Metabolic dysfunction-associated fatty liver disease （MAFLD） has become one of the most prevalent chronic liver diseases worldwide， posing a serious challenge to public health. In this context， the integration of artificial intelligence （AI）， especially intelligent diagnosis and treatment and digital health interventions based on machine learning， can break through the limitations of traditional methods， realize efficient screening of multi-dimensional data such as key genes， biomarkers， and biochemical metabolites， and achieve revolutionary breakthroughs in risk prediction， subtype identification， and therapeutic effect assessment for MAFLD. This article systematically reviews the ground-breaking application of machine learning models in driving the innovation of clinical diagnosis and precise risk prediction of MAFLD， conducts a comprehensive comparative analysis of digital health practice cases of MAFLD in China and globally， and deeply analyzes their advantages and limitations in terms of research subjects， interventions， and management team. Studies have shown that the deep integration of digital health and long-term management of MAFLD is becoming the key engine driving the transformation of disease management modes towards an intelligent， individualized， and precise era， but there are various ethical and technical issues that need to be addressed urgently.

ObjectiveTo investigate the mutation status of the MET gene in tumor patients， with a focus on its expression in lung cancer tissues and its impact on patient prognosis， providing theoretical basis and potential targets for precise diagnosis and treatment of the tumors. MethodsA total of 1，627 tumor patient samples were collected from January 2021 to September 2025. High-throughput sequencing technology was used to conduct DNA-level gene detection on 137 tumor patients with MET mutations. The distribution of mutation sites in exons and introns， as well as the occurrence frequencies of MET gene amplification and gene fusion were analyzed， with particular attention paid to the mutation frequencies of each exon and the high-frequency variant types. Immunohistochemical staining was performed to analyze the expression levels of c-MET protein in tumor tissues of the 137 patients. Immunohistochemical analysis was conducted on tumor tissues of 9 patients with MET gene exon 14 skipping mutations to study the association between c-MET protein expression and this specific mutation type. RNA sequencing results， immunohistochemical staining data， and follow-up prognosis of lung cancer from the GEPIA2 database and TCGA database were comprehensively analyzed to explore the potential relationship between MET gene expression in tumor tissues and patient prognosis. ResultsAmong 1，627 tumor patients， 137 MET mutation patients were detected， including 93 males with an average age of （66.85±9.61） years and 44 females with an average age of （59.43±11.08） years. Lung cancer was the most common tumor type （117 cases）. MET gene expression was lower in breast cancer and glioblastoma multiforme， but higher in lung cancer and colorectal cancer as compared with the expression in normal tissues. Survival analysis showed that patients with high MET gene expression in lung adenocarcinoma had a shorter overall survival time than those with low expression. A total of 961 mutations were detected by next-generation sequencing， including 547 exon mutations and 374 intron mutations. MET gene amplification was found in 13 cases and gene fusion in 27 cases. The highest mutation frequency was in exon 2， followed by exon 5， exon 4， and exon 19. High-frequency variant types included c.2890C>A （p.L964M） on exon 4 and c.3028G>T （p.D1010Y） on exon 5. The c-MET protein expression was closely related to the type of gene mutation. Missense mutations often led to positive or strongly positive c-MET protein expression， while nonsense mutations often resulted in weak or absent c-MET protein expression. Among the 9 patients with MET gene exon 14 skipping mutations， the c-MET protein expression score was mostly 0 - 1， with only 1 case scoring 2. Among the 117 lung cancer patients， the incidence rate was higher in males than in females， and more common among stage Ⅲ-Ⅳ patients than among stage I-Ⅱ patients. Out of the 41 patients treated with surgery + targeted drugs （gutemitinib， savolitinib）， 8 cases of recurrence or metastasis were found during follow-up. ConclusionMET gene mutations exhibit specific mutation characteristics in different tumor types， and its expression levels show significant differences between various tumor tissues and normal tissues. It is closely related to the poor prognosis of lung adenocarcinoma patients. We found that c-MET protein expression is closely related to the type of MET gene mutation， especially in patients with MET gene exon 14 skipping mutations， which presents a characteristic expression pattern. This provides an important basis for the preliminary screening using immunohistochemistry in clinical practice.

Objective:To study the optimum ethanol extraction process. Methods: The extraction rate of extractum and total flavonoids as well scutellarin was choosen as the assessment index. The optimum ethanol extraction process was selected with the orthogonal design. Results: The optimum ethanol extraction process conditions follow as: adding 10 fold of 75% ethanol, extracting for 2.0h and twice in all. Conclusion: The average extraction rate of extractum and total flavonoids as well scutellarin was 13.6%, 91.7%, 78.4%(n=3), respectively.