Data set of gastroesophageal reflux disease caused by caffeine binding to dopamine receptor protein regulating brain addiction

The research team conducted logistic and Cox regression according to the behavioral data of gastroesophageal reflux disease patients who had long been drinking caffeinated coffee drinks, and determined the sensitivity and mathematical rationality of AI prediction model in behavioral science, which can support the research team to build a deep learning neural network and complete the prediction of gastrointestinal tract involvement. The research team designed a controlled trial to analyze the difference of functional reward and reinforcement in the midbrain limbic between people who have coffee drinking habits and blank people. In terms of molecular dynamics, the research team used molecular docking and molecular dynamics technology to analyze the molecular mechanics of caffeine binding to dopamine receptors, which proved the basis of caffeine induced toxic side reactions activating immune verification pathway in the molecular force field. On the basis of interdisciplinary research, the neuropsychological mechanism of caffeine binding to dopamine receptor protein regulating the activation of inflammatory immune factors by the mesolimbic dopamine pathway was verified, and the AI prediction model of gastrointestinal injury was developed at the application level. This paper solves the difficulty of adjusting caffeine intake to predict gastrointestinal injury, and achieves the goal of drinking coffee and caffeine drinks healthily.

研究团队针对长期饮用含咖啡因饮料的胃食管反流病患者的行怍数据，实施了逻辑回归和Cox回归分析，并确定了AI预测模型在行为科学中的敏感性和数学合理性，该模型能够支持研究团队构建深度学习神经网络并完成胃肠道损伤的预测。研究团队设计了一项对照实验，以分析具有咖啡饮用习惯的人与未饮用咖啡的人在中脑边缘系统中功能奖励和强化的差异。在分子动力学方面，研究团队运用分子对接和分子动力学技术分析了咖啡因与多巴胺受体的分子力学相互作用，证明了咖啡因诱导的毒副作用激活免疫验证途径的分子基础。基于跨学科研究，验证了咖啡因与多巴胺受体蛋白结合的中枢边缘多巴胺通路调节炎症免疫因子的神经心理学机制，并在应用层面开发了胃肠道损伤的AI预测模型。本文解决了调整咖啡摄入量以预测胃肠道损伤的难题，实现了健康饮用咖啡和含咖啡因饮料的目标。