Identification of diagnostic biomarkers and dissecting immune microenvironment with crosstalk genes in the POAG and COVID-19 nexus by Changfan Peng, Long Hu, Wanwen Su, Xin Hu

“…Concurrently, gene expression datasets from GEO (POAG: GSE27276; COVID-19: GSE171110, GSE152418) were used to identify 57 crosstalk genes (CGs) via differential expression analysis. Machine learning algorithms (LASSO, SVM-RFE, Random Forest) were applied to screen POAG diagnostic biomarkers from CGs, followed by construction of transcription factor (TF)-microRNA (miRNA)-protein-compound regulatory networks and consensus clustering to characterize COVID-19 immune microenvironment subtypes. …”
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Helicobacter pylori eradication treatment for gastric carcinoma prevention in asymptomatic or dyspeptic adults: systematic review and Bayesian meta-analysis of randomised controlle... by Teruhiko Terasawa, Isao Miyashiro, Katsuaki Kato, Takaki Yoshikawa, Reo Takaku, Hiroshi Nishida

“…However, concerns about supporting unfocused screening and eradication programme in healthy, asymptomatic populations have arisen. …”
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The Role of AI in Nursing Education and Practice: Umbrella Review by Rabie Adel El Arab, Omayma Abdulaziz Al Moosa, Fuad H Abuadas, Joel Somerville

“…First, ethical and social implications were consistently highlighted, with studies emphasizing concerns about data privacy, algorithmic bias, transparency, accountability, and the necessity for equitable access to AI technologies. …”
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Advancing Mn-based electrocatalysts: Evolving from Mn-centered octahedral entities to bulk forms by Huan Li, Jinchao Xu, Liyuan Yang, Wanying Wang, Bin Shao, Fangyi Cheng, Chunning Zhao, Weichao Wang

“…According to the catalytic requirements of an individual entity and its stacking modes, we further developed a search algorithm to identify three-dimensional (3D) structures from 154,718 candidates, pinpointing CaMnO3 as the most effective one among the screened candidates. …”
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Use of artificial intelligence to support prehospital traumatic injury care: A scoping review by Jake Toy, Jonathan Warren, Kelsey Wilhelm, Brant Putnam, Denise Whitfield, Marianne Gausche‐Hill, Nichole Bosson, Ross Donaldson, Shira Schlesinger, Tabitha Cheng, Craig Goolsby

“…Conclusions A small but growing body of literature described AI models based on prehospital features that may support decisions made by dispatchers, Emergency Medical Services clinicians, and trauma teams in early traumatic injury care.…”
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Optimization of Flavor Quality of Lactic Acid Bacteria Fermented Pomegranate Juice Based on Machine Learning by Wenhui ZOU, Fei PAN, Junjie YI, Linyan ZHOU

“…There were 19 key differential volatile compounds screened out by ML. Binary classification models of HWPS and LWPS were established by random forest (RF) and adaptive boosting (AdaBoost) algorithms, and RF algorithm had higher prediction precision and accuracy. …”
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Purine metabolism-associated key genes depict the immune landscape in gout patients by Lin-na Li, Hao Wang, Lu-shan Xiao, Wei-nan Lai

“…Using RNA-seq data of peripheral blood mononuclear cells (PBMCs) from gout patients, we screened the differentially expressed genes (DEGs) of gout patients and found that they were closely involved in purine metabolism. …”
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人工智能融合临床与多组学数据在卒中防治及医药研发中的应用与挑战Applications and Challenges of Integrating Artificial Intelligence with Clinical and Multi-omics Data in Stroke Prevention, Treatment, and Pharmaceut... by 勾岚，姜明慧，姜勇，廖晓凌，李昊，张杰，程丝 (GOU Lan, JIANG Minghui, JIANG Yong, LIAO Xiaoling, LI Hao, ZHANG Jie, CHENG Si)

“…By integrating and analyzing clinical and multi-omics data, AI technology enhances the identification of high-risk populations, optimizes early diagnosis and risk assessment, enables precise subtyping of stroke, facilitates the screening of potential drug targets, and constructs prognostic prediction models. …”
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Utilizing bioinformatics and machine learning to identify CXCR4 gene-related therapeutic targets in diabetic foot ulcers by Hengyan Zhang, Ye Zhou, Heguo Yan, Changxing Huang, Licong Yang, Yangwen Liu

“…We integrated the genes screened by three machine learning models (LASSO, SVM, and Random Forest), and CXCR4 was identified as a key gene with potential therapeutic value in DFUs. …”
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Project quality, regulation quality by Elena Mussinelli

“…These tools legitimise choices where conformity to the standard acts as a screen for the assumption of precise responsibilities. …”
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Exploring the predictive values of CRP and lymphocytes in coronary artery disease based on a machine learning and Mendelian randomization by Yuan Liu, Yuan Liu, Xin Yuan, Xin Yuan, Yu-Chan He, Yu-Chan He, Zhong-Hai Bi, Zhong-Hai Bi, Si-Yao Li, Si-Yao Li, Ye Li, Ye Li, Yan-Li Liu, Yan-Li Liu, Liu Miao, Liu Miao

“…Techniques employed included propensity score matching (PSM), logistic regression, lasso regression, and random forest algorithms (RF). Risk factors were assessed, and the sensitivity and specificity of the models were evaluated using receiver operating characteristic (ROC) curves. …”
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Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation by Zongqi Xia, Prerna Chikersal, Shruthi Venkatesh, Elizabeth Walker, Anind K Dey, Mayank Goel

“…Among the best-performing models with the least sensor data requirement, the ML algorithm predicted depressive symptoms with an accuracy of 80.6% (F1-score=0.76), high global MS symptom burden with an accuracy of 77.3% (F1-score=0.78), severe fatigue with an accuracy of 73.8% (F1-score=0.74), and poor sleep quality with an accuracy of 72.0% (F1-score=0.70). …”
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Implementing Remote Radiotherapy Planning to Increase Patient Flow at a Johannesburg Academic Hospital, South Africa: Protocol for a Prospective Feasibility Study by Duvern Ramiah, Sonwabile Ngcezu, Oluwatosin Ayeni, Okechinyere Achilonu, Mariam Adeleke, Theo Nair, Joseph Otten, Daniel Mmereki

“…Phase 1 (feasibility) encompasses system commissioning, including beam modeling, computed tomography (CT)-to-electron density calibration, multileaf collimator (MLC) optimization, and dose calculations using the anisotropic analytical algorithm. …”
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Lessons from the PROTECT-CH COVID-19 platform trial in care homes by Philip M Bath, Jonathan Ball, Matthew Boyd, Heather Gage, Matthew Glover, Maureen Godfrey, Bruce Guthrie, Jonathan Hewitt, Robert Howard, Thomas Jaki, Edmund Juszczak, Daniel Lasserson, Paul Leighton, Val Leyland, Wei Shen Lim, Pip Logan, Garry Meakin, Alan Montgomery, Reuben Ogollah, Peter Passmore, Philip Quinlan, Caroline Rick, Simon Royal, Susan D Shenkin, Clare Upton, Adam L Gordon

“…Results We initiated the trial including protocol, approvals, insurance, website, database, data algorithms, intervention selection and training materials. …”
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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

“…The rapid development of flexible sensor technology has made flexible sensor arrays a key research area in various applications due to their exceptional flexibility, wearability, and large-area-sensing capabilities. …”
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Meta-analyses of IL1A polymorphisms and the risk of several autoimmune diseases published in databases. by Hang Su, Na Rei, Lei Zhang, Jiaxiang Cheng

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Epidemiology and Association Rules Analysis for Pulmonary Tuberculosis Cases with Extrapulmonary Tuberculosis from Age and Gender Perspective: A Large-Scale Retrospective Multicent... by Wanli Kang, Jiajia Yu, Chen Liang, Quanhong Wang, Liang Li, Jian Du, Hongyan Chen, Jianxiong Liu, Jinshan Ma, Mingwu Li, Jingmin Qin, Wei Shu, Peilan Zong, Yi Zhang, Xiaofeng Yan, Zhiyi Yang, Zaoxian Mei, Qunyi Deng, Pu Wang, Wenge Han, Meiying Wu, Ling Chen, Xinguo Zhao, Lei Tan, Fujian Li, Chao Zheng, Hongwei Liu, Xinjie Li, Ertai A., Yingrong Du, Fenglin Liu, Wenyu Cui, Song Yang, Xiaohong Chen, Junfeng Han, Qingyao Xie, Yanmei Feng, Wenyu Liu, Peijun Tang, Jianyong Zhang, Jian Zheng, Dawei Chen, Xiangyang Yao, Tong Ren, Yan Li, Yuanyuan Li, Lei Wu, Qiang Song, Mei Yang, Jian Zhang, Yuanyuan Liu, Shuliang Guo, Kun Yan, Xinghua Shen, Dan Lei, Yanli Zhang, Youcai Li, Yongkang Dong, Shenjie Tang

“…The multivariable logistic regression models demonstrated that odds ratios of concurrent PTB-EPTB cases varied by gender and age group. …”
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INTERNATIONAL FORUM “OLD AND NEW MEDIA: ALONG THE PATH TOWARDS A NEW AESTHETICS” / МЕЖДУНАРОДНЫЙ ФОРУМ «СТАРЫЕ И НОВЫЕ МЕДИА: ПУТИ К НОВОЙ ЭСТЕТИКЕ»... by BOGATYRYOVA ELENA A.

“…Along with the formulation of the enumerated questions, the presentations of the participants of the conference concretized the conclusions about the influence of new media on the cognitive capabilities of man, his means of action, “new plasticity” and a new way of living. Notice was made of alarming tendencies of transforming consciousness into that of “gamers,” reacting in correlations with certain algorithms, and a transference from discourse and substantiations towards reactions and evaluations. …”
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Identification of biomarkers associated with inflammatory response in Parkinson's disease by bioinformatics and machine learning. by Yatan Li, Wei Jia, Chen Chen, Cheng Chen, Jinchao Chen, Xinling Yang, Pei Liu

“…LASSO, SVM-RFE and Random Forest algorithms were used to screen biomarker genes. Then, ROC curves were drawn and PD risk predicting models were constructed on the basis of the biomarker genes. …”
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Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning by Sizhang Wang, Xiaoyan Wang, Jing Xia, Qiang Mu

“…Then, four overlapping M1 macrophage infiltration-related genes (M1 MIRGs), namely CCDC69, PPP1R16B, IL21R, and FOXP3, were obtained using five machine-learning algorithms. Subsequently, nomogram models were constructed to predict the incidence of Her2-positive breast cancer patients. …”
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