Trends in the epidemiology of diabetic retinopathy in Russian Federation according to the Federal Diabetes Register (2013–2016) by Dmitry V. Lipatov, Olga K. Vikulova, Anna V. Zheleznyakova, Mikhail А. Isakov, Elena G. Bessmertnaya, Anna A. Tolkacheva, Timofey A. Chistyakov, Marina V. Shestakova, Ivan I. Dedov

“…Results: In 2016 the DR prevalence in RF was T1 38,3%, T2 15,0%, with marked interregional differences: 2,6–66,1%, 1,1–46,4%, respectively. …”
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Clinical characteristics, prognosis, and predictive modeling in class IV ± V lupus nephritis by Anjing Wang, Anjing Wang, Yunlong Qin, Yunlong Qin, Yan Xing, Zixian Yu, Liuyifei Huang, Jinguo Yuan, Yueqing Hui, Mei Han, Guoshuang Xu, Jin Zhao, Shiren Sun

“…The prognostic model was developed using machine learning algorithms and Cox regression. The model’s performance was evaluated in terms of discrimination, calibration, and risk classification using the concordance index (C-index), integrated brier score (IBS), net reclassification index (NRI), and integrated discrimination improvement (IDI), respectively.ResultsA total of 313 patients were enrolled for this study, including 156 class IV and 157 class IV+V LN. …”
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Efficiency and safety of the Russian-made KERATOLINK device used to treat patients with stage I–II keratoconus and pellucid marginal corneal degeneration by A. T. Khandzhyan, E. N. Iomdina, A. V. Ivanova, A. S. Sklyarova, N. V. Khodzhabekyan, I. V. Manukyan

“…The analysis of various UVCL programs revealed no difference in the recovery period and showed comparable clinical and functional results. …”
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Root-Zone Salinity in Irrigated Arid Farmland: Revealing Driving Mechanisms of Dynamic Changes in China’s Manas River Basin over 20 Years by Guang Yang, Xuejin Qiao, Qiang Zuo, Jianchu Shi, Xun Wu, Alon Ben-Gal

“…The approach demonstrated high predictive accuracy (<i>R</i>² = 0.96 ± 0.01, root mean squared error <i>RMSE</i> = 0.19 ± 0.03 g kg⁻¹, maximum absolute error <i>MAE</i> = 0.14 ± 0.02 g kg⁻¹) in evaluating <i>SSC</i> drivers. Factors such as initial <i>SSC</i>, crop type distribution, duration of film mulched drip irrigation implementation, normalized difference vegetation index (NDVI), irrigation amount, and actual evapotranspiration (<i>ET_a</i>), with mean (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mfenced close="|" open="|"><mrow><mrow><mi>SHAP</mi><mo> </mo><mi>value</mi></mrow></mrow></mfenced></mrow></semantics></math></inline-formula>) ≥ 0.02 g kg⁻¹, were found to be more closely correlated with root-zone <i>SSC</i> variations than other factors. …”
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A Framework for High-Spatiotemporal-Resolution Soil Moisture Retrieval in China Using Multi-Source Remote Sensing Data by Zhuangzhuang Feng, Xingming Zheng, Xiaofeng Li, Chunmei Wang, Jinfeng Song, Lei Li, Tianhao Guo, Jia Zheng

“…Four machine learning and deep learning algorithms are applied, including Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) networks, and Ensemble Learning (EL). …”
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INTERNATIONAL FORUM “OLD AND NEW MEDIA: ALONG THE PATH TOWARDS A NEW AESTHETICS” / МЕЖДУНАРОДНЫЙ ФОРУМ «СТАРЫЕ И НОВЫЕ МЕДИА: ПУТИ К НОВОЙ ЭСТЕТИКЕ»... by BOGATYRYOVA ELENA A.

“…Notice was made of alarming tendencies of transforming consciousness into that of “gamers,” reacting in correlations with certain algorithms, and a transference from discourse and substantiations towards reactions and evaluations. …”
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