Reduced PI3K(p110α) induces atrial myopathy, and PI3K-related lipids are dysregulated in athletes with atrial fibrillation.

Background: Elucidating mechanisms underlying atrial myopathy, which predisposes individuals to atrial fibrillation (AF), will be critical for preventing/treating AF. In a serendipitous discovery, we identified atrial enlargement, fibrosis, and thrombi in mice with reduced phosphoinositide 3-kinase (PI3K) in cardiomyocytes. PI3K(p110α) is elevated in the heart with exercise and is critical for exercise-induced ventricular enlargement and protection, but the role in the atria was unknown.

Can large language models fully automate or partially assist paper selection in systematic reviews?

Background/aims: Large language models (LLMs) have substantial potential to enhance the efficiency of academic research. The accuracy and performance of LLMs in a systematic review, a core part of evidence building, has yet to be studied in detail.

Methods: We introduced two LLM-based approaches of systematic review: an LLM-enabled fully automated approach (LLM-FA) utilising three different GPT-4 plugins (Consensus GPT, Scholar GPT and GPT web browsing modes) and an LLM-facilitated semi-automated approach (LLM-SA) using GPT4's Application Programming Interface (API).

An optimized plasmalogen modulating dietary supplement provides greater protection in a male than female mouse model of dilated cardiomyopathy.

We previously reported that plasmalogens, a class of phospholipids, were decreased in a setting of dilated cardiomyopathy (DCM). Plasmalogen levels can be modulated via a dietary supplement called alkylglycerols (AG) which has demonstrated benefits in some disease settings. However, its therapeutic potential in DCM remained unknown.

Efficiency and safety of automated label cleaning on multimodal retinal images.

Label noise is a common and important issue that would affect the model's performance in artificial intelligence. This study assessed the effectiveness and potential risks of automated label cleaning using an open-source framework, Cleanlab, in multi-category datasets of fundus photography and optical coherence tomography, with intentionally introduced label noise ranging from 0 to 70%. After six cycles of automatic cleaning, significant improvements are achieved in label accuracies (3.