35 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 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 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.

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.

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 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 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.

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.

The application provides a construction method and application of a preeclampsia animal model, and belongs to the technical field of construction of animal models. The construction method of the preeclampsia animal model provided by the application comprises the following steps: during pregnancy of a female mouse, phthalic acid or an adenovirus is given to inhibit activity of QPRT, and thus the preeclampsia animal model is obtained. The female mouse is a 8-week-old female C57BL / 6 mouse, the female mouse is given the phthalic acid or the adenovirus at the 10.5th day or the 16.5th day of pregnancy, the dosage of the phthalic acid is 50 mg / kg, the dosage of the adenovirus is 10 9 copies / ml. The construction method of the animal model provided by the application has the advantages of simple operation, the drug can be concentrated in the local maternal-fetal interface to play a role by the drug administration mode of uterine cavity perfusion, and the construction method can eliminate the whole-body symptoms caused by intraperitoneal injection and intravenous injection; and the mouse model can better simulate the clinical symptoms and indexes of patients with preeclampsia, and provides a new mouse model for research on the pathogenesis of preeclampsia.

Disclosed are methods and apparatuses for treating a pregnancy related hypertensive disorder, such as pre-eclampsia and eclampsia, using ex vivo treatment with an anti-sEng antibody bound to a solid support in order to reduce blood levels of sEng. The present invention provides a method of treating or preventing a disorder associated with soluble Endoglin (sEng), such as a pregnancy-related hypertensive disorder, in a subject in need thereof comprising providing ex vivo to the subject anti-sEng antibodies or sEng-binding fragments thereof in an amount sufficient and for a time sufficient to decrease the subject's blood levels of sEng to treat or prevent the disorder associated with sEng in the subject.

The invention belongs to the technical field of animal model construction, and particularly relates to a construction method and application of a preeclampsia animal model, and the construction method comprises the following steps: injecting CdCl2 into a gestational animal to construct the preeclampsia animal model. The construction method disclosed by the invention is high in success rate, low in abortion rate, relatively low in toxicity and high in safety, and can be used for long-term intervention, i.e., the change of the early pregnancy can be observed from the early pregnancy intervention, and N-nitro-L-arginine methyl ester used in the prior art is very high in toxicity, so that a very high abortion rate can be caused if the early pregnancy intervention is carried out. The construction method is low in cost, CdCl2 used for injection is low in unit price, economic benefits are high, and the construction method is easy to operate and convenient to popularize.

The invention discloses a method for predicting preeclampsia risk, and relates to the technical field of intelligent medical treatment and predictive medicine. According to the method, a three-order composite mathematical model of static feature weighting, dynamic time attenuation and linear probability mapping is constructed for the first time, the model is complete in structure, the three core problems of feature contribution degree quantification, time effect correction and risk probability nonlinear conversion are scientifically solved, and the limitation of a traditional simple linear model is broken through; according to the method, through multi-dimensional data fusion and machine learning optimization, the model obtains AUCgt on an independent test set; 0.93, the accuracy is gt; the method has excellent performance of 90%, the false positive rate is reduced to 8% or below, the method is far superior to an existing clinical method, and a reliable basis is provided for early accurate intervention; according to the method, the dynamic time attenuation coefficient Ktime is introduced, so that the model has the perception ability in the time dimension, and detection results at different time points during pregnancy can be scientifically corrected and longitudinally compared.

The embodiment of the invention discloses a modeling method of a periodontitis and preeclampsia combined model, which comprises the following steps of: performing first-stage periodontitis modeling on an experimental mouse through continuous orthodontic ligation for 10 days, and performing continuous annular ligation for 10 days on the basis to perform second-stage periodontitis modeling on the experimental mouse, when the expression level of the inflammation gene of the gingival tissue of the experimental mouse is obviously increased, the periodontitis model of the experimental mouse is successfully established. The method comprises the following steps of: performing preeclampsia modeling on the basis of experimental mice of which periodontitis models are successfully established, randomly dividing pregnant female experimental mice into a model group and a control group, and injecting a nitric oxide synthase inhibition solution into the experimental mice in the model group, when the tail artery systolic pressure of the experimental mice in the model group is increased by at least 20mmHg compared with that of the control group and is continuously increased until the end of pregnancy, the periodontitis and preeclampsia combined model of the experimental mice is successfully established, and the model has important academic value and clinical significance for reducing the occurrence rate of complications in the gestation period and improving maternal and fetal prognosis.

The application belongs to the field of biological medicine, and provides application of inosine in preparation of a drug for preventing and / or treating preeclampsia. The pathological state of preeclampsia is simulated by subcutaneous injection of a solution of nitroso-L-arginine methyl ester (125 mg / kg). From the 9th day of pregnancy, inosine is administered by gavage once a day (100 mg / kg), until the end of pregnancy. Compared with drugs such as labetalol, magnesium sulfate and other drugs with single treatment effect, the application proves that oral inosine can not only significantly improve hypertension and proteinuria caused by preeclampsia, repair placental function, promote intrauterine growth and development of the fetus, correct the imbalance of vascular and anti-angiogenic factors, but also reduce the damage to the kidneys and brain of pregnant women induced by preeclampsia, and exhibits a more comprehensive treatment effect, thereby providing a new therapeutic drug for preventing or treating preeclampsia.

The present application relates to the field of noninvasive prenatal diagnosis, and specifically discloses a preeclampsia noninvasive screening method based on deep sequencing of 8bp oligonucleotide double-fragment features, comprising: collecting peripheral blood samples of preeclampsia and healthy pregnant women, extracting free DNA for high-throughput sequencing; extracting core 8-mer sequences 'GTGCGCCC' and 'GATGGGGT' in 150-200bp long fragments through bioinformatics analysis; using integrated support vector machines, K-nearest neighbors, extreme gradient boosting, random forests and multilayer perceptron combined with logistic regression meta-classifier to construct a stacking model, normalizing the frequency of core sequences, performing machine learning analysis, and predicting the risk of preeclampsia. The present application effectively breaks through the limitations of traditional screening methods with low specificity and invasive detection by specifically screening two 8bp oligonucleotide feature fragments and combining a deep learning architecture with multi-model fusion.

The invention relates to the technical field of noninvasive prenatal diagnosis, and particularly discloses a preeclampsia noninvasive screening method based on deep sequencing 4-mer terminal motif spectrum characteristics, which comprises the following steps: collecting preeclampsia and healthy pregnant woman peripheral blood samples, and extracting free DNA for high-throughput sequencing; 12 key terminal 4-mer motifs are extracted and screened out through bioinformatics; and a multi-layer perceptron (MLP) model is adopted to carry out machine learning analysis, and the preeclampsia risk is predicted. According to the method, 12 terminal 4-mer motif spectrums are specifically screened, and the MLP model is combined, so that the limitation that a traditional screening method is low in specificity and invasive in detection is effectively broken through.

This invention belongs to the field of biodetection technology and discloses a risk biomarker for pregnancy complications and its application. The invention provides a risk biomarker for pregnancy complications, namely, serum fluorescence lifetime; the pregnancy complications include preeclampsia, gestational diabetes mellitus, and preeclampsia combined with gestational diabetes mellitus. This invention uses fluorescence lifetime imaging technology to characterize functional changes in the lipid-related microenvironment in maternal serum, enabling the detection of metabolic disturbance signals in the early stages of pregnancy before routine clinical indicators show abnormalities, thereby advancing the disease identification time window. This invention effectively overcomes the problem of difficulty in achieving effective early warning in the subclinical stage of disease in existing technologies, providing a new technical approach for early warning and individualized management of pregnancy complications.

The invention provides a construction method of a pre-eclampsia risk assessment prediction model, a prediction product, a prediction system and related applications thereof. Specifically, a logistic regression analysis model is constructed based on obtained clinical indexes of a pregnant woman, and the prior probability of premature eclampsia or late eclampsia of the pregnant woman is obtained; carrying out median multiple calibration on the obtained MAP and IGFBP1 and / or PLGF of the pregnant woman, and respectively constructing multivariate normal distribution models of the sick pregnant woman and the healthy pregnant woman based on the MAP MoM value and the IGFBP1 MoM value and / or the PLGF MoM value to obtain the conditional probability of the premature or late eclampsia of the pregnant woman; and in combination with the prior probability and the conditional probability, calculating the posterior probability of premature or late eclampsia of the pregnant woman based on a Bayesian model. The detection of related protein markers and the acquisition of maternal clinical information data are relatively easy, the accuracy of predicting early-onset and late-onset preeclampsia is relatively high, clinical popularization and application are facilitated, and the kit has an important value for prevention and early diagnosis of diseases.