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Search for the predictors of fetal growth restriction: from a measuring tape to artificial intellect
Gumeniuk E., Ivshin A., Boldina Y.
Recurrences of uterine fibroids. The modern view on the problems of diagnosis, treatment, and prognosis
Tonoyan N., Kozachenko I., Frankevich V., Chagovets V., Adamyan L.
EARLY AND LATE PREECLAMPSIA: PATHOBIOLOGY PARADIGMS AND CLINICAL PRACTICE
KHODZHAEVA Z., KHOLIN A., VIKHLYAEVA E.
New markers for early prediction of preeclampsia
Timokhina E., Strizhakov A., Belotserkovtseva L., Fedyunina I., Zinin V., Pesegova S.
A Russian model for evaluating the efficiency of the sFlt-1/PlGF test for preeclampsia
Khodzhaeva Z., Kholin A., Shuvalova M., Ivanets T., Demura S., Galichkina I.
SIGNIFICANCE OF AUTOANTIBODIES IN THE PATHOGENESIS OF PREECLAMPSIA
MAKAROV O., BOGATYREV Y., OSIPOVA N.
Possibilities for predicting the effectiveness of assisted reproductive technology programs
Martynova A.
FIRST TRIMESTER PREDICTION OF PREECLAMPSIA BASED ON MATERNAL FACTORS, BIOMARKERS AND 3D POWER DOPPLER OF PLACENTAL BED VASCULARIZATION
Kholin A., Muminova K., Mukhamedovich N., Khodzhaeva Z., Gus A.
THE PREDICTION AND DIAGNOSIS OF THE SEVERE FORMS OF PLACENTAL INSUFFICIENCY
TEZIKOV Y., LIPATOV I.
Using machine learning to analyze the lipid profile of culture medium and predict the efficacy of assisted reproductive technologies
Drapkina Y., Makarova N., Chagovets V., Vasiliev R., Amelin V., Kalinina E.
The significance of lipidomic changes in ovarian neoplasms
Iurova M., Pavlovich S., Khabas G., Chagovets V., Frankevich V., Ashrafyan L.
ASPIRIN IN THE PREVENTION OF PREECLAMPSIA AND ASSOCIATED MATERNAL AND PERINATAL COMPLICATIONS
Khodzhaeva Z., Kholin A., Chulkov V., Muminova K.
PROGNOSTIC CRITERIA FOR SUCCESSFUL OVULATION INDUCTION N PATIENTS WITH PREMATURE OVARIAN FAILURE
ZHAKHUR N., MARCHENKO L., BUTAREVA L., KUZMICHEV L., KINDAROVA L.
Pregestational neural network prediction of fetal growth restriction or small-for-gestational-age fetus with subsequent intensive care of the newborn
Ziyadinov A., Novikova V., Radzinsky V.
Artificial intelligence on guard of reproductive health
Ivshin A., Bagaudin T., Gusev A.
Pathological placentation and prediction of preeclampsia and intrauterine growth restriction in the first trimester
Kholin A., Khodzhaeva Z., Gus A.
POSSIBILITIES FOR PREDICTION OF PREECLAMPSIA
GREBENNIK T., PAVLOVICH S.
Clinical and anamnestic risk factors and prediction models for the development of fetal growth restriction
Gasymova S., Tyutyunnik V., Kan N., Volochaeva M., Donnikov A.
Prediction and prevention of preeclampsia in the city Center for family planning and reproduction
Shalina R., Kasumzade N., Konoplyannikov A., Latyshkevich O., Shekhovtsov D.
3D power doppler placental bed vascularization in the first trimester prediction of preeclampsia
Kholin A., Khodzaeva Z., Gus A.
Prediction of neonatal complications based on quantitative proteome analysisin blood of pregnant women with fetal growth restriction
Volochaeva M., Tokareva A., Bugrova A., Brzhozovskiy A., Kukaev E., Tyutyunnik V., Kan N., Starodubtseva N.
Possibilities for predicting recurrent uterine myoma by plasma lipidomic analysis
Tonoyan N., Tokareva A., Chagovets V., Starodubtseva N., Kozachenko I., Adamyan L., Frankevich V.
A scientifically based system for predicting preeclampsia
Sidorova I., Nikitina N.
Application of various machine learning techniques to the analysis of clinical, anamnestic, and embryological data of patients undergoing assisted reproductive technologies
Drapkina Y., Makarova N., Vasiliev R., Amelin V., Frankevich V., Kalinina E.
Specific features of a metabolomic profile in preeclampsia
Kan N., Lomova N., Amiraslanov E., Chagovets V., Tyutyunnik V., Khachatryan Z., Starodubtseva N., Kitsilovskaya N., Frankevich V.
PREECLAMPSIA IN THE FOCUS OF A PRACTITIONER'S ATTENTION
SIDOROVA I., NIKITINA N.
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