Automated Category and Trend Analysis of Scientific Articles on Ophthalmology Using Large Language Models: Development and Usability Study.

BACKGROUND: In this paper, we present an automated method for article classification, leveraging the power of large language models (LLMs). OBJECTIVE: The aim of this study is to evaluate the applicability of various LLMs based on textual content of scientific ophthalmology papers. METHODS: We developed a model based on natural language processing techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we used zero-shot learning LLMs and compared Bidirectional and Auto-Regressive Transformers (BART) and its variants with Bidirectional Encoder Representations from Transformers (BERT) and its variants, such as distilBERT, SciBERT, PubmedBERT, and BioBERT. To evaluate the LLMs, we compiled a data set (retinal diseases [RenD] ) of 1000 ocular disease-related articles, which were expertly annotated by a panel of 6 specialists into 19 distinct categories. In addition to the classification of articles, we also performed analysis on different classified groups to find the patterns and trends in the field. RESULTS: The classification results demonstrate the effectiveness of LLMs in categorizing a large number of ophthalmology papers without human intervention. The model achieved a mean accuracy of 0.86 and a mean F1-score of 0.85 based on the RenD data set. CONCLUSIONS: The proposed framework achieves notable improvements in both accuracy and efficiency. Its application in the domain of ophthalmology showcases its potential for knowledge organization and retrieval. We performed a trend analysis that enables researchers and clinicians to easily categorize and retrieve relevant papers, saving time and effort in literature review and information gathering as well as identification of emerging scientific trends within different disciplines. Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines.

The role of glucose in cardiac physiology and pathophysiology.

PURPOSE OF REVIEW: Heart failure is one of the major causes of death worldwide and continues to increase despite therapeutics and pharmacology advances. Fatty acids and glucose are used as ATP-producing fuels in heart to meet its energy demands. However, dysregulation of metabolites' use plays a pivotal role in cardiac diseases. How glucose becomes toxic or drives cardiac dysfunction is incompletely understood. In the present review, we summarize the recent findings on cardiac cellular and molecular events that are driven by glucose during pathologic conditions and potential therapeutic strategies to tackle hyperglycemia-mediated cardiac dysfunction. RECENT FINDINGS: Several studies have emerged recently, demonstrating that excessive glucose utilization has been correlated with impairment of cellular metabolic homeostasis primarily driven by mitochondrial dysfunction and damage, oxidative stress, and abnormal redox signaling. This disturbance is associated with cardiac remodeling, hypertrophy, and systolic and diastolic dysfunction. Both human and animal heart failure studies, report that glucose is a preferable fuel at the expense of fatty acid oxidation during ischemia and hypertrophy, but the opposite happens in diabetic hearts, which warrants further investigation. SUMMARY: A better understanding of glucose metabolism and its fate during distinct types of heart disease will contribute to developing novel therapeutic options for the prevention and treatment of heart failure.

WeakMeSH: Leveraging provenance information for weakly supervised classification of biomedical articles with emerging MeSH descriptors.

Medical Subject Headings (MeSH) is a hierarchically structured thesaurus created by the National Library of Medicine of USA. Each year the vocabulary gets revised, bringing forth different types of changes. Those of particular interest are the ones that introduce new descriptors in the vocabulary either brand new or those who come up as a product of a complex change. These new descriptors often lack ground truth articles and rendering learning models that require supervision not applicable. Furthermore, this problem is characterized by its multi label nature and the fine-grained character of the descriptors that play the role of classes, requiring expert supervision and a lot of human resources. In this work, we alleviate these issues through retrieving insights from provenance information about those descriptors present in MeSH to create a weakly labeled train set for them. At the same time, we make use of a similarity mechanism to further filter the weak labels obtained through the descriptor information mentioned earlier. Our method, called WeakMeSH, was applied on a large-scale subset of the BioASQ 2018 data set consisting of 900 thousand biomedical articles. The performance of our method was evaluated on BioASQ 2020 against several other approaches that had given competitive results in similar problems in the past, or apply alternative transformations against the proposed one, as well as some variants that showcase the importance of each different component of our proposed approach. Finally, an analysis was performed on the different MeSH descriptors each year to assess the applicability of our method on the thesaurus.

Cardioprotection by selective SGLT-2 inhibitors in a non-diabetic mouse model of myocardial ischemia/reperfusion injury: a class or a drug effect?

Major clinical trials with sodium glucose co-transporter-2 inhibitors (SGLT-2i) exhibit protective effects against heart failure events, whereas inconsistencies regarding the cardiovascular death outcomes are observed. Therefore, we aimed to compare the selective SGLT-2i empagliflozin (EMPA), dapagliflozin (DAPA) and ertugliflozin (ERTU) in terms of infarct size (IS) reduction and to reveal the cardioprotective mechanism in healthy non-diabetic mice. C57BL/6 mice randomly received vehicle, EMPA (10 mg/kg/day) and DAPA or ERTU orally at the stoichiometrically equivalent dose (SED) for 7 days. 24 h-glucose urinary excretion was determined to verify SGLT-2 inhibition. IS of the region at risk was measured after 30 min ischemia (I), and 120 min reperfusion (R). In a second series, the ischemic myocardium was collected (10th min of R) for shotgun proteomics and evaluation of the cardioprotective signaling. In a third series, we evaluated the oxidative phosphorylation capacity (OXPHOS) and the mitochondrial fatty acid oxidation capacity by measuring the respiratory rates. Finally, Stattic, the STAT-3 inhibitor and wortmannin were administered in both EMPA and DAPA groups to establish causal relationships in the mechanism of protection. EMPA, DAPA and ERTU at the SED led to similar SGLT-2 inhibition as inferred by the significant increase in glucose excretion. EMPA and DAPA but not ERTU reduced IS. EMPA preserved mitochondrial functionality in complex I&II linked oxidative phosphorylation. EMPA and DAPA treatment led to NF-kB, RISK, STAT-3 activation and the downstream apoptosis reduction coinciding with IS reduction. Stattic and wortmannin attenuated the cardioprotection afforded by EMPA and DAPA. Among several upstream mediators, fibroblast growth factor-2 (FGF-2) and caveolin-3 were increased by EMPA and DAPA treatment. ERTU reduced IS only when given at the double dose of the SED (20 mg/kg/day). Short-term EMPA and DAPA, but not ERTU administration at the SED reduce IS in healthy non-diabetic mice. Cardioprotection is not correlated to SGLT-2 inhibition, is STAT-3 and PI3K dependent and associated with increased FGF-2 and Cav-3 expression.