Atherosclerosis continues to be a leading cause of morbidity and mortality globally. The precise evaluation of the extent of an atherosclerotic plaque is essential for forecasting its likelihood of causing health concerns and tracking treatment outcomes. When compared to conventional methods used, nanoparticles offer clear benefits and excellent development opportunities for the detection and characterisation of susceptible atherosclerotic plaques. In this review, we analyse the recent advancements of nanoparticles as theranostics in the management of atherosclerosis, with an emphasis on applications in drug delivery. Furthermore, the main issues that must be resolved in order to advance clinical utility and future developments of NP research are discussed. It is anticipated that medical NPs will develop into complex and advanced next-generation nanobotics that can carry out a variety of functions in the bloodstream.

BACKGROUND Large Language Models (LLM) are AI models that can generate conversational content based on a trained specified source of information (corpus). OBJECTIVE The aim is to use these corpus-trained LLMs to limit the content offered by LLM, then using prompt engineering to teach using Socratic methods. METHODS Two chatbots were created and deployed, powered by OpenAI’s GPT-3.5 model, with a medical-school textbook corpus. The first chatbot generates a brief summary and open-ended question. The second chatbot generates a case vignette from its pre-trained clinical cases, prompting users for a diagnosis. Both chatbots reply to the user’s response, commenting on the accuracy and asks further questions to encourage critical thinking. A randomised controlled trial was conducted on two groups comprising third year medical students. One group used both chatbots for 10 minutes while the other read the medical textbook. A 15-question test was administered to both groups before and after the intervention. RESULTS Forty students participated in the study. The average of the group before and after reading the textbook (n=20) are 3.9 +/- 1.0 and 7.6 +/- 1.5 respectively (p<0.001). The average of the group before and after using the bot (n=20) are 3.9 +/- 0.9 and 12.8 +/- 1.6 respectively (p<0.001). The respective increase in results was 3.7 and 8.9. CONCLUSIONS Medical students’ learning showed a better performance using a LLM based chatbot compared to self-reading of medical information assessed using a standardised test. More studies are required to determine if LLM-based pedagogical methods are superior to standard education.

Body dysmorphic disorder (BDD) is a condition to be assessed preoperatively prior to cosmetic rhinoplasty, as it may influence patient outcomes and satisfaction. The prevalence of BDD in Asians is not well-studied, and the literature consists predominantly of smaller case series. This systematic review and meta-analysis estimated BDD prevalence in Asian rhinoplasty candidates and examined the impact of diagnostic methods and regional factors. Following Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines, five databases were searched up to March 2025. Two reviewers independently screened studies. Meta-analysis using a random-effects model was conducted; study quality and publication bias were assessed. Fifteen studies (n = 1,977) were included. The pooled BDD prevalence was 30.0% (95% CI: 26.8–35.7), with higher rates using the Body Dysmorphic Disorder Questionnaire (36%) versus DSM-V criteria (18%). Substantial heterogeneity was observed and addressed. BDD may affect nearly one in three Asian rhinoplasty candidates, underscoring the need for standardized diagnostic protocols and culturally sensitive preoperative assessment to improve patient outcomes and safety.