Identifying and Validating an Acidosis-Related Signature Associated with Prognosis and Tumor Immune Infiltration Characteristics in Pancreatic Carcinoma by Pingfei Tang, Weiming Qu, Dajun Wu, Shihua Chen, Minji Liu, Weishun Chen, Qiongjia Ai, Haijuan Tang, Hongbing Zhou

“…Univariate Cox regression and the Kaplan–Meier method were applied to screen for prognostic genes. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish the optimal model. …”
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Novel insights into the molecular mechanisms of sepsis-associated acute kidney injury: an integrative study of GBP2, PSMB8, PSMB9 genes and immune microenvironment characteristics by Haiting Ye, Xiang Zhang, Pengyan Li, Mei Wang, Ruolan Liu, Dingping Yang

“…Immune cell infiltration was analyzed using the CIBERSORT algorithm, and potential associations between the hub genes and clinicopathological features were explored based on the Nephroseq database. …”
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Unraveling the oxidative stress landscape in diabetic foot ulcers: insights from bulk RNA and single-cell RNA sequencing data by Jialiang Lin, Linjuan Huang, Weiming Li, Haijun Xiao, Mingmang Pan

“…Furthermore, in vitro experiments successfully established a DFU oxidative stress model of fibroblasts, revealing reduced migration ability in the absence of cell death. …”
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Association between pace of biological aging and cancer and the modulating role of physical activity: a national cross-sectional study by Jingying Nong, Yu Wang, Yi Zhang

“…Epigenetic clocks, derived from sets of DNA methylation CpGs and mathematical algorithms, have demonstrated a remarkable ability to indicate biological aging and age-related health risks. …”
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Identification and validation of efferocytosis-related biomarkers for the diagnosis of metabolic dysfunction-associated steatohepatitis based on bioinformatics analysis and machine... by Chenghui Cao, Chenghui Cao, Wenwu Liu, Xin Guo, Shuwei Weng, Yang Chen, Yonghong Luo, Shuai Wang, Botao Zhu, Botao Zhu, Yuxuan Liu, Yuxuan Liu, Daoquan Peng

“…This analysis was followed by a series of in-depth investigations, including protein–protein interaction (PPI), correlation analysis, and functional enrichment analysis, to uncover the molecular interactions and pathways at play. To screen for biomarkers for diagnosis, we applied machine learning algorithm to identify hub genes and constructed a clinical predictive model. …”
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Comparative assessment of line probe assays and targeted next-generation sequencing in drug-resistant tuberculosis diagnosisResearch in context by Giovanna Carpi, Marva Seifert, Andres De la Rossa, Swapna Uplekar, Camilla Rodrigues, Nestani Tukvadze, Shaheed V. Omar, Anita Suresh, Timothy C. Rodwell, Rebecca E. Colman

“…Interpretation: LPAs demonstrated lower sensitivity and more limited drug resistance detection compared to tNGS workflows, underscoring the advantages of tNGS for improving DR-TB diagnostic algorithms. …”
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Generative and predictive neural networks for the design of functional RNA molecules by Aidan T. Riley, James M. Robson, Aiganysh Ulanova, Alexander A. Green

“…We pair these predictive models with generative adversarial RNA design networks (GARDN), allowing the generative modelling of a diverse range of functional RNA molecules with targeted experimental attributes. …”
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Evaluation of the shielding initiative in Wales (EVITE Immunity): protocol for a quasiexperimental study by Stephen Jolles, Ashley Akbari, Andrew Carson-Stevens, Helen Snooks, Alan Watkins, Adrian Edwards, Ann John, Alison Porter, Victoria Williams, Bridie Angela Evans, Ronan Lyons, Bernadette Sewell, Mark Rhys Kingston, Tony Whiffen, Jane Lyons, Rowena Bailey, Catherine A Thornton, Lesley Bethell, Samantha Bufton, Lucy Dixon

“…Clinically extremely vulnerable people identified through algorithms and screening of routine National Health Service (NHS) data were individually and strongly advised to stay at home and strictly self-isolate even from others in their household. …”
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Review of applications of deep learning in veterinary diagnostics and animal health by Sam Xiao, Navneet K. Dhand, Zhiyong Wang, Kun Hu, Kun Hu, Peter C. Thomson, John K. House, Mehar S. Khatkar, Mehar S. Khatkar

“…Deep learning (DL), a subfield of artificial intelligence (AI), involves the development of algorithms and models that simulate the problem-solving capabilities of the human mind. …”
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Research trends among new investigators at ISOQOL: a bibliometric analysis from 2019 to 2023 by Jae-Yung Kwon, Manraj N. Kaur, Ellen B. M. Elsman, Ava Mehdipour, Lori Suet Hang Lo, Ahmed M. Y. Osman, Sandrine Herbelet, Carrie-Anne Ng, Lotte van der Weijst, on behalf of the New Investigators Special Interest Group Members

“…Methodology Data on publications authored by 56 NI-SIG members between 2019 and 2023 were extracted from Web of Science and Scopus. A two-step screening process, guided by the Wilson and Cleary model of QoL, identified 561 unique documents for analysis. …”
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ATP6V0A4 as a novel prognostic biomarker and potential therapeutic target in oral squamous cell carcinoma by Xiaopu Gao, Jiamin Zhou, Yu Qiao, Chuyin Lin, Guanxiong Zhang, Qiuyu Wu, Zhikang Su, Qianji Zhang, Songkai Huang

“…Methods This study initially integrated TCGA and GEO databases for cross-platform differential gene screening. A prognostic model was constructed using univariate Cox regression and LASSO regression, complemented by random forest algorithms to identify core genes. …”
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Impact of ITH on PRAD patients and feasibility analysis of the positive correlation gene MYLK2 applied to PRAD treatment by Chuanyu Ma, Chuanyu Ma, Guandu Li, Xiaohan Song, Xiaochen Qi, Tao Jiang

“…GO and KEGG pathway enrichment analyses were performed on these 103 positively correlated differentially expressed genes, and the proportion and type of tumour-infiltrating immune cells were assessed by TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL and EPIC algorithms in patients. In addition, we calculated the relevance of immunotherapy and predicted various drugs that might be used for treatment and evaluated the predictive power of survival models under multiple machine learning algorithms through the training set TCGA-PRAD versus the validation set PRAD-FR cohort. …”
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Artificial intelligence in breast oncology by Alexander Mundinger

“…Abstract Artificial intelligence (AI) is based on complex artificial neural networks, characterized by layered network architecture, parallel processing of large data sets and iterative algorithms for processing large data sets. AI-assisted screening studies have demonstrated non-inferior diagnostic performance, reduced human workload by up to 70%, and reduced recall rates by 25% compared to human double reading. …”
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La Inteligencia Artificial en la educación: Big data, cajas negras y solucionismo tecnológico / Artificial Intelligence in Education: Big Data, Black Boxes, and Technological Solut... by Xavier Giró-Gracia, Juana María Sancho-Gil

“…Educators, educational researchers, and policymakers, in general, lack the knowledge and expertise to understand the underlying logic of these new systems, and there is insufficient research based evidence to fully understand the consequences for learners’ development of both the extensive use of screens and the increasing reliance on algorithms in educational settings. …”
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APPLICATION OF NEURAL NETWORKS IN DIAGNOSTICS OF BREAST CANCER ACCORDING TO THE DATA OF MICROWAVE RADIO THERMOMETRY by A. Losev, D. Medvedev

“…However, the analysis of microwave radiothermometry data is a very complex task, which prevents the widespread use of this method in screening. This problem can be solved by creating an effective expert system based on the use of mathematical and computer modeling methods, the capabilities of modern information technologies and, above all, machine learning algorithms. …”
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EDUCATIONAL TRAINING SIMULATOR FOR MONITORING READING TECHNIQUE AND SPEED BASED ON SPEECH-TO-TEXT (STT) METHODS by Olesia Barkovska, Heorhii Ivashchenko, Dmytro Rosinskiy, Daniil Zakharov

“…The results of testing children with dyslalia, meaning the rearrangement of sounds in words when reading, also showed that the Jaro algorithm has a shorter line comparison time for large text arrays (by 7%). …”
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Identification and validation of key biomarkers associated with immune and oxidative stress for preeclampsia by WGCNA and machine learning by Tiantian Yu, Tiantian Yu, Tiantian Yu, Guiying Wang, Guiying Wang, Guiying Wang, Xia Xu, Xia Xu, Xia Xu, Jianying Yan, Jianying Yan, Jianying Yan

“…In the final step, we validated the significant hub gene using independent external datasets, the hypoxia model of the HTR-8/SVneo cell line, and human placental tissue samples.ResultsAt last, leptin (LEP) was identified as a core gene through screening and was found to be upregulated. …”
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Artificial intelligence applications in pediatric ophthalmology: A comprehensive review by Anupama Janardhanan, Meenakshi Ravindran, Allapitchai Fathima, Neelam Pawar

“…Furthermore, it explores existing AI models and their applicability in diagnosing refractive errors and strabismus. …”
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Active Learning for Medical Article Classification with Bag of Words and Bag of Concepts Embeddings by Radosław Pytlak, Paweł Cichosz, Bartłomiej Fajdek, Bogdan Jastrzębski

“…Systems supporting systematic literature reviews often use machine learning algorithms to create classification models to assess the relevance of articles to study topics. …”
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A network toxicology and machine learning approach to investigate the mechanism of kidney injury from melamine and cyanuric acid co-exposure by Zhan Wang, Zhaokai Zhou, Zihao Zhao, Junjie Zhang, Shengli Zhang, Luping Li, Yingzhong Fan, Qi Li

“…Potential target proteins were identified using ChEMBL, STITCH, and GeneCards databases, and hub genes were screened using three machine learning algorithms: LASSO regression, Random Forest, and Molecular Complex Detection. …”
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