A plant flavonol and genetic suppressors rescue a pathogenic mutation associated with kinesin in neurons.

KIF1A, a microtubule-based motor protein responsible for axonal transport, is linked to a group of neurological disorders known as KIF1A-associated neurological disorder (KAND). Current therapeutic options for KAND are limited. Here, we introduced the clinically relevant KIF1A(R11Q) variant into the Caenorhabditis elegans homolog UNC-104, resulting in uncoordinated animal behaviors. Through genetic suppressor screens, we identified intragenic mutations in UNC-104's motor domain that rescued synaptic vesicle localization and coordinated movement. We showed that two suppressor mutations partially recovered motor activity in vitro by counteracting the structural defect caused by R11Q at KIF1A's nucleotide-binding pocket. We found that supplementation with fisetin, a plant flavonol, improved KIF1A(R11Q) worms' movement and morphology. Notably, our biochemical and single-molecule assays revealed that fisetin directly restored the ATPase activity and processive movement of human KIF1A(R11Q) without affecting wild-type KIF1A. These findings suggest fisetin as a potential intervention for enhancing KIF1A(R11Q) activity and alleviating associated defects in KAND.

The role of large language models in medical image processing: a narrative review.

Background and Objective: The rapid advancement of artificial intelligence (AI) has ushered in a new era in natural language processing (NLP), with large language models (LLMs) like ChatGPT leading the way. This paper explores the profound impact of AI, particularly LLMs, in the field of medical image processing. The objective is to provide insights into the transformative potential of AI in improving healthcare by addressing historical challenges associated with manual image interpretation. Methods: A comprehensive literature search was conducted on the Web of Science and PubMed databases from 2013 to 2023, focusing on the transformations of LLMs in Medical Imaging Processing. Recent publications on the arXiv database were also reviewed. Our search criteria included all types of articles, including abstracts, review articles, letters, and editorials. The language of publications was restricted to English to facilitate further content analysis. Key Content and Findings: The review reveals that AI, driven by LLMs, has revolutionized medical image processing by streamlining the interpretation process, traditionally characterized by time-intensive manual efforts. AI's impact on medical care quality and patient well-being is substantial. With their robust interactivity and multimodal learning capabilities, LLMs offer immense potential for enhancing various aspects of medical image processing. Additionally, the Transformer architecture, foundational to LLMs, is gaining prominence in this domain. Conclusions: In conclusion, this review underscores the pivotal role of AI, especially LLMs, in advancing medical image processing. These technologies have the capacity to enhance transfer learning efficiency, integrate multimodal data, facilitate clinical interactivity, and optimize cost-efficiency in healthcare. The potential applications of LLMs in clinical settings are promising, with far-reaching implications for future research, clinical practice, and healthcare policy. The transformative impact of AI in medical image processing is undeniable, and its continued development and implementation are poised to reshape the healthcare landscape for the better.