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Association between new coronavirus infection and fetal growth restriction
Lipatov I.S., Tezikov Y.V., Kalinkina O.B., Tyutyunnik V.L., Kan N.E., Majorova M.O., Yakovleva M.A.
A Russian model for evaluating the efficiency of the sFlt-1/PlGF test for preeclampsia
Khodzhaeva Z.S., Kholin A.M., Shuvalova M.P., Ivanets T.Y., Demura S.A., Galichkina I.V.
POSSIBILITIES FOR PREDICTION OF PREECLAMPSIA
GREBENNIK T.K., PAVLOVICH S.V.
Using machine learning to analyze the lipid profile of culture medium and predict the efficacy of assisted reproductive technologies
Drapkina Y.S., Makarova N.P., Chagovets V.V., Vasiliev R.A., Amelin V.V., Kalinina E.A.
Search for the predictors of fetal growth restriction: from a measuring tape to artificial intellect
Gumeniuk E.G., Ivshin A.A., Boldina Y.S.
Predicting impairment of pre-lactation transformation and preventing lactogenesis failure in metabolic syndrome
Tezikov Y.V., Lipatov I.S., Pechkurov D.V., Tyutyunnik V.L., Kan N.E., Protasov A.D., Kovyazina I.O.
Rationale for using clinical indicators to stratify pregnant women according to risk of Preeclampsia severity
Tezikov Y.V., Lipatov I.S., Khalitova A.I., Tyutyunnik V.L., Kan N.E., Yurasova E.V., Rudneva A.A., Garazhankin A.D.
New markers for early prediction of preeclampsia
Timokhina E.V., Strizhakov A.N., Belotserkovtseva L.D., Fedyunina I.A., Zinin V.N., Pesegova S.V.
ASPIRIN IN THE PREVENTION OF PREECLAMPSIA AND ASSOCIATED MATERNAL AND PERINATAL COMPLICATIONS
Khodzhaeva Z.S., Kholin A.M., Chulkov V.S., Muminova K.T.
Pregestational neural network prediction of fetal growth restriction or small-for-gestational-age fetus with subsequent intensive care of the newborn
Ziyadinov A.A., Novikova V.A., Radzinsky V.E.
Possibilities for predicting the effectiveness of assisted reproductive technology programs
Martynova A.E.
Pathological placentation and prediction of preeclampsia and intrauterine growth restriction in the first trimester
Kholin A.M., Khodzhaeva Z.S., Gus A.I.
Prediction of neonatal complications based on quantitative proteome analysisin blood of pregnant women with fetal growth restriction
Volochaeva M.V., Tokareva A.O., Bugrova A.E., Brzhozovskiy A.G., Kukaev E.N., Tyutyunnik V.L., Kan N.E., Starodubtseva N.L.
Artificial intelligence on guard of reproductive health
Ivshin A.A., Bagaudin T.Z., Gusev A.V.
EARLY AND LATE PREECLAMPSIA: PATHOBIOLOGY PARADIGMS AND CLINICAL PRACTICE
KHODZHAEVA Z.S., KHOLIN A.M., VIKHLYAEVA E.M.
Application of various machine learning techniques to the analysis of clinical, anamnestic, and embryological data of patients undergoing assisted reproductive technologies
Drapkina Y.S., Makarova N.P., Vasiliev R.A., Amelin V.V., Frankevich V.E., Kalinina E.A.
Prediction and prevention of preeclampsia in the city Center for family planning and reproduction
Shalina R.I., Kasumzade N.K., Konoplyannikov A.G., Latyshkevich O.A., Shekhovtsov D.B.
SIGNIFICANCE OF AUTOANTIBODIES IN THE PATHOGENESIS OF PREECLAMPSIA
MAKAROV O.V., BOGATYREV Y.A., OSIPOVA N.A.
Magnetic resonance imaging in the planning and monitoring of the treatment of cervical cancer
Solopova A.E., Bendzhenova B.B.
High-risk pregnancy and perinatal losses
Bezhenar V.F., Ivanova L.A., Grigoryev S.G.
THE PREDICTION AND DIAGNOSIS OF THE SEVERE FORMS OF PLACENTAL INSUFFICIENCY
TEZIKOV Y.V., LIPATOV I.S.
Clinical and anamnestic factors in the prediction and diagnosis of fetal growth restriction
Volochaeva M.V., Kan N.E., Tyutyunnik V.L., Amiraslanov E.Y., Leonova A.A., Soldatova E.E.
Specific features of a metabolomic profile in preeclampsia
Kan N.E., Lomova N.A., Amiraslanov E.Y., Chagovets V.V., Tyutyunnik V.L., Khachatryan Z.V., Starodubtseva N.L., Kitsilovskaya N.A., Frankevich V.E.
PROGNOSTIC CRITERIA FOR SUCCESSFUL OVULATION INDUCTION N PATIENTS WITH PREMATURE OVARIAN FAILURE
ZHAKHUR N.A., MARCHENKO L.A., BUTAREVA L.B., KUZMICHEV L.N., KINDAROVA L.B.
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