The impact of war-related traumatic events on the functional psycho-emotional state of student youth in a front-line city: preliminary findings of the study by T.V. Peresypkina, V.G. Nesterenko, K.G. Pomohaibo, T.V. Merkulova

“…Purpose – determine the features of social risks during the military conflict in Ukraine and the impact of these events on the functional psycho-emotional state of student youth. …”
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Design and Evaluation of ACAFiB-APP, a clinical decision support system for anticoagulant considerations in patients with atrial fibrillation by Ramin Ansari, Sorayya Rezayi, Ali Asghar Safaei, Reza Mollazadeh, Eisa Rezaei, Mahboobeh Khabaz Mafinejad, Soha Namazi, Keyhan Mohammadi

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Modeling the determinants of attrition in a two-stage epilepsy prevalence survey in Nairobi using machine learning by Daniel M. Mwanga, Isaac C. Kipchirchir, George O. Muhua, Charles R. Newton, Damazo T. Kadengye, Abankwah Junior, Albert Akpalu, Arjune Sen, Bruno Mmbando, Charles R. Newton, Cynthia Sottie, Dan Bhwana, Daniel Mtai Mwanga, Damazo T. Kadengye, Daniel Nana Yaw, David McDaid, Dorcas Muli, Emmanuel Darkwa, Frederick Murunga Wekesah, Gershim Asiki, Gergana Manolova, Guillaume Pages, Helen Cross, Henrika Kimambo, Isolide S. Massawe, Josemir W. Sander, Mary Bitta, Mercy Atieno, Neerja Chowdhary, Patrick Adjei, Peter O. Otieno, Ryan Wagner, Richard Walker, Sabina Asiamah, Samuel Iddi, Simone Grassi, Sloan Mahone, Sonia Vallentin, Stella Waruingi, Symon Kariuki, Tarun Dua, Thomas Kwasa, Timothy Denison, Tony Godi, Vivian Mushi, William Matuja

“…These findings will inform development of a web-based algorithm to predict attrition risk and aid in targeted follow-up efforts in similar studies.…”
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Microsatellite instability in endometrial cancer: the role of diagnostic methods and their clinical application. Expert consensus by A. S. Tyulyandina, E. A. Ulrikh, L. A. Kolomiets, S. E. Krasilnikov, A. G. Kedrova, A. A. Rumyantsev, G. A. Raskin, A. I. Nesterova, M. V. Volkonskiy, O. N. Churuksaeva, A. Yu. Goryainova, V. V. Zhavoronkova, V. N. Dmitriev, S. T. Nazranova, A. V. Shkradyuk, K. S. Volkova, A. I. Arutyunova, S. N. Kunitskaya, L. V. Stepura, T. G. Zolotoreva, E. B. Shakhnovich, E. V. Ponomareva, M. A. Strokova, A. S. Danilova, E. S. Martynova

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Correlations between blood group and Rh factor in families and autism spectrum disorder: A comprehensive analysis by Adil Abdul-Rehman Siddiq Al-Salihy

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Artificial intelligence, generative artificial intelligence and research integrity: a hybrid systemic review by Khalid H. Arar, Hamit Özen, Gülşah Polat, Selahattin Turan

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Student Dropout Prediction Using Random Forest and XGBoost Method by Lalu Ganda Rady Putra, Didik Dwi Prasetya, Mayadi Mayadi

“…Conclusion: The findings highlight Random Forest's robustness in handling extensive datasets with diverse attributes, making it a reliable tool for identifying at-risk students. This study underscores the potential of machine learning in addressing educational challenges. …”
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A computational DNA methylation method to remove contaminated DNA from spent embryo culture medium for noninvasive preimplantation genetic testingResearch in context by Yidong Chen, Jin Huang, Fuchou Tang, Lu Wen, Jie Qiao

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Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques by Behzad Vaferi, Mohsen Dehbashi, Reza Yousefzadeh, Ali Hosin Alibak

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Fatty acid metabolism prognostic signature predicts tumor immune microenvironment and immunotherapy, and identifies tumorigenic role of MOGAT2 in lung adenocarcinoma by Denggang Fu, Biyu Zhang, Wenyan Fan, Fanfan Zeng, Fanfan Zeng, Jueping Feng, Xin Wang

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Personalised Prostate Cancer Diagnosis: Evaluating Biomarker-based Approaches to Reduce Unnecessary Magnetic Resonance Imaging and Biopsy Procedures by Timo F.W. Soeterik, Xiaobo Wu, Roderick C.N. Van den Bergh, Claudia Kesch, Fabio Zattoni, Ugo Falagario, Alberto Martini, Marcin Miszczyk, Vittorio Fasulo, Martina Maggi, Veeru Kasivisvanathan, Pawel Rajwa, Giancarlo Marra, Giorgio Gandaglia, Peter K.F. Chiu

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Exercise-related immune gene signature for hepatocellular carcinoma: machine learning and multi-omics analysis by Cheng Pu, Lei Pu, Xiaoyan Zhang, Qian He, Jiacheng Zhou, Jianyue Li

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USE OF ARTIFICIAL INTELLIGENCE TO IDENTIFY AND CORRECT MISCONCEPTIONS ABOUT RADIATION by Oleksandr Tymoshchuk

“…The experiment involved presenting students with a series of statements designed to identify misconceptions related to factual knowledge (e.g., radiation units, background levels), conceptual understanding (e.g., the difference between radiation and radioactivity, effects of low-dose exposure), and application/evaluation (e.g., risk assessment, protective measures). AI tools, including natural language processing models for text analysis and machine learning algorithms for misconceptions classification, were used to provide personalised feedback and targeted corrective information. …”
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Poverty‐Armed Conflict Nexus: Can Multidimensional Poverty Data Forecast Intrastate Armed Conflicts? by Çağlar Akar, Doğa Başar Sarıipek, Gökçe Cerev

“…While not guaranteeing absolute certainty in forecasting future armed conflicts, the model shows a high degree of accuracy in assessing security risks related to intrastate conflicts. It utilizes a machine‐learning algorithm and annually published fragility data to forecast future intrastate armed conflicts. …”
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Long-Term (2015–2024) Daily PM_2.5 Estimation in China by Using XGBoost Combining Empirical Orthogonal Function Decomposition by Jiacheng Jiang, Jiaxin Dong, Yu Ding, Wenjia Ni, Jie Yang, Siwei Li

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Post COVID-19 Conditions and Neurocognitive Impairment—Current Findings and Clinical Implications by Tarek Jebrini, Michael Ruzicka, Hans Stubbe, Kristina Adorjan

“…This perspective emphasizes the need for evidence-based diagnostic algorithms that integrate both subjective and objective NCI, explicitly addressing the risk of stigmatization. …”
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Case Report: Button battery ingestion—an underestimated emergency in children by Karin Konzett, Stefanie Gang, Lukas Poyntner, Eberhard Reithmeier, Susanne Dertinger, Burkhard Simma

“…A multidisciplinary treatment algorithm for this fatal complication should be implemented and trained in pertinent hospitals. …”
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Optimising test intervals for individuals with type 2 diabetes: A machine learning approach. by Sasja Maria Pedersen, Nicolai Damslund, Trine Kjær, Kim Rose Olsen

“…We examine fairness across income and education levels and evaluate the risk of false-positives and false-negatives.…”
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Pharmacological correction of hepatotoxicity induced by anti-tuberculosis drugs by D. S. Sukhanov, E. V. Timofeev, Y. S. Alekseeva, K. A. Nagornova

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Text mining of hypertension researches in the west Asia region: a 12-year trend analysis by Mohammad Rezapour, Mohsen Yazdinejad, Faezeh Rajabi Kouchi, Masoomeh Habibi Baghi, Zahra Khorrami, Morteza Khavanin Zadeh, Elmira Pourbaghi, Hassan Rezapour

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