Surgical thyroid pathology in Crimea and Sevastopol: the way COVID-19 pandemic altered the frequency and structure of diseases by O. R. Khabarov, D. V. Zima, O. F. Bezrukov, E. Yu. Zyablitskaya, E. R. Asanova, P. E. Maksimova

“…These findings highlight the importance of adapting healthcare systems to global crises, including reserving resources for oncology care and developing early diagnostic algorithms.…”
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Using Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: Systematic Review by Rongrong Zhong, XiaoHui Wu, Jun Chen, Yiru Fang

“…For each included study, the following information was extracted: demographic characteristics, diagnostic criteria or psychiatric assessments, details of the technological tools and data types, duration of data collection, data preprocessing methods, selected variables or features, machine learning algorithms or statistical tests, validation, and main findings. …”
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Predicting p53 Status in IDH‐Mutant Gliomas Using MRI‐Based Radiomic Model by Jiamin Li, Zhihong Lan, Xiao Zhang, Xiaoyun Liang, Hanwei Chen, Xiangrong Yu

“…We aimed to investigate the diagnostic efficacy of radiomics utilizing pre‐surgery contrast‐enhanced T1‐weighted imaging (CE‐T1WI) for predicting p53 status in IDH‐mt gliomas. …”
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Mechanisms of drug resistance to neoadjuvant chemotherapy in breast cancer by K. A. Aliev, E. Yu. Zyablitskaya, T. P. Makalish, L. E. Sorokina, E. R. Asanova

“…The presented results can serve as the basis for the creation of diagnostic algorithms that have predictive value regarding the effectiveness of NCT, and also to help identify new targets to justify the use of combined breast cancer treatments in the early stage.…”
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Identifying Therapeutic Targets and Potential Drugs for Diabetic Retinopathy: Focus on Oxidative Stress and Immune Infiltration by Peng H, Hu Q, Zhang X, Huang J, Luo S, Zhang Y, Jiang B, Sun D

“…Analysis of protein-protein interaction (PPI) networks and machine learning algorithms were used to identify hub genes. Single-gene Gene Set Enrichment Analysis (GSEA) identified biological functions, while nomograms and ROC curves assessed diagnostic potential. …”
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Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study by Zhu Zhu, Kaiying Wu, Jian Lu, Sunxian Dai, Dabo Xu, Wei Fang, Yixing Yu, Wenhao Gu

“…Conclusion Based on Gd-EOB-DTPA-enhanced MRI images, we focused on developing a radiomics model that combines bi-regional features and two machine learning algorithms (CR and DLR). The application of the new model will provide a more accurate and non-invasive diagnostic solution for medical imaging. …”
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Automating the amino acid identification in elliptical dichroism spectrometer with Machine Learning. by Ridhanya Sree Balamurugan, Yusuf Asad, Tommy Gao, Dharmakeerthi Nawarathna, Umamaheswara Rao Tida, Dali Sun

“…Advanced data preprocessing techniques and ML algorithms to learn patterns from the absorption profiles that distinguish different amino acids were investigated to prove the feasibility of this approach. …”
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Pathogenetic approach to the treatment of coagulopathic bleeding in obstetrics by T. A. Sushko, M. S. Zainulina

“…A prospective cohort study was conducted involving 52 patients with postpartum hemorrhage (PPH) who gave a child birth between 2021 and 2023. Diagnostics and treatment of coagulopathy was carried out according to the algorithm using thromboelastometry (TEM) parameters. …”
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Classification of intracranial tumors based on optical-spectral analysis by I. D. Romanishkin, T. A. Savelieva, A. Ospanov, K. G. Linkov, S. V. Shugai, S. A. Goryajnov, G. V. Pavlova, I. N. Pronin, V. B. Loschenov

“…Importantly, the classification task can be used in two applications  – to differentiate between different tumors and to differentiate between different parts of the same (center, perifocal zone, normal) tumor. …”
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Artificial intelligence in clinical decision support and the prediction of adverse events by S. P. Oei, T. H. G. F. Bakkes, M. Mischi, R. A. Bouwman, R. A. Bouwman, R. J. G. van Sloun, S. Turco

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Classification of Individuals With COVID-19 and Post–COVID-19 Condition and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near–Real-Time Monitoring C... by Carlos Alberto Sanches, Andre Felipe Henriques Librantz, Luciana Maria Malosá Sampaio, Peterson Adriano Belan

“…Classification models were developed using supervised machine learning algorithms (decision tree, support vector machines, k-nearest neighbor, and neural networks) and evaluated through cross-validation. …”
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Multi-omic signatures of host response associated with presence, type, and outcome of enterococcal bacteremia by Charlie Bayne, Dominic McGrosso, Concepcion Sanchez, Leigh-Ana Rossitto, Maxwell Patterson, Carlos Gonzalez, Courtney Baus, Cecilia Volk, Haoqi Nina Zhao, Pieter Dorrestein, Victor Nizet, George Sakoulas, David J. Gonzalez, Warren Rose

“…However, our study also established that systemic features to distinguish Enterococcus faecalis from Enterococcus faecium EcB show only a moderate degree of discriminatory accuracy, unlikely to significantly improve upon current diagnostic methods. Comparisons of differences in the plasma proteome relative to healthy samples between bacteremia caused by Enterococcus and Staphylococcus aureus suggest the presence of bacteria-specific responses alongside conserved inflammatory reactions.…”
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EEG microstate analysis in children with prolonged disorders of consciousness by Yi Zhang, Zhichong Hui, Yuwei Su, Weihang Qi, Guangyu Zhang, Liang Zhou, Jiamei Zhang, Kaili Shi, Yonghui Yang, Lei Yang, Gongxun Chen, Sansong Li, Mingmei Wang, Dengna Zhu

“…CRS-R scores differed significantly between VS/UWS and MCS (p < 0.001). …”
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Anxiety-depressive syndrome in patients with arterial hypertension and chronic obstructive pulmonary disease with frequent exaccations by L. A. Shpagina, I. S. Shpagin, L. D. Khidirova, A. A. Vasilenko

“…Computer analysis of the results of this study was carried out using the SAS, USA statistical software package using standard variational algorithms. statistics. When analyzing intergroup differences in indicators measured on an interval scale, the values of Student's t-test were calculated using the corresponding formulas.Results. …”
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Application of Hardware and Software Complex for Individualisation of Students’ Sport and Recreational Physical Activities by O. N. Moskovchenko, L. V. Zakharova, N. V. Tretyakova, N. V. Lyulina, O. A. Kattsin, G. S. Savolaynen

“…The criteria to determine a vegetative tonus of students (eutony, vagotony, sympathicotony) were identified on the basis of nonparametric algorithms of automatic classification, recognition of samples and theorem of statistical hypothesis tests. …”
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PD-L1 expression predicts the efficacy of PD-1 blockade plus chemotherapy versus chemotherapy alone in treatment-naïve advanced or metastatic gastric cancer: a pooled analysis of r... by Wei Zhou, Zeng-Zhi Cai, Zhuolin Fan, Xu Zheng, Yu-Tong Chen

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High concentration formulation developability approaches and considerations by Jonathan Zarzar, Tarik Khan, Maniraj Bhagawati, Benjamin Weiche, Jasmin Sydow-Andersen, Alavattam Sreedhara

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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

“…As the main directions of eye care development in diabetes it is necessary to standardize primary care in the regions, to unify the examination algorithms and methods of early diagnostic, to increase the continuity and interaction of endocrinologists and ophthalmologists in managing patients with diabetes in order to prevent the development of new cases of vision loss.…”
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The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment: A Machine Learning Approach by Abed Mutemi, Fernando Bacao

“…Under a variety of hyperparameter configurations, we test and compare the outcomes of eight different machine learning classification algorithms, seven individual classifiers, and a stacked ensemble. …”
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Advanced intrusion detection technique (AIDT) for secure communication among devices in internet of medical things (IoMT) by M. Ramya, Pradeep Sudhakaran, Yuvaraj Sivagnanam, C. Santhana Krishnan

“…By employing the integrated patient sensing and network traffic datasets, our research achieves a superior accuracy rate of 96.4% in network intrusion detection compared to the competing algorithms. Furthermore, we incorporated a comprehensive analysis of the implementation of different classification algorithms in IoMT network intrusion detection, further supporting the claim that the proposed framework performs marginally better than alternative models.…”
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