Assessment of the impact of a new sequential approach to antimicrobial use in young febrile children in the emergency department (DIAFEVERCHILD): a French prospective multicentric... by Elsa Tavernier, Elise Launay, Christèle Gras Le Guen, Gaelle Hubert, Cécile Feildel Fournial, Anne Chauvire-Drouard, Fleur Lorton

“…The aims of this study are to demonstrate that using a procalcitonin (PCT) rapid test-based prediction rule (1) would not be inferior to usual practice in terms of morbidity and mortality (non-inferiority objective) and (2) would result in a significant reduction in antibiotic use (superiority objective).Methods and analysis This prospective multicentric cluster-randomised study aims to include 7245 febrile children aged 6 days to 3 years with a diagnosis of fever without source in 26 participating EDs in France and Switzerland during a 24-month period. …”
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Geographical features and management strategies for microplastic loads in freshwater lakes by Huike Dong, Ruixuan Zhang, Xiaoping Wang, Jiamin Zeng, Lei Chai, Xuerui Niu, Li Xu, Yunqiao Zhou, Ping Gong, Qianxue Yin

“…To address this gap, our study utilizes Machine Learning (the random forest algorithm), combined with number-to-mass transformation techniques to generate a global prediction. …”
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Development and Application of a Senolytic Predictor for Discovery of Novel Senolytic Compounds and Herbs by Jinjun Li, Kai Zhao, Guotai Yang, Haohao Lv, Renxin Zhang, Shuhan Li, Zhiyuan Chen, Min Xu, Naixue Yang, Shaoxing Dai

“…For TCMbank, 714 potential senolytic compounds were predicted and 81 medicinal herbs with possible senolytic properties were identified. …”
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UBE2N as a novel prognostic and therapeutic biomarker of lung adenocarcinoma by Haofeng Yin, Yibo Xue, Chen Wang, Yanqin Wu, Yuchen Guo, Chunzhen Li, Yunyan Zhang, Shulei Yin, Tiejun Zhao

“…The role of UBE2N in predicting tumor therapeutic susceptibility was characterized using bioinformatics algorithms combined with publicly available CRISPR screening datasets and immunotherapy cohorts. …”
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Comprehensive Analysis of the Role of Metabolic Features in Osteoporosis: A Multi-Omics Analysis by Chang S, Tao W, Shi P, Wu H, Liu H, Xu J, Chen J, Zhu J

“…Additionally, 13 differentially expressed TFs were predicted to regulate the expression levels of these 23 miRNAs. …”
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A fully automated, expert-perceptive image quality assessment system for whole-body [18F]FDG PET/CT by Cong Zhang, Xin Gao, Xuebin Zheng, Jun Xie, Gang Feng, Yunchao Bao, Pengchen Gu, Chuan He, Ruimin Wang, Jiahe Tian

“…In predicting slice-level image quality across all body regions, the model achieved an average accuracy of 0.832 for PET and 0.902 for CT. …”
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A Sparse Bayesian Technique to Learn the Frequency-Domain Active Regressors in OFDM Wireless Systems by Carlos Crespo-Cadenas, María José Madero-Ayora, Juan A. Becerra, Elías Marqués-Valderrama, Sergio Cruces

“…The FD-SBL algorithm is proposed to first identify the active FD regressors and estimate the coefficients of the PA model using a given symbol, and then, the coefficients are employed to predict the distortion of successive OFDM symbols. …”
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The significance of the leaf area index for evapotranspiration estimation in SWAT-T for characteristic land cover types of West Africa by F. Merk, T. Schaffhauser, F. Anwar, Y. Tuo, J.-M. Cohard, M. Disse

“…Moreover, the benchmarking highlights that, if it only accounts for the LAI but disregards AET data, an optimization process's prediction of AET still yields an adequate performance with SWAT-T for all PET methods and land cover types. …”
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A systematic review of the role of quantitative CT in the prognostication and disease monitoring of interstitial lung disease by Giles Dixon, Hannah Thould, Matthew Wells, Krasimira Tsaneva-Atanasova, Chris J. Scotton, Michael A. Gibbons, Shaney L. Barratt, Jonathan C.L. Rodrigues

“…A modified CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) checklist was used for data extraction. …”
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THE RELATIONSHIP BETWEEN THE INDIVIDUAL GENETICALLY DETERMINED FACIAL PROFILE OF PATIENTS WITH A DISTAL OCCLUSION (ENGLE CLASS II) AND THE TYPE OF GROWTH OF THEIR FACIAL SKULL AND... by S.I. Doroshenko, O.Y. Opekha, V.V. Volkova

“…Jarabak methods has enabled a more detailed diagnosis of distal occlusion (Class II malocclusion) and the development of a comprehensive treatment algorithm with improved outcome prediction. …”
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Identification of Exosome-Associated Biomarkers in Diabetic Foot Ulcers: A Bioinformatics Analysis and Experimental Validation by Tianbo Li, Lei Gao, Jiangning Wang

“…Analyses included nomogram construction, functional and correlation analyses, immune infiltration, GSEA, gene co-expression and regulatory network construction, drug prediction, molecular docking, and RT-qPCR validation in clinical samples. …”
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Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic... by Peng Lyu, Na Xie, Xu-peng Shao, Shuai Xing, Xiao-yue Wang, Li-yun Duan, Xue Zhao, Jia-min Lu, Rong-fei Liu, Duo Zhang, Wei Lu, Kai-liang Fan

“…Abstract This study aims to predict and diagnose pediatric septic shock through the screening of immune infiltration-related biomarkers. …”
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Catalyzing early ovarian cancer detection: Platelet RNA-based precision screening by Eunyong Ahn, Se Ik Kim, Sungmin Park, Sarah Kim, Hyejin Lee, Yeochan Kim, Sangick Park, Suyeon Lee, Dong Won Hwang, Heeyeon Kim, HyunA Jo, Untack Cho, Juwon Lee, Cheol Lee, TaeJin Ahn, Yong-Sang Song

“…If integrated with current screening methods, our algorithm holds promise for identifying ovarian or endometrial cancer in its early stages.…”
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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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Synergizing Attribute-Guided Latent Space Exploration (AGLSE) with Classical Molecular Simulations to Design Potent Pep-Magnet Peptide Inhibitors to Abrogate SARS-CoV-2 Host Cell E... by Farhan Ullah, Aobo Xiao, Shahid Ullah, Na Yang, Min Lei, Liang Chen, Sheng Wang

“…Using artificial intelligence (AI), specifically variational autoencoders (VAE) and Wasserstein autoencoders (WAE), we were able to generate a latent space plot that can be surveyed for peptides with known properties and interpolated across a predictive vector between two defined points to identify novel peptides that exhibit dose-responsive antiviral activity. …”
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Association of estimated glucose disposal rate with atrial fibrillation, heart failure and cardiovascular mortality in patients with diabetes: a prospective cohort study from the U... by Zhen Tan, Yijun Liu, Lei Liu, Shuang Li, Xinrui Xue, Xiaoping Li, Hongqiang Ren

“…Polygenic Risk Score (PRS) analysis evaluated the joint effects of eGDR and PRS. Boruta algorithm filtered key predictive variables. Subgroup analysis was performed using cardiovascular high-risk factors, and mediation analysis explored the relationships of eGDR with the outcomes.ResultsSubjects with higher eGDR were more likely to be female, younger, more physically active, non-smoker, and non-drinker. …”
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PDT-regulated immune gene prognostic model reveals tumor microenvironment in colorectal cancer liver metastases by Jiachi Xu, Hui Zhou, Zhongtao Liu, Yunpeng Huang, Zijian Zhang, Heng Zou, Yongxiang Wang

“…Subsequently, utilizing single-cell sequencing data about liver metastasis, the colorectal cancer immune microenvironment reflected in the predictive model was analyzed, and a key gene set of the model was identified. …”
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Type 1 diabetes prevention clinical trial simulator: Case reports of model‐informed drug development tool by Juan Francisco Morales, Marian Klose, Yannick Hoffert, Jagdeep T. Podichetty, Jackson Burton, Stephan Schmidt, Klaus Romero, Inish O'Doherty, Frank Martin, Martha Campbell‐Thompson, Michael J. Haller, Mark A. Atkinson, Sarah Kim

“…Using individual‐level data collected from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies, we previously developed a quantitative joint model to predict the time to T1D onset. We then applied trial‐specific inclusion/exclusion criteria, sample sizes in treatment and placebo arms, trial duration, assessment interval, and dropout rate. …”
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Multimodal identification of a rare head and neck cancer patient cohort in the clinical data warehouse of Greater Paris Teaching Hospital by A. La Rosa, M. Verdoux, P. Riebler, I. Lolli, C. Daniel, X. Tannier, S. Atallah, B. Baujat, E. Kempf

“…It showed a 91% sensitivity and a 95% specificity when relying on multiple data sources, with a 76% positive predictive value observed for rare histology identification compared with 43% for rare topography. …”
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Scalable Precision Psychiatry With an Objective Measure of Psychological Stress: Prospective Real-World Study by Helena Wang, Norman Farb, Bechara Saab

“…Algorithm predictions were compared against the efficacy of the individual’s self-selected sessions. …”
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