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Decoding seasonal irrigation suitability of drain water using integrated indexical, statistical, graphical and machine learning approaches.

SCOPE: Rapid urbanization and industrialization have exacerbated freshwater scarcity while simultaneously increasing wastewater generation, making the safe and judicious reuse of wastewater a viable strategy to address both challenges. OBJECTIVES: The present study aimed to evaluate the seasonal irrigation suitability of the Kala Sanghian drain using integrated (graphical, indexical, statistical a...

A quantum graph-based differential model for drug-drug interaction prediction.

Predicting clinically significant drug-drug interactions (DDIs) continues to be an unresolved challenge in contemporary pharmacovigilance, primarily due to the inadequacy of current computational frameworks in addressing the nonlinear, multi-scale characteristics of simultaneous drug metabolism. This paper presents the Quantum Graph-Differential (QGD) model an exact mathematical framework that com...

Personalized machine learning-guided radiation dose escalation in newly diagnosed glioblastoma: prospective pilot study.

Glioblastoma is a highly aggressive primary brain tumor with near-universal recurrence despite maximal safe resection followed by standard chemoradiat...

Fostering Cultural Safety Through AI-Based Simulations: A Mixed-Methods Study in Nursing Education.

BACKGROUND: Increasing cultural diversity requires nurses to ensure cultural safety in patient care. Artificial intelligence (AI)-based simulations of...

Do predictors of motor recovery differ between robotic and conventional post-stroke rehabilitation?

BACKGROUND: While research on robotic rehabilitation has largely focused on evaluating device effectiveness, there remains a clear need to investigate...

Predicting biliary complications after liver transplantation: a machine learning and nomogram-based exploratory study.

BACKGROUND: Accurate risk stratification of biliary complications (BCs) after liver transplantation (LT) remains challenging. This study aimed to deve...

Explaining sleep disorders in children after earthquakes using SHAP modelling: Container City modelling.

BACKGROUND: It is well-known that sleep disturbances and sleep-related traumas are the most common symptoms experienced by children following a trauma...

CT-based radiomic markers to predict late-onset seizures after traumatic brain injury.

Post-traumatic epilepsy (PTE) is a major long-term complication of traumatic brain injury (TBI), but early risk prediction remains imprecise. Radiomic...

An engineering-informed voting ensemble framework for rapid seismic risk screening of reinforced concrete structures.

Rapid evaluation of existing building stocks is essential for reducing earthquake-related disaster risk, particularly in seismically vulnerable cities...

Enhancing Anatomy Learning Through Enhanced 3D Visualization: A Study on Student Perception and Engagement in Museum-Based Education.

Effective anatomy education is essential for medical training. While traditional anatomy instruction provides essential foundational knowledge, its ef...

Structural imaging predictors of ketamine response in treatment-resistant depression: a machine learning approach.

Ketamine has demonstrated rapid antidepressant efficacy in treatment-resistant depression (TRD), but clinical decision-making is challenging due to va...

A TyG-UHR-based machine learning model for screening lean MAFLD: development and external validation.

BACKGROUND: Lean metabolic dysfunction-associated fatty liver disease (MAFLD) is increasingly recognized but often goes unnoticed during health checku...

Artificial Intelligence in Breast Cancer Lymph Node Evaluation: Is Neoadjuvant Chemotherapy the Achilles' Heel?

CONTEXT.—: Artificial intelligence (AI) has demonstrated high accuracy in detecting lymph node (LN) metastases in treatment-naïve invasive breast canc...

Unveiling the Microvasculature: The Index of Microcirculatory Resistance and Its Expanding Role in Cardiovascular Care.

Index of microcirculatory resistance (IMR) is a cutting-edge, wire-based tool that advances the capability assessment of coronary microvascular functi...

A Review on Artificial Intelligence in Pharmaceutical Sciences: Opportunities & Challenges.

SignificanceThe review aims to systematically explore the transformative impact of artificial intelligence (AI) on pharmaceutical sciences. It Address...

Clinician engagement shapes the impact of AI-based ECG screening for chronic liver disease in primary care.

Artificial intelligence (AI)-based screening tools show promise for early identification of chronic liver disease (CLD), yet their effectiveness in re...

Occupational and socioeconomic predictors of myocardial infarction and coronary heart disease: a machine learning analysis.

BACKGROUND: Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality worldwide. Although traditional cardiovascular risk models...

Development of AI-assisted 3D-printed degradable hydrogel microneedles for transdermal delivery of progesterone-loaded solid lipid nanoparticles: a novel approach to slowing Alzheimer's disease progression.

Progesterone (PG) is used to slow the progression of neurodegenerative diseases, particularly Alzheimer's disease (AD) in postmenopausal women. Howeve...

Ventral hernia repair in emergency settings. A machine learning model to predict post-operative complications.

BACKGROUND: Emergency ventral hernia repair remains a challenging procedure due to patient instability, contaminated surgical fields, and heterogeneit...

Artificial Intelligence for Regulatory Evidence: A Systematic Document Analysis of European Medicines Agency Regulatory Advice and Public Reports.

Artificial intelligence (AI) is receiving increasing attention across the entire lifecycle of medicines, from early development to postauthorization u...