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Metabolic-associated steatohepatitis (MASH) and pancreatic inflammation are key complications of obesity-related metabolic syndrome. Elevated IL-6; a proinflammatory cytokine, contributes to liver steatosis and pancreatic β-islet cells dysfunction. This study explores pancreatic tissue-resident (tr)NK cells IL-6 receptor (IL-6R) in pancreatic injury in a murine MASH model. MASH models were established using male mice fed a high-fat diet ( ; 60.3% kcal from fat) for 4 weeks and using immunocompromised NOD-SCID IL2rγnull (NSG) mice fed with HFD for 16 weeks and injected with 10 × 10 pancreatic trNK and treated with IL-6R antagonizing antibody on week 12. Biochemical assays assessed serum ALT, AST, lipids, glucose, and insulin levels. Pancreatic injury was analyzed through mRNA expression of Reg1, Reg3, oxidative stress marker of tissue malondialdehyde (MDA) and β-islet cells' proliferation and apoptosis. Fibrotic markers of α-SMA, Collagen-I, and Fibronectin were assessed via RT-PCR and trNK cell activation (CD107a, NKp46, IFN-γ) were assessed by flow cytometry. mice exhibited increased serum cholesterol, triglycerides, fasting blood glucose, and liver injury enzymes. Markers of pancreatic injury of Reg1/Reg3 and pancreatic MDA with β-islet cells apoptosis were significantly elevated compared to littermates' control. These results were accompanied by a decline in trNK counts and activations (P < 0.05). In an adoptive transfer model, NSG mice fed with HFD and transplanted with trNK cells from donors (expressing high IL-6) exhibited similar pancreatic injury markers, whereas those receiving trNK cells from mice pre-treated with an IL-6R antagonist showed marked reductions in Reg1/Reg3 (∼2-fold), MDA (∼1.77-fold), and β-islet cells apoptosis (∼2.2-fold). Moreover, phenotypic characterization of the NSG mice fed an HFD transplanted with IL-6R antagonizing antibody showed an increase in the NK cell activation marker CD107a (∼2.3-fold) and amelioration in pancreatic fibrotic profile of α-SMA mRNA expressions of 1.6 -fold when compared to its counterparts. Our data highlights the importance of IL-6R modulation on trNK cells in remodeling pancreatic tissue after liver injury, emphasizing the liver-pancreas axis as a therapeutic target to prevent pancreatic damage, β-islet cells dysfunction and fibrosis and reduce the risk of diabetes and metabolic syndrome.

Objectives We aimed to assess undergraduate medical students’ knowledge, attitude, and perception regarding artificial intelligence (AI) in medicine. Methods A multi-national, multi-center cross-sectional study was conducted from March to April 2022, targeting undergraduate medical students in nine Arab countries. The study utilized a web-based questionnaire, with data collection carried out with the help of national leaders and local collaborators. Logistic regression analysis was performed to identify predictors of knowledge, attitude, and perception among the participants. Additionally, cluster analysis was employed to identify shared patterns within their responses. Results Of the 4492 students surveyed, 92.4% had not received formal AI training. Regarding AI and deep learning (DL), 87.1% exhibited a low level of knowledge. Most students (84.9%) believed AI would revolutionize medicine and radiology, with 48.9% agreeing that it could reduce the need for radiologists. Students with high/moderate AI knowledge and training had higher odds of agreeing to endorse AI replacing radiologists, reducing their numbers, and being less likely to consider radiology as a career compared to those with low knowledge/no AI training. Additionally, the majority agreed that AI would aid in the automated detection and diagnosis of pathologies. Conclusions Arab medical students exhibit a notable deficit in their knowledge and training pertaining to AI. Despite this, they hold a positive perception of AI implementation in medicine and radiology, demonstrating a clear understanding of its significance for the healthcare system and medical curriculum. Clinical relevance statement This study highlights the need for widespread education and training in artificial intelligence for Arab medical students, indicating its significance for healthcare systems and medical curricula. Key Points • Arab medical students demonstrate a significant knowledge and training gap when it comes to using AI in the fields of medicine and radiology . • Arab medical students recognize the importance of integrating AI into the medical curriculum. Students with a deeper understanding of AI were more likely to agree that all medical students should receive AI education. However, those with previous AI training were less supportive of this idea . • Students with moderate/high AI knowledge and training displayed increased odds of agreeing that AI has the potential to replace radiologists, reduce the demand for their services, and were less inclined to pursue a career in radiology, when compared to students with low knowledge/no AI training .

ObjectivesWe aimed to assess undergraduate medical students’ knowledge, attitude, and perception regarding artificial intelligence (AI) in medicine.MethodsA multi-national, multi-center cross-sectional study was conducted from March to April 2022, targeting undergraduate medical students in nine Arab countries. The study utilized a web-based questionnaire, with data collection carried out with the help of national leaders and local collaborators. Logistic regression analysis was performed to identify predictors of knowledge, attitude, and perception among the participants. Additionally, cluster analysis was employed to identify shared patterns within their responses.ResultsOf the 4492 students surveyed, 92.4% had not received formal AI training. Regarding AI and deep learning (DL), 87.1% exhibited a low level of knowledge. Most students (84.9%) believed AI would revolutionize medicine and radiology, with 48.9% agreeing that it could reduce the need for radiologists. Students with high/moderate AI knowledge and training had higher odds of agreeing to endorse AI replacing radiologists, reducing their numbers, and being less likely to consider radiology as a career compared to those with low knowledge/no AI training. Additionally, the majority agreed that AI would aid in the automated detection and diagnosis of pathologies.ConclusionsArab medical students exhibit a notable deficit in their knowledge and training pertaining to AI. Despite this, they hold a positive perception of AI implementation in medicine and radiology, demonstrating a clear understanding of its significance for the healthcare system and medical curriculum.Clinical relevance statementThis study highlights the need for widespread education and training in artificial intelligence for Arab medical students, indicating its significance for healthcare systems and medical curricula.Key Points• Arab medical students demonstrate a significant knowledge and training gap when it comes to using AI in the fields of medicine and radiology.• Arab medical students recognize the importance of integrating AI into the medical curriculum. Students with a deeper understanding of AI were more likely to agree that all medical students should receive AI education. However, those with previous AI training were less supportive of this idea.• Students with moderate/high AI knowledge and training displayed increased odds of agreeing that AI has the potential to replace radiologists, reduce the demand for their services, and were less inclined to pursue a career in radiology, when compared to students with low knowledge/no AI training.