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Background: Tirzepatide is a novel dual glucose-dependent insulinotropic peptide (GIP)-glucagon-like peptide 1 (GLP-1) receptor agonist being evaluated for the treatment of various metabolic disorders. We performed a meta-analysis of randomized data on the effects of tirzepatide on serum lipid levels. Methods: We systematically searched the PubMed and ClinicalTrials.gov databases for relevant data from randomized controlled clinical trials. All articles were screened, reviewed, and extracted by at least two independent authors, with conflicts resolved by consensus. Four hundred and thirty-three records were identified in the initial literature search; 18 of them were identified for full-text review, and 14 of those were systematically reviewed and included in the analysis. The meta-analysis was performed using an inverse variance random-effects model. Results: Fourteen articles that reported data from 13 randomized controlled clinical trials were included in the review. Nine trials had a low risk of bias, two had a moderate risk, and two had a high risk of bias. The pooled analysis showed that tirzepatide was efficacious at improving all lipid markers, including cholesterol and triglycerides. Moreover, a clear dose response trend was visible across results from groups taking 5, 10, and 15 mg of tirzepatide. Conclusion: There is growing evidence to support the use of tirzepatide in patients with metabolic diseases such as type 2 diabetes mellitus, metabolic syndrome, and obesity. Our results demonstrate that tirzepatide significantly improves all aspects of patient metabolism and might be superior in this regard to conventional agents such as insulin formulations or traditional GLP-1 agonists. [ABSTRACT FROM AUTHOR]

Background Traditional assessments often lack flexibility, personalized feedback, real-world applicability, and the ability to measure skills beyond rote memorization. These may not adequately accommodate diverse learning styles and preferences, nor do they always foster critical thinking or creativity. The inclusion of Artificial Intelligence (AI), especially Generative Pre-trained Transformers, in medical education marks a significant shift, offering both exciting opportunities and notable challenges for authentic assessment practices. Various fields, including anatomy, physiology, pharmacy, dentistry, and pathology, are anticipated to employ the metaverse for authentic assessments increasingly. This innovative approach will likely enable students to engage in immersive, project-based learning experiences, facilitating interdisciplinary collaboration and providing a platform for real-world application of knowledge and skills. Methods This commentary paper explores how AI, authentic assessment, and Student-as-Partners (SaP) methodologies can work together to reshape assessment practices in medical education. Results The paper provides practical insights into effectively utilizing AI tools to create authentic assessments, offering educators actionable guidance to enhance their teaching practices. It also addresses the challenges and ethical considerations inherent in implementing AI-driven assessments, emphasizing the need for responsible and inclusive practices within medical education. Advocating for a collaborative approach between AI and SaP methodologies, the commentary proposes a robust plan to ensure ethical use while upholding academic integrity. Conclusion Through navigating emerging assessment paradigms and promoting genuine evaluation of medical knowledge and proficiency, this collaborative effort aims to elevate the quality of medical education and better prepare learners for the complexities of clinical practice. [ABSTRACT FROM AUTHOR]

Background: Traditional medical education often lacks contextual experience, hindering students' ability to effectively apply theoretical knowledge in real-world scenarios. The integration of the metaverse into medical education holds great enormous promise for addressing educational disparities, particularly in lower-middle-income countries (LMICs) accompanied by rapid technological advancements. This commentary paper aimed to address the potential of the metaverse in enhancing basic sciences education within the constraints faced by universities in LMICs. We also addressed learning design challenges by proposing fundamental design elements and a suggested conceptual framework for developing metaverse-based teaching methods. The goal is to assist educators and medical practitioners in comprehensivley understanding key factors in immersive teaching and learning. Discussion: By immersing medical students in virtual scenarios mimicking real medical settings and patient interactions, the metaverse enables practice in clinical decision-making, interpersonal skills, and exposure to complex medical situations in a controlled environment. These simulations can be customized to reflect local healthcare challenges, preparing medical students to tackle specific community needs. Various disciplines, including anatomy, physiology, pharmacy, dentistry, and pathology, have begun leveraging the metaverse to offer immersive learning experiences, foster interdisciplinary collaborations, and facilitate authentic assessments. However, financial constraints pose a significant barrier to widespread adoption, particularly in resource-limited settings like LMICs. Addressing these challenges is crucial to realizing the full potential of metaverse technology in medical education. Conclusion: The metaverse offers a promising solution for enhancing medical education by providing immersive, context-rich learning experiences. This paper proposes a conceptual framework and fundamental design elements to aid faculty educators and medical practitioners in effectively incorporating metaverse technology into their teaching methods, thus improving educational outcomes in LMICs. [ABSTRACT FROM AUTHOR]

The World Wide Web and the advancement of computer technology in the 1960s and 1990s respectively set the ground for a substantial and simultaneous change in many facets of our life, including medicine, health care, and medical education. The traditional didactic approach has shifted towards more dynamic and interactive methods, leveraging technologies such as simulation tools, virtual reality, and online platforms. At the forefront is the remarkable evolution that has revolutionized how medical knowledge is accessed, disseminated, and integrated into pedagogical practices. The COVID-19 pandemic also led to rapid and large-scale adoption of e-learning and digital resources in medical education because of widespread lockdowns, social distancing measures, and the closure of medical schools and healthcare training programs. This review paper examines the evolution of medical education from the Flexnerian era to the modern digital age, closely examining the influence of the evolving WWW and its shift from Education 1.0 to Education 4.0. This evolution has been further accentuated by the transition from the static landscapes of Web 2D to the immersive realms of Web 3D, especially considering the growing notion of the metaverse. The application of the metaverse is an interconnected, virtual shared space that includes virtual reality (VR), augmented reality (AR), and mixed reality (MR) to create a fertile ground for simulation-based training, collaborative learning, and experiential skill acquisition for competency development. This review includes the multifaceted applications of the metaverse in medical education, outlining both its benefits and challenges. Through insightful case studies and examples, it highlights the innovative potential of the metaverse as a platform for immersive learning experiences. Moreover, the review addresses the role of emerging technologies in shaping the post-pandemic future of medical education, ultimately culminating in a series of recommendations tailored for medical institutions aiming to successfully capitalize on revolutionary changes. [ABSTRACT FROM AUTHOR]

This study evaluated effect of mental rotation (MR) training on learning outcomes and explored effectiveness of teaching via three-dimensional (3D) software among medical students with diverse spatial intelligence. Data from n = 67 student volunteers were included. A preliminary test was conducted to obtain baseline level of MR competency and was utilized to assign participants to two experimental conditions, i.e., trained group (n = 25) and untrained group (n = 42). Data on the effectiveness of training were collected to measure participants' speed and accuracy in performing various MR activities. Six weeks later, a large class format (LCF) session was conducted for all students using 3D software. The usefulness of technology-assisted learning at the LCF was evaluated via a pre- and post-test. Students' feedback regarding MR training and use of 3D software was acquired through questionnaires. MR scores of the trainees improved from 25.9±4.6 points to 28.1±4.4 (P = 0.011) while time taken to complete the tasks reduced from 20.9±3.9 to 12.2±4.4 minutes. Males scored higher than females in all components (P = 0.016). Further, higher pre- and post-test scores were observed in trained (9.0±1.9 and 12.3±1.6) versus untrained group (7.8±1.8; 10.8±1.8). Although mixed-design analysis of variance suggested significant difference in their test scores (P < 0.001), both groups reported similar trend in improvement by means of 3D software (P = 0.54). Ninety-seven percent of students reported technology-assisted learning as an effective means of instruction and found use of 3D software superior to plastic models. Software based on 3D technologies could be adopted as an effective teaching pedagogy to support learning across students with diverse levels of mental rotation abilities.

Background: More than 75 genetic susceptibility loci have been implicated with hyperglycemia. SNPs in genes linked with satiety, insulin sensitivity, BMR (Adiponectin, FTO, and MC4R), and inflammation and autoimmunity (TNF-a) have been shown to have a relationship with metabolic syndrome. Objectives: To measure the frequency of ADIPOQ, FTO, MCR4, and TNF SNPs in metabolic syndrome patients and controls, and to assess the association of these with hyperglycemia and adiposity status. Methods: A cross-sectional study was conducted. Baseline investigations including lipid profiles, blood glucose levels, and BMI calculation were conducted for n=113 Metabolic Syndrome and n=47 healthy controls. Tetra ARMS PCR and gel electrophoresis were conducted for Adiponectin rs266729, Adiponectin rs1501299, FTO rs9939609, MCR4 rs1297013, and TNF rs1800629. Statistical analysis was performed using SPSS version 26. Results: Difference was observed in lipid profiles (including LDL, triglycerides, and cholesterol but not HDL), body fat percentage (p= 0.034), and both fasting (p=0.000) and random (p=0.000) blood glucose levels among metabolic syndrome patients versus controls. Adiponectin rs1501299, FTO rs9939609, and MCR4 rs1297013 showed genotype frequency differences between groups. Spearman's correlation was applied to test any association of risk allele with study variables and MCR4 rs1297013 showed an independent association with Fasting Blood Glucose Level (r=0.238; p=0.024) irrespective of age, weight, or gender. Conclusion: MCR4 rs1297013 polymorphism is an independent risk factor leading to hyperglycemia in the study population, however, no other SNPs were identified to carry a significant risk. Large-scale genome-wide studies are required to identify the unique set of risk genes for the Pakistani population. [ABSTRACT FROM AUTHOR]