Use of Machine Learning to Predict the Incidence of Type 2 Diabetes Among Relatively Healthy Adults: A 10-Year Longitudinal Study in Taiwan by Ying-Qiang Liu, Tzu-Wei Chang, Lung-Chun Lee, Chia-Yu Chen, Pi-Shan Hsu, Yu-Tse Tsan, Chao-Tung Yang, Wei-Min Chu

“…Ultimately, 6687 adults were included in the final analysis, where we implemented three different ML algorithms, including logistic regression (LR), random forest (RF) and extreme gradient boosting (XGBoost) in order to predict diabetes. …”
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Enhanced deep learning model for apple detection, localization, and counting in complex orchards for robotic arm-based harvesting by Tantan Jin, Xiongzhe Han, Pingan Wang, Zhao Zhang, Jie Guo, Fan Ding

“…The results demonstrate the enhanced model's reliable performance and heightened precision for robotic arm-based apple harvesting in complex and challenging orchard environments. The study also provides a comprehensive analysis of the model's strengths, limitations, and avenues for future research. …”
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Multi-omics analysis identifies SNP-associated immune-related signatures by integrating Mendelian randomization and machine learning in hepatocellular carcinoma by Qingyan Kou, Zhichao Wu, Wenbin Zhao, Zhenyuan Liu, Shengxian Qiao, Qiang Mu, Xu Zhang

“…Machine learning analysis was performed on the genes identified through Mendelian randomization (MR) and survival association analysis, using 101 algorithms to construct a robust prognostic model. A novel riskScore model was developed by integrating genetic, clinical, and immune cell infiltration data. …”
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Artificial intelligence tools for engagement prediction in neuromotor disorder patients during rehabilitation by Simone Costantini, Anna Falivene, Mattia Chiappini, Giorgia Malerba, Carla Dei, Silvia Bellazzecca, Fabio A. Storm, Giuseppe Andreoni, Emilia Ambrosini, Emilia Biffi

“…This study aimed at methodologically exploring the performance of artificial intelligence (AI) algorithms applied to structured datasets made of heart rate variability (HRV) and electrodermal activity (EDA) features to predict the level of patient engagement during RAGR. …”
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Machine learning identification of key genes in cardioembolic stroke and atherosclerosis: their association with pan-cancer and immune cells by Tianxiang Zhang, Chunhui Yuan, Mo Chen, Jinjiang Liu, Wei Shao, Ning Cheng

“…The correlation between biomarkers and clinical features was also evaluated. Results A total of 69 and 39 FRDEGs were identified in CS and AS, respectively. …”
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Analysis and Validation of Autophagy-Related Gene Biomarkers and Immune Cell Infiltration Characteristic in Bronchopulmonary Dysplasia by Integrating Bioinformatics and Machine Lea... by Xiao S, Ding Y, Du C, Lv Y, Yang S, Zheng Q, Wang Z, Zheng Q, Huang M, Xiao Q, Ren Z, Bi G, Yang J

“…Subsequently, the hub genes were identified by Lasso and Cytoscape with three machine-learning algorithms (MCC, Degree and MCODE). In addition, hub genes were validated with ROC, single-cell sequence and IHC in hyperoxia mice. …”
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Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences by Eugenia Mylona, Dimitrios I. Zaridis, Charalampos Ν. Kalantzopoulos, Nikolaos S. Tachos, Daniele Regge, Nikolaos Papanikolaou, Manolis Tsiknakis, Kostas Marias, ProCAncer-I Consortium, Dimitrios I. Fotiadis

“…Feature selection method impacts radiomics models’ performance more than ML algorithms. Best feature selection methods: RFE, LASSO, RF, and Boruta. …”
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Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa by Charles Agyemang, Frederick Wekesah, Calistus Wilunda, Gershim Asiki, Peter Otieno, Richard E Sanya, Welcome Wami

“…Clusters of lifestyle risk factors: harmful salt intake, physical inactivity, obesity, tobacco and alcohol use were also computed. Prevalence ratios (PR) from modified Poisson regression were used to assess the association of cardiometabolic multimorbidity with sociodemographic and lifestyle risk factors.Results Two distinct classes of CMDs were identified: relatively healthy group with minimal CMDs (95.2%) and cardiometabolic multimorbidity class comprising participants with high blood sugar, hypercholesterolaemia, hypertension and CVDs (4.8%). …”
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Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study by Ting Peng, Rujia Miao, Hao Xiong, Yanhui Lin, Duzhen Fan, Jiayi Ren, Jiangang Wang, Yuan Li, Jianwen Chen

“…A total of 5 machine learning algorithms, namely random forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting, Gradient Boosting Machine, and CatBoost were used. …”
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Picometre-level surface control of a closed-loop, adaptive X-ray mirror with integrated real-time interferometric feedback by Ioana-Theodora Nistea, Simon G. Alcock, Andrew Foster, Vivek Badami, Riccardo Signorato, Matteo Fusco

“…Aside from demonstrating the extreme sensitivity of the interferometer sensors, this study also highlights the voltage repeatability and stability of the programmable high-voltage power supply, the accuracy of the correction-calculation algorithms and the almost instantaneous response of the bimorph mirror to command voltage pulses. …”
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Technology Requirements for an Alamouti-Coded 100 Gb/s Digital Coherent Receiver Using 3 × 3 Couplers for Passive Optical Networks by Md. Saifuddin Faruk, David J. Ives, Seb J. Savory

“…In addition, we present the adaptive DSP algorithms required to recover data for a single-carrier Alamouti-coded signal. …”
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Flavivirus and Filovirus EvoPrinters: New alignment tools for the comparative analysis of viral evolution. by Thomas Brody, Amarendra S Yavatkar, Dong Sun Park, Alexander Kuzin, Jermaine Ross, Ward F Odenwald

“…<h4>Methodology/principal finding</h4>We have adapted EvoPrinter alignment algorithms for the rapid comparative analysis of Flavivirus or Filovirus sequences including Zika and Ebola strains. …”
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Elucidating the dynamic tumor microenvironment through deep transcriptomic analysis and therapeutic implication of MRE11 expression patterns in hepatocellular carcinoma by Ruiqiu Chen, Chaohui Xiao, Zizheng Wang, Guineng Zeng, Shaoming Song, Gong Zhang, Lin Zhu, Penghui Yang, Rong Liu

“…Publicly available single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics were utilized to explore MRE11’s dynamic mechanisms in the tumor microenvironment (TME) of both primary and post-immunotherapy cases. We also screened for differentially expressed genes and constructed a robust HCC prognosis model using 101 machine-learning algorithms. …”
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The photometry and kinematics studies of NGC 2509 derived from Gaia DR3 by Nasser M. Ahmed, A. L. Tadross

“…Moreover, the $$\alpha _2$$ is closest to the Salpeter value. Also, we identified 20 member stars as red clump that have G magnitudes between 12.6 and 13.1 mag and slightly higher temperatures than typical giants. …”
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CELIAC DISEASE SCREENING IN A LARGE DOWN SYNDROME COHORT: COMPARISON OF DIAGNOSTIC YIELD OF DIFFERENT SEROLOGICAL SCREENING TESTS by Dilek Uludağ Alkaya, Seçil Sözen, Birol Öztürk, Nuray Kepil, Tülay Erkan, Hüseyin Tufan Kutlu, Beyhan Tüysüz

“…Additionally, EMA was also measured in patients with elevated tTGIgA or tTG-IgG levels. …”
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Few-shot crop disease recognition using sequence- weighted ensemble model-agnostic meta-learning by Junlong Li, Quan Feng, Junqi Yang, Jianhua Zhang, Jianhua Zhang, Sen Yang

“…Experimental results show that SWE-MAML demonstrates strong competitiveness compared to state-of-the-art algorithms on the PlantVillage dataset. Compared to the original MAML, SWE-MAML improves accuracy by 3.75%–8.59%. …”
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Construction and performance of a clinical prediction rule for ureteral stone without the use of race or ethnicity: A new STONE score by Christopher L. Moore, Cary P. Gross, Louis Hart, Annette M. Molinaro, Deborah Rhodes, Dinesh Singh, Cristiana Baloescu

“…Considering the potential adverse effects of propagating racial bias in clinical algorithms, we recommend using the revised STONE score. …”
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Mathematical model of a lightweight three-axle off-road vehicle construction for Arctic zone of Russia by I. E. Agureev, V. N. Bondarenko

“…The work uses numerical methods to solve the equations of the constructed model, which enables to gradually weaken the accepted assumptions and build more general calculation algorithms. The main integration method for ensuring the stability of solutions is the multi-step Adams method, which, with the correct choice of step, ensures the necessary stability of the solution over sufficiently long model times. …”
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The Management of IgG4-Related Disease in Children: A Systematic Review by Evdoxia Sapountzi, Eleni P. Kotanidou, Vasiliki-Rengina Tsinopoulou, Lampros Fotis, Liana Fidani, Assimina Galli-Tsinopoulou

“…<b>Background/Objectives</b>: IgG4-related disease (IgG4-RD) is a multi-organ disease with greatly varying therapeutic approaches and a lack of specific treatment algorithms. This systematic review aimed to determine the therapeutic approaches for pediatric IgG4-RD in real-word practice. …”
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Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patients by Ran Du, Lijun Jing, Denggang Fu

“…Immune cell infiltration analysis revealed distinct patterns in high- and low-risk groups, with the high-risk group showing a more aggressive and immunosuppressive tumor microenvironment. The signature also effectively stratified low-grade glioma patients across four independent datasets. …”
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