66 results about "Eclampsia" patented technology

The invention discloses a pre-eclampsia prediction method and system based on machine learning and early pregnancy indexes. The pre-eclampsia prediction method comprises the following steps: acquiring early pregnancy laboratory index data and FMF model evaluation parameters of a to-be-predicted pregnant woman; based on a preset preeclampsia type, performing feature screening on the early pregnancy laboratory index data by using a Boruta algorithm to construct corresponding original features; inputting the original features into a trained first-level machine learning prediction model corresponding to the pre-eclampsia type to obtain a risk score; according to the risk score and an FMF model evaluation parameter, obtaining a joint feature; and inputting the joint features into a trained second-stage machine learning prediction model corresponding to the pre-eclampsia type to obtain a corresponding pre-eclampsia risk prediction result. According to the method, information of different sources and different physiological dimensions is deeply fused, analyzed and judged, and high-precision and low-cost early prediction of different pre-eclampsia types is realized.

The invention provides a construction method and application of a gene Idh1 conditional knockout mouse model and a preeclampsia heart injury animal model, and relates to the technical field of biology. According to the preeclampsia heart injury animal model, flox mice with conditional gene Idh1 knockout and mice with placental trophoblast specific expression Cre recombinase comate, PE-related phenotypes and typical symptoms of heart injury spontaneously appear in the double-positive pregnant mouse model, and the double-positive pregnant mouse model can be used for research of disease causes, diagnosis methods and drug research and development.

The invention relates to the technical field of prenatal diagnosis, in particular to a biomarker for early prediction of preeclampsia based on body fluid free DNA and application of the biomarker, and particularly relates to a biomarker for prediction or diagnosis of pregnancy diseases, especially preeclampsia, identified through a machine learning model based on a sample set. The biomarker can be eclampsia specific motif and related motif variation trend, so that early prediction of preeclampsia with low cost, high efficiency and high accuracy is realized.

The invention discloses a wearable monitoring bracelet for preeclampsia early warning, and relates to the technical field of wearable monitoring bracelets.The wearable monitoring bracelet comprises a monitoring bracelet body, a touch control circular screen is arranged in the middle of the top face of the monitoring bracelet body, and splicing end blocks are fixedly connected to the two ends of the monitoring bracelet body; the end parts of the two splicing end blocks are both provided with expansion splicing boxes, the interior of the expansion splicing box at the tail end of the monitoring bracelet main body is filled with an installation elastic rubber box, and one side of the interior of the installation elastic rubber box is provided with a gas pressure intensity sensor. Meanwhile, a universal splicing structure is utilized at the splicing part of the expansion splicing box and the splicing end block, so that a sensor in the expansion splicing box can be replaced as required, and the monitoring bracelet can be used for specially and accurately detecting other vital signs while detecting eclampsia symptoms; and the use safety and smoothness of the monitoring bracelet are further improved.

The invention discloses a method and a system for predicting preeclampsia risk of a pregnant woman based on computer implementation. The method integrates the concentration level of vitamin D (especially 25-hydroxyvitamin D) in serum of pregnant women, angiogenesis biomarkers (such as sFlt-1 / PlGF ratio), conventional clinical indexes (such as mean arterial pressure and body mass index) and personal medical history data. A machine learning algorithm (such as a random forest or a gradient lifting model) is adopted to carry out weighted analysis on the multi-dimensional data, and a dynamic and high-precision prediction model is constructed. The system comprises a data acquisition module, an intelligent prediction module and a clinical decision support module, and can provide real-time risk assessment and personalized intervention suggestions (such as a vitamin D supplement scheme) for medical staff, so that early warning and effective management of preeclampsia are realized, and the risk of pregnancy outcome caused by maternal and infant poor pregnancy is reduced.

The invention discloses a urine glycosylation protein marker for early auxiliary diagnosis of preeclampsia and application of the urine glycosylation protein marker. The invention discloses a urine glycosylation protein marker for early auxiliary diagnosis of preeclampsia. The urine glycosylation protein marker is selected from any one or more of glycosylation modified SERPINC1 and SERPING1. The invention also discloses application of the reagent for detecting the urine glycosylated protein marker in preparation of a kit for early auxiliary diagnosis of preeclampsia. According to the present invention, the high-throughput screening method is adopted to analyze the glycosylated proteins in the urine of the normal pregnancy pregnant woman and the preeclampsia pregnant woman, such that the glycosylated proteins capable of indicating the preeclampsia disease are screened, and the research basis is provided for the development and the clinical application of the glycosylated protein marker in the urine; besides, in the research method, urine is relatively simple and non-invasive to collect and is easier to obtain, so that the urine is easier to accept by a patient, continuous dynamic monitoring can be performed, changes of markers can be tracked, clinical transformation is easy, and the method is suitable for popularization and application.

The invention provides a computer-implemented method for predicting and assessing a health risk parameter, such as the risk of diabetes, in particular gestational diabetes and / or preeclampsia, of a woman based on at least one pregnancy-related parameter, the method comprising the steps of: if the pregnancy-related parameter satisfies a first condition; if so, providing an assessment of the health risk parameter using a first machine learning model (1); providing an assessment of the health risk parameter using a second machine learning model (2) if the pregnancy-related parameter satisfies a second condition; and the health risk parameters are made available.

The invention provides an application of a pair of immune costimulatory factors TNFSF9 / TNFRSF9 as markers in preparation of a product for early prediction of preeclampsia, and the immune costimulatory factors comprise a tumor necrosis factor superfamily member 9 (TNFSF9) and a tumor necrosis factor receptor superfamily member 9 (TNFRSF9). The product is used for carrying out early warning on the preeclampsia occurrence risk 20 weeks before pregnancy. The product is used for detecting the immune costimulatory factor through at least one of serum, plasma, whole blood and placental tissue. The invention aims to improve the early prediction efficiency of preeclampsia by detecting the abnormal expression level of the pair of markers.

Provided are methods and compositions for using tissue kallikrein-1 (KLK1) to treat or prevent pregnancy disorders such as fetal growth restriction (FGR) and preeclampsia.

The invention discloses a machine learning-based pregnant woman preeclampsia subtype classification probability prediction method and system. According to the method, the one-carbon metabolic cycle core metabolite is combined with multi-source clinical indexes for the first time, and cross-modal deep feature fusion is realized by constructing derivative composite features with clear biological significance; in the model level, a double-layer self-adaptive model integration strategy is adopted, firstly, multiple base classifiers are subjected to self-adaptive weighted fusion based on cross validation performance, then further optimization is carried out through stacking generalization, and a final prediction model with high precision and high robustness is formed; besides, the system integrates SHAP interpretability analysis and risk positioning visualization based on historical distribution, not only outputs the classification probability, but also provides visual clinical decision-making assistance. According to the method, the problems of insufficient subtype identification ability, weak model generalization ability and poor result interpretability in the prior art are effectively solved, and reliable technical support is provided for health management and clinical decision-making in the gestation period.

The invention discloses a preeclampsia biomarker and an application of the preeclampsia biomarker. The preeclampsia biomarker comprises at least one of a fusion gene PSPC1-MRPS31P2 and a fusion gene LINC00630-AL035494. The preeclampsia biomarker can be used for detecting preeclampsia. The preeclampsia biomarker provided by the invention can provide a non-invasive diagnosis scheme for clinical prenatal preeclampsia, and preeclampsia specific high-expression fusion genes (PSPC1-MRPS31P2 and LINC00630-AL035494) in peripheral blood of a pregnant woman are detected through real-time quantitative PCR (Polymerase Chain Reaction), so that preeclampsia early prediction, disease diagnosis and monitoring of the state of a patient in the illness period are realized; and in combination with the existing clinical diagnosis scheme, the accuracy of pre-eclampsia diagnosis is improved.

The application provides use of zanamivir in preparation of a medicine for treating or preventing preeclampsia, and a corresponding medicine and treatment method; zanamivir can effectively improve high blood pressure, proteinuria, creatinine, neuraminidase and sialic acid elevation symptoms of preeclampsia, increase NO level, and improve pregnancy outcome. Zanamivir and hydroxyprogesterone caproate in combination show better effects.

The invention discloses application of C1INH in preparation of a product for prediction or auxiliary diagnosis of preeclampsia and the product, belongs to the technical field of biological medicines, and aims to detect the content of C1INH in a blood sample on a protein level by taking the blood sample of a subject as a detection object. According to the invention, C1INH molecules in blood are taken as a detection index, prediction and auxiliary diagnosis of preeclampsia of pregnant women can be realized, high sensitivity and specificity are realized, rapid detection can be realized, and a new way is provided for early screening and diagnosis of preeclampsia in the prior art.

This invention discloses a method, prediction method, and prediction device for constructing a preeclampsia prediction model based on temporal multimodal data, relating to the field of preeclampsia risk prediction technology. The construction method includes: acquiring multimodal temporal feature data of pregnant women with known pregnancy outcomes to construct a time-series dataset; the pregnant women with known pregnancy outcomes include those with and without preeclampsia; training a graph attention network and a fully connected network using the time-series dataset; embedding the resulting missing value fitting model as an embedding layer into a gated recurrent unit; and obtaining the preeclampsia prediction model after training with the time-series dataset. In this invention, the prediction model processes missing data through a missing value fitting model, effectively recovering missing information and ensuring the model can more accurately handle incomplete medical data. Simultaneously, the combination of a gated recurrent unit for capturing and predicting temporal features effectively improves the prediction accuracy of preeclampsia.

The invention develops a construction method of a novel preeclampsia prediction model based on insulin growth binding factor binding protein 1 (IGFBP1) and placental growth factor (PlGF) and related application of the novel preeclampsia prediction model. Specifically, according to the method, IGFBP1 and / or PLGF of a pregnant woman, clinical information and MAP data serve as input, whether the pregnant woman suffers from preeclampsia or not serves as output, model construction is conducted through a machine learning method, and a preeclampsia prediction model is obtained. As effective detection can be carried out only by adopting one or two protein predictive markers without depending on the uterine artery pulsation index, the cost is low, and on the whole, clinical popularization is more convenient, the medical expenditure is reduced, the mom and infant outcome is effectively improved, and the method has important significance on reducing the mortality rate of pregnant and lying-in women and infants.

The invention relates to a method for constructing a preeclampsia prediction model by using clinical laboratory data and a related application thereof. Specifically, the preeclampsia risk prediction model is constructed based on a plurality of clinical indexes of pregnant women, a plurality of clinical laboratory data and insulin growth binding factor binding protein 1 and / or placental growth factors. The clinical laboratory data comprises leukocyte count and / or urine protein, and further comprises any one or combination of platelet count, monocyte proportion, glutamic-straw / glutamic-pyruvic transaminase ratio, glutamyltranspeptidase, uric acid and high-density lipoprotein. The constructed preeclampsia risk prediction model can provide a reliable implementation means for early prevention and early treatment of preeclampsia. Meanwhile, the sensitivity of the prediction model is 88.0% when the specificity of early eclampsia is 85.04% and the sensitivity of the prediction model is 62.5% when the specificity of late eclampsia is 85.04% in a complete independent verification set, and the detection performance of the prediction model is higher than that of the current detection performance.

The present application relates to the technical field of medical data fusion processing, and discloses a method and system for predicting adverse pregnancy outcomes of preeclampsia by fusing placental microenvironment image features and serum biomarkers, wherein the method comprises: obtaining digital whole field image of placental tissue section and serum biomarker detection data at the same period; constructing a multi-modal sample data set; extracting multi-scale structure features of placental microenvironment; constructing serum biomarker discriminant features; dynamically fusing placental image features and serum marker features; constructing a prediction model for adverse pregnancy outcomes of preeclampsia and outputting a prediction probability. Compared with the prior art which only relies on single image information or single serum biomarker for risk assessment, it is difficult to realize stable prediction of the coordinated changes of placental microenvironment abnormalities and biochemical abnormalities. Due to the multi-scale image structure analysis, the model generalization ability for predicting adverse pregnancy outcomes of preeclampsia is improved.

The present invention includes methods and computer programs for use in the detection preeclampsia and / or determining an increased risk for preeclampsia in a pregnant female, the methods including identifying in a biosample obtained from the pregnant female cell-free DNA signals, including concentration, fetal fraction, and fragment size distribution. These methods provide for the identification of patients at risk of preeclampsia in the first trimester of pregnancy.

Provided is a method for diagnosing or predicting preeclampsia in a pregnant woman or determining if a pregnant woman has a risk of preeclampsia, comprising: a) obtaining at least one (such as two or more) maternal or fetal parameter or factor of the pregnant woman, selected from a group comprising: (i) one or more of mean artery pressure (MAP), BMIsampling, ΔBMI, Gestational age and ΔBMI / GA; (ii) Maternal age; (iii) one or more of Urinary protein level and the ratio of Urinary protein level (such as Urine Total Protein (UTP) ) / Urinary Creatinine level (Cre) (UTP / Cre); (iv) one or more of preeclampsia in previous pregnancy (PEhis) and having received antihypertensive medication (Antihyper); and (v) any combination of (i) - (iv), b) determining if the urine of said pregnant woman contains misfolded proteins or misfolded protein aggregates, and c) combining the at least one maternal or fetal parameter or factor obtained in step a) and the content level of misfolded proteins or misfolded protein aggregates in the urine of said pregnant woman to diagnose or predict preeclampsia or determine the risk of preeclampsia.

The invention relates to the field of intelligent medical treatment, in particular to a pregnancy disease risk prediction method and device based on biochemical indexes and a program product. Comprising the following steps: S1, acquiring serum biochemical index data of a pregnant woman; s2, comparing the serum biochemical indexes with a preset threshold value; the serum biochemical indexes include aspartate aminotransferase, total protein, albumin, lactic dehydrogenase, alkaline phosphatase, total bile acid, creatine kinase, blood sugar, urea nitrogen, uric acid, creatinine, glomerular filtration rate, potassium, chlorine, total calcium, serum magnesium, inorganic phosphorus, total cholesterol, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol and homocysteine. When one or more of the myoglobin, the hypersensitive troponin I, the prealbumin, the cystatin C and the creatine kinase isoenzyme MB are abnormal, judging that the pregnancy eclampsia exists. The method is simple, convenient and rapid to operate and low in cost, can be clinically popularized to predict the health state of the pregnant woman on a large scale, and has good clinical value.

The present disclosure relates to a method and device for predicting the risk of preeclampsia by utilizing an artificial intelligence model. One embodiment of the present disclosure may provide a method for predicting the risk of preeclampsia by utilizing an artificial intelligence model, the method comprising the steps of: inputting data about an expectant mother to a preeclampsia prediction model generated on the basis of a training data set; and acquiring an incidence rate of the preeclampsia from the preeclampsia prediction model, wherein the data about the expectant mother includes final variables determined from a plurality of initial variables of the training data set.

The invention relates to the technical field of early pregnancy disease prediction, in particular to a preeclampsia onset risk prediction method, which comprises the following steps: S1, determining contribution degrees of different clinical information to preeclampsia; s2, collecting clinical information of multiple groups of pregnant women; s3, based on the acquired clinical information and the contribution degree of different clinical information to preeclampsia, the preeclampsia incidence probability corresponding to each group of data is determined, and each group of clinical information and the corresponding preeclampsia incidence probability form a data set; s4, constructing and training an XGBoost prediction model by taking the clinical information in the data set as an input feature and taking the preeclampsia incidence probability as a target variable; and S5, inputting clinical information of a new case into the XGBoost prediction model, and outputting the preeclampsia morbidity probability and morbidity risk level by the XGBoost prediction model. The prediction result is more detailed and accurate, the prediction result can be rapidly and dynamically output according to the clinical information of the new case, and compared with a traditional method, the prediction efficiency is higher.

Provided herein are methods for diagnosing pre-eclampsia, methods for determining the risk of a subject developing pre-eclampsia, as well as methods for preventing and treating pre-eclampsia should the subject be found to have, or be at risk of developing, pre-eclampsia. The methods involve determining a level of at least three transposable element subfamilies in a sample, wherein differential expression of the transposable element subfamilies relative to reference values indicates that the subject is at risk of developing, or has, pre-eclampsia.

Provided are a pre-eclampsia biomarker and the use thereof. The pre-eclampsia biomarker comprises at least one of the fusion gene PSPC1-MRPS31P2 and the fusion gene LINC00630-AL035494. The pre-eclampsia biomarker provided can provide a non-invasive diagnostic scheme for clinical pre-eclampsia. By detecting the highly expressed pre-eclampsia-specific fusion genes (PSPC1-MRPS31P2 and LINC00630-AL035494) in the peripheral blood of pregnant women via real-time quantitative PCR, early prediction of pre-eclampsia, disease diagnosis, and monitoring of the status of patients during the illness can be realized. The marker can be combined with existing clinical diagnostic schemes to improve the accuracy of pre-eclampsia diagnosis.

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.

A method comprising: receiving or otherwise obtaining input data for a subject with suspected or confirmed pre-eclampsia, wherein the input data comprises data representing a plurality of input variables comprising clinical data variables and / or other input variables associated with the subject and / or healthcare setting, wherein the input variables are grouped into a plurality of variable data groups, wherein the method comprises: processing the input data to identify each of the plurality of variable data groups as one of: empty, partially complete or complete; performing a data completion process to complete the input data for the one or more partially complete variable group; combining the data of the complete variable group and the completed data of the partially complete variables groups to form combined data; performing a model selection process based on the received or otherwise obtained input data to select at least one model from a plurality of trained models; applying the selected model to at least some of the combined data to obtain at least a risk level and / or probability associated with one or more adverse maternal outcomes for a subject with suspected or confirmed pre-eclampsia.

The invention provides a gene marker combination and application thereof in a risk prediction model of premature eclampsia. The gene marker combination comprises a combination selected from GTF2H1, CD48, PBX1, PLA2G2A, ACVR1B, HMGB1, ERBB2, XRCC5, TGFBR2, NFKB1, EGF, TLR6, EZH2, IKZF1, EGFR and CD36, a combination of HIPK1, MTHFR, IL6R, RAB27A, UBE2I, CREBBP, SOX8, HNF4A, ICOS, LIF, TMPRSS6, DNMT3A, WWTR1, RASSF1, FOXP1, TLR2 and MAPK14, AGRN, ATP2B1, ELAC2, HDAC5, GABARAP, FXR2, NFE2L2,

The present invention relates to determining the carrier status of Annexin A5 M2 haplotype of parents (both male and female) prior to and / or after pregnancy to minimize the risk of pregnancy complications, including, but not limited to, recurrent pregnancy loss (RPL), infertility, miscarriage, in vitro fertilization (IVF) failure, IUI failure, implantation failure, foetal growth restriction (FGR), small for gestational age (SGA) newborn, intra-uterine foetal death (IUFD), gestational hypertension (GH), pre-eclampsia (PE) and / or venous thromboembolism (VTE). Once M2 carrier status is determined, methods of intervention, including administration of low molecular weight heparin (LMWH) and / or other anti-coagulants can be administered either prior to and / or after pregnancy. Methods of detecting the carrier status as well as method of diagnosing and or predicting prognosis based on the M2 carrier status of a patient and / or couple is also contemplated.

The present disclosure relates to a disease-specific lipidome for the diagnosis and severity determination of pre-eclampsia. A composition for diagnosing pre-eclampsia and a composition for determining the severity of pre-eclampsia, according to an aspect, can diagnose pre-eclampsia and significantly determine the severity of the disease, by measuring and comparing the level of a lipidome that changes in patients.

The invention discloses a blood mtDNACN-based system, method and application for evaluating an adverse pregnancy outcome risk, and the system comprises a data obtaining module which is used for obtaining blood mtDNACN detection data; the risk assessment module is used for storing a pre-constructed causal risk assessment model; and the result output module is used for outputting a risk assessment result. The model is constructed based on a Mendel randomization method, SNP significantly related to blood mtDNACN is used as a tool variable, and the lactic acid metabolism level hybrid effect is innovatively corrected, so that the lactic acid metabolism level hybrid effect of a subject is corrected without depending on lactic acid parameters of the subject. The causal association between the blood mtDNACN and the specific adverse pregnancy outcome (including gestational hypertension, preeclampsia, premature rupture of fetal membranes and the like) is accurately determined. According to the scheme provided by the invention, more reliable and anti-hybrid individualized risk assessment can be realized, and a new tool is provided for health management in the gestation period.