49 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 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 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 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.

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 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.

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.

The application provides a construction method of a preeclampsia rat model with pregnancy and concurrent thrombocytopenia, relates to the technical field of disease animal model construction, and comprises the following steps: obtaining a pregnancy model by mating a pregnant female mouse with a male mouse; injecting an adenovirus into the uterus of the model group pregnant female mouse in the pregnancy cycle of the pregnancy model, and injecting a carrier virus into the control group; and observing and measuring typical clinical characteristics of preeclampsia of the rat. After being verified by a systematic preeclampsia animal model evaluation method, the preeclampsia-like mouse constructed by the method has typical clinical characteristics of preeclampsia such as thrombocytopenia, is highly similar to the performance of a preeclampsia patient, has good clinical symptom simulation, and is of great significance for in-depth study of the cause, occurrence and development mechanism and treatment scheme of PE.

The present invention relates to a method for the prognosis, prediction, risk assessment and / or risk stratification of pre-eclampsia in a pregnant subject, comprising determining a level of soluble fms-like tyrosine kinase-1 (sFlt-1) or a fragment thereof in a sample isolated from said pregnant subject.The present invention further relates to a method for the prognosis, prediction, risk assessment and / or risk stratification of early onset pre-eclampsia in a pregnant subject, comprising determining a level of (sFlt-1) or a fragment thereof in a sample isolated from said pregnant subject, wherein the sample is isolated from the subject before the end of the 12th week of gestation, and wherein the level of sFlt-1 or a fragment thereof is indicative of the likelihood of early onset pre-eclampsia occurring before the end of the 33rd week of gestation.The present invention further relates to a combined measurement of sFlt-1 and PlGF, optionally in combination with consideration of one or more further factors selected from maternal age, body mass index, uterine artery Doppler measurement and / or mean arterial pressure (MAP). The present invention further relates to a kit for carrying out the method of the present invention, comprising detection reagents for determining the level of sFlt-1 or a fragment thereof in a sample from a subject, and optionally for determining the level of at least one additional biomarker described herein.

The invention discloses a traditional Chinese medicine composition for preventing and treating pregnancy complications as well as a preparation method and application thereof. The composition is composed of medicinal and edible traditional Chinese medicines including radix rehmanniae recen, fructus gardeniae, gastrodia elata, fructus lycii and exocarpium citri rubrum, and mulberry leaves, ramulus uncariae cum uncis and the like can be selectively added. The preparation method comprises the steps of extracting volatile oil by steam distillation, leaching by a solvent, concentrating, mixing and the like, and can be combined with ultrasonic crushing to strengthen extraction. The composition is based on the treatment method of nourishing kidney, containing wood, clearing heat, calming liver, dissolving turbidity and preventing miscarriage in traditional Chinese medicine, has the effects of multi-target synergistic pressure reduction, proteinuria improvement and fetal development promotion aiming at the pathogenesis of yin deficiency and yang hyperactivity in the gestation period, is high in safety and free of fetal toxic and side effects, and is suitable for prevention and treatment of hypertensive diseases in the gestation period such as preeclampsia and the like.

A method and device for measuring intra-abdominal pressure in a pregnant woman to assess likelihood or occurrence of pre-eclampsia. The method includes providing a catheter having first and second lumens and a balloon, inserting the catheter into a bladder of the patient, injecting gas into the first lumen of the catheter to expand the balloon, obtaining a first pressure reading of the bladder based on deformation of the balloon to thereby monitor pressure within an abdomen of the mother to assess if pre-eclampsia is occurring or likely to occur and transmitting the first pressure reading to an external monitor connected to the catheter. The pressure reading is indicative of the presence and / or risk of pre-eclampsia to determine when intervention should occur to prevent morbidity and mortality of the woman and baby.

Provided herein compositions and methods for the treatment and prevention of preeclampsia or hypertensive disorders of pregnancy. In particular, provided herein are regulators of the Hippo pathway (e.g., TEAD inhibitors; VGLL2 regulators; verteporfin) that find use in the treatment and prevention of preeclampsia or hypertensive disorder of pregnancy.

A method for assessing the risk of preeclampsia in a pregnant subject is provided. The method can comprise: obtaining at least one blood pressure pulse waveform from at least one arterial site of the subject; determining a risk of preeclampsia in the subject, based at least in part on the at least one blood pressure pulse waveform; and generating an output indicative of the determined risk.

The application discloses a feature selection method and device for predicting preeclampsia risk of pregnant women, and relates to the technical field of data processing.The application uses an adaptive network topology strategy to construct a topology structure, and dynamically constructs sparse adjacency relations between individuals based on fitness and similarity to generate a topology population.The topology structure reflects the internal interaction correlation between features, can guide the algorithm to perform self-organizing directional search according to a search state, and realizes the unified balance of exploration and development.Then, a positive sine search mechanism is used to perform periodic jump search between global search and local search under the guidance of a global optimal solution, and a multi-level information interaction strategy is used, global items promote convergence to the optimal solution, local items strengthen mining of potential local areas, global information dissemination and local neighborhood learning are fused and unified, and dynamic balance search between global search and local search is realized, so that key features are finally identified.

The invention provides a pharmaceutical composition. The pharmaceutical composition is prepared from an anti-platelet medicine, folic acid, 5-methyltetrahydrofolic acid and acceptable auxiliary materials. The composition has the advantages that the composition can be used for preventing and treating preeclampsia, and is particularly suitable for primary lying-in women, hypertension pregnant women and diabetes pregnant women. The composition provided by the invention aims at specific pregnant women, realizes precise medical treatment, improves the curative effect, reduces the side effect and increases the medication compliance.