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The tire antioxidant derivative 6PPD-quinone exacerbates IBD by targeting NR1H4-mediated lipid metabolism and mitochondrial dysfunction in human colon epithelial cells
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The tire antioxidant derivative 6PPD-quinone exacerbates IBD by targeting NR1H4-mediated lipid metabolism and mitochondrial dysfunction in human colon epithelial cells

N- (1,3-Dimethylbutyl)-N'-phenyl-p-phenylenediamine quinone (6PPD-Q), a tire rubber antioxidant derivative, accumulates in air, soil, and water and has been found in urine, blood, and cerebrospinal fluid, posing significant health risks. Although 6PPD-Q exhibits intestinal toxicity, its role in inflammatory bowel disease (IBD) remains unclear. The objective of this study was to identify key molecular targets of 6PPD-Q in IBD and to validate their involvement in 6PPD-Q-induced intestinal epithelial cell injury. U…

Food and Chemical Toxicology
Predicting longitudinal depressive symptom trajectories among older Chinese adults with chronic health conditions: An interpretable machine learning study
Evidence-backed gain

Predicting longitudinal depressive symptom trajectories among older Chinese adults with chronic health conditions: An interpretable machine learning study

Objective This study leveraged interpretable machine learning (ML) to map heterogeneous trajectories of depressive symptoms in Chinese older adults with chronic diseases, aiming to develop an interpretable, prediction-oriented framework for personalized mental health interventions. Methods We analyzed four-wave longitudinal data from 5492 participants in the China Health and Retirement Longitudinal Study. Following trajectory identification, 10 ML algorithms were compared. A 50-iteration bootstrap Recursive Feat…

Prognostic Value of Admission D-dimer Levels and Total Bleeding Volume in Aneurysmal Subarachnoid Hemorrhage: A Retrospective Cohort Study with Machine Learning-Based Modeling
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Prognostic Value of Admission D-dimer Levels and Total Bleeding Volume in Aneurysmal Subarachnoid Hemorrhage: A Retrospective Cohort Study with Machine Learning-Based Modeling

Background Plasma D-dimer levels are independently associated with poor prognosis following aneurysmal subarachnoid hemorrhage (aSAH). However, the underlying mechanisms contributing to early D-dimer elevation remain unclear. This study aimed to evaluate the association between admission D-dimer levels and total bleeding volume (TBV) and to further explore their combined predictive power for functional outcomes using interpretable machine learning approaches. Methods We analyzed data from 473 patients with aSAH…

A CECT-Based 2PI System as a Novel Noninvasive Prognostic Tool for Hepatocellular Carcinoma: A Dual-Validation Study
Evidence-backed gain

A CECT-Based 2PI System as a Novel Noninvasive Prognostic Tool for Hepatocellular Carcinoma: A Dual-Validation Study

This study aimed to develop and validate machine learning (ML) models integrating clinical parameters and the 2PI system (Pathology and Prognosis-Informed Imaging System) for predicting postoperative recurrence risk in hepatocellular carcinoma (HCC). The multicenter retrospective study included 496 patients with solitary HCC (≤ 5 cm). Surgical resection (SR) patients from the primary center constituted the training set; radiofrequency ablation (RFA) patients from the same center formed the internal test set; and…

The Challenges of Predicting Rare Outcomes: A Critical Appraisal of Machine Learning Using the Pediatric Resuscitation and Trauma Outcome (PRESTO) Model in a Tanzanian Injury Registry
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The Challenges of Predicting Rare Outcomes: A Critical Appraisal of Machine Learning Using the Pediatric Resuscitation and Trauma Outcome (PRESTO) Model in a Tanzanian Injury Registry

Background Injuries are responsible for 950,000 deaths per year among children and adolescents under 18 years old. Trauma prediction scores are useful in determining severity and prognosis of injury patients. The pediatric resuscitation and trauma outcome (PRESTO) score was developed as a simple score for short-term mortality prediction in pediatric populations in low- and middle-income countries (LMICs). Using variables available at the bedside in resource-limited settings, PRESTO has been validated in South Af…

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From Biomedical Datasets to Fairness-Aware Recommendations: An Integrated Data Orchestration Pipeline for Binary Clinical Predictions

Many problems in biomedicine can be posed as binary classification. When they are addressed using artificial intelligence methods, though, average performance alone does not show whether a dataset is artificial intelligence ready, whether the endpoint is clinically valid, or whether errors are unevenly distributed across patient subgroups. This article presents the Fairness-Aware Data Orchestration Pipeline (FADOP), a reusable workflow that analyzes biomedical datasets, trains baseline binary classifiers, audits…

From Biomedical Datasets to Fairness-Aware Recommendations: An Integrated Data Orchestration Pipeline for Binary Clinical Predictions
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Accuracy of General-Use Multimodal AI Platforms for Pell and Gregory Classification of Impacted Mandibular Third Molars

Purpose The purpose of this study was to evaluate the performance of two general-use artificial intelligence models, ChatGPT and Grok, in classifying impacted mandibular third molars using the Pell and Gregory system on panoramic radiographs, compared with a resident consensus reference standard. Materials and methods One hundred panoramic radiographic images of impacted mandibular third molars were independently classified by two blinded resident reviewers using the Pell and Gregory classification system. Resid…

Accuracy of General-Use Multimodal AI Platforms for Pell and Gregory Classification of Impacted Mandibular Third Molars
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Artificial intelligence-driven decision-making after endoscopic resection for early gastric cancer

Early gastric cancer (EGC) is increasingly managed by endoscopic resection (ER); however, lymph node metastasis (LNM), which occurs in approximately 5%-10% of cases, remains the key determinant for recommending additional gastrectomy. Current guideline-based strategies, including the eCura system, provide structured risk stratification but rely on categorical decision-making and may lead to overtreatment, as nearly 90% of patients undergoing additional surgery do not have LNM. Artificial intelligence (AI) has em…

Artificial intelligence-driven decision-making after endoscopic resection for early gastric cancer
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A novel use of AI for prediction of clinical deterioration in a post-acute hospital

There is a growing literature on the prediction of risk of deterioration in hospital settings, including by leveraging artificial intelligence (AI) models. However, this literature has focused on acute-care hospitals, rather than post-acute facilities, where the risk of deterioration remains high. Post-acute facilities tend to have lower digital maturity and poorer data foundations, as well as less rich physiologic data, making the implementation of AI tools for deterioration challenging. In this study, we demon…

A novel use of AI for prediction of clinical deterioration in a post-acute hospital
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Integrative bioinformatic analysis delineates a mitochondrial-hematopoietic gene signature for diagnosis and immune characterization in myelodysplastic syndromes

Objective This study aimed to develop a mitochondrial and hematopoiesis-related differentially expressed genes (MH-related DEGs) signature for Myelodysplastic syndromes (MDS) diagnosis and to characterize its regulatory network and immune microenvironment. Methods MH-related DEGs were defined as the intersection of differentially expressed genes from three integrated microarray datasets (GSE145733, GSE19429, GSE81173) with a curated set of mitochondrial- and hematopoiesis-related genes from public databases. Fun…

Integrative bioinformatic analysis delineates a mitochondrial-hematopoietic gene signature for diagnosis and immune characterization in myelodysplastic syndromes
Health

Physics-Informed Neural Networks Meet Multimodal Large Language Models: Biomechanical Simulation in Aortic Aneurysm

Axial dissections of the thoracic artery are common causes of death in people diagnosed with aortic dissection; however, decisions to intervene on ascending thoracic aortic patients are determined by the size of the ascending thoracic aorta based on its diameter. Diameter-based criteria fail to take into consideration the biomechanical properties of the aorta as well as other characteristics of the patient, and finite element analysis (in determining aortic wall stresses) would ideally address the issues associa…

Physics-Informed Neural Networks Meet Multimodal Large Language Models: Biomechanical Simulation in Aortic Aneurysm
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Machine learning-integrated explainable artificial intelligence for survival prediction in urothelial carcinoma with enfortumab vedotin: an exploratory real-world analysis

Background Enfortumab vedotin (EV) has transformed treatment for advanced urothelial carcinoma (aUC), but outcomes vary. Machine learning (ML) with explainable artificial intelligence (XAI) may improve survival prediction. Methods Data from 544 aUC patients receiving EV after platinum chemotherapy and immunotherapy (51 centers, 24 countries) were analyzed. Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20…

Machine learning-integrated explainable artificial intelligence for survival prediction in urothelial carcinoma with enfortumab vedotin: an exploratory real-world analysis