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Background and purpose: Gastric cancer is the third most common cancer in Iran and the second leading cause cancer-related death worldwide. Shark cartilage prevents angiogenesis in vivo and in vitro, however, its role on angiogenesis or anti-tumor responses in human is not clear yet. We studied the effects of oral treatment of shark cartilage on peripheral Treg frequency and TGF-β as suppressor activity and Treg cells inducer associated with TH1/TH2 cytokines pattern in patients with gastric cancer. Materials and methods: This study included 23 patients suffering from intestinal gastric cancer who were assigned into intervention (n=14) and control group (n=9). A month after conventional treatment the patients in intervention group were given three tablets (150mg) of shark cartilage daily for 20 days. Then flow cytometry was employed to determine whether the peripheral blood mononuclear cells such as CD4+CD25+Foxp3highT regulatory cells in patients with gastric cancer were changed correspondingly. Results: The results demonstrated that γ-IFN increased and IL-4 decreased in the intervention group. But, no changes were seen in Treg cells frequency and amounts of TGF-β. The evaluations for control group showed no significant difference. Conclusion: Shark cartilage amplified anti-tumor responses in patients with gastric cancer by increase in γ-IFN (TH1 immunity) and decrease in IL-4 (TH2 immunity).

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Background and purpose: Artificial intelligence (AI) is transforming numerous fields, particularly healthcare. In Iran, where AI is an emerging discipline, there is a notable gap in knowledge and understanding in this area. This study aimed to explore medical students' acceptance, knowledge, attitudes, and readiness regarding medical artificial intelligence.
Materials and methods: This descriptive cross-sectional study was conducted on 117 medical students selected through convenience sampling. The study utilized a structured questionnaire comprising four sections: demographic characteristics, readiness (22 items rated on a five-point Likert scale), acceptance (28 items rated on a five-point Likert scale), knowledge (8 items rated on a three-point Likert scale), and attitude toward artificial intelligence (13 items rated on a five-point Likert scale). Data were analyzed using SPSS version 27, employing descriptive statistics, independent t-tests, Pearson correlation tests, and regression analysis. A significance level of P<0.05 was considered statistically significant.
Results: The findings indicated that the mean levels of readiness (50.66±84.13), knowledge (23.17±27.3), and acceptance (25.95±63.14) were moderate, while the mean attitude level (51.46±01.6) was good. A direct and statistically significant relationship was observed among readiness, knowledge, acceptance, and attitude toward artificial intelligence (P<0.05), except for the relationship between readiness and attitude, which was not statistically significant (P=0.516). Regression analysis showed that attending artificial intelligence training courses (Beta=22.5, P=0.013) and knowledge about artificial intelligence (Beta=0.41, P<0.001) were strong predictors of readiness for medical artificial intelligence. These relationships remained statistically significant in both simple and multivariate linear regression analyses. Additionally, artificial intelligence usage and acceptance were identified as independent predictors of readiness in simple linear regression.
Conclusion: Medical students at Babol University of Medical Sciences showed a positive attitude toward artificial intelligence, indicating its growing relevance in medical education. These findings suggest that education planners should focus on improving students’ knowledge, readiness, and acceptance of AI through well-structured courses and training programs. Such efforts could help better prepare students for the increasing role of AI in healthcare.