Sodium Propionate Alleviates Atopic Dermatitis by Inhibiting Ferroptosis via Activation of LTBP2/FABP4 Signaling Pathway by Xie A, Li W, Ye D, Yin Y, Wang R, Wang M, Yu R

“…The identified module includes genes with correlated expression patterns specifically linked to AD. Analysis using three algorithms identified potential therapeutic targets of SP. …”
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Using random forests to forecast daily extreme sea level occurrences at the Baltic Coast by K. Bellinghausen, B. Hünicke, E. Zorita

“…<p>We have designed a machine learning method to predict the occurrence of daily extreme sea level at the Baltic Sea coast with lead times of a few days. …”
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Predictive performance of risk prediction models for lung cancer incidence in Western and Asian countries: a systematic review and meta-analysis by Yah Ru Juang, Lina Ang, Wei Jie Seow

“…In addition, 14.8% (8/54) of the studies directly compared biomarker-based models with those incorporating only traditional risk factors, demonstrating improved discrimination. Machine-learning algorithms were applied in eight Western models and two Asian models. …”
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Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review by Kapongo D. Lumamba, Gordon Wells, Delon Naicker, Threnesan Naidoo, Adrie J. C. Steyn, Mandlenkosi Gwetu

“…We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. …”
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Investigating potential biomarkers associated with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis using Mendelian randomization and transcriptomic analysis by Yujia Wang, Zhimin Chen, Kaiqi Huang, Keng Ye, Shiwei He, Yanfang Xu, Hong Chen

“…Genes with a significant causal relationship were selected as candidate genes for further analysis. Machine learning algorithms, ROC curve analysis, and expression evaluation were employed to screen for biomarkers. …”
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Correction of TRMM 3B43 Monthly Precipitation Data Using Quantile Regression Model in the Urmia Lake Basin by Sima Kazempour Choursi, Mahdi Erfanian, Hirad Abghari, Mirhassan Miryaghoobzadeh, khadijeh Javan

“…The studies conducted confirmed this overestimation pattern across different time scales. Linear Regression was not enough to correct the TRMM data, as the corrected data still showed both underestimation and overestimation. …”
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Effects of urban sprawl on land use change in the peripheral villages of Tehran metropolis (case study: Tehran-Damavand axis) by ِAshkan Mohammadi, Naser Shafiei Sabet, Alireza Shakiba

“…After that, the supervised classification operation was monitored by the SVM algorithm and the detection and determination of the sprawl pattern in the study area. …”
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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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Rice seed integrity evaluation: Developing a rapid onsite system to check seed fraud using a portable NIR spectroscopic device coupled with smartphone technology by Ernest Teye, Charles Lloyd Yeboah Amuah, Vida Gyimah Boadu, Kwadwo Anokye Dompreh, Maxwell Darko Asante, Francis Padi Lamptey, Stephen Narh, Daniel Dzorkpe Gamenyah, George Oduro Nkansah, Selorm Akaba

“…After applying various pre-processing techniques and principal component analysis (PCA), linear discriminant functions 1 and 2 successfully revealed distinct clustering patterns for both the varieties and categories (rice seed and paddy). …”
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Leveraging AI for early cholera detection and response: transforming public health surveillance in Nigeria by Adamu Muhammad Ibrahim, Mohamed Mustaf Ahmed, Shuaibu Saidu Musa, Usman Abubakar Haruna, Mohammed Raihanatu Hamid, Olalekan John Okesanya, Aishat Muhammad Saleh, Don Eliso Lucero-Prisno III

“…AI technologies, including predictive modeling and ML algorithms such as random forests and convolutional neural networks (CNNs), can analyze diverse data sources—such as meteorological, environmental, and health records—to detect patterns and predict outbreaks. …”
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Rethinking the Paradigm of Using Ps for Diagnosing Compartment Syndrome by Yasser Bouklouch, BSc, MPH, July Agel, MA, ATC, William T. Obremskey, MD, MPH, MMHC, Andrew H. Schmidt, MD, Kathy Liu, MB, ChB, Jerald R. Westberg, MPH, Matthew Zakariah, BSc, Eli Bunzel, MD, Greer Henry, MSc, Andres Fidel Diaz, MD, Thierry Bégué, MD, Mitchell Bernstein, MD, Edward J. Harvey, MDCM, MSc

“…The combinations were tested for predictive power using 2 machine learning algorithms. Results:. Pressure on palpation was the strongest clinical predictor of ACS while pain was the weakest. …”
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Development of a deep learning system for predicting biochemical recurrence in prostate cancer by Lu Cao, Ruimin He, Ao Zhang, Lingmei Li, Wenfeng Cao, Ning Liu, Peisen Zhang

“…Finally, patient-level artificial intelligence models were developed by integrating deep learning -generated pathology features with several machine learning algorithms. Results The BCR prediction system demonstrated great performance in the testing cohort (AUC = 0.911, 95% Confidence Interval: 0.840–0.982) and showed the potential to produce favorable clinical benefits according to Decision Curve Analyses. …”
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AirQuaNet: A Convolutional Neural Network Model With Multi-Scale Feature Learning and Attention Mechanisms for Air Quality-Based Health Impact Prediction by Sreeni Chadalavada, Suleyman Yaman, Abdulkadir Sengur, Ravinesh C. Deo, Abdul Hafeez-Baig, Tracy Kolbe-Alexander, Niranjana Sampathila, U. Rajendra Acharya

“…It achieved outstanding results, with an R2 of 0.9997 on regression tasks and a classification accuracy of 94.21%, outperforming traditional machine learning algorithms and DL baselines. These results highlight the model&#x2019;s robustness under diverse data environments and its ability for high generalization across varied temporal scales and types of contaminants. …”
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Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity by Xiaochen Tang, Yanyan Wei, Jiaoyan Pang, Lihua Xu, Huiru Cui, Xu Liu, Yegang Hu, Mingliang Ju, Yingying Tang, Bin Long, Wei Liu, Min Su, Tianhong Zhang, Jijun Wang

“…A total of 239 participants from the Shanghai At Risk for Psychosis (SHARP) program were enrolled, consisting of 151 CHR individuals and 88 matched healthy controls (HCs). Functional connectivity (FC) features that were correlated with symptom severity were subjected to the single-cell interpretation through multikernel learning (SIMLR) algorithm in order to identify latent homogeneous subgroups. …”
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Removal mechanism and damage evolution of SiCp/Al composites based on FEM-MD model considering 3D random polyhedral particles in orthogonal cutting by Ming Li, Qingguang Li, Xianchao Pan, Jiaqi Wang, Zixuan Wang, Shengzhi Xu, Yunguang Zhou, Lianjie Ma, Tianbiao Yu

“…The polyhedral particle model demonstrated superior predictive accuracy over spherical approximations, particularly in capturing edge-driven stress concentrations and anisotropic debonding patterns. Experimental validation confirmed the multi-scale model's predictive accuracy for machining-induced surface damage. …”
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Bioinformatics&amp;#x2011;Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis by Yang M, Zhang X, Zhou C, Du Y, Zhou M, Zhang W

“…Least Absolute Shrinkage and Selection Operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to screen hub genes. …”
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Taurine-mediated metabolic immune crosstalk indicates and promotes immunosuppression with anti-PD-1 resistance in bladder cancer by Zhengfang Liang, Fengwei Nong, Fengwei Nong, Zhenjie Li, Runmin Chen, Haoxu Zhao, Yongping Huang, Yongping Huang

“…Immuno-infiltration patterns and immunotherapeutic responsiveness were quantified via algorithms including ESTIMATE and TIDE. …”
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Identification and evaluation of metabolic mRNAs and key miRNAs in colorectal cancer liver metastasis by Guanxuan Chen, Shiwen Wang, Meng Zhang, Wenna Shi, Ruoyu Wang, Wanqi Zhu

“…Their expression patterns and diagnostic performance were validated across multiple datasets. …”
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From Mountains to Basins: Asymmetric Ecosystem Vulnerability and Adaptation to Extreme Climate Events in Southwestern China by Qingao Lu, Yuandong Zhang, Wei Sun, Jingxuan Wei, Kun Xu

“…The increasing frequency of both singular and compound extreme climate events driven by global warming has profoundly impacted terrestrial ecosystems. Using machine learning-based Random Forest algorithms and moving correlation analysis, this study quantifies the impacts of extreme climate indices (ECIs) on two ecological indicators (EIs), the NDVI and GPP, from 1982 to 2019. …”
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Modeling the effects of climate change scenarios on the potential distribution of Vespa crabro Linnaeus, 1758 (Hymenoptera: Vespidae) in a Mediterranean biodiversity hotspot by Erika Bazzato, Arturo Cocco, Emanuele Salaris, Ignazio Floris, Alberto Satta, Michelina Pusceddu

“…We focused on Sardinia, a Mediterranean island with a heterogeneous morphological, geological, and climatic pattern, where V. crabro was accidentally introduced.We used 316 verified citizen science occurrences, high-resolution bioclimatic variables (40 × 40 m) specifically developed for the island, and two future climate and socio-economic scenarios for two temporal horizons (2040 and 2060) to model climatic suitability using an ensemble framework with three machine learning algorithms: Artificial Neural Networks (ANN), Generalized Boosting Model (GBM), and Random Forest (RF). …”
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