Correlation between metformin use and mortality in acute respiratory failure: a retrospective ICU cohort study by Yunlin Yang, Jinfeng Liu, Yi Hou, Yuxun Wei, Liang Huang, Liang Huang, Wei Wei

“…Propensity score matching (PSM) and machine learning algorithms were used for confounder adjustment and feature selection.ResultsAfter PSM, 1,429 patients with ARF were included (374 metformin users; 1,055 non-users). …”
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Integrated edge-to-exascale workflow for real-time steering in neutron scattering experiments by Junqi Yin, Viktor Reshniak, Siyan Liu, Guannan Zhang, Xiaoping Wang, Zhongcan Xiao, Zachary Morgan, Sylwia Pawledzio, Thomas Proffen, Christina Hoffmann, Huibo Cao, Bryan C. Chakoumakos, Yaohua Liu

“…We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. …”
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Unraveling morphological brain network disparities Parkinsonian tremor from essential tremor: an artificial intelligence approach for clinical differentiation by Moxuan Zhang, Siyu Zhou, Huizhi Wang, Pengda Yang, Jinli Ding, Xiaobo Wang, Xuzhu Chen, Chaonan Zhang, Anni Wang, Yuan Gao, Qiang Liu, Yueping Li, Tianqi Xu, Zeyu Ma, Yin Jiang, Lin Shi, Chunlei Han, Yuchen Ji, Guoen Cai, Tao Feng, Jianguo Zhang, Fangang Meng

“…Finally, by incorporating these specific characteristics, we developed a machine learning model capable of accurately distinguishing between different tremor types, providing valuable insights for clinical practice.…”
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Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks by Bowen Bao, Hui Yang, Qiuyan Yao, Lin Guan, Jie Zhang, Mohamed Cheriet

“…In this paper, benefiting from machine learning, we propose a resource allocation with edge-cloud collaborative traffic prediction (TP-ECC) in integrated radio and optical networks, where an efficient resource allocation scheme (ERAS) is designed based on the prediction results with the gated recurrent unit model. …”
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Recent advances in metal oxide-biochar composites for water and soil remediation: A review by Hermann Tamaguelon Dzoujo, Victor Odhiambo Shikuku, Sylvain Tome, Aurelle Clandy Ntinkam Simo, Emily C. Ng'eno, Zachary M. Getenga, Marie Annie Etoh, David Daniel Joh Dina

“…Remediation mechanisms for various adsorbates in aqueous media and soils generally include electrostatic attraction, oxidation/reduction, complexation and precipitation. Life cycle assessment (LCA), pilot-scale, cost analysis, potential environmental risks, and machine learning modelling studies are found to be lacking for metal-biochar composites and provide areas for future research.…”
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Single-cell multiomics reveals simvastatin inhibits pan-cancer epithelial-mesenchymal transition via the MEK/ERK pathway in XBP1+ mast cells by Sen Lin, Huimin Zhang, Ruiqi Zhao, Zhulin Wu, Weiqing Zhang, Mengjiao Yu, Bei Zhang, Lanyue Ma, Danfei Li, Lisheng Peng, Weijun Luo

“…A prognostic model was established using WGCNA and 12 machine learning algorithms to identify potential mast cell targets. …”
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Navigating the Maze of Social Media Disinformation on Psychiatric Illness and Charting Paths to Reliable Information for Mental Health Professionals: Observational Study of TikTok... by Alexandre Hudon, Keith Perry, Anne-Sophie Plate, Alexis Doucet, Laurence Ducharme, Orielle Djona, Constanza Testart Aguirre, Gabrielle Evoy

“…ResultsDisinformation was predominantly found in videos about neurodevelopment, mental health, personality disorders, suicide, psychotic disorders, and treatment. A machine learning model identified weak predictors of disinformation, such as an initial perceived intent to disinform and content aimed at the general public rather than a specific audience. …”
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Tropical Cyclone Size Prediction and Development of An Error Correction Method by Guo Ruichen, Xu Jing, Wang Yuqing

“…Based on this relationship, a machine learning model, XGBoost, is used to develop an R17 size correction scheme that incorporate initial and forecast intensity, inner-core and outer-core sizes, and initial errors as predictors to estimate and correct model-predicted size errors. …”
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Mapping Windthrow Severity as Change in Canopy Cover in a Temperate Eucalypt Forest by Nina Hinko-Najera, Paul D. Bentley, Samuel Hislop, Alison C. Bennett, Jamie E. Burton, Thomas A. Fairman, Sacha Jellinek, Julio C. Najera Umana, Lauren T. Bennett

“…We assessed percentage canopy cover from high-resolution aerial images of 455 randomly selected plots in disturbed and undisturbed areas to train a model and machine learning framework to predict landscape scale canopy cover from Sentinel-2 images. …”
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A secure and energy-efficient routing using coupled ensemble selection approach and optimal type-2 fuzzy logic in WSN by S. Ambareesh, Pundalik Chavan, S. Supreeth, Rajesh Nandalike, P. Dayananda, S. Rohith

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A comprehensive IoT cloud-based wind station ready for real-time measurements and artificial intelligence integration by Décio Alves, Fábio Mendonça, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias

“…The proposed cloud-computing Internet of Things-based Automated Weather Station framework demonstrates significant potential for accurate and efficient wind measurement and monitoring, paving the way for future advancements in high temporal resolution wind monitoring systems capable of producing big data prepared for subsequent machine learning model approaches.…”
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Host‐Microbial Cometabolite Ursodeoxycholic Acid Protects Against Poststroke Cognitive Impairment by Xuxuan Gao, Feng Zhang, Jiafeng Zhang, Yu Ma, Yiting Deng, Jiaying Chen, Yueran Ren, Huidi Wang, Boxin Zhao, Yan He, Jia Yin

“…Patients with mild acute ischemic stroke who developed PSCI exhibited significant alterations in gut microbiota and plasma bile acid profiles during the acute stroke phase, including a notable reduction in UDCA level. Through feature selection and machine learning, we constructed a predictive model for PSCI incorporating plasma UDCA level, the relative abundance of Clostridia, Bacilli, and Bacteroides, as well as age, educational level, and the presence of moderate to severe white matter lesions. …”
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Thermal comfort and energy related occupancy behavior in Dutch residential dwellings by Anastasios Ioannou

“…The future in understanding the energy related occupancy behaviour, and therefore using it towards a more sustainable built environment, lies in the advances of sensor technology, big data gathering, and machine learning. Technology will enable us to move from big population models to tailor made solutions designed for each individual occupant.   …”
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Enhancing Attention Network Spatiotemporal Dynamics for Motor Rehabilitation in Parkinson’s Disease by Guangying Pei, Mengxuan Hu, Jian Ouyang, Zhaohui Jin, Kexin Wang, Detao Meng, Yixuan Wang, Keke Chen, Li Wang, Li-Zhi Cao, Shintaro Funahashi, Tianyi Yan, Boyan Fang

“…The identified brain spatiotemporal neural markers were validated using machine learning models to assess the efficacy of MIRT in motor rehabilitation for PD patients, achieving an average accuracy rate of 86%. …”
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Determining optimal strategies for primary prevention of cardiovascular disease: a synopsis of an evidence synthesis study by Olalekan A Uthman, Lena Al-Khudairy, Chidozie Nduka, Rachel Court, Jodie Enderby, Seun Anjorin, Hema Mistry, G J Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…An umbrella review summarised evidence from 95 systematic reviews. A machine learning study developed a parallel Convolutional Neural Network algorithm with 96.4% recall and 99.1% precision for study screening. …”
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Reconsidering the use of race, sex, and age in clinical algorithms to address bias in practice: A discussion paper by Reanna Panagides, Jessica Keim-Malpass

“…By applying a framework for understanding sources of harm throughout the machine learning life cycle and presenting case studies, this paper aims to examine sources of potential harms (i.e. representational and allocative harm) associated with including sex and age in clinical decision-making algorithms, particularly risk calculators. …”
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Prenatal exposure to criteria air pollution and traffic-related air toxics and risk of autism spectrum disorder: A population-based cohort study of California births (1990–2018) by Karl O’Sharkey, Sanjali Mitra, Ting Chow, Seung-a Paik, Laura Thompson, Jason Su, Myles Cockburn, Beate Ritz

“…Methods: Utilizing CA Department of Public Health birth registry data from 1990 to 2018, linked with ASD diagnoses from the CA Department of Developmental Services (n = 13,591,003 children; ASD cases = 138,460, identified from birth year through 2022, allowing for a follow-up ranging from a minimum of 4 to a maximum of 32 years) we assessed prenatal exposure to PM2.5, NO2, O3, and six traffic-related air toxics (benzene, 1,3-butadiene, chromium, lead, nickel, zinc) using machine learning-enhanced land-use regression models. …”
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A recurrent multimodal sparse transformer framework for gastrointestinal disease classification by V. Sharmila, S. Geetha

“…Further, the model employs principal component analysis (PCA) for dimensionality reduction and gradient boosting machines (GBMs) for semantic conflict resolution. …”
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Therapy response prediction of focal cortex stimulation based on clinical parameters: a multicentre, non-interventional study protocol by Andreas Schulze-Bonhage, Ann Mertens, Felix von Podewils, Martin Hirsch, Yaroslav Winter, Susanne Knake, Ekaterina Pataraia, Thomas Mayer, Yvonne G Weber, Rainer Surges, Jan Wagner, Hajo M Hamer, Tim Wehner, Christoph Baumgartner, Lukas Imbach, Bernhard J Steinhoff, Sotirios Kalousios, Jürgen Hesser, Matthias Dümpelmann, Elisabeth Kaufmann, Josua Kegele, Georg Leonhardt, Carlos M Quesada, Berthold R Voges

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Remote-Management of COPD: Evaluating the Implementation of Digital Innovation to Enable Routine Care (RECEIVER): the protocol for a feasibility and service adoption observational... by David J Lowe, Grace McDowell, Anna Taylor, Stephanie Lua, Shane Burns, Paul McGinness, Christopher M Carlin

“…The digital infrastructure will also provide a foundation to explore the feasibility of approaches to predict outcomes and exacerbation in people with COPD through machine learning analysis.Ethics and dissemination Ethical approval for this clinical trial has been obtained from the West of Scotland Research Ethics Service. …”
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