Publications by authors named "G P Gill"

Phenolic acid-rich fraction from Anisopus mannii (PhAM) contains abundance of ferulic acid, gallic acid, protocatechuic acid, and syringic acid. Among other glycolytic enzymes, in vitro, PhAM counteracted the binding of sodium orthovanadate to phosphofructokinase 1 (PFK-1), improving its activities. In a rat model of diet-induced diabetes, PhAM monotherapy reduced HbA1c by an average of 0.

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This paper investigates the potential of artificial intelligence (AI) and machine learning (ML) to enhance the differentiation of cystic lesions in the sellar region, such as pituitary adenomas, Rathke cleft cysts (RCCs) and craniopharyngiomas (CP), through the use of advanced neuroimaging techniques, particularly magnetic resonance imaging (MRI). The goal is to explore how AI-driven models, including convolutional neural networks (CNNs), deep learning, and ensemble methods, can overcome the limitations of traditional diagnostic approaches, providing more accurate and early differentiation of these lesions. The review incorporates findings from critical studies, such as using the Open Access Series of Imaging Studies (OASIS) dataset (Kaggle, San Francisco, USA) for MRI-based brain research, highlighting the significance of statistical rigor and automated segmentation in developing reliable AI models.

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Complications of percutaneous coronary intervention (PCI) can lead to significant morbidity and mortality. In-depth understanding of the mechanisms and management options of these complications as well as timely recognition and action can sometimes be lifesaving. In this review we discuss the mechanisms, prevention methods, diagnosis, and management of three major PCI complications: a) perforation b) acute vessel closure, and c) equipment loss.

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Alzheimer's disease (AD) and other neurodegenerative illnesses place a heavy strain on the world's healthcare systems, particularly among the aging population. With a focus on research from January 2022 to September 2023, this scoping review, which adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-Scr) criteria, examines the changing landscape of artificial intelligence (AI) applications for early AD detection and diagnosis. Forty-four carefully chosen articles were selected from a pool of 2,966 articles for the qualitative synthesis.

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Introduction: The emergence of large language models (LLMs) has led to significant interest in their potential use as medical assistive tools. Prior investigations have analyzed the overall comparative performance of LLM versions within different ophthalmology subspecialties. However, limited investigations have characterized LLM performance on image-based questions, a recent advance in LLM capabilities.

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