This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

Background Depressive symptoms have become a significant factor affecting the physical and mental health of the occupational population, and workers in petroleum refining enterprises face multiple stressors in their work environment. Objective To explore the impact of occupational noise exposure on depressive symptoms among workers in a petroleum refining enterprise. Methods This cross-sectional study was conducted in July 2024 using a questionnaire survey among workers of a petroleum refining enterprise in Hainan Province. Basic information of the subjects was collected. The Center for Epidemiologic Studies Depression Scale (CES-D) was used to measure depressive symptoms, the Chinese version of the Pittsburgh Sleep Quality Index (PSQI) scale was used to assess sleep quality, and the Chinese version of the Effort-Reward Imbalance (ERI) scale was used to evaluate occupational stress. Chi-square test was employed to compare the differences in reporting depressive symptoms among populations with different characteristics. Binary logistic regression models were used to analyze the impact of occupational noise exposure and other factors on depressive symptoms. Results The overall positive rate of depressive symptoms in the study population was 42.7%. The results of the multifactor analysis indicated that compared with the control group, employees in both the low-exposure and high-exposure groups had elevated odds of depressive symptoms, with OR (95%CI) of 2.244 (1.131, 4.454) and 1.970 (1.009, 3.850), respectively. This association remained robust after adjusting for potential confounders, including gender, age, work tenure, and other occupational exposures. Additionally, female [OR (95%CI)=1.483 (1.039, 2.118)], exposure to benzene, toluene, or xylene [OR (95%CI)=1.621 (1.208, 2.174)], sleep disturbance [OR (95%CI)=3.772 (2.942, 4.838)], and occupational stress [OR (95%CI)=2.018 (1.575, 2.585)] were also significantly associated with higher odds of depressive symptoms. Conclusion The positive rate of depressive symptoms is relatively high among employees in this petrochemical enterprise, and occupational noise exposure may be a risk factor for depressive symptoms.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

This article systematically reviews and examines the historical evolution of Bambusae Succus as a medicinal material， covering aspects such as nomenclature， origin， geographical distribution， harvesting and processing methods， quality assessment， therapeutic effects and indications， by consulting ancient herbal texts， medical compendia， and modern literature. The aim is to provide a reference for the development and utilization of famous classical formulas containing this herb. Research indicated that Bambusae Succus was first documented in the Shennong Bencaojing during the Han dynasty， with Zhuli being the standard name used throughout history， alongside aliases like Zhuzhi， Zhuyou and Huoquan. Historically， the primary source of Bambusae Succus has been Phyllostachys nigra var. henonis（Danzhu）， although other species such as Pleioblastus amarus and Bambusa emeiensis have also been used medicinally. Ancient records predominantly noted its origin in Yizhou（present-day Chengdu and surrounding areas in Sichuan） and the Wuling region（between present-day Hunan， Guangdong， Guangxi and Jiangxi provinces）， while contemporary sources are mainly from regions south of the Yangtze River and southwestern China. Traditionally， Bambusae Succus was harvested from bamboo that had grown for exactly one year， today， it can be collected year-round without strict age requirements. Ancient preparation methods included direct fire roasting or dry distillation， whereas modern industrial production employs dry distillation， reflux extraction， and percolation. In terms of quality evaluation， ancient texts considered a sweet taste to be superior， while today， clarity and transparency are prioritized. Historically， Bambusae Succus was characterized as sweet and cold nature， targeting the lung and stomach meridians， with uses evolving from clearing heat and resolving phlegm to nourishing Yin， moistening dryness， and relaxing tendons and unblocking meridians. Modern descriptions classify it as sweet， bitter， and cold in nature， affecting the heart， liver， and lung meridians， with functions including clearing heat， resolving phlegm， and facilitating orifices. It is indicated for conditions such as stroke with phlegm confusion， lung heat with phlegm congestion， convulsions， epilepsy， excessive phlegm in febrile diseases， high fever with thirst， irritability during pregnancy， and tetanus， with more clearly defined applications. Based on the results of the research， it is recommended that when developing and utilizing famous classical formulas containing Bambusae Succus， the one-year-old Phyllostachys nigra var. Henonis， which has been highly praised throughout history， should be selected as the source material. Industrial production should adopt the dry distillation method. Furthermore， in-depth research should be conducted on the modern technological characterization of the traditional quality control indicator of sweet taste， and reasonable modern quality control standards should be established.

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.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Breast cancer has become the malignant tumor with the highest incidence rate among women， seriously threatening the life and health of women all over the world. The pathogenic factors and development mechanisms of breast cancer are complex and diverse. The development of breast cells from ordinary hyperplasia to atypical hyperplasia， and from pre-cancerous lesions to cancerous lesions， is a long-term progressive process. Therefore， early screening and prevention of breast cancer is particularly important. Western medicine has a relatively mature treatment program for breast cancer， which is mainly based on surgery and systemic treatment， whereas the ensuing complications and adverse reactions often bring a heavy burden to patients. For the precancerous lesions of breast cancer， surgery is also the mainstay of treatment. In recent years， traditional Chinese medicine （TCM） has increasingly highlighted its advantages in the prevention and treatment of breast cancer. Increasing studies have shown that in the prevention and treatment of breast cancer and pre-cancerous lesions， TCM compound prescriptions， single herbs or herb pairs， and active components are able to regulate a variety of intracellular signaling pathways through multi-targets to inhibit the proliferation and invasion， promote the apoptosis and autophagy of tumor cells， and regulate the cell cycle and the immune microenvironment， thus exerting anti-tumor effects. At the same time， they can significantly attenuate the toxic side effects of radiotherapy and drug resistance of patients. However， the specific mechanisms of TCM in the prevention and treatment of breast cancer and precancerous lesions have not been fully clarified. The available studies are tanglesome regarding the TCM inhibition of tumor development through the regulation of classical signaling pathways such as phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt）/mammalian target of rapamycin （mTOR）， Wnt/β-catenin， and Notch， which still need to be verified by a large number of clinical and experimental studies. Therefore， this paper reviews the research progress in the prevention and treatment of breast cancer and precancerous lesions by TCM through interfering with the relevant signaling pathways in recent years， aiming to summarize the possible mechanisms of TCM in the prevention and treatment of breast cancer and provide references for subsequent studies.