Contrast-enhanced MRI-based intratumoral heterogeneity assessment for predicting lymph node metastasis in resectable pancreatic ductal adenocarcinoma by Junjian Shen, Qing Li, Lei Li, Tianyu Lu, Jun Han, Zongyu Xie, Peng Wang, Zirui Cao, Mengsu Zeng, Jianjun Zhou, Tianzhu Yu, Yaolin Xu, Haitao Sun

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Trajectories of mHealth-Tracked Mental Health and Their Predictors in Female Chronic Pelvic Pain Disorders by Leventhal EL, Nukavarapu N, Elhadad N, Bakken SR, Elovitz MA, Hirten RP, Rodrigues J, Danieletto M, Landell K, Ensari I

“…Despite its prevalence and morbidity, CPPDs are often inadequately managed with few patients experiencing relief from any medical intervention. Characterizing mental health symptom trajectories and lifestyle predictors of mental health is a starting point for enhancing patient self-efficacy in managing symptoms. …”
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Deep Learning for Detecting and Subtyping Renal Cell Carcinoma on Contrast-Enhanced CT Scans Using 2D Neural Network with Feature Consistency Techniques by Amit Gupta, Rohan Raju Dhanakshirur, Kshitiz Jain, Sanil Garg, Neel Yadav, Amlesh Seth, Chandan J. Das

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Predicting Renal Cell Carcinoma Subtypes and Fuhrman Grading Using Multiphasic CT-Based Texture Analysis and Machine Learning Techniques by Amit Gupta, Sanil Garg, Neel Yadav, Rohan Raju Dhanakshirur, Kshitiz Jain, Rishi Nayyar, Seema Kaushal, Chandan J. Das

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Whole-genome sequencing analysis reveals new susceptibility loci and structural variants associated with progressive supranuclear palsy by Hui Wang, Timothy S. Chang, Beth A. Dombroski, Po-Liang Cheng, Vishakha Patil, Leopoldo Valiente-Banuet, Kurt Farrell, Catriona Mclean, Laura Molina-Porcel, Alex Rajput, Peter Paul De Deyn, Nathalie Le Bastard, Marla Gearing, Laura Donker Kaat, John C. Van Swieten, Elise Dopper, Bernardino F. Ghetti, Kathy L. Newell, Claire Troakes, Justo G. de Yébenes, Alberto Rábano-Gutierrez, Tina Meller, Wolfgang H. Oertel, Gesine Respondek, Maria Stamelou, Thomas Arzberger, Sigrun Roeber, Ulrich Müller, Franziska Hopfner, Pau Pastor, Alexis Brice, Alexandra Durr, Isabelle Le Ber, Thomas G. Beach, Geidy E. Serrano, Lili-Naz Hazrati, Irene Litvan, Rosa Rademakers, Owen A. Ross, Douglas Galasko, Adam L. Boxer, Bruce L. Miller, Willian W. Seeley, Vivanna M. Van Deerlin, Edward B. Lee, Charles L. White, Huw Morris, Rohan de Silva, John F. Crary, Alison M. Goate, Jeffrey S. Friedman, Yuk Yee Leung, Giovanni Coppola, Adam C. Naj, Li-San Wang, P. S. P. genetics study group, Clifton Dalgard, Dennis W. Dickson, Günter U. Höglinger, Gerard D. Schellenberg, Daniel H. Geschwind, Wan-Ping Lee

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EUROPEAN UNION’S LIMITED COMPETENCE IN HARMONIZING LABOUR – HOW THIS MAY AFFECT THE NEW WAYS OF WORKING WITHIN THE MEMBER STATES by Cristiana CHELU-PRODESCU, Dragoș-Adrian BANTAȘ

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Pemantauan Status Gizi Balita Melalui Peningkatan Kegiatan Posyandu Harapan Ibu 6, Kelurahan Mata Air Kota Padang by Yeffi Masnarivan, Rahmi Awalina, Resmiati Resmiati

“…The method used monitoring, counselling, and intervention methods for toddlers. Respondents were taken by total sampling: all toddlers in the working area of ​​Posyandu Harapan Ibu 6. …”
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Retroperitoneal leiomyosarcoma invading the renal hilum: A case report and literature review by Hongyan Song, MB, Fang Nie, MD

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Association Between Language Skills and Statistical Learning in Aphasia by Talat BULUT, Emine BAHAR

“…The findings have the potential to influence aphasia assessment and intervention.…”
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Early detection of cognitive decline with deep learning and graph-based modeling by Sunita Patil, Swetta Kukreja

“…In today’s world, increasing stress and depression significantly impact cognitive well-being, making early detection of cognitive impairment essential for timely intervention. This work introduces a Multimodal Fusion Cognitive Assessment Framework that leverages advanced deep learning and graph intelligence to enhance early identification accuracy. …”
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Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics by Gangfeng Zhu, Yipeng Song, Zenghong Lu, Qiang Yi, Rui Xu, Yi Xie, Shi Geng, Na Yang, Liangjian Zheng, Xiaofei Feng, Rui Zhu, Xiangcai Wang, Li Huang, Yi Xiang

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An in-depth understanding of stakeholders’ experiences about their participation in the co-production of ‘Maze Out’: a serious game for the treatment of eating disorders by Maria Mercedes Guala, Aida Bikic, Kim Bul, David Clinton, Anette Søgaard Nielsen, Helene Nygaard Nielsen, Martina Scarpelli, Luciana Schneider, Regina Christiansen

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Efectos de un programa de ejercicio físico multicomponente sobre la condición física, la autoestima, la ansiedad y la depresión de personas adultas-mayores (Effects of a multicomp... by Miguel Ángel Araque-Martínez, Pedro Jesús Ruiz-Montero, Eva María Artés-Rodríguez

“…Physical exercise has showed to be an useful tool to fight against changes of ageing process. Both Multiple Intelligence Theory and Intelligence XXI manual were used to develop physical, cognitive and emotional factors. …”
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Deep learning-based applications in metal additive manufacturing processes: Challenges and opportunities–A review by Tuğrul Özel

“…The ultimate objective is to develop intelligent AM systems capable of using real-time process data for automated control decisions and interventions, advancing towards more reliable defect-free manufacturing outcomes.…”
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Photobiomodulation therapy (PBMT) in skeletal muscle regeneration: A comprehensive review of mechanisms, clinical applications, and future directions by Haohua Liu, Umber Cheema, Darren J. Player

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Cohort protocol: risk assessment of maternal inflammation and early brain development in infants and young children based on multi-source data modeling by Xianghui Huang, Xianghui Huang, Cuimin Su, Ying Lin, Ying Lin, Tianyi Zhou, Ruming Ye, Ruming Ye, Dan Li, Dan Li, Miaoshuang Liu, Guanhong Wu, Wanting Li, Namei Xie, Xiaofang Deng, Nanxi Zhu, Shaohong Lin, Qin Li, Kai Yan, Deyi Zhuang, Deyi Zhuang

“…Children's cognitive, emotional, and linguistic development was evaluated, and blood samples were obtained for whole-exome sequencing and exposure-related biomarker analysis.ConclusionIn this study, we used deep learning artificial intelligence to construct an early risk assessment model for infant brain development based on the developmental trajectory and developmental results of early brain structure, function, and connections under the complex interaction of “gene-image-environment-behavior” multi-factors, which can improve the early identification and precise intervention of problems in this period, and improve infants cognitive learning and work performance in childhood, adolescence and even adulthood.Clinical trial registrationhttps://www.clinicaltrials.gov/; identifier [NCT05040542].…”
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A Social Network Analysis on Venture Capital Alliance’s Exit from an Emerging Market by Jing Wu, Chuan Luo, Ling Liu

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Deep learning in oral surgery for third molar extraction: empirical evidence and original model by Deyan Neychev, Ralitsa Raycheva, Nadezhda Kafadarova

“…Preemptive analgesia is an analgesic intervention to influence postoperative pain sensation. …”
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Advancements in the Application of Convolutional Neural Networks in Ultrasound Imaging for Breast Cancer Diagnosis and Treatment by An Zichen, Li Fan

“…Breast ultrasound (US) imaging technology plays a crucial role in the early diagnosis and intervention treatment of breast cancer patients. Deep learning (DL), as one of the most powerful machine learning techniques in the field of artificial intelligence (AI), has the ability to automatically select features from raw data, achieving remarkable advancements in breast US imaging. …”
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Large Language Models in Systematic Review Screening: Opportunities, Challenges, and Methodological Considerations by Carlo Galli, Anna V. Gavrilova, Elena Calciolari

“…The findings presented aim to balance the strengths of LLM-driven automation with structured human checks, ensuring that systematic reviews retain their methodological integrity while leveraging the efficiency gains made possible by recent advances in artificial intelligence.…”
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