Objective:To observe the efficacy of Tegafur gimeracil oteracil potassium(Gio) capsules combined with oxaliplatin in the treatment of advanced gastric cancer,and explore its effect on matrix metalloproteinase-9 (MMP-9) expression in cancer tissues. Methods: Totally 120 patients with advanced gastric cancer were randomly divided into the observation group and the control group, and both groups were treated with neoadjuvant chemotherapy for 3 cycles as follows:the observation group was given Gio capsules and oxaliplatin,and the control group was given 5-fluorouracil combined with oxaliplatin. The short term efficacy,adverse reactions and ex-pression of MMP-9 in cancer tissue before and after the chemotherapy were observed in the two groups. Results:After the treatment, the objective response rate in the two groups had no significant difference(P>0.05);the clinical benefit rate of the observation group was significantly higher than that of the control group(P<0.05);the incidence of severe bone marrow suppression and liver and kid-ney dysfunction in the observation group was significantly lower than that in the control group(P<0.05);after the treatment,the pos-itive expression of MMP-9 in the observation group was significantly lower than that in the control group(P<0.05). Conclusion:Gio capsules combined with oxaliplatin can improve the clinical benefit rate of the patients with advanced gastric cancer,and effectively re-duce the expression of MMP-9 in cancer tissue.

Stroke ranks as the third leading cause of death and the fourth leading cause of disability worldwide. Its high disability rate and prolonged recovery period not only severely impact patients' quality of life but also impose a significant burden on families and society. Primary prevention is the cornerstone of stroke control, as early intervention on risk factors can effectively reduce its incidence. Therefore, the development of predictive models for first-ever stroke risk holds substantial clinical value. In recent years, advancements in big data and artificial intelligence technologies have opened new avenues for stroke risk prediction. This article reviews the current research status of traditional methods and machine learning models in predicting first-ever stroke risk and outlines future development trends from three perspectives: First, emphasis should be placed on technological innovation by incorporating advanced algorithms such as deep learning and large models to further enhance the accuracy of predictive models. Second, there is a need to diversify data types and optimize model architectures to construct more comprehensive and precise predictive models. Lastly, particular attention should be given to the clinical validation of models in real-world settings. This not only enhances the robustness and generalizability of the models but also promotes physicians' understanding of predictive models, which is crucial for their application and dissemination.