Potential Involvement of Apelin/APJ System in Addiction and Neuroprotection Against Drugs of Abuse.

Addiction, characterized by compulsive drug-seeking behavior and impaired self-control, remains a significant public health concern. Understanding the neurobiology of addiction is crucial for identifying novel therapeutic targets and further developing effective treatments. Recently, the apelin/APJ system, an emerging signaling pathway, has attracted attention for its involvement in various neuropsychiatric disorders. The cross-talk between the apelin/APJ system and hypothalamic mu opioid signaling, as well as its heterodimerization with kappa opioid receptors (KORs), supports the potential relevance of this system to addiction. Moreover, several protective effects of apelin against various addictive substances, including methamphetamine, morphine, and alcohol, underscore the need for further investigation into its role in substance use disorder. Understanding the contribution of the apelin/APJ system in addiction may offer valuable insights into the underlying neurobiology and pave the way for novel therapeutic interventions in substance use disorders. This review provides a concise overview of the apelin/APJ system, emphasizing its physiological roles and highlighting its relevance to addiction research.

Advancing psychiatric nosology through philosophical inquiry.

Here, we have utilised the concept of fuzzy logic and Karl Popper's notion of verisimilitude to advocate navigating the complexity of psychiatric nosology, emphasising that psychiatric disorders defy Boolean logic. We underscore the importance of embracing imprecision and collecting extensive data for a more nuanced understanding of psychiatric disorders, asserting that falsifiability is crucial for scientific progress. We encourage the advancement of personalised psychiatric taxonomy, urging the continual accumulation of data to inform emerging advancements like artificial intelligence in reshaping current psychiatric nosology.

Unveiling Medical Insights: Advanced Topic Extraction from Scientific Articles.

In the ever-evolving landscape of medical research and healthcare, the abundance of scientific articles presents both a treasure trove of knowledge and a daunting challenge. Researchers, clinicians, and data scientists grapple with vast amounts of unstructured information, seeking to extract meaningful insights that can drive advancements in the biomedical domain including, research trends, patient care, drug discovery, and disease understanding. This paper utilizes the topic extraction algorithms on Breast Cancer Research to shed light on the current trends and the path to follow in this field. We utilized TextRank and Large Language Models (LLM) using the TripleA tool to extract topics in the field, analyzing and comparing the results.