Subclinical features of atherosclerotic remodeling of arteries in patients with rheumatoid arthritis by I. M. Fushtei, O. V. Tkachenko, S. L. Podsevakhina, O. I. Palamarchuk

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Features of a new Coronavirus infection in children of different ages by M. A. Shakmaeva, T. M. Chernova, V. N. Timchenko, T. A. Nachinkina, K. V. Tetyushin, T. A. Kaplina, M. D. Subbotina, O. V. Bulina, O. I. Afanasyeva

“…According to available statistics, complications are less common among children, asymptomatic or mild forms of the disease prevail more often.This article presents the features of the viral landscape of the upper respiratory tract in children with ARVI in a pandemic, the clinical and laboratory features of the course of COVID-1 9 in children of different ages.It was found that SARS-CoV-2 is detected only in a third (32.9%) of hospitalized patients with respiratory symptoms, in 4.3% of cases — in combination with seasonal CoV-OC43 / CoV-229E, in 1 1.6% — with other respiratory viruses. …”
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Clinical features of patients with multiple myeloma complicated with renal impairment by SHAO Wen, ZHOU Xiao-shuang

“…In the meanwhile of application of the traditional test indices, concern about β₂-MG, LDH and blood calcium may help detect early renal impairment.…”
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The Specific Features of the Pathogenesis and Correction of Critical Conditions in Obstetric Care by Yu. S. Podolsky

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Systemic Amyloidosis with Cardiac Involvement: Features of Course and Diagnostic Difficulties by E. V. Voloshinova, I. Yu. Khorkina, A. M. Dzuban, E. V. Yakovleva

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Features of women’s reproductive dysfunctions associated with prolonged stressful situations by O. H. Horbatiuk, A. S. Shatkovska, A. P. Hryhorenko, O. V. Vaskiv, I. O. Bets, I. M. Kustovska, A. I. Petrash

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Features of the clinical course and prognosis of tongue cancer in young women by M. A. Kropotov, L. P. Yakovleva, D.  A. Safarov, O. A. Saprina, D. A. Peshko, A.  V. Khodos

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Clinical features and rare complications in 132 patients with hepatic glycogenosis by Deniz Kor, Fatma Derya Bulut, Burcu Köşeci, Esra Kara, Ezgi Burgaç, İrem Kaplan, Nazmiye Tüzel Gündüz, Halise Neslihan Önenli Mungan

“…Non-hepatic malignancy was detected in three patients with GSD type III. The twin rate was 6.1%. …”
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Gender and age features of dyslipidemia in the population of the Nizhny Novgorod region by N. Yu. Borovkova, V. K. Kurashin, A. S. Tokareva, T. E. Bakka, N. K. Pershina, E. S. Timoshchenko, A. A. Nekrasov, E. A. Ovchinnikova, N. N. Savickaya, M. A. Mironov, Yu. A. Balanova, A. E. Imaeva, A. V. Kontsevaya

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Features of Diagnostics and Course of Hypertrophic Cardiomyopathy in Real Clinical Practice by E. V. Reznik, T. L. Nguyen, M. S. Dikaeva, I. O. Sirenova, A. V. Salikov, O. V. Gavrylova, E. N. Platonova, G. N. Golukhov

“…The aim of our work was to analyze the features of the diagnosis and course of HCM in real clinical practice. …”
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THE FEATURES OF SURGICAL TREATMENT AND MORPHOLOGICAL ASPECTS OF EPULIS AND PAPILLOMA IN CHILDREN by P.I. Tkachenko, I.I. Starchenko, S.O. Bilokon, N.M. Lokhmatova, O.B. Dolenko, N.M. Korotych, Yu.V. Popelo, N.P. Bilokon

“… The paper presents the findings of the study on the features of surgical treatment and morphological structure of epulis and papillomas in children. …”
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Clinicopathological features and outcome in elderly patients with idiopathic membranous nephropathy by Jinxiu Liang, Wenke Hao, Fangxiao Xia, Zhi Zhao, Yanhua Wu, Feng Yu, Wenxue Hu, Xiaowu Fang, Wei Liu

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AI-CMCA: a deep learning-based segmentation framework for capillary microfluidic chip analysis by Mahmood Khalghollah, Azam Zare, Esmaeil Shakeri, Behrouz Far, Amir Sanati-Nezhad

“…Here, we present AI-CMCA, an artificial intelligence framework designed for capillary microfluidic chip analysis, which automates fluid path detection and tracking using deep learning-based segmentation. …”
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RSCIWANet: Regional Spatial-Channel Information Weighted Attention Network for Video SAR and Large-Scale SAR Image Targets Detection by Hao Chang, Ping Lang, Xiongjun Fu, Kunyi Guo, Xinqing Sheng, Jialin Guan, Chuyi Liu

“…Synthetic aperture radar (SAR) encounters distinct challenges in airborne surveillance (dynamic scene variations, target edge blurring) and spaceborne observation (large-scale analysis, high-resolution processing). Both traditional methods and contemporary deep learning-based solutions exhibit limitations: inadequate dynamic target adaptability, weak small-target detection, and redundant recognition in large-scale scenarios, stemming from challenges like target ambiguity, occlusion, and interclass similarity. …”
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Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets by Inam Abousaber, Haitham F. Abdallah, Hany El-Ghaish

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Generating Synthetic Datasets with Deep Learning Models for Human Physical Fatigue Analysis by Arsalan Lambay, Ying Liu, Phillip Morgan, Ze Ji

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Machine-Learning-Assisted Nanozyme-Based Sensor Arrays: Construction, Empowerment, and Applications by Jinjin Liu, Xinyu Chen, Qiaoqiao Diao, Zheng Tang, Xiangheng Niu

“…With the catalytic signal amplification feature, nanozymes not only find wide use in traditional “lock-and-key” single-target detection but hold great potential in high-throughput multiobjective analysis via fabricating sensor arrays. …”
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Intrusion Detection System for Internet of Medical Things Using GRU with Attention Mechanism based Hybrid Deep Learning Technique by Naveen Saran, Nishtha Kesswani

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Recurrent Neural Network Optimized by Grasshopper for Accurate Audio Data-Based Diagnosis of Parkinson's Disease by Saif Wali Ali Alsudani, Ghassan Khudair Saud

“…Incorporated within the system is a dimensionality reduction technique called Linear Discriminant Analysis. This approach allows the system to optimally utilize the features extracted from the given speech signal while ensuring that all the conditions necessary for successful classification are met. …”
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EmoNet: Deep Attentional Recurrent CNN for X (Formerly Twitter) Emotion Classification by Md. Shakil Hossain, Md. Mithun Hossain, Md. Shakhawat Hossain, M. F. Mridha, Mejdl Safran, Sultan Alfarhood

“…Emotion classification from social media data is critical for market research, sentiment analysis, and understanding human behavior, yet the unstructured nature of Twitter data poses significant challenges for conventional models. …”
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