pregnancy-induced diabetes (GDM) is realized.

The invention is applicable to the technical field of biological medicines, provides application of ophiopogon japonicus borneol glycoside in preparation of a medicine for preventing and treating gestational metabolic diseases, and aims to solve the technical problems that the conventional medicine is limited in use during pregnancy, and the curative effect and the safety are difficult to take into account. According to verification of multi-model systems such as animals, cells and human-derived trophoblast organs, it is found that ophiopogon japonicus borneol glycoside can significantly improve gestational glycometabolism disorder, improve insulin sensitivity and reduce insulin resistance, and therefore effective intervention on gestational diabetes mellitus is achieved. As a natural component derived from the Zhejiang radix ophiopogonis, the radix ophiopogonis borneol glycoside has definite pharmacological activity and excellent safety, and the Zhejiang radix ophiopogonis is listed in a national homology of medicine and food, so that reliable safety guarantee is provided for use during pregnancy.

The invention belongs to the technical field of analysis and detection, and particularly relates to a method for determining glyceric acid in a biological sample, which comprises the following steps: mixing the biological sample with an extracting solution, and extracting to obtain supernate; evaporating and concentrating the supernate, re-dissolving the supernate in an acetonitrile aqueous solution, and filtering to obtain a solution to be detected; the extracting solution comprises an internal standard solution and a solvent, the internal standard solution comprises DL-glyceric acid-2, 3, 3-d3 calcium salt dihydrate and a methanol / water solution, and the solvent is acetonitrile; and testing the to-be-tested solution through liquid chromatography-tandem mass spectrometry, and obtaining the content of glyceric acid in the to-be-tested solution according to the standard curve. The determination steps are simple and convenient, and a large number of samples can be rapidly and simultaneously treated. By adopting an isotope internal standard method for quantification, the matrix effect can be greatly eliminated, and accurate quantification can be achieved. And reference can be provided for clinical early diagnosis of gestational diabetes mellitus.

Certain aspects of the present disclosure provide a therapy management system configured to monitor at least glucose concentration levels of a patient during a first time period using one or more glucose sensors to obtain glucose data, and determine a diabetic state classification of the patient based on the glucose data. The diabetic state classification indicates a diabetic state of the patient and comprises one of: a healthy classification, a pre-diabetic classification, a mild type 2 diabetes (T2D) classification, a severe T2D classification, or a gestational diabetes classification. The therapy management system is further configured to provide personalized feedback to the patient based on the determined diabetic state classification, wherein the personalized feedback comprises a recommendation for at least one of an exercise session, a dietary change, a lifestyle modification, a medication adjustment, or a continuous glucose monitoring wear schedule.

The invention relates to the technical field of digital medical treatment, in particular to a gestational diabetes risk early warning method based on multi-modal data fusion. Blood sugar time sequence data, exercise amount, body weight, placenta thickness and other multi-modal data of the pregnant woman in the gestation period are obtained, the blood sugar falling tendency degree is quantified by analyzing the peak value and falling characteristics of the blood sugar time sequence and combining the exercise amount, and the dynamic metabolism control ability is accurately reflected; further, on the basis of the placenta thickness data, determining placenta thickness estimation value time sequence data in combination with the body weight change condition, the exercise amount and the blood glucose falling tendency degree, and improving physiological related characteristics among various data; and finally, fusing the blood glucose fall-back trend mean value and the placenta thickness continuous increasing trend characteristic, revealing the correlation characteristic of blood glucose and placenta abnormal development, and training a neural network model to capture a multi-factor nonlinear relationship, thereby realizing early and accurate risk prediction of gestational diabetes mellitus (GDM).

This invention relates to healthcare information processing technology and discloses an intelligent blood glucose management system for early warning and intervention of gestational diabetes mellitus. The system includes a data acquisition unit, a parameter mapping unit, and a state prediction unit. The data acquisition unit acquires gene polymorphism characteristic parameters and a continuous real-time blood glucose monitoring signal stream. The parameter mapping unit maps the characteristic parameters to the initial state transition matrix of the blood glucose prediction model and determines the process noise covariance matrix. The state prediction unit generates a blood glucose prediction trajectory, calculates the sliding variance of the residual sequence, and triggers covariance adaptive adjustment logic when the sliding variance continuously exceeds a preset threshold. By increasing the diagonal elements of the noise covariance matrix, the blood glucose prediction trajectory is corrected. This invention utilizes a statistically driven adaptive adjustment mechanism to effectively map the nonlinear evolution of the mother's physiological state, eliminate the mismatch between genetic priors and real-time monitoring data, and suppress false positive alarms.

A computer implemented method of early prediction of risk of pre-eclampsia in a pregnant obese woman is described. The method comprises the steps of inputting abundance values for a panel of obese pregnancy specific metabolite biomarkers obtained from an assayed biological sample into a computational model, in which the biological sample is obtained from an obese pregnant woman at 8 to 24 weeks of pregnancy, and inputting a patient parameter for the pregnant obese woman into the computational model selected from at least one of ethnicity, risk of gestational diabetes, fetal sex, number of pregnancies and level of obesity. The computational model is configured to select a subset comprising at least two of the obese pregnancy specific metabolite biomarkers based on the patient parameter input, correlate abundance values for the subset of obese pregnancy specific metabolite biomarkers with risk of pre-eclampsia, and output a predicted risk of pre-eclampsia for the pregnant obese woman.

The invention discloses application of beta-carotenoid in preventing or treating progeny autism spectrum disorder behaviors caused by gestational diabetes mellitus. The invention develops new application of beta-carotenoid in preparation of medicines for preventing or treating progeny autism spectrum disorder behaviors caused by gestational diabetes mellitus, not only can the blank of existing treatment means be filled, but also a safe, effective and easily popularized treatment scheme can be provided, and the beta-carotenoid has important clinical value and social significance. The beta-carotenoid is applied to preparation of the medicine for preventively improving gestational diabetes mellitus progeny autism, and a new medicine is provided for treating the autism.

The invention belongs to the field of marker detection, and particularly relates to a gestational diabetes diagnosis marker combination and application thereof, a kit, a device and an evaluation model. One purpose of the invention is to provide an application of a detection marker in predicting gestational diabetes mellitus. The detection marker comprises fructose glycine and / or lysophosphatidyl ethanolamine LysoPE (20: 5 (5Z, 8Z, 11Z, 14Z, 17Z) / 0: 0). The application comprises the following steps: detecting gestational diabetes mellitus or related diseases; predicting the risk of suffering from gestational diabetes mellitus or related diseases; or the prognosis effect of gestational diabetes mellitus or related diseases can be predicted. The diagnostic marker constructed by the invention is helpful to clinically identify high-risk GDM people, and provides important clinical early warning for prevention and treatment of gestational diabetes mellitus patients.

The invention relates to a postpartum follow-up monitoring method and system for gestational diabetes mellitus, in particular to the field of biochemical sensors, and aims to realize accurate cleaning and anomaly recognition of blood glucose data by fusing multi-source information and spatial-temporal characteristics, realize a personalized calibration mechanism based on context awareness and dynamically optimize data correction by utilizing reinforcement learning, and improve the accuracy of postpartum follow-up monitoring of gestational diabetes mellitus. The accuracy of single-point measurement is remarkably improved, the federal element learning framework aggregates individual experience on the premise of protecting privacy, drives a calibration model to continuously evolve, enhances generalization ability, constructs a credible quality closed loop for block chain evidence storage and intelligent contract monitoring, and automatically triggers verification and feedback, so that the accuracy of the single-point measurement is improved. High-quality data injection is obtained in the abnormal recognition and model optimization links, and a self-perfect monitoring system is formed.

The invention relates to a nutritional composition. The nutritional composition consists of fucosyllactose, neutral non-fucosyllactose and sialic acid lactose. According to the nutritional composition disclosed by the invention, by additionally supplementing the nutritional composition in daily diet, the insulin sensitivity is further improved and / or the insulin resistance is reduced, so that a synergistic effect is achieved, and the blood glucose homeostasis of pregnant women is improved.

The invention discloses application of indoleacetic acid in diagnosis and treatment of gestational diabetes mellitus. Clinical queue research finds that the concentration of serum indoleacetic acid of pregnant women in a gestational diabetes mellitus group is remarkably lower than that of pregnant women in a healthy control group, and high diagnosis efficiency is achieved. Animal experiments further prove that in a high fat diet induced mouse model with gestational diabetes mellitus, the exogenous indoleacetic acid is supplemented, so that the fasting blood glucose of a female mouse with gestational diabetes mellitus can be reduced, abnormal glucose tolerance and insulin resistance can be improved, and the indoleacetic acid is free of embryotoxicity, safe and reliable. The application provides a new thought and means for early diagnosis and effective treatment of gestational diabetes mellitus, and has a wide application prospect.

The invention provides a GLP-1R / GIPR / GCGR triple receptor agonist and an application of the GLP-1R / GIPR / GCGR triple receptor Specifically, the invention provides a polypeptide with GLP-1 (glucagon-like peptide-1) R / GIPR / GCGR triple receptor agonist activity or a pharmaceutically acceptable salt thereof, and the polypeptide or the pharmaceutically acceptable salt thereof has obvious triple agonist activity of a glucagon-like peptide-1 (GLP-1) receptor, a gastric inhibitory polypeptide (GIP) receptor and a glucagon (GCG) receptor. The compound can be used for preparing pharmaceutical compositions for preventing or treating metabolic diseases such as type I diabetes mellitus, type II diabetes mellitus, gestational diabetes mellitus, obesity, non-alcoholic fatty liver disease (NAFLD), obesity, hyperlipidemia and the like.

The invention relates to the technical field of food measurement, in particular to an obstetrical gestational diabetes mellitus food measuring device which comprises a storage bucket, the storage bucket is hollow, a feeding port is formed in the inner side wall of the storage bucket, a mounting port is formed in the side wall of the storage bucket, and a detection machine is slidably connected to the inner side wall of the mounting port. The detection machine is provided with a measuring groove, the measuring groove is located in the feeding port, the storage barrel is provided with a juice pressing mechanism, the juice pressing mechanism comprises supporting arms arranged on the two sides of the storage barrel, and through the arrangement of the juice pressing mechanism, when the sugar degree of the food of the pregnant woman needs to be measured, the food can be placed in the juice pressing barrel; after food enters the juicing barrel, the pressing disc can move downwards to squeeze the food in the juicing barrel, squeezed juice can drop onto the measuring groove, the measuring groove is used for measuring the sugar degree, the whole measuring process is simple and rapid, and the measuring efficiency is greatly improved.