Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study by Ting Peng, Rujia Miao, Hao Xiong, Yanhui Lin, Duzhen Fan, Jiayi Ren, Jiangang Wang, Yuan Li, Jianwen Chen

“…MethodsA cross-sectional study was conducted for model development, and a retrospective cohort study was used for validation. …”
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Development and validation of machine learning-based diagnostic models using blood transcriptomics for early childhood diabetes prediction by Xin Huang, Xin Huang, Di Ouyang, Weiming Xie, Huawei Zhuang, Siyu Gao, Pan Liu, Lizhong Guo

“…This study aimed to develop and validate machine learning algorithms utilizing transcriptomic signatures to predict T1DM onset in children up to 46 months before clinical diagnosis.MethodsWe analyzed 247 peripheral blood RNA-sequencing samples from pre-diabetic children and age-matched healthy controls. …”
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Depression Recognition Using Daily Wearable-Derived Physiological Data by Xinyu Shui, Hao Xu, Shuping Tan, Dan Zhang

“…Data collected include pulse wave, skin conductance, and triaxial acceleration. For comparison, we also utilized data from fifty-eight matched healthy controls from a publicly available dataset, collected using the same devices over equivalent durations. …”
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Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: a study protocol for the DiCAYA Network by Hui Zhou, Manmohan Kamboj, Yi Guo, Angela D Liese, Rebecca Anthopolos, Lu Zhang, John Chang, Anna Roberts, Tessa Crume, Brian E Dixon, Hui Shao, David C Lee, Lorna E Thorpe, Dimitri Christakis, Eneida A Mendonca, Katie Allen, Dana Dabelea, Giuseppina Imperatore, Mark Weiner, Meredith Akerman, Rong Wei, Kristi Reynolds, Annemarie G Hirsch, Jasmin Divers, Tianchen Lyu, Alex Ewing, Shaun Grannis, Yuan Luo, Bo Cai, Anthony Wong, Brian S Schwartz, Meda Pavkov, Meredith Lewis, Sarah Conderino, Jiang Bian, Yonghui Wu, Jihad S Obeid, Harold P Lehmann, Charles Bailey, Theresa Anderson, Elizabeth A Shenkman, Elizabeth Nauman, Christopher Forrest, Mattia Prosperi, Seho Park, Cara M Nordberg, Tessa L Crume, Anna Bellatorre, Stefanie Bendik, Marc Rosenman, Levon Utidjian, Mitch Maltenfort, Amy Shah, G Todd Alonso, Sara Deakyne-Davies, Tim Bunnell, Anne Kazak, Melody Kitzmiller, Daksha Ranade, Joseph J DeWalle, H Lester Kirchner, Dione G Mercer, Amy Poissant, Nimish Valvi, Jeff Warvel, Ashley Wiensch, Tamara Hannon, Eva Lustigova, Don McCarthy, Matthew T Mefford, George Lales, Allison Zelinski, Pedro Rivera, Thomas Carton, Victor W Zhong, Andrew Fair, Jessica Guillaume, Shahidul Islam, Alan Jacobson, Chinyere Okpara, Anand Rajan, Andrea Titus, Rebecca Conway, Toan Ong, Jack Pattee, Shawna Burgett, Bethlehem Shiferaw, Sarah J Bost, William T Donahoo, William R Hogan, Piaopiao Li, Lisa Knight, Caroline Rudisill, Jessica Stucker, Deborah Bowlby, Elaine Apperson, Deborah B Rolka

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Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion by Rocío Aznar-Gimeno, Jose Luis Perez-Lasierra, Pablo Pérez-Lázaro, Irene Bosque-López, Marina Azpíroz-Puente, Pilar Salvo-Ibáñez, Martin Morita-Hernandez, Ana Caren Hernández-Ruiz, Antonio Gómez-Bernal, María de la Vega Rodrigalvarez-Chamarro, José-Víctor Alfaro-Santafé, Rafael del Hoyo-Alonso, Javier Alfaro-Santafé

“…<b>Methods</b>: A cross-sectional case-control study was conducted involving 42 individuals aged 60 years or older. …”
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Development of Engineering Models of Nanosatellites for Student Training by V. Е. Evchik, A. A. Spiridonov, D. V. Ushakov, V. S. Baranova, I. A. Shalatonin, V. A. Saechnikov

“…It has reduced development costs, flexible equipment reconfiguration, and easier access to the simulator's internal architecture for demonstration purposes.The developed complexes allow students to practically study the ultra-small satellite components design and ground stations, methods for receiving and processing telemetry and scientific information, attitude determination and control algorithms. The complexes allow to conduct of research in the development of individual onboard systems and special-purpose equipment of the nanosatellite and their testing in the loop. …”
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Research on Maneuvering Motion Prediction for Intelligent Ships Based on LSTM-Multi-Head Attention Model by Dongyu Liu, Xiaopeng Gao, Cong Huo, Wentao Su

“…Finally, we perform generalization testing on the optimized motion prediction model using test sets for zigzag and turning conditions. The results demonstrate that our proposed model significantly improves the accuracy of ship maneuvering predictions compared to standalone LSTM and MHAM algorithms and exhibits superior generalization performance.…”
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Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review by Olga Adriana Caliman Sturdza, Florin Filip, Monica Terteliu Baitan, Mihai Dimian

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Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review by Natalie S Blencowe, Neil J Smart, George E Fowler, Rhiannon C Macefield, Conor Hardacre, Mark P Callaway

“…While a large amount of work has been undertaken discussing the role of AI little is understood regarding the performance of such applications in the clinical setting. This systematic review aims to critically appraise the diagnostic performance of AI algorithms to identify disease from cross-sectional radiological images of the abdominopelvic cavity, to identify current limitations and inform future research.Methods and analysis A systematic search will be conducted on Medline, EMBASE and the Cochrane Central Register of Controlled Trials to identify relevant studies. …”
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Identification of Diagnostic Biomarkers and Therapeutic Targets in Sepsis-Associated ARDS via Combining Bioinformatics with Machine Learning Analysis by Liu T, Gao L, Li X

“…Three machine learning algorithms were applied to refine the intersected genes. …”
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Human Action Recognition Method Based on Multi-channel Fusion by Zhiyong TAO, Xijun GUO, Xiaokui REN, Ying LIU, Zemin WANG

“…This result highlights the model’s effectiveness in distinguishing different actions in controlled settings. Tests are also conducted in various natural settings to validate the model’s adaptability to real-world environments, including self-built laboratories, classrooms, and corridors. …”
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Artificial intelligence in vaccine research and development: an umbrella review by Rabie Adel El Arab, May Alkhunaizi, May Alkhunaizi, Yousef N. Alhashem, Alissar Al Khatib, Munirah Bubsheet, Salwa Hassanein, Salwa Hassanein

“…Nonetheless, persistent challenges emerged—data heterogeneity, algorithmic bias, limited regulatory frameworks, and ethical concerns over transparency and equity.Discussion and implicationsThese findings illustrate AI’s transformative potential across the vaccine lifecycle but underscore that translating promise into practice demands five targeted action areas: robust data governance and multi‑omics consortia to harmonize and share high‑quality datasets; comprehensive regulatory and ethical frameworks featuring transparent model explainability, standardized performance metrics, and interdisciplinary ethics committees for ongoing oversight; the adoption of adaptive trial designs and manufacturing simulations that enable real‑time safety monitoring and in silico process modeling; AI‑enhanced public engagement strategies—such as routinely audited chatbots, real‑time sentiment dashboards, and culturally tailored messaging—to mitigate vaccine hesitancy; and a concerted focus on global equity and pandemic preparedness through capacity building, digital infrastructure expansion, routine bias audits, and sustained funding in low‑resource settings.ConclusionThis umbrella review confirms AI’s pivotal role in accelerating vaccine development, enhancing efficacy and safety, and bolstering public acceptance. …”
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Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics by Yushu Xia, Jonathan Sanderman, Jennifer D. Watts, Megan B. Machmuller, Andrew L. Mullen, Charlotte Rivard, Arthur Endsley, Haydee Hernandez, John Kimball, Stephanie A. Ewing, Marcy Litvak, Tomer Duman, Praveena Krishnan, Tilden Meyers, Nathaniel A. Brunsell, Binayak Mohanty, Heping Liu, Zhongming Gao, Jiquan Chen, Michael Abraha, Russell L. Scott, Gerald N. Flerchinger, Patrick E. Clark, Paul C. Stoy, Anam M. Khan, E. N. Jack Brookshire, Quan Zhang, David R. Cook, Thomas Thienelt, Bhaskar Mitra, Marguerite Mauritz‐Tozer, Craig E. Tweedie, Margaret S. Torn, Dave Billesbach

“…Bayesian calibration was conducted using quality‐controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, and grass‐tree mixture). …”
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Using the power of artificial intelligence to improve the diagnosis and management of nonmelanoma skin cancer by Fahimeh Abdollahimajd, Fatemeh Abbasi, Alireza Motamedi, Narges Koohi, Reza Mohamoud Robati, Mona Gorji

“…Building patient trust is also essential for the successful implementation of AI in the clinical settings. AI algorithms may outperform experts in controlled environments but can fall short in the real-world clinical applications, indicating a need for more prospective studies to evaluate their effectiveness in the practical scenarios. …”
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