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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Bioinformatic analysis, clinical implications and experimental validation of ferroptosis-related feature gene in IgA nephropathy: focus on DUSP1 by Tingting Liu, Tingting Pan, Mingxin Chang, Shaojie Fu, Hongzhao Xu, Hao Wu, Zhonggao Xu, Yanli Cheng

“…Among them, dual specificity phosphatase 1 (DUSP1) was screened as FFG by three machine learning algorithms. …”
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Identifying neural substrates of competitive interactions and sequence transitions during mechanosensory responses in Drosophila. by Jean-Baptiste Masson, François Laurent, Albert Cardona, Chloé Barré, Nicolas Skatchkovsky, Marta Zlatic, Tihana Jovanic

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Machine learning approach effectively discriminates between Parkinson’s disease and progressive supranuclear palsy: Multi-level indices of rs-fMRI by Weiling Cheng, Xiao Liang, Wei Zeng, Jiali Guo, Zhibiao Yin, Jiankun Dai, Daojun Hong, Fuqing Zhou, Fangjun Li, Xin Fang

“…Various rs-fMRI indices were extracted, followed by a comprehensive feature screening for each index. We constructed fifteen distinct combinations of indices and selected four machine learning algorithms for model development. …”
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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

“…Multiple analytical algorithms indicated that SLCs were associated with immune cell infiltration and immune checkpoint gene expression. …”
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Combined Analysis of Transcriptome and Mendelian Randomization Reveals AKT1 and PPARG as Biomarkers Related to Glucose Metabolism in Sepsis by Ma J, Li W, Ma Q, Ding L, Wang Z, Wang R, Huang Y, Ma G, Gao J

“…Jie Ma,1,2,* Wendi Li,3 Qianqian Ma,1 Liying Ding,4 Zhaoyun Wang,1 Rong Wang,1 Yanan Huang,1 Gang Ma,2,* Jun Gao1,* 1Department of Anesthesia and Perioperative Medicine, First People’s Hospital of Yinchuan, The Second Clinical Medical College of Ningxia Medical University, Yinchuan, Ningxia, 750001, People’s Republic of China; 2Department of Anesthesia and Perioperative Medicine, General Hospital of Ningxia Medical University, The First Clinical Medical College of Ningxia Medical University, Yinchuan, Ningxia, 750004, People’s Republic of China; 3Department of Child Rehabilitation Education, Ningxia Rehabilitation Center for the Disabled, Yinchuan, Ningxia, 750002, People’s Republic of China; 4Department of Gynecology, Gynecology Clinic of Li Ying, Wuzhong, Ningxia, 751100, People’s Republic of China*These authors contributed equally to this workCorrespondence: Gang Ma, Department of Anesthesia and Perioperative Medicine, General Hospital of Ningxia Medical University, The First Clinical Medical College of Ningxia Medical University, Yinchuan, Ningxia, 750004, People’s Republic of China, Email magang2671@163.com Jun Gao, Department of Anesthesia and Perioperative Medicine, First People’s Hospital of Yinchuan, The Second Clinical Medical College of Ningxia Medical University, Yinchuan, Ningxia, 750001, People’s Republic of China, Email gaojun1605@sina.comIntroduction: This study aimed to identify diagnostic and therapeutic biomarkers related to glucose metabolism in sepsis, as hyperglycemia and blood glucose fluctuations influence sepsis progression.Methods: Datasets from public databases were analyzed using various methods, including differential expression analysis, PPI network screening, machine learning algorithms and Mendelian randomization. …”
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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

“…There may need to be differential cancer screening algorithms for patients with different inflammatory conditions and TNF-I exposure.…”
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PRKDC regulates cGAMP to enhance immune response in lung cancer treatment by Zhanghao Huang, Zhanghao Huang, Zhanghao Huang, Runqi Huang, Runqi Huang, Runqi Huang, Jun Zhu, Jun Zhu, Youlang Zhou, Youlang Zhou, Jiahai Shi, Jiahai Shi

“…This study aimed to investigate the antitumor effects of 2’,3’-cGAMP in LUAD.MethodHerein, patients with LUAD were screened for prognostic biomarkers, which were then assessed for sensitivity to immunotherapy and chemotherapy utilizing the “TIDE” algorithm and CellMiner database. …”
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Association between the development of sepsis and the triglyceride-glucose index in acute pancreatitis patients: a retrospective investigation utilizing the MIMIC-IV database by Lin Xu, Xuejing Li, Na Zhang, Chunmei Guo, Pan Wang, Min Gao, Yanhui Zhang, Lixin Zhao

“…Utilizing the formula ln[(triglycerides mg/dl) × (glucose mg/dl)/2], the TyG index was calculated. The Boruta algorithm and Xgboost model were used for feature selection in order to pinpoint the important variables affecting results. …”
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Nanomaterial isolated extracellular vesicles enable high precision identification of tumor biomarkers for pancreatic cancer liquid biopsy by Zachary F. Greenberg, Samantha Ali, Andrew Brock, Jinmai Jiang, Thomas D. Schmittgen, Song Han, Steven J. Hughes, Kiley S. Graim, Mei He

“…Through modelling the ATP6V0B cycling threshold, we reported 3 models with AUCs between 0.86 and 0.88, showcasing an enabling and clinically translatable liquid biopsy approach for early detection of pancreatic cancer using circulating EVs. …”
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A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis by Almas Begum, V. Dhilip Kumar, Junaid Asghar, D. Hemalatha, G. Arulkumaran

“…It is one of the significant reasons among ladies, regardless of huge endeavors to stay away from it through screening developers. An automatic detection system for disease helps doctors to identify and provide accurate results, thereby minimizing the death rate. …”
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An EfficientNet integrated ResNet deep network and explainable AI for breast lesion classification from ultrasound images by Kiran Jabeen, Muhammad Attique Khan, Ameer Hamza, Hussain Mobarak Albarakati, Shrooq Alsenan, Usman Tariq, Isaac Ofori

“…Explainable artificial intelligence‐based analysed the performance of trained models. After that, a new feature selection technique is proposed based on the cuckoo search algorithm called cuckoo search controlled standard error mean. …”
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Biophysical and nutritional combination treatment for myosteatosis in patients with sarcopenia: a study protocol for single-blinded randomised controlled trial by Maoyi Tian, Ling Qin, Simon Kwoon Ho Chow, Wing Hoi Cheung, A A Welch, Can Cui, Parco M Siu, Meng Chen Michelle Li, Yu Kin Cheng, Ronald Man Yeung Wong, Timothy CY Kwok, Minghui Yang, Clinton Rubin, Sheung Wai Law

“…The findings of this study will demonstrate the effect of combination treatment as an alternative for managing sarcopenia.Methods and analysis In this single-blinded randomised controlled trial, subjects will be screened based on the Asian Working Group for Sarcopenia (AWGS) 2019 definition. 200 subjects who are aged 65 or above and identified sarcopenic according to the AWGS algorithm will be recruited. …”
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Improving synergistic drug combination prediction with signature-based gene expression features in oncology by Mozhgan Mozaffarilegha, Sajjad Gharaghani

“…We compared their performance with that of conventional drug signatures and chemical structure-based descriptors.Results:Our results demonstrate that models incorporating DRS features consistently outperform traditional approaches across all evaluated algorithms. …”
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U-shaped relationship between frailty and non-HDL-cholesterol in the elderly: a cross-sectional study by Yu Pan, Yan Yuan, Juan Yang, Zhu Qing Feng, Xue Yin Tang, Yi Jiang, Gui Ming Hu, Jiang Chuan Dong

“…The variables underwent screening through Least Absolute Shrinkage and Selection Operator (LASSO) regression, univariate logistic regression, and Light Gradient Boosting Machine (LightGBM), with models developed through multivariate logistic regression and the LightGBM algorithm. …”
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Rapid Resilience Assessment and Weak Link Analysis of Power Systems Considering Uncertainties of Typhoon by Wenqing Ma, Xiaofu Xiong, Jian Wang

“…Second, for the resilience assessment process, the impact increment method is used to reduce the dimensionality of multiple fault state analysis in the power system, and resilience indexes are calculated by screening the contingency set based on depth-first traversal through a backtracking algorithm. …”
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Identification of the immune infiltration and biomarkers in ulcerative colitis based on liquid–liquid phase separation-related genes by Zhixing Hong, Shilin Fang, Haihang Nie, Jingkai Zhou, Yuntian Hong, Lan Liu, Qiu Zhao

“…We identified the hub LLPS-RGs (DE-LLPS-RGs) (HSPB3, SLC16A1, TRIM22, SRI, PLEKHG6, GBP1, PADI2) by machine learning algorithms. Hub genes were screened that displayed high prediction accuracy of UC patients. …”
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Callback time preference for prescreening visits among Black residents in the Boston area: findings from two randomized controlled trials by Ruth Zeto, Oluwagbemisola Ibikunle, Jingyi Cao, Hannah Col, Dhrumil Patil, Ruth-Alma Turkson-Ocran, Mingyu Zhang, Timothy B. Plante, Stephen P. Juraschek

“…Staff call attempts and participant screening status were logged prospectively. Gender was estimated based on first name, using a published algorithm. …”
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Key factors determination of hyperuricemia and association analysis among patients with breast cancer: results from NHANES data by Ting-ting Meng, Wen-rui Wang, Yan-qing Zheng, Guan-dong Liu

“…ObjectivesTo explore the factors influencing hyperuricemia in breast cancer patients based on the National Health and Nutrition Examination Survey (NHANES) database.MethodsThe univariate and multivariate generalized linear regression were used to screen the influencing factors of hyperuricemia. Logistic and XGBoost algorithms were used to rank the importance of influencing factors. …”
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Comparison of sample preparation methods for higher heating values in various sugarcane varieties using near-infrared spectroscopy by Kantisa Phoomwarin, Khwantri Saengprachatanarug, Jetsada Posom, Seree Wongpichet, Kittipong Laloon, Arthit Phuphaphud

“…Spectral data were pre-processed using seven techniques to minimize noise, and four variable selection algorithms–Variable Importance in Projection, Successive Projection Algorithm, Genetic Algorithm, and correlation-based selection via Partial Least Squares Regression–were employed to improve modelling accuracy.In parallel, four machine learning models–AdaBoost Regressor, Gradient Boosting, K-Nearest Neighbors, and Random Forest–were applied to the same dataset for Higher heating value prediction. …”
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