Machine learning to improve HIV screening using routine data in Kenya by Jonathan D. Friedman, Jonathan M. Mwangi, Kennedy J. Muthoka, Benedette A. Otieno, Jacob O. Odhiambo, Frederick O. Miruka, Lilly M. Nyagah, Pascal M. Mwele, Edmon O. Obat, Gonza O. Omoro, Margaret M. Ndisha, Davies O. Kimanga

“…We trained four machine learning algorithms including logistic regression, Random Forest, AdaBoost and XGBoost. …”
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Assessment of enthesitis in patients with psoriasis: Relationships with clinical features, screening questionnaries results, and quality of life: An ultrasound study by Dulović Dragan, Rančić Nemanja, Božić Ksenija, Stamatović Ratko, Mijušković Željko, Pešić Jasna, Kremić Zorana, Vojinović Radiša, Petronijević Milan

“…Often asymptomatic, enthesitis can be an integral feature of the wide clinical spectrum in psoriasis as well as an early sign of development of psoriatic arthritis (PsA). It may be difficult to clinically recognize enthesitis in patients with psoriasis or distinguish it from other causes of extraarticular pain. …”
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Single-cell profiling of SLC family transporters: uncovering the role of SLC7A1 in osteosarcoma by Yan Liao, Junkai Chen, Hao Yao, Ting Zheng, Jian Tu, Weidong Chen, ZeHao Guo, Yutong Zou, Lili Wen, Xianbiao Xie

“…Pharmacological inhibition of SLC7A1 may be a feasible therapeutic approach for osteosarcoma.…”
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Exploring the Global and Regional Factors Influencing the Density of <i>Trachurus japonicus</i> in the South China Sea by Mingshuai Sun, Yaquan Li, Zuozhi Chen, Youwei Xu, Yutao Yang, Yan Zhang, Yalan Peng, Haoda Zhou

“…Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of <i>T. japonicus</i> density. …”
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A Novel 14-Gene Panel Associated With Efferocytosis for Predicting Pancreatic Cancer Prognosis Through Bulk and Single-Cell Databases by Yaheng Wu, Lin Zhao, Dingyan Yi, Zhihua Tian, Bin Dong, Chunxiang Ye, Jingtao Liu, Huachong Ma, Wei Zhao

“…Conclusion: Our findings underscore the importance of considering a novel prognostic signature comprising 14 ER genes in the context of the TME when investigating the biology of PDAC. Future studies may explore how modulating these interactions could lead to novel therapeutic opportunities.…”
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MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems by Abdul Jabbar, Marco Cocconcelli, Gianluca D’Elia, Davide Borghi, Luca Capelli, Jacopo Cavalaglio Camargo Molano, Matteo Strozzi, Riccardo Rubini

“…In industrial applications where hundreds of carts may operate simultaneously, monitoring a large number of bearings becomes highly complex, making fault identification and localization particularly difficult. …”
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Predictive Modelling of nephrotoxicity from vancomycin and piperacillin-tazobactam (VPT) combination and vancomycin as monotherapy: an artificial intelligence (AI) Approach by Dr Winnie Lee, Mr William J Bolton, Mr Richard C Wilson, Professor Pantelis Georgiou, Professor Alison H Holmes, Dr Nina Zhu, Dr Timothy M Rawson

“…Conclusion: Our results may provide clinicians with actionable insights into optimising empirical antimicrobial regimens while minimising nephrotoxicity risks through the use of AI, which can support better clinical decision-making.…”
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Comprehensive Analysis of Programmed Cell Death-Related Genes in Diagnosis and Synovitis During Osteoarthritis Development: Based on Bulk and Single-Cell RNA Sequencing Data by Zhou J, Jiao S, Huang J, Dai T, Xu Y, Xia D, Feng Z, Chen J, Li Z, Hu L, Meng Q

“…Using machine learning algorithms, Hub PCD-related differentially expressed genes (Hub PCD-DEGs) were identified. …”
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Application of machine learning for the analysis of peripheral blood biomarkers in oral mucosal diseases: a cross-sectional study by Huiyu Yao, Zixin Cao, Liangfu Huang, Haojie Pan, Xiaomin Xu, Fucai Sun, Xi Ding, Wan Wu

“…Future research should focus on validating these findings in larger cohorts and exploring alternative machine-learning algorithms to improve diagnostic accuracy further.…”
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In-Hospital Stroke in a Multidisciplinary Hospital by S. S. Petrikov, G. R. Ramazanov, S. B. Binnatova, M. V. Tikhomirova

“…However, patients with in-hospital stroke may have a large number of contraindications to this type of therapy. …”
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Long-term risk of de novo malignancy with tumor necrosis factor alpha (TNF) inhibitor immunosuppression: a multicenter, retrospective cohort study by Conor B. Driscoll, Jordan M. Rich, Christopher Yang, Joseph Nicolas, Dylan Isaacson, Philip Silberman, Xinlei Mi, Sai Kaushik Shakar Ramesh Kumar, Hui Zhang, Steven Belknap, William H. Temps, Edward M. Schaeffer, Shilajit D. Kundu

“…This is consistent with previous melanoma, skin SCC and BCC data and demonstrates novel findings for leukemia, NHL, and PCa. There may need to be differential cancer screening algorithms for patients with different inflammatory conditions and TNF-I exposure.…”
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Intracranial aneurysm instability prediction model based on 4D-Flow MRI and HR-MRI by Fei Peng, Jiaxiang Xia, Fandong Zhang, Shiyu Lu, Hao Wang, Jiashu Li, Xinmin Liu, Yao Zhong, Jiahuan Guo, Yonghong Duan, Binbin Sui, Chuyang Ye, Yi Ju, Shuai Kang, Yizhou Yu, Xin Feng, Xingquan Zhao, Rui Li, Aihua Liu

“…We introduce a specialized ensemble learning framework, termed the Hybrid Model, which synergistically combines two heterogeneous base learning algorithms: 4D-Flow logistic regression (4D-Flow-LR) and Multi-crop Attention Branch Network (MicroAB-Net). …”
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Cellular hierarchy framework based on single-cell and bulk RNA sequencing reveals fatty acid metabolic biomarker MYDGF as a therapeutic target for ccRCC by Ning Wang, Ziyu Xu, Lina Zhang, Lina Zhang, Yanfang Lu, Yanfang Lu, Yanliang Wang, Yanliang Wang, Lei Yan, Lei Yan, Huixia Cao, Huixia Cao, Limeng Wang, Limeng Wang, Fengmin Shao, Fengmin Shao

“…Functional enrichment algorithms, including AUCell, UCell, singscore, ssGSEA, and AddModuleScore, along with hdWGCNA analysis, were used to identify hub genes influencing high FAM of ccRCC. …”
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Protocol for an intervention development and pilot implementation evaluation study of an e-health solution to improve newborn care quality and survival in two low-resource settings... by Hassan Haghparast-Bidgoli, Michelle Heys, Caroline Crehan, Erin Kesler, Monica Lakhanpaul, Emma Wilson, Fabiana Lorencatto, Felicity Fitzgerald, Mario Cortina Borja, Msandeni Chiume, Hannah Gannon, Simbarashe Chimhuya, Gwendoline Chimhini, Tim Hull-Bailey, Tarisai Chiyaka, Yali Sassoon, Deliwe Nkhoma, Nushrat Khan, Alexander G. Stevenson, Kristina Curtis

“…Digital health technologies may offer an implementation solution. Since 2014, we have worked in Bangladesh, Malawi, Zimbabwe and the UK to develop and pilot Neotree: an android app with accompanying data visualisation, linkage and export. …”
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The osteosarcoma immune microenvironment in progression: PLEK as a prognostic biomarker and therapeutic target by Yunpeng Zou, Yunpeng Zou, Jianning Kang, Shaopeng Zhu, Xuechen Ren, Zheng Li, Jiayao Niu, Xuanzhe Qin, Hongbo Li, Lu Xiang, Wei Jiang, Jiangbo Zhong, Ying Zhang, Ying Zhang, Kai Zhao

“…Protein–protein interaction networks, GO/KEGG enrichment, and CytoHubba algorithms identified core hub genes. Tumor-infiltrating immune cells were profiled via TIMER. …”
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Protocol and research program of the European registry and biobank for interstitial lung diseases (eurILDreg) by Ekaterina Krauss, Silke Tello, Jennifer Naumann, Sandra Wobisch, Clemens Ruppert, Stefan Kuhn, Poornima Mahavadi, Raphael W. Majeed, Philippe Bonniaud, Maria Molina-Molina, Athol Wells, Nik Hirani, Carlo Vancheri, Simon Walsh, Matthias Griese, Bruno Crestani, Andreas Guenther, on behalf of further eurILDreg investigators, RARE-ILD investigators

“…Despite limited therapeutic options beyond antifibrotic drugs and immunosuppressants, accurate diagnosis is challenging, often necessitating invasive procedures that may not be feasible for certain patients. Drawn against this background, experts across pediatric and adult ILD fields have joined forces in the RARE-ILD initiative to pioneer novel non-invasive diagnostic algorithms and biomarkers. …”
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Identification and validation of TUBB, CLTA, and FBXL5 as potential diagnostic markers of postmenopausal osteoporosis by Yue Tan, Yujing Wang, Qin Zhu, Yan Xue, Xuhao Ji, Zhenkun Li, Jiawen Shen, Chengming Sun, Shiqi Ren, Chenlin Zhang, Jianfeng Chen

“…The findings contribute to understanding the underlying molecular mechanisms of CLTA, TUBB, and FBXL5 in PMOP and may facilitate the development of novel therapeutic strategies and improved monitoring of the disease. …”
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FGFR2 identified as a NETs-associated biomarker and therapeutic target in diabetic foot ulcers by Linrui Dai, Shunli Rui, Mengling Yang, Shiyan Yu, Qingqing Chen, Hongyan Wang, Bo Deng, Liling Deng, Wei Hao, Xiaohua Wu, David G. Armstrong, Zhidong Cao, Xiaodong Duan, Wuquan Deng

“…LASSO logistic regression and Random Forest algorithms were applied to the NETDEGs to select key feature genes. …”
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Integrating ultrasound radiomics and clinicopathological features for machine learning-based survival prediction in patients with nonmetastatic triple-negative breast cancer by Wenwen, Zekun Jiang, Jingyan Liu, Dingbang Liu, Yiyue Li, Yushuang He, Haina Zhao, Lin Ma, Yixin Zhu, Qiongxian Long, Jun Gao, Honghao Luo, Heng Jiang, Kang Li, Xiaorong Zhong, Yulan Peng

“…A thorough chart review was conducted for each patient to collect clinicopathological and sonographic features, and ultrasound radiomics features were obtained by PyRadiomics. Deep learning algorithms were utilized to delineate ROIs on ultrasound images. …”
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Development of a Predictive Model of Occult Cancer After a Venous Thromboembolism Event Using Machine Learning: The CLOVER Study by Anabel Franco-Moreno, Elena Madroñal-Cerezo, Cristina Lucía de Ancos-Aracil, Ana Isabel Farfán-Sedano, Nuria Muñoz-Rivas, José Bascuñana Morejón-Girón, José Manuel Ruiz-Giardín, Federico Álvarez-Rodríguez, Jesús Prada-Alonso, Yvonne Gala-García, Miguel Ángel Casado-Suela, Ana Bustamante-Fermosel, Nuria Alfaro-Fernández, Juan Torres-Macho

“…XGBoost, LightGBM, and CatBoost algorithms were used to train different prediction models, which were subsequently validated in a hold-out dataset. …”
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