Metabolic dysfunction-associated fatty liver disease (MAFLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is characterized by hepatic fat accumulation by metabolic dysfunction. The rising prevalence of MAFLD, especially among Asians, may be associated with changes in gut microbiota. We investigated gut microbiota characteristics and potential mechanisms leading to MAFLD development according to enterotypes. Case-control studies examining the gut microbiota composition between MAFLD and non-MAFLD participants were searched in public databases until July 2023. Gut microbiota was categorized into two enterotypes by principal component analysis. According to the enterotypes, LEfSe, ALDEx2, XGBoost, and DCiPatho were utilized to identify differential abundances and pathogenic microbes in the gut between the MAFLD and non-MAFLD groups. We analyzed microbial community networks with the SprCC module and predicted microbial functions. In the Prevotella enterotype (ET-P), 98.6% of Asians and 65.1% of Caucasians were associated with MAFLD (p = 0.049). MAFLD incidence was correlated with enterotype, age, obesity, and ethnicity (p < 0.05). Asian MAFLD patients exhibited decreased Firmicutes and Akkermansia muciniphila and increased Bacteroidetes and P. copri. The pathogenicity scores were 0.006 for A. muciniphila and 0.868 for P. copri. The Asian MAFLD group showed decreased stability and complexity in the gut microbiota network. Metagenome function analysis revealed higher fructose metabolism and lipopolysaccharide (LPS) biosynthesis and lower animal proteins and α-linolenic acid metabolism in Asians with MAFLD compared with the non-MAFLD group. LPS biosynthesis was positively correlated with P. copri (p < 0.05). In conclusion, P. copri emerged as a potential microbial biomarker for MAFLD. These findings enhance our understanding of the pathological mechanisms of MAFLD mediated through the gut microbiota, providing insights for future interventions.

The integration of artificial intelligence (AI) in medical diagnostics represents a transformative advancement in healthcare, with projected market growth reaching $188 billion by 2030. This comprehensive review examines the latest developments in AI-driven diagnostic technologies across multiple disease domains, particularly focusing on cancer, Alzheimer’s disease (AD), and diabetes. Through systematic bibliometric analysis using GraphRAG methodology, we analyzed research publications from 2022 to 2024, revealing the distribution and impact of AI applications across various medical fields. In cancer diagnostics, AI systems have achieved breakthrough performances in analyzing medical imaging and molecular data, with notable advances in early detection capabilities across 19 different cancer types. For AD diagnosis, AI-powered tools have demonstrated up to 90 % accuracy in risk detection through non-invasive methods, including speech pattern analysis and blood-based biomarkers. In diabetes care, AI-integrated systems incorporating deep neural networks and electronic nose technology have shown remarkable accuracy in predicting disease onset before clinical manifestation. These developments collectively indicate a paradigm shift toward more precise, efficient, and accessible diagnostic approaches. However, challenges remain in standardization, data quality, and clinical implementation. This review synthesizes current progress while highlighting the potential for AI to revolutionize medical diagnostics through enhanced accuracy, early detection, and personalized patient care.

We investigated the molecular mechanism by which Houttuynia cordata Thunb (HCT) may intervene in coronavirus disease 2019 (COVID-19) and COVID-19-induced cytokine storms using network pharmacology and molecular docking approaches. Using the Traditional Chinese medicine Systems Pharmacology Database and Analysis Platform (TCMSP), a “component–target–pathway” topology map of HCT for COVID-19 treatment was constructed using Cytoscape. Core target genes were analyzed using the STRING database, and the signal pathway map and biological mechanism of COVID-19 therapy were obtained using cluster profilers. Active components of HCT were docked with severe respiratory syndrome coronavirus 2 (SARS-CoV-2) 3C-like protease (3CL ^pro ) and RNA-dependent RNA polymerase (RdRp) using AutoDockTools. Data visualization and statistical analysis were conducted using the R program. A molecular dynamic simulation was carried out with the Groningen Machine for Chemical Simulation program. HCT had six active anti-COVID-19 ingredients and 45 molecular targets. Their crucial target proteins for COVID-19 treatment were the RELA (nuclear factor kappa B [NF- κ B] p65 subunit), interleukin 6, and mitogen-activated protein kinase 1. In functional enrichment analysis, the potential molecular targets of active components of HCT for COVID-19 treatment belonged to 18 signaling pathways (adjusted P = 2.12E-11). Gene ontology obtained by Kyoto Encyclopedia of Genes and Genome enrichment screening showed that the primary mechanism of COVID-19 treatment was upregulation of protein kinase C followed by downregulations of T cell differentiation and proliferation and NF- κ B signaling. Molecular docking showed that the active components of HCT (quercetin and kaempferol) had similar binding affinities for SARS-CoV-2 3CL ^pro and SARS-CoV-2 RdRp, primary COVID-19 target proteins as did clinically used drugs. These results were confirmed with molecular dynamics simulation. In conclusion, multiple components of HCT, especially quercetin and kaempferol, have the potential to treat COVID-19 infection and COVID-19-induced cytokine storm by targeting multiple proteins.

Metabolic syndrome (MetS) risk is influenced by genetic and environmental factors. The present study explored genetic risk scores (GRS) of genetic variants that influence the MetS and the effect of interactions between GRS and nutrient intake on MetS risk. The genetic variants that influence MetS risk were selected by genome-wide association study after adjusting for age, sex, area of residence and BMI in 8840 middle-aged adults. GRS were calculated by summing the risk alleles of the selected SNP and divided into low (0–1), medium (2–3) and high (4–7) risk groups, and the relationships between the MetS and GRS were determined by logistic regression after adjusting covariates involved in MetS risk. We also analysed the interaction between GRS and lifestyles. Four genetic variants (APOA5_rs651821, EFCAB4B_rs4766165, ZNF259_rs2160669 and APOBEC1_rs10845640) were selected because they increased MetS risk after adjusting for covariates. Individuals with medium-GRS and high-GRS alleles had a higher MetS risk by 1·48- and 2·23-fold, respectively, compared with those with low-GRS after adjusting for covariates. The increase in MetS risk was mainly related to serum TAG and HDL-cholesterol concentrations. The GRS had an interaction with carbohydrate (CHO) and Na intakes and daily physical activities for MetS risk. In conclusion, Asian middle-aged adults with high-GRS alleles were at increased MetS risk mainly due to dyslipidaemia. High daily physical activity (≥1 h moderate activity per d) reduced the MetS risk but a low-CHO diet (<65 % of total energy intake) increased the risk in carriers with high-GRS alleles. Low Na intake (<1·6 g Na intake/4 MJ) did not decrease its risk.