+ HEALTH Background Discharge letters are essential for continuity of care, yet patients often misunderstand their con…± HEALTH Hematoxylin and Eosin (H&E) glass slide preparation is fundamental to histopathology. Pathologists' expertise…+ CLIMATE Traditional soil heavy metal risk assessments suffer from fragmented indices and poor logical integration. Co…± HEALTH Background Neonatal pneumothorax can progress rapidly, yet detection on supine chest radiographs remains chal…+ HEALTH T cell exhaustion has widespread implications for the progression and treatment of chronic diseases including…+ HEALTH Background Standard pulmonary arterial hypertension (PAH) risk models may not fully apply to PAH associated w…+ HEALTH Objectives To map and synthesize the scientific evidence regarding the use of Augmented Reality (AR) and Mixe…± HEALTH Objective To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovas…
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1045 published stories · page 7 of 70

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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 · Health

The tire antioxidant derivative 6PPD-quinone exacerbates IBD by targeting NR1H4-mediated lipid metabolism and mitochondrial dysfunction in human colon epithelial cells
Canadian Medical Students Interested in Radiology Report Greater Perceived Importance of Procedural Roles and Greater Career Sustainability Amid Artificial Intelligence
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Canadian Medical Students Interested in Radiology Report Greater Perceived Importance of Procedural Roles and Greater Career Sustainability Amid Artificial Intelligence

Rationale and objectives Artificial intelligence (AI) integration and perceptions of radiology's procedural scope may influence medical students' interest in radiology. We aimed to determine whether perceptions of radiology's scope and AI integration differed according to student interest in radiology. Methods An anonymous cross-sectional survey containing binary, Likert-scale, and open-ended questions was distributed to medical students at a Canadian institution (N=73; 19.7% effective response rate). Responses…

Health
"Reports in Medical Illustration (REMIL) in Musculoskeletal Radiology: An Evaluation of Evolving AI Models"
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"Reports in Medical Illustration (REMIL) in Musculoskeletal Radiology: An Evaluation of Evolving AI Models"

Objective Radiology reports remain predominantly text-based, requiring clinicians and patients to mentally reconstruct imaging findings. Reports in Medical Illustration (REMIL) represent an emerging approach in which artificial intelligence (AI) generates simplified visual summaries directly from report text. This study aimed to evaluate the feasibility, anatomical accuracy, and clinical utility of AI-generated REMIL in musculoskeletal (MSK) radiology. Methods Twenty-five MSK imaging cases were selected. Identic…

Health
Digital pathology, image analysis, and artificial intelligence in liver disease
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Digital pathology, image analysis, and artificial intelligence in liver disease

Advances in digital pathology, image analysis, and artificial intelligence (AI) are rapidly transforming how pathologists and researchers interact with tissue samples and enable the development of diagnostic tools that harness high-resolution whole-slide images; these advances are in turn creating new opportunities for research, education, and routine clinical care globally. Liver disease is no exception, and digital pathology and AI have many applications in the diagnosis of liver cancer and liver diseases and…

Health
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…

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

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…

Health
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…

Health

The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers?

Abstract This study aimed to explore the experiences, perceptions, knowledge, concerns, and intentions of Generation Z (Gen Z) students with Generation X (Gen X) and Generation Y (Gen Y) teachers regarding the use of generative AI (GenAI) in higher education. A sample of students and teachers were recruited to investigate the above using a survey consisting of both open and closed questions. The findings showed that Gen Z participants were generally optimistic about the potential benefits of GenAI, including enh…

Education
The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers?

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…

Health
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

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…

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

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…

Health
Accuracy of General-Use Multimodal AI Platforms for Pell and Gregory Classification of Impacted Mandibular Third Molars

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…

Health
Artificial intelligence-driven decision-making after endoscopic resection for early gastric cancer