River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model by Khabat Khosravi, Salim Heddam, Changhyun Jun, Sayed M. Bateni, Dongkyun Kim, Essam Heggy

“…Each model is trained and evaluated at one station and validated at the second station to assess transferability and generalization capability. …”
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Retrotransposon methylation profiles and survival in Black women with high-grade serous ovarian carcinoma by Christelle Colin-Leitzinger, Katherine A. Lawson-Michod, Courtney E. Johnson, Irma M. Vlasac, Sean Yoder, Tania Mesa, Dana Roeber, Chad Huff, Michelle A. T. Hildebrandt, Kristin Haller, Anthony J. Alberg, Elisa V. Bandera, Melissa Bondy, Michele L. Cote, Theresa Hastert, Edward S. Peters, Paul D. Terry, Andrew B. Lawson, Andrew Berchuck, Brooke L. Fridley, Jing-Yi Chern, Jennifer A. Doherty, Jeffrey R. Marks, Joellen M. Schildkraut, Brock C. Christensen, Lucas A. Salas, Lauren C. Peres

“…Methods Methylation levels of LTR, LINE-1, and Alu (type of SINE) in 200 HGSOC tumors were predicted using a random forest approach and clustered using multiple consensus algorithms. Associations between RE methylation clusters and survival were evaluated using Cox proportional hazard regression, adjusting for age, stage, and debulking status. …”
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Combined endodontic treatment: microbiological aspects by using scanning electronical microscopy by V. N. Tsarev, A. V. Mitronin, M. S. Podporin, D. A. Ostanina, E. V. Ippolitov, V. A. Mitronin

“…To determine possible alternative algorithms for root canal disinfection through the combined use of chemical and physiotherapeutic irrigation techniques under in vitro conditions.Materials and methods. …”
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A multidimensional assessment of adverse events associated with paliperidone palmitate: a real-world pharmacovigilance study using the FAERS and JADER databases by Siyu Lou, Zhiwei Cui, Yingyong Ou, Junyou Chen, Linmei Zhou, Ruizhen Zhao, Chengyu Zhu, Li Wang, Zhu Wu, Fan Zou

“…Utilizing disproportionality analyses such as the reporting odds ratios (ROR), proportional reporting ratios (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item Poisson shrinkage (MGPS), significant associations between ADEs and paliperidone palmitate were evaluated. Results A total of 27,672 ADE reports related to paliperidone palmitate were identified in FAERS, with 285 significantly disproportionate preferred terms (PTs) identified by all four algorithms. …”
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Comparative assessment of line probe assays and targeted next-generation sequencing in drug-resistant tuberculosis diagnosisResearch in context by Giovanna Carpi, Marva Seifert, Andres De la Rossa, Swapna Uplekar, Camilla Rodrigues, Nestani Tukvadze, Shaheed V. Omar, Anita Suresh, Timothy C. Rodwell, Rebecca E. Colman

“…Complete assay failure rates were low for LPAs (4.9%) and ONT (5.0%) and moderately higher for GenoScreen (8.6%), with differences in single-target failures across all assays. …”
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Exploring fecal microbiota signatures associated with immune response and antibiotic impact in NSCLC: insights from metagenomic and machine learning approaches by Wenjie Han, Wenjie Han, Yuhang Zhou, Yuhang Zhou, Yiwen Wang, Yiwen Wang, Xiaolin Liu, Tao Sun, Tao Sun, Junnan Xu, Junnan Xu, Junnan Xu

“…Among eight machine learning algorithms evaluated, the optimal model was selected to construct a predictive framework for immunotherapy response.ResultsMicrobial α-diversity was significantly elevated in responders compared to non-responders, with antibiotic administration further amplifying this difference—most notably at the species level. …”
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What has changed in the attitude of health workers towards vaccination after the pandemic COVID-19 by S. M. Kharit, L. V. Romanov, Yu. E. Konstantinova, S. A. Bogdan

“…There is a significant difference between confirming the answer about the need for vaccination and vaccinating yourself, as well as evaluating the effectiveness of the vaccine (even for measles vaccines, 90.7% say they need to be vaccinated, only 83.5% are vaccinated and only 76.7% are confident in the effectiveness of the vaccine. …”
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Predicting determinants of unimproved water supply in Ethiopia using machine learning analysis of EDHS-2019 data by Jember Azanaw, Mihret Melese, Eshetu Abera Worede

“…Using machine-learning algorithms, specifically Random Forest, has yielded significant insights into the factors influencing Ethiopia’s unimproved water supply. …”
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The effect of implementing parenteral nutrition guideline on growth and clinical outcomes in preterm infants: a comparative study by Majid Mahallei, Lida Gorbani, Mohammad Bagher Hoseini, Elnaz Shaseb, Bahareh Mehramuz, Khatereh Rezazadeh

“…The PRE group received individualized PN formulations based on clinician discretion, while the POST group received PN guided by a newly introduced, stepwise algorithmic protocol aiming to optimize early protein and energy intake. …”
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Copper Metabolism-Related Genes as Biomarkers in Colon Adenoma and Cancer by Zhang T, Fu Y

“…Five machine-learning algorithms were employed to identify biomarkers. The degree of immune infiltration was evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA), and the expression profiles of these biomarkers across various cell types were further characterized using single-cell RNA sequencing (scRNA-seq). …”
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Conflict management training for future educators by N. A. Sokolova, N. V. Sivrikova, E. G. Chernikova, T. G. Ptashko, E. M. Harlanova, S. V. Roslyakova

“…Most students (97,6%) learned algorithms to solve typical interpersonal conflicts and began to choose more constructive strategies to solve them. …”
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Estimating vegetation indices and biophysical parameters for Central European temperate forests with Sentinel-1 SAR data and machine learning by Daniel Paluba, Bertrand Le Saux, Francesco Sarti, Přemysl Štych

“…In the comparison of ML models, the traditional ML algorithms, Random Forest Regressor and Extreme Gradient Boosting (XGB) slightly outperformed the Automatic Machine Learning (AutoML) approach, auto-sklearn, for all forest parameters, achieving high accuracies (R2 between 70% and 86%) and low errors (0.055–0.29 of mean absolute error). …”
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An approach to forecasting damage due to unfavorable circumstances associated with indistinguishability of source data by V. F. Zolotukhin, A. V. Matershev, L. A. Podkolzina

“…The results obtained are focused on the construction of analytical algorithms for establishing indistinguishability under the monitoring, modeling, forecasting state-related processes and complex dynamic multiparameter objects.…”
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Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…In this study, we investigate the predictive efficacy of distinguishing between benign and malignant thyroid nodules by employing traditional machine learning algorithms and a deep transfer learning model, aiming to advance the diagnostic paradigm in this field. …”
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PIC2O-Sim: A physics-inspired causality-aware dynamic convolutional neural operator for ultra-fast photonic device time-domain simulation by Pingchuan Ma, Haoyu Yang, Zhengqi Gao, Duane S. Boning, Jiaqi Gu

“…Optical simulation plays an important role in photonic hardware design flow. The finite-difference time-domain (FDTD) method is widely adopted to solve time-domain Maxwell equations. …”
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Gender and age characteristics and atrial fibrillation during long-term telemonitoring of the ECG by V.M. Bogomaz, I.O. Berdnyk, L.I. Lysa

“…The aim – to evaluate the possibilities of long-term patch monitoring of the electrocardiogram (ECG) to determine gender and age characteristics of the frequency of detection of atrial fibrillation (AF). …”
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Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study by Juan Chen, Xiong Chen, Kai Fu, Lan Zhou, Shichao Long, Mengsi Li, Linhui Zhong, Aerzuguli Abudulimu, Wenguang Liu, Deng Pan, Ganmian Dai, Yigang Pei, Wenzheng Li

“…This study aimed to elucidate biological differences related to pTLS density and develop a radiomic classifier for predicting pTLS density in HCC, offering new insights for clinical diagnosis and treatment.Methods Spatial transcriptomics (n=4) and RNA sequencing data (n=952) were used to identify critical regulators of pTLS density and evaluate their prognostic significance in HCC. …”
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Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis by ShinYe Kim, Winson Fu Zun Yang, Zishan Jiwani, Emily Hamm, Shreya Singh

“…We also evaluated the predictive power of these linguistic features using machine learning and identified key thematic structures through semantic network analysis. …”
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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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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

“…Purpose of the study. To evaluate the impact of the COVID-19 pandemic on the structure of surgical thyroid pathology among the population of the Republic of Crimea and Sevastopol City in 2019–2024 through a retrospective data analysis.Patients and methods. …”
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