GLUcose COntrol Safety & Efficacy in type 2 DIabetes, a systematic review and NETwork meta-analysis. by Guillaume Grenet, Shams Ribault, Giao Bao Nguyen, Faustine Glais, Augustin Metge, Thomas Linet, Behrouz Kassai-Koupai, Catherine Cornu, Théodora Bejan-Angoulvant, Sylvie Erpeldinger, Rémy Boussageon, Aurore Gouraud, Fabrice Bonnet, Michel Cucherat, Philippe Moulin, François Gueyffier

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High-flow nasal cannula therapy versus continuous positive airway pressure for non-invasive respiratory support in paediatric critical care: the FIRST-ABC RCTs by Padmanabhan Ramnarayan, Alvin Richards-Belle, Karen Thomas, Laura Drikite, Zia Sadique, Silvia Moler Zapata, Robert Darnell, Carly Au, Peter J Davis, Izabella Orzechowska, Julie Lester, Kevin Morris, Millie Parke, Mark Peters, Sam Peters, Michelle Saull, Lyvonne Tume, Richard G Feltbower, Richard Grieve, Paul R Mouncey, David Harrison, Kathryn Rowan

“…Background Despite the increasing use of non-invasive respiratory support in paediatric intensive care units, there are no large randomised controlled trials comparing two commonly used non-invasive respiratory support modes, continuous positive airway pressure and high-flow nasal cannula therapy. …”
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Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications by Can Qi, Can Qi, Ziwei Zhao, Lin Chen, Le Wang, Yun Zhou, Guochen Duan, Guochen Duan

“…A total of five biomarkers,[Selenoprotein P1 (SEPP1), Fibrinogen-like protein 2 (FGL2), NK cell lectin-like receptor K1 (KLRK1), ATP-binding cassette transporters 6(ABCA6) and Galectins(GAL)], were screened, and a risk model based on the biomarkers was created. …”
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LncRNAs regulates cell death in osteosarcoma by Ping’an Zou, Zhiwei Tao, Zhengxu Yang, Tao Xiong, Zhi Deng, Qincan Chen, Li Niu

“…Three machine learning algorithms—Support Vector Machine, Random Forest, and Generalized Linear Model—were utilized to select feature genes. …”
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Prognostic Risk Signature and Comprehensive Analyses of Endoplasmic Reticulum Stress-Related Genes in Lung Adenocarcinoma by CaiZhen Yang, YuHui Wei, WenTao Li, JinMei Wei, GuoXing Chen, MingPeng Xu, GuangNan Liu

“…A total of 1034 samples from TCGA and GEO were used to screen differentially expressed genes. Further, Random Forest algorithm was utilized to screen characteristic genes related to prognosis. …”
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MYOPIA PREVALENCE AMONG STUDENTS DURING COVID-19 PANDEMIC. A SYSTEMATIC REVIEW AND META-ANALYSIS by Natasha Hana Savitri, Adinda Sandya Poernomo, Muhammad Bagus Fidiandra1, Eka Candra Setyawan1, Arinda Putri Auna Vanadia1, Bulqis Inas Sakinah1, Lilik Djuari

“…Data retrieval used the PICO method and journal adjustments were selected using the PRISMA algorithm. Data analysis was performed using a random-effects model. …”
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Joint analysis of single-cell RNA sequencing and bulk transcriptome reveals the heterogeneity of the urea cycle of astrocytes in glioblastoma by Minfeng Tong, Qi Tu, Lude Wang, Huahui Chen, Xing Wan, Zhijian Xu

“…For bulk RNA-seq, univariate Cox and LASSO analyses were undertaken to screen prognostic genes, while multivariate Cox regression analysis was applied to set up a prognostic model. …”
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Effect of miR-200c on inducing autophagy and apoptosis of HT22 cells from mouse hippocampal neurons via regulating PRDM1 protein: a bioinformatics analysis by W. Wu, J. Fu, Q. Liu, Q. Wang, S. Gao, X. Deng, C. Shen

“…The Support Vector Machine (SVM) algorithm in the Weka software was used to process, model, and screen the available miRNA data. …”
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New insights into biomarkers and risk stratification to predict hepatocellular cancer by Katrina Li, Brandon Mathew, Ethan Saldanha, Puja Ghosh, Adrian R. Krainer, Srinivasan Dasarathy, Hai Huang, Xiyan Xiang, Lopa Mishra

“…Through human studies compiled with animal models and mechanistic insight in pathways such as the TGF-β pathway, the biological progression from chronic liver disease to cirrhosis and HCC can be delineated. …”
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Immune Evasion Mechanism Mediated by ITPRIPL1 and Its Prognostic Implications in Glioma by Zou Xiaoyun, Ye Wenhao, Wu Huan, Yang Yuanyuan, Liu Changqing, Wen Hebao, Ma Caiyun

“…Ninety‐eight machine learning algorithm combinations were screened to identify the optimal predictive model. …”
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Case-control study combined with machine learning techniques to identify key genetic variations in GSK3B that affect susceptibility to diabetic kidney diseases by Jinfang Song, Yi Xu, Liu Xu, Tingting Yang, Ya Chen, Changjiang Ying, Qian Lu, Tao Wang, Xiaoxing Yin

“…On the other hand, the expression levels and kinase activity of GSK3β in exosomes differed significantly between patients with different genotypes of the GSK3B, suggesting that the effect of GSK3B gene polymorphisms on GSK3β expression and activity may be an important mechanism leading to individual differences in susceptibility to DKD. XG Boost algorithm model identified rs60393216 and rs1488766 as important biomarkers for clinical early warning of DKD.…”
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Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning by Qiao Tang, Yanwei Ji, Zhongyuan Xia, Yuxi Zhang, Chong Dong, Chong Dong, Qian Sun, Shaoqing Lei

“…The ERDEGs diagnostic model was developed based on a combination of LASSO and Random Forest approaches, and the diagnostic performance was evaluated by the area under the receiver operating characteristic curve (ROC-AUC) and validated against external datasets. …”
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Automated Detection of Reduced Ejection Fraction Using an ECG-Enabled Digital Stethoscope by Ling Guo, PhD, Gregg S. Pressman, MD, Spencer N. Kieu, BS, Scott B. Marrus, MD, PhD, George Mathew, PhD, John Prince, PhD, Emileigh Lastowski, MS, Rosalie V. McDonough, MD, MSc, Caroline Currie, BA, John N. Maidens, PhD, Hussein Al-Sudani, MD, Evan Friend, BA, Deepak Padmanabhan, MD, Preetham Kumar, MD, Edward Kersh, MD, Subramaniam Venkatraman, PhD, Salima Qamruddin, MD

“…Recently, electrocardiogram-based algorithms have shown promise in detecting ALVSD. Objectives: The authors developed and validated a convolutional neural network (CNN) model using single-lead electrocardiogram and phonocardiogram inputs captured by a digital stethoscope to assess its utility in detecting individuals with actionably low ejection fractions (EF) in a large cohort of patients. …”
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Multi-Target Mechanism of Compound Qingdai Capsule for Treatment of Psoriasis: Multi-Omics Analysis and Experimental Verification by Qiao Y, Li C, Chen C, Wu P, Yang Y, Xie M, Liu N, Gu J

“…CQC ingredients-targets network was constructed using these ingredients and their targets. Screening of CQC anti-psoriasis core targets using machine learning algorithm. …”
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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

“…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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Unveiling diagnostic biomarkers and therapeutic targets in lung adenocarcinoma using bioinformatics and experimental validation by Sixuan Wu, Yuanbin Tang, Qihong Pan, Yaqin Zheng, Yeru Tan, Junfan Pan, Yuehua Li

“…In addition, a machine learning model constructed based on Stepglm[backward] with the random forest algorithm achieved the highest C-index (0.999) and screened eight core genes, among which ST14 was noted for its excellent predictive ability. …”
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Smart driving assistance system for mining operations in foggy environments by Swades Kumar Chaulya, Monika Choudhary, Naresh Kumar, Vikash Kumar, Abhishek Chowdhury

“…Finally, the screen fitted in the dashboard is forward-facing to the operator's seat and displays the final output. …”
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Exhaled volatile organic compounds as novel biomarkers for early detection of COPD, asthma, and PRISm: a cross-sectional study by Jiaxin Tian, Qiurui Zhang, Minhua Peng, Leixin Guo, Qianqian Zhao, Wei Lin, Sitong Chen, Xuefei Liu, Simin Xie, Wenxin Wu, Yijie Li, Junqi Wang, Jin Cao, Ping Wang, Min Zhou

“…Subsequently, classification models were established by machine learning algorithms, based on these VOC markers along with baseline characteristics. …”
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Cell death-related signature genes: risk-predictive biomarkers and potential therapeutic targets in severe sepsis by Yanan Li, Yuqiu Tan, Zengwen Ma, Zengwen Ma, Weiwei Qian, Weiwei Qian

“…Further combining cell death-related gene screening and four machine learning algorithms (including LASSO-logistic, Gradient Boosting Machine, Random Forest and xGBoost), nine SeALAR-characterized cell death genes (SeDGs) were screened and a risk prediction model based on SeDGs was constructed that demonstrated good prediction performance. …”
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Opening closed doors: using machine learning to explore factors associated with marital sexual violence in a cross-sectional study from India by Anita Raj, Abhishek Singh, Nandita Bhan, Lotus McDougal, Nabamallika Dehingia, Julian McAuley

“…Analyses included iterative thematic analysis (L-1 regularised regression followed by iterative qualitative thematic coding of L-2 regularised regression results) and neural network modelling.Outcome measure Participants reported their experiences of sexual violence perpetrated by their current (or most recent) husband in the previous 12 months. …”
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