BACKGROUND: Caffeine is related to the incidence of neuro immune gastrointestinal diseases. Coffee consumption needs to be optimized in order to reduce the incidence rate.
PURPOSE: By using KEEG analysis to explore potential molecular signaling pathways involved in the progression of neurological immune gastrointestinal diseases, and analyzing the details of this signaling Pathway using molecular simulation results, which can support AI system for doctor.
METHODS: The research team designed a controlled experiment to analyze the differences in reward and reinforcement of Brain pleasure/addiction and dopamine related signaling pathways function between multiple groups of people with different coffee drinking habits and a blank control group. The study team used molecular dynamics methods to investigate the signaling route that links coffee with the binding of dopamine receptor D3.AI is used to predict the prevalence of gastric reflux disease.
RESULTS: Human experiments have shown a correlation between caffeine intake and gastroesophageal reflux disease. AI algorithm results can provide clinical support, and molecular simulation results are consistent with human experimental results. Caffeine and DRD3 protein have a stable interaction system.
CONCLUSION: The research team elucidated the intermolecular interaction between caffeine and DRD3, and AI algorithms can predict the likelihood of disease occurrence, providing a new strategy for clinical practice. This study has passed ethical approval at Chifeng Cancer Hospital, and the ethical documents for this study have been submitted to the World Health Organization for filing.
BACKGROUND: Smoking behavior can affect the pathogenesis and prognosis of neuroimmune gastrointestinal diseases.
AIMS: The purpose of developing tools to assist clinical practice is to avoid the harm of cigarettes to the human body.
METHODS: Molecular dynamics method was used to elucidate the biophysical mechanism of TP53 gene mutation caused by harmful ingredients, and the signaling pathway of midbrain edge excitation was determined by molecular dynamics of nicotine and dopamine receptor D3. The possible involvement of nicotine in neuronal damage was determined through the molecular interaction between nicotine and ACHE. Molecular pathways were analyzed based on the aforementioned biological principles, developed artificial intelligence systems and brain computer interface systems.
RESULTS: Several signaling pathways were elucidated, and effective AI algorithms were developed.
CONCLUSION: The accuracy of artificial intelligence systems is over 70%. This study provides clinical doctors with a new precision medicine strategy and tool to regulate patient behavior and reduce disease risk. Other: This project was approved by the Ethics Committee of Chifeng Cancer Hospital and reported to the WHO.