Radiological Assessment of Charcot Neuro-Osteoarthropathy in Diabetic Foot: A Narrative Review by Antonio Mascio, Chiara Comisi, Virginia Cinelli, Dario Pitocco, Tommaso Greco, Giulio Maccauro, Carlo Perisano

“…Nuclear imaging, including bone scintigraphy, radiolabeled leukocyte scans, and FDG-PET/CT, offers additional diagnostic precision in complex cases, especially when differentiating CNO from infections or evaluating patients with metal implants. …”
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Identification and validation of cellular senescence-related genes and immune cell infiltration characteristics in intervertebral disc degeneration by Hao Li, Lei Miao, Jiayuan Wu, Ye Wang, Jiyuan Xia, Da He

“…The former was used for the analysis of differentially expressed genes (DEGs) and the latter was used as the validation set. …”
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Identification of potential metabolic biomarkers and immune cell infiltration for metabolic associated steatohepatitis by bioinformatics analysis and machine learning by Haoran Xie, Junjun Wang, Qiuyan Zhao

“…Methods: This study extracted multiple datasets from the GEO database to identify metabolism-related differentially expressed genes (MRDEGs). Protein-Protein Interaction (PPI) network and machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest (RF), were applied to screen for signature MRDEGs. …”
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FAR1 as a ferroptosis-related biomarker and potential therapeutic target in acute kidney injury: integrated bioinformatics and experimental validation by Hao Duan, Jie Yan, Xingyu Fan, Yijun Du, Xing Zhong, Tianrong Pan, Yue Wang

“…Eight diagnostic biomarkers were selected using multiple algorithms, and their predictive accuracy was validated through ROC curve analysis. …”
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Analysis and validation of hub genes for atherosclerosis and AIDS and immune infiltration characteristics based on bioinformatics and machine learning by Qirui Zheng, Yupeng Wu, Xiaojiao Zhang, Yuzhu Zhang, Zaihan Zhu, Bo Luan, Peizhuo Zang, Dandan Sun

“…Hub genes were filtered using topological and machine learning algorithms and validated in external cohorts. Then immune infiltration and correlation analysis of hub genes were constructed. …”
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Spatial pattern and heterogeneity of green view index in mountainous cities: a case study of Yuzhong district, Chongqing, China by Aiping Gou, Xuyuan Wang, Jiangbo Wang, Chenjie Wang, Guanzheng Tan

“…Abstract The Green View Index (GVI) is utilized to evaluate urban street value and ecosystem services and to gauge public perceptions of street greening. …”
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The 2024 revised clinical guidelines on the management of thyroid tumors by the Japan Association of Endocrine Surgery by Iwao Sugitani, Naomi Kiyota, Yasuhiro Ito, Naoyoshi Onoda, Tomo Hiromasa, Kiyomi Horiuchi, Seigo Kinuya, Tetsuo Kondo, Sueyoshi Moritani, Kiminori Sugino, Hisato Hara

“…Following a comprehensive search of the literature and systematic review to evaluate the overall evidence, we aimed to present optimal recommendations by considering the balance of benefits and harms from the patient’s perspective. …”
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An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope Sensors by Atcharawan Rattanasak, Talit Jumphoo, Wongsathon Pathonsuwan, Kasidit Kokkhunthod, Khwanjit Orkweha, Khomdet Phapatanaburi, Pattama Tongdee, Porntip Nimkuntod, Monthippa Uthansakul, Peerapong Uthansakul

“…Data were collected from 35 pregnant women at Suranaree University of Technology (SUT) Hospital. This study evaluated ten signal extraction methods, six machine learning algorithms, and four feature selection techniques to enhance classification performance. …”
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EEG microstate analysis in children with prolonged disorders of consciousness by Yi Zhang, Zhichong Hui, Yuwei Su, Weihang Qi, Guangyu Zhang, Liang Zhou, Jiamei Zhang, Kaili Shi, Yonghui Yang, Lei Yang, Gongxun Chen, Sansong Li, Mingmei Wang, Dengna Zhu

“…Support vector machine (SVM) models were trained using combined temporal and spatial microstate features, optimized via grid search and random search algorithms. Model performance was evaluated using standard metrics, and features were ranked by permutation importance. …”
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Utilizing Artificial Intelligence for Microbiome Decision-Making: Autism Spectrum Disorder in Children from Bosnia and Herzegovina by Džana Bašić-Čičak, Jasminka Hasić Telalović, Lejla Pašić

“…Model accuracy was evaluated, and an external dataset was introduced to test model generalizability. …”
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RETRACTED ARTICLE: Molecular mechanism and diagnostic marker investigation of endoplasmic reticulum stress on periodontitis by Qianqian Sun, Enqiang Zhu

“…Two machine learning algorithms were used to reveal potential ERS diagnostic markers of periodontitis. …”
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Optimizing deep learning for accurate blood cell classification: A study on stain normalization and fine-tuning techniques by Mohammed Tareq Mutar, Jaffar Nouri Alalsaidissa, Mustafa Majid Hameed, Ali Almothaffar

“…BACKGROUND: Deep learning’s role in blood film screening is expanding, with recent advancements including algorithms for the automated detection of sickle cell anemia, malaria, and leukemia using smartphone images. …”
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A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer. by Yifei Wang, Bingbing Chen, Jinhai Yu

“…Subsequently, models were constructed using six different machine learning algorithms. Finally, the discrimination, calibration, and clinical applicability of each model were evaluated to determine the optimal model.…”
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Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning by Tiantian He, Jie Geng, Chuandong Hou, Hongyi Li, Hong Zhang, Peng Zhao, Peifeng He, Xuechun Lu

“…Additionally, 12 machine learning algorithms were employed to construct predictive models for DLBCL, with performance evaluated using AUC and F-score metrics. …”
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Associations between organophosphorus pesticides exposure and age-related macular degeneration risk in U.S. adults: analysis from interpretable machine learning approaches by Yu-Xin Jiang, Si-Yu Gui, Xiao-Dong Sun

“…CONCLUSION: Urinary DAP metabolites of OPPs exposure are associated with AMD risk and ML algorithms show the excellent generalizability and differentiability in the course of AMD risk prediction.…”
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A Pilot Study on Mixed-Reality Approaches for Detecting Upper-Limb Dysfunction in Multiple Sclerosis: Insights on Cerebellar Tremor by Etty Sabatino, Miriam Moschetta, Andrea Lucaroni, Giacinto Barresi, Carlo Ferraresi, Jessica Podda, Erica Grange, Giampaolo Brichetto, Anna Bucchieri

“…Data from twelve healthy individuals served as benchmarks, while nine PwMS, including three with cerebellar tremor and one with ataxia, were tested to evaluate the tool’s diagnostic potential. Clustering algorithms applied to the kinematic data classified participants into distinct groups, showing that PwMS without cerebellar symptoms sometimes displayed behavior similar to healthy controls. …”
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Integrative bioinformatics and machine learning identify key crosstalk genes and immune interactions in head and neck cancer and Hodgkin lymphoma by Meiling Qin, Xinxin Li, Xun Gong, Yuan Hu, Min Tang

“…The intersection of HNSCC DEGs and HL-related modules was evaluated using protein–protein interaction (PPI) network analysis. …”
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Development and validation of an early diagnosis model for severe mycoplasma pneumonia in children based on interpretable machine learning by Si Xie, Mo Wu, Yu Shang, Wenbin Tuo, Jun Wang, Qinzhen Cai, Chunhui Yuan, Cong Yao, Yun Xiang

“…S100A8/A9 can also be used as a biomarker for validity differentiation of SMPP when medical conditions are limited.…”
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Editorial by Bulent Cavas

“…The fifth article, “Problem Solving in High School Biology: Students’ Agentic Response to Differentiation,” by English et al. (Finland), investigates how students select problems based on perceived ability when offered differentiated biology tasks. …”
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Lipid-Metabolism-Related Gene Signature Predicts Prognosis and Immune Microenvironment Alterations in Endometrial Cancer by Zhangxin Wu, Yufei Nie, Deshui Kong, Lixiang Xue, Tianhui He, Kuaile Zhang, Jie Zhang, Chunliang Shang, Hongyan Guo

“…Tumor immune infiltration patterns were evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA), Estimation of Stromal and Immune Cells in Malignant Tumors using Expression Data (ESTIMATE), and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms. …”
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