This paper outlines the development of artificial intelligence （AI） and its applications in traditional Chinese medicine （TCM） research， and elucidates the roles and advantages of large language models， knowledge graphs， and natural language processing in advancing syndrome identification， prescription generation， and mechanism exploration. Using spleen-stomach diseases as an example， it demonstrates the empowering effects of AI in classical literature mining， precise clinical syndrome differentiation， efficacy and safety prediction， and intelligent education， highlighting an upgraded research paradigm that evolves from data-driven and knowledge-driven approaches to intelligence-driven models. To address challenges related to privacy protection and regulatory compliance in cross-institutional data collaboration， a "trusted federated evidence-based analysis platform for TCM spleen-stomach diseases" is proposed， integrating blockchain-based smart contracts， federated learning， and secure multi-party computation. The deep integration of AI with privacy-preserving computing is reshaping research and clinical practice in TCM spleen-stomach diseases， providing feasible pathways and a technical framework for building a high-quality， trustworthy TCM big-data ecosystem and achieving precision syndrome differentiation.

OBJECTIVE To explore the mechanism of action of Qingre huatan huoxue decoction against atherosclerosis （AS）based on macrophage polarization. METHODS Using atorvastatin served as the positive control， the drug-containing serum of the Qingre huatan huoxue decoction was prepared to treat RAW264.7 macrophages. Macrophage viability， apoptosis rate， and the fluorescence intensities of CD86 and CD206 were measured， along with the levels of tumor necrosis factor-α （TNF-α） and interleukin-1β （IL-1β）. Apolipoprotei n E-deficient （ApoE －/－ ） mice （AS model mice） fed with a high-fat diet were randomly assigned to model group， atorvastatin group （2.6 mg/kg）， and low-， medium- and high-dose groups of Qingre huatan huoxue decoction （90， 180， 360 mg/kg）， respectively. C57BL/6J mice fed with a standard diet served as the normal control group， with 10 mice per group. The treatment group mice were administered the corresponding drugs intragastrically， once daily， for 8 consecutive weeks. Serum levels of TNF-α and IL-1β were measured in all groups. Lipid deposition in the aorta （assessed by the percentage of plaque in the entire aorta and aortic root） and morphological changes in the aortic root were observed. Expression levels of CD86 and CD206 in aortic tissue， as well as the protein expression levels of inducible nitric oxide synthase （iNOS）， arginase-1 （Arg-1）， AMP-activated protein kinase （AMPK）， phosphorylated AMPK （p-AMPK）， and peroxisome proliferator-activated receptor γ （PPAR-γ） in aortic tissues were all detected. RESULTS Cell experiment results showed that， at concentrations of 5-100 μg/mL， the drug-containing serum of the Qingre huatan huoxue decoction significantly increased RAW264.7 cell viability （ P ＜0.05）. The drug-containing serum of the Qingre huatan huoxue decoction at concentrations of 10， 50， and 100 μg/mL， along with atorvastatin， significantly reduced apoptosis rates， CD86 fluorescence intensity， and TNF-α and IL-1β levels in RAW264.7 cells， while markedly enhancing CD206 fluorescence intensity （ P ＜0.05）. Animal experiment results showed that， compared with the model group， all dosage groups of Qingre huatan huoxue decoction and the atorvastatin group showed significantly reduced/down-regulated levels of TNF-α and IL-1β in serum， along with decreased aortic total and root plaque percentages， CD86 expression， and iNOS protein expression. CD206 expression and Arg-1， p-AMPK/AMPK， PPAR-γ protein expression were significantly up-regulated （ P ＜0.05）. Pathological morphology of the aorta showed varying degrees of improvement. CONCLUSIONS The formula of Qingre huatan huoxue decoction exerts its anti-AS effects by regulating macrophage polarization， increasing the proportion of M2 macrophages， thereby effectively inhibiting AS plaque formation and reducing inflammatory responses.

In recent years， hyperuricemia （HUA） has shown a rapidly increasing incidence and tends to occur in increasingly young people， with a wide range of cardiac， renal， joint， and cancerous hazards and all-cause mortality associations. Western medicine treatment has limitations such as large liver and kidney damage， medication restriction， and easy recurrence. The intestine is the major extra-renal excretion pathway for uric acid （UA）， and the intestinal microecology can be regulated to promote UA degradation. It offers great potential to develop UA-lowering strategies that target the intestinal microecology， which are promising to provide safer and more effective therapeutic approaches. Traditional Chinese medicine （TCM） can treat HUA via multiple targets and multiple pathways from a holistic view， with low toxicity and side effects. Studies have shown that intestinal microecology is a crucial target for TCM in the treatment of HUA. However， its specific mechanism of action has not been fully elucidated. Focusing on the key role of intestinal microecology in HUA， this review explores the relationship between intestinal microecology and HUA in terms of intestinal flora， intestinal metabolites， intestinal UA transporters， and intestinal barriers. Furthermore， we summarize the research progress in TCM treatment of HUA by targeting the intestinal microecology， with the aim of providing references for the development of TCM intervention strategies for HUA and the direction of future research.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective To establish a liquid chromatography method for the simultaneous determination of 13 aromatic amine compounds (AAs) in workplace air. Methods A total of 13 AAs in both vapor and aerosol phases were collected in workplace air using a new GDH-6 sampling tube. Samples were desorbed and eluted with methanol, separated using a Symmetry Shield™ RP₁₈ reversed-phase liquid chromatography column, and detected with a diode array detector. Quantification was performed using an external standard method. Results The linear range of the 13 AAs measured by this method was 0.02-373.60 μg/L with the correlation coefficients greater than 0.999 0. The minimum detection concentration was 0.09-14.37 μg/m³, and the minimum quantitative concentration was 0.31-47.90 μg/m³ (both calculated based on sampling 15.0 L of air and 3.0 mL of elution volume). The average desorption and elution efficiency ranged from 97.46% to 101.23%. The within-run relative standard deviation (RSD) was 0.10%-5.99%, and the between-run RSD was 0.17%-2.71%. Samples could be stably stored in sealed conditions at 2-8 ℃ for more than seven days. Conclusion This method is suitable for the simultaneous determination of 13 AAs in workplace air, including both vapor and aerosol phases.

Objective: To explore the network structure of eating behaviors with anxiety, depression and perceived stress in adolescents, so as to provide a basis for effective prevention and intervention of eating behavior problems and negative emotions in adolescents. Methods: Based on the Psychology and Behavior Investigation of Chinese Residents (2021) database, the study was conducted among 3 087 adolescents. Sakata Eating Behavior Scale Short From(EBS-SF) was used to investigate their eating behaviors. The Patient Health Questionnaire-9(PHQ-9), Generalized Anxiety Disorder Scale-7 Item(GAD-7), and Perceived Stress Questionnaire-3 Item (PSQ-3) were used to evaluate their depression, anxiety and perceived stress. Network analysis method was applied to construct a network of eating behaviors and negative emotional symptoms among adolescents, so as to evaluate the centrality, bridge strength, stability and accuracy of each item. Results: The total scores of eating behaviors, depression,anxiety and stress perception in adolescents were 17.41±4.53,6.95±6.08,4.86±5.03,9.34±3.80,respectively. The symptom with the highest intensity and expected impact was "I am only satisfied when I buy more food than I need", with a node intensity and expected impact value of 4.37. The nodes Depression and Anxiety were the most closely connected(weight=0.87). There were no statistically significant differences in the network structure( M =0.13,0.11) and network connection strength(female and male:4.16,4.06, s =0.10;urban and rural areas:4.08,4.07, s =0.01) between different sexes and residents ( P >0.05). Conclusion The negative impact of comorbidities such as anxiety, depression, perceived stress and eating behaviors among adolescents can be reduced through targeted prevention and intervention of core symptoms and bridging symptoms.

AIM: To identify the primary active components and underlying mechanisms of Yijing Decoction(YJD)in treating early diabetic retinopathy(DR)based on liquid chromatography-mass spectrometry and network pharmacology.METHODS: Active components of YJD were characterized through LC-MS. Components with optimal ADME(absorption, distribution, metabolism, excretion)properties were selected as key bioactive candidates. Network pharmacology approaches were employed to predict YJD-DR therapeutic targets. Protein-protein interaction(PPI)networks, gene ontology(GO)enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis were subsequently conducted to predict core targets and networks. Critical targets and pathways were experimentally validated through Western blot.RESULTS: Ten core therapeutic targets were identified, including TNF, Alb, EGFR, STAT3, PTGS2, ESR1, PPAR, MMP9, TLR4, and MAPK. YJD was related to cancer-related signaling, fluid shear stress and atherosclerosis, and neurodegenerative diseases, encompassing key biological processes such as inflammatory response regulation, programmed cell death activation, and enhanced cell migration. Furthermore, Western blot analysis confirmed that YJD significantly inhibited high glucose-induced phosphorylation of STAT3(P-STAT3/STAT3)and ERK(P-ERK/ERK)in rat retinal microvascular endothelial cells.CONCLUSION: This study revealed YJD's pharmacodynamical basis and its multi-component, multi-target, and multi-paths pharmacology. YJD exerts therapeutic effects on DR by coordinately regulating critical signaling pathways and alleviating intraocular inflammation, thus preserving retinal vascular endothelial cells, maintaining blood-retinal barrier integrity, and facilitating retinal neurovascular repair.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

With the rising incidence of breast cancer among women, neoadjuvant chemotherapy (NAC) is becoming increasingly crucial as a preoperative treatment modality, enabling tumor downstaging and volume reduction. However, its efficacy varies significantly among patients, underscoring the importance of predicting pathological complete response (pCR) following NAC. Early research relied on statistical methods to integrate clinical data for predicting treatment outcomes. With the advent of artificial intelligence (AI), traditional machine learning approaches were subsequently employed for efficacy prediction. Deep learning emerged to dominate this field, and demonstrated the capability to automatically extract imaging features and integrate multimodal data for pCR prediction. This review comprehensively examined the applications and limitations of these three methodologies in predicting breast cancer pCR. Future efforts must prioritize the development of superior predictive models to achieve precise predictions, integrate them into clinical workflows, enhance patient care, and ultimately improve therapeutic outcomes and quality of life.
Humans ; Breast Neoplasms/pathology* ; Neoadjuvant Therapy ; Artificial Intelligence ; Female ; Machine Learning ; Deep Learning ; Chemotherapy, Adjuvant ; Treatment Outcome

Instrument separation is a critical complication during root canal therapy, impacting treatment success and long-term tooth preservation. The etiology of instrument separation is multifactorial, involving the intricate anatomy of the root canal system, instrument-related factors, and instrumentation techniques. Instrument separation can hinder thorough cleaning, shaping, and obturation of the root canal, posing challenges to successful treatment outcomes. Although retrieval of separated instrument is often feasible, it carries risks including perforation, excessive removal of tooth structure and root fractures. Effective management of separated instruments requires a comprehensive understanding of the contributing factors, meticulous preoperative assessment, and precise evaluation of the retrieval difficulty. The application of appropriate retrieval techniques is essential to minimize complications and optimize clinical outcomes. The current manuscript provides a framework for understanding the causes, risk factors, and clinical management principles of instrument separation. By integrating effective strategies, endodontists can enhance decision-making, improve endodontic treatment success and ensure the preservation of natural dentition.
Humans ; Root Canal Therapy/adverse effects* ; Consensus ; Root Canal Preparation/adverse effects*