54 results about "Gestational diabetes" patented technology

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 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 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 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.

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.

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 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 discloses a blood glucose monitoring whole-process management and control system for gestational diabetes patients, relates to the technical field of blood glucose supervision, and is characterized in that the gestational diabetes patients are subjected to continuous subcutaneous insulin infusion treatment and continuous glucose monitoring to obtain respective patient dynamic associated treatment data; a transmission path network is constructed for transmitting all the patient dynamic association treatment data, multi-factor data analysis is performed on the patient dynamic association treatment data, gestational diabetes patients at abnormal and normal blood glucose levels are monitored respectively, corresponding decision operations are decided according to different blood glucose levels and executed, and the gestational diabetes treatment data are obtained. The method comprises the following steps: establishing a respective data board of each gestational diabetes patient, executing respective blood glucose level data management through the data boards, establishing a follow-up visit window, performing whole-course follow-up visit on each gestational diabetes patient through the follow-up visit window, and performing personalized management and control on the gestational diabetes patients with substandard follow-up visit.

The invention provides an intelligent prediction system and method for prenatal complications of an obstetrical department based on multi-modal fusion of sclera characteristics and clinical indexes, and is applied to the technical field of data processing. The method comprises the following steps: performing preprocessing and fusion calculation on feature information in multi-modal data to generate fusion calculation data; the prenatal complications (such as gestational diabetes mellitus, gestational hypertension, premature rupture of fetal membranes and premature delivery) of the obstetrics department are dynamically predicted, and prediction characteristic parameters are generated; performing abnormal value filtering processing on the cross-modal feature weight, dynamically optimizing the weight in combination with a clinical and image feature correlation matrix, and generating feature weight data; processing the feature weight data to generate feature importance dynamic change data and abnormal features; processing the feature importance dynamic change data based on a preset risk judgment rule and the abnormal features to generate risk judgment data; and processing the risk judgment data and the prediction characteristic parameters to generate risk early warning information and a complication intervention instruction.

The present application belongs to the field of marker detection, and particularly relates to a diagnosis marker combination for gestational diabetes, and application, kit, device and evaluation model thereof. One of the purposes of the present application is to provide application of the detection marker in prediction of gestational diabetes, and the detection marker comprises fructose glycine and / or lysophosphatidyl ethanolamine LysoPE (20:5 (5Z, 8Z, 11Z, 14Z, 17Z) / 0:0). The application comprises: detection of gestational diabetes or related diseases; prediction of the risk of suffering from gestational diabetes or related diseases; or prediction of the prognosis effect of suffering from gestational diabetes or related diseases. The diagnosis marker constructed by the present application is helpful for identification of high-risk GDM population in clinic, and provides important clinical early warning for prevention and treatment of patients with gestational diabetes.

Decorin or a composition containing decorin for use in the treatment of type 1 diabetes, type 2 diabetes or gestational diabetes is described herein. Decorin or a composition containing decorin is also described for use in the context of transplanting pancreatic islet cells.

The present application relates to the field of biological agents, in particular to the use of docosahexaenoic acid in the preparation of external preparation products, the external preparation is used for preventing and / or treating gestational diabetes. According to the use of the present application, by adding DHA in the external preparation product, the application concentration of local DHA is improved while avoiding potential excessive intake of DHA, and then the safe, efficient and controlled application amount of functional components of gestational diabetes intervention treatment are realized, and the application prospect is wide.

The invention relates to the technical field of gestational diabetes mellitus detection, and discloses a gestational diabetes mellitus serum marker detection data analysis system. The system comprises a serum biochemical data acquisition module, a detection device is used for acquiring a serum sample of a pregnant woman, and a serum biochemical data set containing blood sugar concentration, insulin level and glycosylated hemoglobin indexes is constructed; a multi-dimensional index integration module combines a historical detection time sequence and the physiological parameter information of the current detection time period to generate an integrated data set containing time dimension correlation features; the abnormal risk analysis module processes the time dimension correlation features in the integrated data set, and calculates the risk deviation value of gestational diabetes in the current detection period; when the risk deviation value is abnormal, the risk grading module generates a plurality of candidate intervention classification schemes based on the serum biochemical data set, and outputs the intervention influence degree of each candidate scheme; and an intervention scheme generation module screens the candidate scheme with the lowest intervention influence degree and generates an optimal intervention classification scheme in the current detection time period.

The invention relates to non-systemic TGR5 agonist useful in the treatment of chemotherapy-induced diarrhea, diabetes, Type II diabetes, gestational diabetes, impaired fasting glucose, impaired glucose tolerance, insulin resistance, hyperglycemia, obesity, metabolic syndrome, ulcerative colitis, Crohn's disease, disorders associated with parenteral nutrition especially during short bowel syndrome, and irritable bowel syndrome (IBS), and other TGR5 associated diseases and disorders, having the Formula:where R1, R2, R2′, R3, R4, X1, X2, X3, X4, Q, and n are described herein.

This invention discloses a method for identifying gestational diabetes mellitus based on fundus images. One implementation of this method includes: first, using labeled fundus images as training samples; acquiring first-scale and second-scale images corresponding to the training samples; the labels include at least a "normal" label and a "gestational diabetes mellitus" label; second, extracting features from the first-scale and second-scale images respectively to generate corresponding first and second feature representations; then, based on two different attention models, cross-weighting the first and second feature representations according to a cross-attention strategy to obtain weighted features; finally, using the true labels of the training samples as a reference, supervised model training is performed based on the classification prediction results corresponding to the weighted features to generate an unbiased classification model. Therefore, gestational diabetes mellitus can be accurately identified and classified based on fundus images, thus providing effective advice to doctors in clinical practice.

The present disclosure relates to novel compounds comprising one or more aromatic boron-containing groups, including diborates, and methods of making the disclosed compounds. The present disclosure also relates to pharmaceutical compositions comprising the disclosed compounds, and their use in the prevention and treatment of diseases and disorders, such diseases and disorders include hyperglycemia, type 2 diabetes mellitus, impaired glucose tolerance, type 1 diabetes mellitus, obesity, metabolic syndrome X or dyslipidemia, gestational diabetes mellitus, prediabetes mellitus, Alzheimer's disease, MODY 1, MODY 2 or MODY 3 diabetes mellitus, mood disorders, and mental disorders.

The application belongs to the technical field of medicine preparation, and particularly relates to application of cellulose nanofiber in preparation of medicine for preventing or treating gestational diabetes mellitus. In the application, the CNF has significant adsorption capacity for micro / nano plastic due to its high specific surface area and unique surface characteristics. Moreover, the CNF intervention can effectively reduce the deposition of microplastics in organs such as liver and placenta, and reduce the trans-barrier transport and biological toxicity of harmful particles from the source. In addition, the CNF can effectively repair the intestinal barrier damage caused by microplastic exposure or high-fat diet, and reduce the systemic inflammation level. Furthermore, the CNF intervention can significantly reduce the fasting blood glucose of the microplastic exposure model mouse during pregnancy, improve the area under the curve (AUC) of insulin tolerance test (ITT) and oral glucose tolerance test (OGTT), and restore the insulin sensitivity.