TMSB4X is a regulator of inflammation-associated ferroptosis, and promotes the proliferation, migration and invasion of hepatocellular carcinoma cells by Linlin Tang, Yangli Jin, Jinxu Wang, Xiuyan Lu, Mengque Xu, Mingwei Xiang

“…Univariate Cox regression analysis was conducted to screen prognostic genes, and 10 machine learning algorithms were combined to find the optimal strategy to evaluate the prognosis of the patients based on the prognosis-related genes. …”
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Developing a Prediction Model for Real-Time Incident Detection Leveraging User-Oriented Participatory Sensing Data by Md Tufajjal Hossain, Joyoung Lee, Dejan Besenski, Branislav Dimitrijevic, Lazar Spasovic

“…Real crash data from the New Jersey Department of Transportation (NJDOT) and crowdsourced data from Waze were matched using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to differentiate true and false alerts. A binary logit model was constructed to reveal significant predictors such as time categories around peak hours, road type, report ratings, and crash type. …”
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Screening of potential biomarkers for polycystic ovary syndrome and identification of expression and immune characteristics. by Shuang Liu, Xuanpeng Zhao, Qingyan Meng, Baoshan Li

“…Recursive feature elimination (RFE) algorithm and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to acquire feature genes as potential biomarkers. …”
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Predicting Neoplastic Polyp in Patients With Gallbladder Polyps Using Interpretable Machine Learning Models: Retrospective Cohort Study by Zhaobin He, Shengbiao Yang, Jianqiang Cao, Huijie Gao, Cheng Peng

“…This study employed nine ML algorithms to construct predictive models. Subsequently, model performance was evaluated and compared using several metrics, including the area under the receiver operating characteristic curve (AUC). …”
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Integrated multiple machine learning and Mendelian randomization reveal LTF gene as a prognostic biomarker for nonspecific orbital inflammation by Zixuan Wu, Jinfeng Xu, Yuan Gao, Kang Tan, Xiaolei Yao, Qinghua Peng

“…To further investigate the correlation between LTF and immune-related biological processes, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate immune microenvironment characteristics of each sample. …”
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CMSS1: A RNA binding protein with pivotal roles in non-small cell lung cancer progression and prognosis by Zhe Fan, Wanyu Liu, Zhiwei Gao, Youfa Liu, Hongyang Hai, Zhenyang Lv

“…The relationship between the CMSS1 gene and tumor-infiltrating immune cells was assessed using the ImmuCellAI algorithm. Additionally, a loss-of-function assay was performed to investigate the functional role of CMSS1 in NSCLC cells. …”
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Diagnosis of parotid gland tumors using a ternary classification model based on ultrasound radiomics by Xiaoling Liu, Weihan Xiao, Chen Yang, Zhihua Wang, Dong Tian, Gang Wang, Xiachuan Qin

“…Two-step LASSO-BNB and voting ensemble learning modeling algorithm with recursive feature elimination feature selection method (RFE-Voting) models were then applied for classification. …”
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Identification of hub genes in myocardial infarction by bioinformatics and machine learning: insights into inflammation and immune regulation by Juan Yang, Xiang Li, Li Ma, Jun Zhang

“…These genes were then subjected to functional enrichment analysis, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA). The CIBERSORT algorithm was utilized to evaluate immune cell infiltration patterns. …”
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Neutrophil extracellular traps-related genes contribute to sepsis-associated acute kidney injury by Tang Shaoqun, Yu Xi, Wang Wei, Luo Yaru, Lei Shaoqing, Qiu Zhen, Yang Yanlin, Sun Qian, Xia Zhongyuan

“…The OS-related genes were obtained by weighted gene co-expression network analysis. Differentially expressed genes were screened by “limma” package in R. …”
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Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma by Guangjie Yang MD, Aidi Gong BS, Pei Nie MD, Lei Yan BS, Wenjie Miao BS, Yujun Zhao BS, Jie Wu MD, Jingjing Cui BS, Yan Jia BS, Zhenguang Wang MD

“…The 2D and 3D CTTA models were constructed with the least absolute shrinkage and selection operator algorithm and texture scores were calculated. The diagnostic performance of the 2D and 3D CTTA models was evaluated with respect to calibration, discrimination, and clinical usefulness. …”
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A systematic review of data privacy in Mobility as a Service (MaaS) by Zineb Garroussi, Antoine Legrain, Sébastien Gambs, Vincent Gautrais, Brunilde Sansò

“…Using the PRISMA framework, a comprehensive literature search across Web of Science, Elsevier, and IEEE Xplore databases resulted in the selection of 32 studies for detailed analysis.The review is structured around three main themes: (1) Privacy-Preserving Techniques, including anonymization strategies (k-anonymity, differential privacy, obfuscation), encryption methods (blockchain, cryptographic protocols), federated learning for decentralized data processing, and advanced algorithms for optimizing privacy budgets and balancing utility-privacy trade-offs; (2) User Trust and Privacy Perceptions, highlighting that trust in service providers is essential for MaaS adoption, privacy concerns may impact adoption but do not necessarily prevent it (the “privacy paradox”), and awareness of data misuse affects user trust and willingness to adopt MaaS; and (3) Regulatory Frameworks, focusing on the importance of GDPR compliance to ensure strict data protection through consent and transparency, and embedding privacy-by-design principles within MaaS architectures to safeguard user data from the outset.This review emphasizes the need for a holistic approach, integrating technological innovation, user-centered design, and strong regulatory oversight to effectively address privacy challenges in MaaS. …”
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Metabolomic Profiling Reveals Serum Tryptophan as a Potential Therapeutic Target for Systemic Lupus Erythematosus by Wang K, Zhu R, Xu M, Zhu K, Li J, Li C, Meng D, Chen H, Sun L

“…Kai Wang,1,2 Rujie Zhu,1 Min Xu,3 Kexin Zhu,1 Ju Li,2 Chang Li,4 Deqian Meng,2 Hongwei Chen,1,3,5 Lingyun Sun1,5 1Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, People’s Republic of China; 2Department of Rheumatology and Immunology, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, 223001, People’s Republic of China; 3Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Clinical College of Nanjing University of Chinese Medicine, Nanjing, 210008, People’s Republic of China; 4Department of Medical Laboratory, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, 223001, People’s Republic of China; 5Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, People’s Republic of ChinaCorrespondence: Lingyun Sun, Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Zhongshan Road 321, Nanjing, 210008, People’s Republic of China, Email lingyunsun@nju.edu.cn Hongwei Chen, Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, People’s Republic of China, Email chenhw@nju.edu.cnObjective: This study aimed to identify potential diagnostic biomarkers for systemic lupus erythematosus (SLE) using metabolomics approaches and machine learning algorithms, and to evaluate therapeutic targets for SLE treatment.Methods: Serum samples from 44 SLE patients with lupus nephritis, 40 rheumatoid arthritis patients, 39 primary Sjögren’s syndrome patients, and matched healthy controls were analyzed using ultra-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS). …”
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Validity of the International Classification of Diseases 10th revision code for hospitalisation with hyponatraemia in elderly patients by Amit X Garg, Salimah Z Shariff, Sonja Gandhi, Jamie L Fleet, Matthew A Weir, Arsh K Jain

“…Objective To evaluate the validity of the International Classification of Diseases, 10th Revision (ICD-10) diagnosis code for hyponatraemia (E87.1) in two settings: at presentation to the emergency department and at hospital admission.Design Population-based retrospective validation study.Setting Twelve hospitals in Southwestern Ontario, Canada, from 2003 to 2010.Participants Patients aged 66 years and older with serum sodium laboratory measurements at presentation to the emergency department (n=64 581) and at hospital admission (n=64 499).Main outcome measures Sensitivity, specificity, positive predictive value and negative predictive value comparing various ICD-10 diagnostic coding algorithms for hyponatraemia to serum sodium laboratory measurements (reference standard). …”
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Revealing key regulatory factors in lung adenocarcinoma: the role of epigenetic regulation of autophagy-related genes from transcriptomics, scRNA-seq, and machine learning by Xianchang Zeng, Lingyun Wei, Lu Lv, Di Wu, Yingying Shen, Xinliang Lu, Xianghui Kong, Zhijian Cai, Jianli Wang, Jianli Wang

“…Single-cell RNA sequencing was further employed to evaluate the heterogeneity of immune cells. Machine learning algorithms were utilized to construct and identify diagnostic markers for LUAD, which were then validated by receiver operating characteristic (ROC) curve analysis. …”
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Integrated DDPG-PSO energy management systems for enhanced battery cycling and efficient grid utilization by Oladimeji Ibrahim, Mohd Junaidi Abdul Aziz, Razman Ayop, Wen Yao Low, Nor Zaihar Yahaya, Ahmed Tijjani Dahiru, Temitope Ibrahim Amosa, Shehu Lukman Ayinla

“…The results show that the integrated DDPG-PSO EMS outperforms the traditional DDPG in terms of battery scheduling and grid utilization efficiency. Cost evaluations under critical peak tariffs indicate that both EMS algorithms achieved a 34 % cost saving compared to a grid-only system. …”
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Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis by Liping Tang, Dong Pang, Chengbang Wang, Jiali Lin, Shaohua Chen, Jiangchun Wu, Junqi Cui

“…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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Causal estimation of the relationship between reproductive performance and the fecal bacteriome in cattle by Yutaka Taguchi, Haruki Yamano, Yudai Inabu, Hirokuni Miyamoto, Koki Hayasaki, Noriyuki Maeda, Yoshiro Kanmera, Seiji Yamasaki, Noboru Ota, Kenji Mukawa, Atsushi Kurotani, Shigeharu Moriya, Teruno Nakaguma, Chitose Ishii, Makiko Matsuura, Tetsuji Etoh, Yuji Shiotsuka, Ryoichi Fujino, Motoaki Udagawa, Satoshi Wada, Jun Kikuchi, Hiroshi Ohno, Hideyuki Takahashi

“…Artificial insemination (AI) was performed after 300 days of age, and the number of AI required for pregnancy (AI number) was evaluated. The relationship of the fecal bacteriome at 150 and 300 days of age and reproductive performance was visualized using statistical structural equation modelling between traits based on four types of machine-learning algorithms (linear discriminant analysis, association analysis, random forest, and XGBoost). …”
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Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach by Li X, Xiao Y, Yang M, Zhang X, Yuan Z, Zhang Z, Zhang H, Liu L, Zhao M

“…Functional enrichment analyses were conducted to explore the biological roles of these genes. LASSO and SVM-RFE algorithms identified shared diagnostic genes, which were evaluated using receiver operating characteristic (ROC) curves. …”
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Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study by Zhen Lu, Binhua Dong, Hongning Cai, Tian Tian, Junfeng Wang, Leiwen Fu, Bingyi Wang, Weijie Zhang, Shaomei Lin, Xunyuan Tuo, Juntao Wang, Tianjie Yang, Xinxin Huang, Zheng Zheng, Huifeng Xue, Shuxia Xu, Siyang Liu, Pengming Sun, Huachun Zou

“…We trained a supervised machine learning model and developed pathways to classify individuals before evaluating its diagnostic validity and usability on an external cohort. …”
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Layer-by-Layer Multifractal Scanning of Optically Anisotropic Architectonics of Blood Plasma Films: Fundamental and Applied Aspects by Alexander Ushenko, Natalia Pavlyukovich, Oksana Khukhlina, Olexander Pavlyukovich, Mykhaylo Gorsky, Iryna Soltys, Alexander Dubolazov, Yurii Ushenko, Olexander Salega, Ivan Mikirin, Jun Zheng, Zhebo Chen, Lin Bin

“…The practical aspect of this work is to evaluate the diagnostic potential of the Jones-matrix theziography method for identifying and differentiating changes in the birefringence of supramolecular networks in blood plasma facies caused by the long-term effects of COVID-19. …”
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