ObjectiveTo observe the clinical efficacy and safety of Yuyin Lidan granules （YYLD） in preventing the recurrence of common bile duct stones （CBDS） in patients with liver and gallbladder dampness-heat syndrome following endoscopic retrograde cholangiopancreatography （ERCP）. MethodsThis randomized， parallel， controlled trial enrolled postoperative CBDS-ERCP patients who met the inclusion and exclusion criteria. Sixty-four patients were randomly assigned to an observation group or a control group， with 32 cases in each. Both groups received conventional Western medical treatment after ERCP， while the observation group additionally received YYLD for 8 weeks. The follow-up period lasted for 1 year. The efficacy indicators included bile bilirubin levels， traditional Chinese medicine （TCM） syndrome scores， clinical efficacy rate， pancreatitis and inflammation markers， postoperative liver function， and CBDS recurrence rate at 1-year follow-up， which were used to jointly evaluate the clinical efficacy and safety of both groups. ResultsA total of 56 patients completed the study and were included in the final analysis， i.e.， 29 in the observation group and 27 in the control group. Baseline characteristics were comparable between the two groups. Compared with pre-treatment and with the control group after treatment， the bile bilirubin level in the observation group significantly decreased （P<0.05）. After treatment， the clinical cure and marked improvement rates were higher in the observation group than in the control group， showing a statistically significant difference in overall clinical efficacy （P<0.05）. Compared with pre-treatment， the primary and secondary symptoms in the observation group， as well as the primary symptom and the secondary symptom of nausea and vomiting in the control group （weeks 4 and 8）， were significantly reduced （P<0.05）. Compared with the control group after treatment， the observation group showed significant reductions in the primary symptom of loose stools/constipation （day 5 and week 4） and in three secondary symptoms， i.e.， bitter taste and sticky dry mouth， abdominal distension and poor appetite （throughout the treatment period）， and general heaviness and fatigue （day 5 and week 4）， with statistical differences （P<0.05）. Compared with pre-treatment， both groups showed decreased lipase and urinary amylase levels （P<0.05）. However， no significant between-group differences were observed in pancreatitis or inflammation-related indices after treatment. Compared with pre-treatment， all liver function indicators in the observation group and alanine aminotransferase （ ALT ）， γ-glutamyl transferase （ γ-GT ）， alkaline phosphatase （ALP）， and conjugated bilirubin in the control group significantly decreased at weeks 4 and 8 （P<0.05）. Compared with the control group after treatment， only serum total bilirubin and unconjugated bilirubin were significantly reduced in the observation group during the treatment period （P<0.05）. ConclusionYYLD combined with conventional Western medical treatment can effectively regulate bilirubin metabolism （in bile and serum）， improve TCM clinical symptoms， and prevent CBDS recurrence after ERCP in patients with liver and gallbladder dampness-heat syndrome. This regimen is safe and effective and is worthy of further clinical research and promotion.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

OBJECTIVE To analyze the distribution characteristics of mirtazapine steady-state trough concentration and concentration-to-dose ratio （ C / D ）， and to investigate the influence of clinical and genetic factors on C / D . METHODS A retrospective study was conducted on hospitalized patients with depression who received mirtazapine treatment and underwent therapeutic drug monitoring at the First Hospital of Hebei Medical University from May 2022 to May 2025. The collected data included patients’ gender， age， body mass index， daily dose， steady-state trough concentration， smoking status， history of liver disease， drug type， concomitant medications， and CYP2D6 metabolic phenotype. The C / D was calculated. Spearman rank correlation was used to analyze the relationship between mirtazapine steady-state trough concentration and daily dose. Univariate analysis and multiple linear regression model were employed to screen the factors potentially influencing the C / D of mirtazapine. RESULTS A total of 226 patients were included. The daily dose of mirtazapine was 25.00 （24.82， 30.00） mg/d， the steady-state trough concentration was 44.46 （20.00， 70.00） ng/mL， and the C / D was 1.83 （1.00， 2.00） （ng·d）/（mL·mg）. Steady-state trough concentrations were within the reference range （30-80 ng/mL） in 121 patients （53.54%）， below the lower limit in 80 patients （35.40%）， and above the upper limit in 25 patients （11.06%）. A positive correlation was observed between mirtazapine steady-state trough concentration and daily dose （coefficient of determination was 0.320 8， P ＜0.001）. Gender， smoking status， and CYP2D6 metabolic phenotype were significantly associated with the mirtazapine C / D （ P ＜0.05）. CONCLUSIONS Significant interindividual variability exists in mirtazapine steady-state trough concentrations. Gender， smoking status， and CYP2D6 metabolic phenotype are identified as independent influencing factors for the mirtazapine C / D ， with higher C / D ratios observed in females， non-smokers， and intermediate metabolizers.

Esophageal cancer （EC） is a highly prevalent malignant tumor in China. The phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt） signaling pathway， as one of the key oncogenic pathways， can promote the cell cycle progression， proliferation， migration， and invasion， induce chemoresistance， and inhibit apoptosis and autophagy of EC cells. Traditional Chinese medicine （TCM）， with the advantages of targeting multiple points with multiple components to delay cancer progression， can target the PI3K/Akt signaling pathway for EC treatment. This article preliminarily discusses the molecular mechanism and role of the PI3K/Akt signaling pathway in EC and elaborates on the specific targets and efficacy of TCM in treating EC through intervention in the PI3K/Akt signaling pathway in the past five years. TCM materials and extracts inhibiting the PI3K/Akt signaling pathway in EC include Borneolum， spore powder of Ganoderma lucidum without spore coat， extract of Celastrus orbiculatus， root extract of Taraxacum， and Bruceae Fructus oil emulsion. TCM active ingredients exerting the effect include flavonoids， terpenoids， saponins， phenols， polysaccharides， alkaloids， and other compounds. TCM compound prescriptions with such effect include Qige San， Huqi San， Xuanfu Daizhetang， Tongyoutang and its decomposed prescriptions， Liujunzi Tang， and Xishenzhi Formula. In addition， TCM injections such as Compound Kushen Injection and Kang'ai injection also inhibit the PI3K/Akt signaling pathway in EC. This paper summarizes the role of the PI3K/Akt signaling pathway in EC and the TCM interventions， aiming to provide reference for the research and clinical application of new drugs for EC.

Extracellular vesicles (EVs) are pivotal mediators of intercellular communication within the tumor immune microenvironment (TME). They are broadly categorized into exosomes, microvesicles, and apoptotic bodies based on their distinct biogenesis pathways. Exosomes originate from the endosomal system via multivesicular body fusion, microvesicles bud directly from the plasma membrane, and apoptotic bodies are released during programmed cell death. By shuttling diverse bioactive cargoes—including proteins, lipids, and nucleic acids such as mRNA, miRNA, and DNA—EVs exert dual modulatory effects on tumor initiation, progression, and immune evasion. Importantly, EVs exhibit remarkable compositional heterogeneity that is intrinsically linked to their cellular origin. Tumor-derived EVs (TDEVs) are typically enriched with immunosuppressive molecules like PD-L1, TGF‑β, and miR-21, which promote tumor immune escape and metastasis. In contrast, EVs derived from immune cells, such as dendritic cells or cytotoxic T lymphocytes, often carry immunostimulatory components including antigens, co-stimulatory molecules, and granzymes, thereby potentiating anti-tumor immunity. This review systematically delineates the biogenesis and molecular composition of EVs, with a particular emphasis on their dynamic regulatory functions within the TME. Specifically, we discuss how EVs mediate intricate crosstalk between immune and tumor cells, facilitating signal transfer that reshapes immune surveillance. For instance, TDEVs can induce macrophage polarization toward an M2-like pro-tumor phenotype, while also suppressing natural killer cell cytotoxicity and dendritic cell maturation. The clinical utility of EV-associated biomarkers in liquid biopsy is increasingly recognized. Circulating EVs carry tumor-specific molecular signatures that mirror the genetic and proteomic alterations of primary tumors, enabling non-invasive early diagnosis, molecular subtyping, and real-time monitoring of therapeutic responses. Their natural biocompatibility, low immunogenicity, and intrinsic ability to traverse biological barriers make them ideal candidates for drug delivery systems. This review explores cutting-edge applications, including the use of EVs in immune checkpoint blockade therapy—for instance, engineered EVs displaying anti-PD-1 antibodies or carrying siRNA to silence immunosuppressive genes. Moreover, EV-based tumor vaccines are being developed, leveraging dendritic cell-derived EVs loaded with tumor antigens to elicit potent T cell responses. The feasibility of loading EVs with therapeutic molecules such as chemotherapeutic agents, oncolytic viruses, or CRISPR-Cas9 components is also under active investigation. The advent of engineered EVs has further expanded their therapeutic potential. Through surface modification or cargo encapsulation, EVs can be tailored for targeted delivery and controlled release, enhancing precision immunotherapy. However, several hurdles impede clinical translation. Current isolation and purification methods, such as ultracentrifugation and size-exclusion chromatography, suffer from low yield and purity. Distinguishing EV subpopulations remains technically challenging due to overlapping size and marker expression. Moreover, the lack of standardized protocols for EV production, characterization, and quality control poses significant barriers to regulatory approval and clinical adoption. Looking forward, the convergence of multi-omics technologies with artificial intelligence offers a powerful approach to decipher EV heterogeneity and identify robust diagnostic signatures. Machine learning algorithms can integrate proteomic, transcriptomic, and lipidomic data from large patient cohorts to construct predictive models for cancer diagnosis and prognosis. Concurrently, advances in bioengineering are enabling the design of next-generation EVs with enhanced targeting specificity, on-demand drug release, and reduced off-target effects. Future efforts should also focus on establishing good manufacturing practice (GMP)‑compliant production processes and conducting rigorous preclinical and clinical evaluations. In summary, this review provides a comprehensive overview of EV biology, their multifaceted roles in the TME, and their transformative potential in cancer diagnostics and therapeutics. By addressing current challenges and leveraging emerging technologies, EV-based strategies are poised to revolutionize precision oncology.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective: To investigate the factors influencing follow-up visit behavior among patients with chronic periodontitis after supragingival scaling, and to provide evidence for early identification of high-risk dropouts and improvement of treatment continuity. Methods: This study was approved by the Institutional Medical Ethics Committee, and written informed consent was obtained from all participants. A retrospective analysis was conducted on 572 patients with chronic periodontitis who received supragingival scaling at the Outpatient Department of The Affiliated Stomatological Hospital of Nanjing University School of Medicine from July 2022 to July 2023 and required subsequent periodontal sequential therapy. Data regarding sex, age, monthly income, education level, oral health literacy, reason for visit, and satisfaction with care were collected. Patients were divided into a follow-up group and a dropout group based on whether they adhered to the scheduled appointments within 12 months. Chi-square tests and independent samples t-tests were used for univariate comparisons between the groups. Variables with P<0.05 in the univariate analysis (gender, education level, monthly income, reason for visit, oral health literacy, and satisfaction with care), along with age (as a covariate), were included in a multivariate logistic regression model to analyze the factors influencing follow-up behavior. Results: Among the 572 patients, 239 attended follow-up visits, while 333 dropped out, resulting in a dropout rate of 58.22%. Univariate analysis revealed statistically significant differences between the two groups in terms of gender, education level, monthly income, reason for visit, oral health literacy, and satisfaction with care (P<0.05). Multivariate logistic regression analysis showed that patients referred for treatment had significantly lower compliance (OR=0.241, 95% CI: 0.154-0.377, P<0.001). Conversely, higher oral health literacy scores (OR=1.159, 95% CI: 1.056-1.272, P=0.002) and higher satisfaction with care (OR=4.090, 95% CI: 1.732-9.656, P=0.001) were associated with better follow-up adherence. Conclusion Follow-up behavior in patients with chronic periodontitis after supragingival scaling is primarily influenced by the reason for visit, oral health literacy, and satisfaction with care. These factors can serve as intervention targets to reduce dropout rates in clinical practice.

OBJECTIVE To investigate the effect and mechanism of Tangzhi formula（TZF） on improving glucose and lipid metabolism disorders in type 2 diabetes mellitus （T2DM） rats.METHODS Thirty SPF-grade SD rats were used to establish a T2DM rat model through high-fat diet feeding combined with intraperitoneal injection of streptozotocin. After successful modeling， the rats were divided into the model group， metformin group （positive control， 0.18 g/kg）， and TZF low-， medium-， and high-dose groups （5， 10， and 20 g/kg）， with 6 rats in each group. An additional 6 rats were selected as the blank group. The rats in each group were orally administered the corresponding volume of drug solution or saline， and continuous dosing was performed for 6 weeks， once daily. Fasting blood glucose （FBG）， area under the curve of oral glucose tolerance test （OGTT-AUC）， area under the curve of intraperitoneal insulin tolerance test （IPITT-AUC）， serum total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， and high-density lipoprotein cholesterol （HDL-C） levels were measured. Hematoxylin-eosin （HE） staining and oil red O staining were used to observe hepatic pathological morphology and lipid deposition. Differentially expressed proteins in liver tissues were screened by proteomics analysis， and key proteins in signaling pathways were verified by Western blot and immunofluorescence staining.RESULTS Compared with the blank group， the levels of FBG， OGTT-AUC， IPITT-AUC， LDL-C， TG， and TC in the model group were significantly increased （ P ＜0.05）， while HDL-C level was significantly decreased （ P ＜0.05）； the liver tissues showed disordered hepatic cords， punctate necrosis of hepatocytes， and significant lipid deposition， the NAFLD activity score and positive area of lipid droplets were significantly increased（ P ＜0.05）. Compared with the model group， the above indicators in TZF low-， medium-， and high-dose groups and metformin group were significantly reversed （ P ＜0.05）； hepatic pathological injury and lipid deposition were alleviated. Proteomics results showed that a total of 442 differentially expressed proteins were screened between the model group and the blank group， and 358 differentially expressed proteins were screened between the TZF high-dose group and the model group. Among these differentially expressed proteins， multiple proteins closely related to glucose and lipid metabolism showed a reversal trend after TZF intervention. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that the peroxisome proliferator-activated receptor （PPAR） signaling pathway was significantly enriched. The validation experiment results showed that TZF could significantly up-regulate the expression of PPARα， PPARγ， and apolipoprotein A1 （ApoA1） in rat liver tissues （ P ＜0.05）， and down-regulate the expression of stearoyl-CoA desaturase 1 （SCD1） and perilipin 2 （PLIN2） （ P ＜0.05）.CONCLUSIONS TZF can improve glucose and lipid metabolism disorders in T2DM rats； its mechanism may be related to the activation of the PPAR signaling pathway and the regulation of downstream lipid metabolism-related proteins SCD1， PLIN2， and ApoA1.

Metabolic dysfunction-associated fatty liver disease （MAFLD） has become the most common chronic liver disease worldwide. Its pathological process is centered on the “two-hit” theory， with gut-liver axis dysregulation running through the entire disease course from steatosis to liver cirrhosis and even hepatocellular carcinoma， and it forms an extensive cross-system regulatory mechanism with the metabolic， cardiovascular， renal， and psychological and nervous systems through multi-dimensional pathways such as the “gut-liver-brain axis”， the “liver-kidney axis”， and the “liver-heart axis”. Disease progression is not limited to the liver itself， and it also involves functional imbalance of multiple organ systems throughout the body. This article systematically elaborates on the association mechanism of “liver pathological progression-multi-axis regulation-comorbidity occurrence” in MAFLD， proposes synergistic management strategies integrating early screening based on risk stratification， targeted intervention， and multidisciplinary diagnosis and treatment， and analyzes the limitations of current research and future development directions， in order to provide a theoretical basis and practical guidance for precise diagnosis and treatment and individualized management of MAFLD.