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Association of a Single-Nucleotide Variant rs11100494 of the NPY5R Gene with Antipsychotic-Induced Metabolic Disorders
Dobrodeeva, V.S., Shnayder, N.A., Novitsky, M.A., Asadullin, A.R., Vaiman, E.E., Petrova, M.M., Limankin, O.V., Neznanov, N.G., Garganeeva, N.P., Nasyrova, R.F. (2022)
in the chain of interaction to target receptors for APs, agouti-related peptide receptors
Circulating miRNAs as Diagnostic and Prognostic Biomarkers in High-Grade Gliomas
Wu, JN, Al-Zahrani, A, Beylerli, O, Sufianov, R, Talybov, R, Meshcheryakova, S, Sufianova, G, Gareev, I, Sufianov, A (2022)
polymerase chain reaction (qRT-PCR) was carried out to measure the pre- and postoperative plasma levels of mi
The role of pathogenic germline mutations of BRCA1/2, NBS1, and CHEK2 genes in gastric cancer development within the Volga-Ural region of Russia
Nurgalieva, A.Kh., Petrova, Sabina G., Gallyamova, Lilia F., Ekomasova, Natalia V., Sakaeva, Dina D., Fedorova, Yulia Yu, Dzhaubermezov, Murat A., Abdeev, Rustem R., Rakhimov, Radmir R., Khusnutdinova, Elza K., Prokofyeva, Darya S. (2025)
-chloroform extraction. Genotyping of mutations was performed using real-time polymerase chain reaction with DNA melt
Artificial intelligence for direct-to-physician reporting of ambulatory electrocardiography
Johnson, L.S., Zadrozniak, P., Jasina, G., Grotek-Cuprjak, A., Andrade, J.G., Svennberg, E., Diederichsen, S.Z., McIntyre, W.F., Stavrakis, S., Benezet-Mazuecos, J., Krisai, P., Iakobishvili, Z., Laish-Farkash, A., Bhavnani, S., Ljungström, E., Bacevicius, J., Vreeswijk, N.L., Rienstra, M., Spittler, R., Marx, J.A., Oraii, A., Miracle Blanco, A., Lozano, A., Mustafina, I., Zafeiropoulos, S., Bennett, R., Bisson, J., Linz, D., Kogan, Y., Glazer, E., Marincheva, G. l., Rahkovich, M., Shaked, E., Ruwald, M.H., Haugan, K., Węcławski, J., Radoslovich, G., Jamal, S., Brandes, A., Matusik, P.T., Manninger, M., Meyre, P.B., Blum, S., Persson, A., Måneheim, A., Hammarlund, P., Fedorowski, A., Wodaje, T., Lewinter, C., Juknevicius, V., Jakaite, R., Shen, C., Glotzer, T., Platonov, P., Engström, G., Benz, A.P., Healey, J.S. (2025)
) and an ensemble AI model, called DeepRhythmAI. To compare the performance of the AI model and the technicians, a
СПЕКТР И ЧАСТОТА МУТАЦИЙ В ГЕНАХ BRCA1, BRCA2, CHEK2, PALB2 И RAD50 У ПАЦИЕНТОК С РАКОМ МОЛОЧНОЙ ЖЕЛЕЗЫ В РЕСПУБЛИКЕ БАШКОРТОСТАН
SPECTRUM AND FREQUENCY OF BRCA1, BRCA2, CHEK2, PALB2, RAD50 MUTATIONS IN BREAST CANCER PATIENTS IN THE REPUBLIC OF BASHKORTOSTAN
ПУШКАРЁВ, А.В., СУЛТАНБАЕВА, Н.И., ПУШКАРЁВ, В.А., НАСРЕТДИНОВ, А.Ф., МЕНЬШИКОВ, К.В., МУСИН, Ш.И., МИННИАХМЕТОВ, И.Р., ГИЛЯЗОВА, И.Р., ИЗМАЙЛОВ, А.А., СУЛТАНБАЕВ, А.В., PUSHKAREV, A.V., SULTANBAEVA, N.I., PUSHKAREV, V.A., NASRETDINOV, A.F., MENSHIKOV, K.V., MUSIN, SH.I., MINNIAKHMETOV, I.R., GILYAZOVA, I.R., IZMAILOV, A.A., SULTANBAEV, A.V. (2020)
EVALUATION OF TISSUE METAL AND TRACE ELEMENT CONTENT IN A RAT MODEL OF NON-ALCOHOLIC FATTY LIVER DISEASE USING ICP-DRC-MS
Gatiatulinaa, E.R., Popova, E.V., Polyakova, V.S., Skalnaya, A.A., Agletdinovd, E.F., Nikonorovae, A.A., Skalnyefgh, A.V., Tinkov, A.A. (2017)
EVALUATION OF TISSUE METAL AND TRACE ELEMENT CONTENT IN A RAT MODEL OF NON-ALCOHOLIC FATTY LIVER
МОРФОФУНКЦИОНАЛЬНОЕ СОСТОЯНИЕ И ФИЗИЧЕСКАЯ ПОДГОТОВЛЕННОСТЬ СТУДЕНТОВ С РАЗЛИЧНЫМИ ТОТАЛЬНЫМИ РАЗМЕРАМИ ТЕЛА И ДВИГАТЕЛЬНОЙ АКТИВНОСТЬЮ
PHYSICAL EDUCATION CUSTOMIZABLE TO STUDENTS’ TOTAL BODY DIMENSIONS, FUNCTIONALITY, PHYSICALITY AND PHYSICAL ACTIVITY
GAINULLIN, R.A., ISAEV, A.P., ABZALILOV, R.YA., Гайнуллин Р.А., Исаев А.П., Абзалилов Р.Я. (2019)
Modern physical education models customizable to the special requirements of medical university
Remaining Useful Life Prediction of Lithium-Ion Battery Based on Adaptive Fractional Lévy Stable Motion with Capacity Regeneration and Random Fluctuation Phenomenon
Song, Wanqing, Chen, Jianxue, Wang, Zhen, Kudreyko, Aleksey, Qi, Deyu, Zio, Enrico (2023)
–Stieltjes transform and Monte Carlo simulation. The proposed degradation model exhibits flexibility for capturing long
Predictors of Adverse Outcomes in Ischemic Stroke Associated with COVID-19
Novikova, L.B. ;, Latypova, R.F. (2023)
dehydrogenase, and C-reactive protein levels. Use of a model based on multivariate analysis allowed
Tool Degradation Prediction Based on Semimartingale Approximation of Linear Fractional Alpha-Stable Motion and Multi-Feature Fusion
Yuan, Yuchen, Chen, Jianxue, Rong, Jin, Cattani, Piercarlo, Kudreyko, Aleksey, Villecco, Francesco (2023)
with a differential iterative model with it as the noise term is constructed according to the fractional

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