Metabolomic Profiling Reveals Serum Tryptophan as a Potential Therapeutic Target for Systemic Lupus Erythematosus by Wang K, Zhu R, Xu M, Zhu K, Li J, Li C, Meng D, Chen H, Sun L

“…In the mouse model, tryptophan supplementation improved renal histology, reduced proteinuria, increased naïve T cells and central memory T cells, and decreased effector T cell frequencies in both peripheral blood and spleen.Conclusion: This study demonstrates the successful application of machine learning algorithms to metabolomics data for SLE classification and identifies tryptophan and beta-alanine as potential SLE-specific biomarkers. …”
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Predicting postoperative trauma-induced coagulopathy in patients with severe injuries by machine learning by Xiaohui Du, Wei Wang, Bo Xu, Jiang Zheng, Victor W. Xia, Yi Guo, Shuai Feng, Qingxiang Mao, Hong Fu

“…Utilizing machine learning may enhance clinical decision-making and improve management strategies for postoperative coagulation issues.…”
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Unraveling anoikis in glioblastoma: insights from single-cell sequencing and prognostic modeling by Qikai Tang, Chenfeng Ma, Jiaheng Xie, Qixiang Zhang, Bingtao Zhang, Weiqi Bian, Qingyu Lu, Zeyu Wan, Wei Wu

“…The developed prognostic model offers a promising tool for personalized treatment strategies and warrants further exploration of targeted therapies to improve outcomes for GBM patients.…”
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Thermal ablation in the management of oligometastatic colorectal cancer by Thierry De Baere, Lambros Tselikas, Alexandre Delpla, Charles Roux, Eloi Varin, Adrian Kobe, Steven Yevich, Frederic Deschamps

“…As ablation techniques and technology improve, the role of percutaneous thermal ablation may expand even into surgically resectable disease. …”
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NeuroRF FarmSense: IoT-fueled precision agriculture transformed for superior crop care by Tarun Vats, Shrey Mehra, Uday Madan, Amit Chhabra, Akashdeep Sharma, Kunal Chhabra, Sarabjeet Singh, Utkarsh Chauhan

“…In light of the ongoing global hunger crisis, it is imperative to improve food production in accordance with Sustainable Development Goal 2.0, which aims to eliminate hunger while promoting sustainable agricultural practices. …”
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Age and Saving Lives in Crisis Standards of Care: A Multicenter Cohort Study of Triage Score Prognostic Accuracy by Michael Hermsen, MD, MS, Patrick G. Lyons, MD, MS, Govind Persad, JD, PhD, Alice F. Bewley, MS, Chengsheng Mao, PhD, Kaveri Chhikara, MS, Anoop Mayampurath, PhD, Matthew Churpek, MD, PhD, Monica E. Peek, MD, MPH, MSc, Yuan Luo, PhD, William F. Parker, MD, PhD

“…To determine if adding age to triage protocols used in disaster scenarios improves the identification of critically ill patients likely to survive. …”
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Cancer patients' experiences with and opinions on the process 'Screening of Distress and Referral Need' (SDRN) in clinical practice: A quantitative observational clinical study. by Floor M van Nuenen, Stacey M Donofrio, Harry B M van de Wiel, Josette E H M Hoekstra-Weebers

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A Public Dataset of Annotated Orcinus orca Acoustic Signals for Detection and Ecotype Classification by K. J. Palmer, Emma Cummings, Michael G. Dowd, Kait Frasier, Fabio Frazao, Alex Harris, April Houweling, Jasper Kanes, Oliver S. Kirsebom, Holger Klinck, Holly LeBlond, Lauren Laturnus, Craig Matkin, Olivia Murphy, Hannah Myers, Dan Olsen, Caitlin O’Neill, Bruno Padovese, James Pilkington, Lucy Quayle, Amalis Riera Vuibert, Krista Trounce, Svein Vagle, Scott Veirs, Val Veirs, Jen Wladichuk, Jason Wood, Tina Yack, Harald Yurk, Ruth Joy

“…This curated dataset aims to improve the accuracy of killer whale detection algorithms, support conservation efforts, and advance our understanding of killer whale acoustic communication across different populations.…”
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Analyzing Satellite Imagery to Target Tuberculosis Control Interventions in Densely Urbanized Areas of Kigali, Rwanda: Cross-Sectional Pilot Study by Mauro Faccin, Caspar Geenen, Michiel Happaerts, Sien Ombelet, Patrick Migambi, Emmanuel André

“… Abstract BackgroundEarly diagnosis and treatment initiation for tuberculosis (TB) not only improve individual patient outcomes but also reduce circulation within communities. …”
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Design, Fabrication, and Application of Large-Area Flexible Pressure and Strain Sensor Arrays: A Review by Xikuan Zhang, Jin Chai, Yongfu Zhan, Danfeng Cui, Xin Wang, Libo Gao

“…Real-time data processing requires innovative solutions such as edge computing and machine learning algorithms, ensuring low-latency, high-accuracy data interpretation while preserving the flexibility of sensor arrays. …”
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Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology by Raoof Nopour

“…We also assessed features based on the best-performing model to improve clinical usability. To show the generalizability of the established prediction model for predicting CRC metastasis, we leveraged the data of 115 CRC patients from Imam Khomeini Hospital in Sari City. …”
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Predicting cognitive decline in cognitively impaired patients with ischemic stroke with high risk of cerebral hemorrhage: a machine learning approach by Eun Namgung, Young Sun Kim, Sun U. Kwon, Dong-Wha Kang, Dong-Wha Kang

“…Recognizing these high-risk individuals and their risk and protective factors allows for timely and targeted interventions to improve prognosis in PSCI patients.…”
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可穿戴设备在卒中风险预测及卒中后管理中的应用进展 Research Progress on the Application of Wearable Devices in Stroke Risk Prediction and Post-Stroke Management... by 吴雅婷1，魏宸铭1，桑振华1，陈乐1，梁怡凡1，武剑1，2，3 (WU Yating1, WEI Chenming1, SANG Zhenhua1, CHEN Le1, LIANG Yifan1, WU Jian1,2,3)

“…By integrating with health management platforms and artificial intelligence algorithms, wearable devices can significantly enhance the accuracy of risk assessment, optimize rehabilitation treatment plans, and thus improve the patients’ outcomes. …”
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Early Warning of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Multi-Omics Signature: A Machine Learning-Based Retrospective Study by Ke Z, Shen L, Shao J

“…The findings of this study can help clinicians identify patients with higher risk of ALN metastasis and provide personalized perioperative management to assist preoperative decision-making and improve patient prognosis.Keywords: breast cancer, axillary lymph node metastasis, radiomics, pathomics, nomogram, random forest, machine learning…”
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A novel perspective on survival prediction for AML patients: Integration of machine learning in SEER database applications by Zheng-yi Jia, Maierbiya Abulimiti, Yun Wu, Li-na Ma, Xiao-yu Li, Jie Wang

“…Finally, we used 11 machine learning algorithms to predict the survival rate of AML patients at 1, 2, and 3 years, respectively. …”
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Utilizing an NLP-supported mobile reflection application to explore academic engagement, application engagement, and performance in engineering and physics courses by Saira Anwar, Ahmed Ashraf Butt, Muhsin Menekse

“…Abstract Background Technology-enhanced classrooms now integrate a range of educational apps designed to improve student outcomes. The effectiveness of these applications is influenced by multiple factors related to the courses and the applications themselves. …”
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Multimodal data-driven prognostic model for predicting long-term outcomes in older adult patients with sarcopenia: a retrospective cohort study by Mengdie Liu, Wen Guo, Jin Peng, Jinhui Wu

“…This study aims to utilize machine learning techniques that incorporate sociodemographic factors, health-related metrics, lifestyle variables, and biomarker data to improve risk stratification and management in older adults with SP.MethodsWe analyzed data from the NHANES from 1999–2006 and 2010–2018, including a total of 1,619 older adult patients with SP, with a 10-year follow-up period for this population, during which 541 (33%) patients died and 1,078 (67%) survived. …”
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Simultaneous multi-class detection of interplanetary space weather events by Nguyen Gautier, Bernoux Guillerme, Ferlin Antoine

“…This, in turn, helps improve our understanding of their impact on the near-Earth space environment, including effects on human activities. …”
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Enhancing stroke prediction models: A mixing of data augmentation and transfer learning for small-scale dataset in machine learning by Imam Tahyudin, Ade Nurhopipah, Ades Tikaningsih, Puji Lestari, Yaya Suryana, Edi Winarko, Eko Winarto, Nazwan Haza, Hidetaka Nambo

“…Therefore, additional data acquisition techniques are needed to increase data size and improve model quality.This study applied Data Augmentation and Transfer Learning to solve small-scale dataset problems in analyzing stroke patient information in The Banyumas Regional General Hospital (RSUD Banyumas). …”
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A Soft Sensor Based Inference Engine for Water Quality Assessment and Prediction by Micheal A Ogundero, Theophilus A Fashanu, Foluso O Agunbiade, Kehinde Orolu, Ahmed A Yinusa, Usman A Daudu, Muhammed O H Amuda

“…It becomes imperative to implement reliable, real-time, water quality assessment and prediction systems to resolve known and emerging water quality issues in order to improve the quality of life in such communities. Thus, this work developed an inference engine for robust water quality assessment and prediction using some statistical and intelligent discriminant functions. …”
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