Kazantseva, A.,
Enikeeva, R.,
Takhirova, Z.,
Davydova, Y.,
Mustafin, R.,
Malykh, S.,
Karunas, A.,
Kanapin, A.,
Khusnutdinova, E. (2023) and neurological symptoms in anamnesis comprised the study sample.
Logistic regression was performed with COVID-19
Machine Learning Methods and
Models for Recognizing Lung Inhomogeneity from Computed Tomography
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
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
Korytina, G.F.,
Aznabaeva, Y.G.,
Kochetova, O.V.,
Nasibullin, T.R.,
Akhmadishina, L.Z.,
Khusnutdinova, N.N.,
Zagidullin, N. Sh.,
Victorova, T.V. (2023) controls. Significant associations with COPD in the study group under an additive genetic
model were
Kazantseva, A.V.,
Davydova, Yu. D.,
Enikeeva, R.F.,
Yakovleva, D.V.,
Mustafin, R.N.,
Lobaskova, M.M.,
Malykh, S.B.,
Khusnutdinova, E.K. (2023) . The final
model was based on a combined effect of PGS of TERT, TNF, SLC6A4, smoking and maternal protection