Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement lo... by Qing Huang, Zihao Jiang, Bo Shi, Jiaxu Meng, Li Shu, Fuyong Hu, Jing Mi

“…Five machine learning (ML) algorithms were employed for risk prediction. Data preprocessing included missing value imputation via random forest. …”
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Predicting p53 Status in IDH‐Mutant Gliomas Using MRI‐Based Radiomic Model by Jiamin Li, Zhihong Lan, Xiao Zhang, Xiaoyun Liang, Hanwei Chen, Xiangrong Yu

“…The predictive performance of the models was evaluated using receiver operating characteristic (ROC) curve analysis. …”
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Surgical interventions in velopharyngeal dysfunction: comparative perceptual speech and nasometric outcomes for three techniques by Ryan Instrum, Agnieszka Dzioba, Anne Dworschak-Stokan, Murad Husein

“…Abstract Background The aim of this study was to evaluate speech outcomes following surgical intervention for velopharyngeal dysfunction (VPD). …”
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Advancing personalized, predictive, and preventive medicine in bladder cancer: a multi-omics and machine learning approach for novel prognostic modeling, immune profiling, and ther... by Han Yan, Xinyu Ji, Bohan Li

“…Survival analysis, immune infiltration, pathway enrichment, and drug sensitivity were evaluated to validate the model.ResultsThe ICDRS, based on eight key genes (IL32, AHNAK, ANXA5, FN1, GSN, CNN3, FXYD3, CTSS), effectively stratified BLCA patients into high- and low-risk groups with significant differences in overall survival (OS, P < 0.001). …”
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Prognosis and immune landscape of bladder cancer can be predicted using a novel miRNA signature associated with cuproptosis by Zhilei Zhang, Fang Liu, Yongbo Yu, Fei Xie, Tao Zhu

“…Additionally, we developed a nomogram incorporating clinical characteristics and the miRNA signature to further assess its prognostic value. We evaluated the tumor microenvironment (TME) of every patient using immune ESTIMATE, CIBERSORT, and ssGSEA algorithms. …”
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Validation of Automated Respiratory Event Scoring in Type 3 Home Sleep Apnea Testing by Shiroshita N, Obata R, Kawana F, Kato M, Sato A, Ishiwata S, Yatsu S, Matsumoto H, Shitara J, Murata A, Shimizu M, Kato T, Suda S, Tomita Y, Hiki M, Naito R, Kasai T

“…Nanako Shiroshita,1,* Ryoko Obata,1,2,* Fusae Kawana,1 Mitsue Kato,1 Akihiro Sato,3 Sayaki Ishiwata,3 Shoichiro Yatsu,3 Hiroki Matsumoto,3,4 Jun Shitara,3 Azusa Murata,3 Megumi Shimizu,3 Takao Kato,3,4 Shoko Suda,1,3,4 Yasuhiro Tomita,3– 5 Masaru Hiki,3 Ryo Naito,3,4 Takatoshi Kasai1,3,4 1Cardiovascular Respiratory Sleep Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; 2Philips Japan, Tokyo, Japan; 3Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; 4Sleep and Sleep-Disordered Breathing Center, Juntendo University Hospital, Tokyo, Japan; 5Sleep Center, Toranomon Hospital, Tokyo, Japan*These authors contributed equally to this workCorrespondence: Takatoshi Kasai, Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan, Tel +81-3-3813-3111, Fax +81-3-5689-0627, Email kasai-t@mx6.nisiq.netPurpose: Home sleep apnea tests (HSATs) using polygraphy devices are becoming increasingly important for evaluating obstructive sleep apnea. Alice NightOne, a widely used polygraphy device, includes automatic scoring software; however, more reliable scoring results can be provided by incorporating advanced algorithmic systems like Somnolyzer. …”
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Trust in Artificial Intelligence–Based Clinical Decision Support Systems Among Health Care Workers: Systematic Review by Hein Minn Tun, Hanif Abdul Rahman, Lin Naing, Owais Ahmed Malik

“…Barriers to trust included algorithmic opacity, insufficient training, and ethical challenges, while enabling factors for health care workers’ trust in AI-CDSS tools were transparency, usability, and clinical reliability. …”
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Bioinformatics‑Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis by Yang M, Zhang X, Zhou C, Du Y, Zhou M, Zhang W

“…Least Absolute Shrinkage and Selection Operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to screen hub genes. Immune cell infiltration was evaluated using CIBERSORT. …”
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Development of explainable artificial intelligence based machine learning model for predicting 30-day hospital readmission after renal transplantation by Nasser Alnazari, Omar Ibrahim Alanazi, Muath Owaidh Alosaimi, Ziyad Mohamed Alanazi, Ziyad Mohammed Alhajeri, Khaled Mohammed Alhussaini, Abdulkarim Mekhlif Alanazi, Ahmed Y. Azzam

“…Our methodology included a four-stage machine learning pipeline: data processing, feature preparation, model development using stratified 5-fold cross-validation, and clinical validation. Multiple algorithms were evaluated, with gradient boosting demonstrating superior performance. …”
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Identification and Validation of Key Genes Involved in the Coupling of Mitochondria-Associated Endoplasmic Reticulum Membrane in Hemorrhoidal Disease by Mao L, Rao Z, Wang Y, Yang J, He J, Zheng Z, Chen L

“…Additionally, CIBERSORT algorithms were utilized to evaluate immune cell infiltration abundance, differences, and correlations in the samples. …”
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Neurofibromatosis-Noonan syndrome: a prospective monocentric study of 26 patients and literature review by Didier Bessis, Dominique Vidaud, Pierre Meyer, Laurence Pacot, de La Villeon G, Adeline Alice Bonnard, Yline Capri, Christine Coubes, Fanchon Herman, Didier Lacombe, Nicolas Molinari, Laura Poujade, Agathe Roubertie, Julien Van Gils, Alain Verloes, David Geneviève, Hélène Cavé, Marjolaine Willems

“…Secondary objectives include evaluating inter-rater diagnostic agreement among experienced clinicians and assessing the utility of deep-learning algorithms (Face2Gene® [F2G]). …”
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A gene signature related to programmed cell death to predict immunotherapy response and prognosis in colon adenocarcinoma by Lei Zheng, Jia Lu, Dalu Kong, Yang Zhan

“…Immune infiltration of the samples was evaluated using CIBERSORT and Microenvironment Cell Populations (MCP)-counter algorithms. …”
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Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in precision me... by Yutong Fang, Rongji Zheng, Yefeng Xiao, Qunchen Zhang, Junpeng Liu, Jundong Wu

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”
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Linking Immunological Parameters and Recovery of Patient’s Motor and Cognitive Functions In The Acute Period of Ischemic Stroke by A. M. Tynterova, N. N. Shusharina, A. M. Golubev, E. M. Moiseeva, L. S. Litvinova

“…Objective. To evaluate the relationship between immunological parameters and functional outcome in patients with varying severity of ischemic stroke based on statistical methodology.Materials and methods. …”
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Exploring the Ethical Challenges of Conversational AI in Mental Health Care: Scoping Review by Mehrdad Rahsepar Meadi, Tomas Sillekens, Suzanne Metselaar, Anton van Balkom, Justin Bernstein, Neeltje Batelaan

“…The following 10 themes were distinguished: (1) safety and harm (discussed in 52/101, 51.5% of articles); the most common topics within this theme were suicidality and crisis management, harmful or wrong suggestions, and the risk of dependency on CAI; (2) explicability, transparency, and trust (n=26, 25.7%), including topics such as the effects of “black box” algorithms on trust; (3) responsibility and accountability (n=31, 30.7%); (4) empathy and humanness (n=29, 28.7%); (5) justice (n=41, 40.6%), including themes such as health inequalities due to differences in digital literacy; (6) anthropomorphization and deception (n=24, 23.8%); (7) autonomy (n=12, 11.9%); (8) effectiveness (n=38, 37.6%); (9) privacy and confidentiality (n=62, 61.4%); and (10) concerns for health care workers’ jobs (n=16, 15.8%). …”
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Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques by Liuqing Yang, Liuqing Yang, Liuqing Yang, Rui Xuan, Rui Xuan, Rui Xuan, Dawei Xu, Dawei Xu, Dawei Xu, Aming Sang, Aming Sang, Aming Sang, Jing Zhang, Jing Zhang, Jing Zhang, Yanfang Zhang, Xujun Ye, Xinyi Li, Xinyi Li, Xinyi Li

“…CIBERSORT was utilized to evaluate the inflammatory and immunological condition of sepsis. …”
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An improved hybrid approach involving deep learning for urban greening tree species classification with Pléiades Neo 4 imagery—A case study from Nanjing, Eastern China by Min Sun, Stephane G.P. Debulois, Zhengnan Zhang, Xiaolei Cui, Zhili Chen, Mingshi Li

“…The distribution of urban tree species shapes spatial differences in urban eco-environmental benefits and liveability. …”
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Can Stereoscopic Density Replace Planar Density for Forest Aboveground Biomass Estimation? A Case Study Using Airborne LiDAR and Landsat Data in Daxing’anling, China by Xuan Mu, Dan Zhao, Zhaoju Zheng, Cong Xu, Jinchen Wu, Ping Zhao, Xiaomin Li, Yong Pang, Yujin Zhao, Tianyu An, Yuan Zeng, Bingfang Wu

“…Forest aboveground biomass (AGB) is a key indicator for evaluating carbon sequestration capacity and forest productivity. …”
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Intelligent multi-modeling reveals biological relationships and adaptive phenotypes for dairy cow adaptation to climate change by Robson Mateus Freitas Silveira, Angela Maria de Vasconcelos, Concepta McManus, Luiz Paulo Fávero, Iran José Oliveira da Silva

“…In this study, we develop a systematic methodology with multivariate models and machine learning algorithms to (i) model complex patterns of relationships or multi-phenotypic differences between the thermal environment and thermoregulatory, hormonal, biochemical, hematological and productive responses; and (ii) identify potential associations among biological relationships that may underlie shared and specific phenotypic patterns of adaptive responses. …”
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ImmuProgML: machine learning-based dissection of cancer-immune dynamics during tumor progression to improve immunotherapy by Hanxiao Zhou, Lan Mei, Qianyi Lu, Yakun Zhang, Yue Sun, Caiyu Zhang, Han Jiang, Jiajun Zhou, Xia Li, Yunpeng Zhang, Shangwei Ning

“…We introduced the DNEX score, which combines expression changes with immunotherapy-driven network topologies, and employed machine learning algorithms for prognostic and immunotherapy response predictions. …”
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