Modelling the Impact of NETosis During the Initial Stage of Systemic Lupus Erythematosus.

The development of autoimmune diseases often takes years before clinical symptoms become detectable. We propose a mathematical model for the immune response during the initial stage of Systemic Lupus Erythematosus which models the process of aberrant apoptosis and activation of macrophages and neutrophils. NETosis is a type of cell death characterised by the release of neutrophil extracellular traps, or NETs, containing material from the neutrophil's nucleus, in response to a pathogenic stimulus. This process is hypothesised to contribute to the development of autoimmunogenicity in SLE. The aim of this work is to study how NETosis contributes to the establishment of persistent autoantigen production by analysing the steady states and the asymptotic dynamics of the model by numerical experiment.

Participation of transgender and gender diverse persons in cardiovascular clinical trials.

Study objective: Transgender persons face increased risk in developing cardiovascular diseases due to administration of hormonal therapy used for gender expression, or due to the presence of other risk factors, such as minority stress and difficulty to have full access to health care. Even though the need for gender diversity in research has been identified, the number of clinical trials including transgender persons remains low. The aim of this study was to highlight gaps in inclusion of transgender individuals in cardiovascular clinical research. Design setting: A search in the pubmed.com database, as well as in the clinicaltrials.gov repository, was performed with search terms regarding transgender persons and cardiovascular diseases. Main outcome measures: The inclusion of transgender persons in cardiovascular clinical trials was evaluated. Results and conclusions: This study revealed that there is only a small number of cardiovascular clinical trials including or studying transgender persons. This finding demonstrates the overall lack of clinical trials regarding cardiovascular health in transgender individuals and is indicative of their under-representation in clinical research.

Machine learning for catalysing the integration of noncoding RNA in research and clinical practice.

The human transcriptome predominantly consists of noncoding RNAs (ncRNAs), transcripts that do not encode proteins. The noncoding transcriptome governs a multitude of pathophysiological processes, offering a rich source of next-generation biomarkers. Toward achieving a holistic view of disease, the integration of these transcripts with clinical records and additional data from omic technologies ("multiomic" strategies) has motivated the adoption of artificial intelligence (AI) approaches. Given their intricate biological complexity, machine learning (ML) techniques are becoming a key component of ncRNA-based research. This article presents an overview of the potential and challenges associated with employing AI/ML-driven approaches to identify clinically relevant ncRNA biomarkers and to decipher ncRNA-associated pathogenetic mechanisms. Methodological and conceptual constraints are discussed, along with an exploration of ethical considerations inherent to AI applications for healthcare and research. The ultimate goal is to provide a comprehensive examination of the multifaceted landscape of this innovative field and its clinical implications.