ObjectiveTo investigate the feasibility， safety， and efficacy of surgery-assisted transjugular intrahepatic portosystemic shunt （SA-TIPS） in the treatment of portal hypertension comorbid with complex portal vein thrombosis， including cavernous transformation of the portal vein （CTPV）. MethodsAn analysis was performed for the data of 36 patients with portal hypertension and complex portal vein thrombosis who underwent SA-TIPS in Beijing Shijitan Hospital， Capital Medical University， from November 2023 to January 2025， including general status， technical data of the surgical process （surgical success rate， puncture times， time of operation， the number of stents used， and the length of shunt）， perioperative complications， and surgical recovery. The change in portal pressure gradient （PPG） after shunt was compared， and the rate of reaching the standard for PPG reduction was calculated， as well as stent patency rate within 1 week after surgery. The paired samples t-test was used for comparison of continuous data between two groups. ResultsAmong the 36 patients， 34 （94.4%） underwent SA-TIPS successfully. The incidence rate of perioperative complications was 16.7% （6/36）， including 3 cases of thoraco-abdominal hemorrhage， 2 cases of intraoperative arrhythmia， and 1 case of incision infection. There was a significant reduction in PPG after SA-TIPS （t=19.85， P<0.01）， and the patients achieving a ≥50% reduction in PPG accounted for 76.5% （26/34）. Imaging reexamination within 1 week showed a shunt patency rate of 100%. ConclusionSA-TIPS has a high technical success rate， a favorable safety profile， and good efficacy in the treatment of portal hypertension comorbid with complex portal vein thrombosis （including CTPV）， and therefore， it holds promise for clinical application.

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.

Objective To explore the differences in heartbeat-evoked response (HER) under drug-related cues and neutral cues in individuals with heroin use disorder (HUD), and analyze the correlation between HER potentials and immediate cue-induced craving scores. Methods Fifty HUD participants were recruited from the Chang’an Compulsory Isolation Drug Rehabilitation Center in Shaanxi Province from June to September 2024. Simultaneous acquisition of 64-channel electroencephalography (EEG) and electrocardiogram signals was performed. Twenty alternating segments of drug-related and neutral cue videos were presented, and participants rated their subjective craving after each segment using visual analogue scale (VAS) scores. Scalp EEG data were source analyzed to obtain cortical EEG signals and corresponding HER. Short-time Fourier transform was used to calculate the power spectral density (PSD) of EEG within a time window from 100 ms before the R-peak to 500 ms after it, using the R-peak as the time zero point. Cluster-based permutation testing was used to analyze PSD differences between drug-related and neutral cues in the HUD individuals. Pearson correlation analysis was performed to evaluate the correlation between HER potentials and VAS scores. Results In the 350–420 ms time window, HER potentials in the left posterior parietal, temporal, and posterior cingulate cortices were significantly lower under drug-related cues compared to neutral cues (P＜0.01); in the 140–210 ms time window, HER potentials in the right prefrontal cortex were significantly higher under drug-related cues compared to neutral cues (P＜0.01). Correlation analysis showed that HER potentials in the left temporal and left posterior cingulate cortices were significantly negatively correlated with VAS scores (P＜0.05). Drug-related cues enhanced PSD of γ power (30–100 Hz) in salience network (fronto-insular), parietal and occipital regions (P＜0.05). PSD integrations of low-γ power (40–60 Hz) in parietal region (350–400 ms) and high-γ power (70–100 Hz) in left salience network (fronto-parietal) and occipital regions (300–350 ms) were positively correlated with VAS scores (P＜0.05). Conclusions Drug-related cues may modulate cortical activity related to heartbeat perception in HUD individuals, and such dynamic changes in both time and frequency domains are stably associated with subjective craving.

Objective To study the five-year survival status and influencing factors of elderly patients with lung cancer complicated with chronic obstructive pulmonary disease (COPD). Methods A cohort study was conducted to follow up 450 patients with lung cancer and chronic obstructive pulmonary disease who were hospitalized in our hospital from January 2018 to December 2023. The endpoint of the follow-up was the end of a five-year period or death. The Life Tables method was used to calculate survival rates and plot survival curves. The Cox proportional hazards model was used to analyze the influencing factors of five-year survival. Results The results indicated that the overall five-year survival rate of patients was 4.89%, and it decreased year by year. Cox regression analysis showed that age, gender, family functioning, and psychological status significantly influenced patient survival rate (all P<0.05). Stratified analysis found that the smoking status, family functioning, and psychological status of male patients all had an impact on survival rate (all P<0.05), while the psychological status of female patients had a more significant impact on survival (P=0.008). Conclusion This study provides a scientific basis for comprehensive intervention of elderly lung cancer patients with COPD. It is recommended that clinical attention should be paid to psychological and family factors to improve patient prognosis.

Objective: To investigate the factors influencing intraoperative blood transfusion in patients with hip fractures and to develop a machine learning (ML) model for predicting this risk. Methods: A total of 424 patients with hip fractures who underwent surgical treatment between November 2022 and March 2025 in our hospital were selected. Key feature variables of intraoperative blood transfusion risk were identified using the Boruta algorithm. Four different ML algorithms—support vector machine (SVM), linear discriminant analysis (LDA), mixed discriminant analysis (MDA), and extreme gradient boosting (XGBoost)—were used to develop predictive models for intraoperative blood transfusion risk. The predictive performance of the four ML models were evaluated using accuracy, precision, receiver operating characteristic (ROC) curves, precision-recall curves (PRC), precision-recall gain curves (PRGC), and F1 scores. Shapley additive interpretation (SHAP) was used to interpret the final model. Results: Among the 424 patients, 77(18.2%) received intraoperative blood transfusion. The Boruta algorithm identified albumin (ALB), activated partial thromboplastin time (APTT), types of anesthesia, types of fracture, and hemoglobin (Hb) as key feature variables for predicting intraoperative blood transfusion risk. In model evaluation, the SVM model outperforms the other three models across multiple metrics, including the area under the receiver operating characteristic curve (AUC), recall, recall gain, accuracy, precision, F1 score, and the area under the precision-recall curve (PRC-AUC). The SVM model, interpreted and visualized based on SHAP values, effectively predicted intraoperative blood transfusion risk in patients with hip fracture. A visual online application was developed based on the SVM model (https://pbo-nomogram.shinyapps.io/blood/). Conclusion: Preoperative low ALB and Hb levels, prolonged APTT, general anesthesia, and intertrochanteric fractures are risk factors for intraoperative blood transfusion in hip fracture patients. The risk prediction model for intraoperative blood transfusion constructed based on the SVM algorithm has optimal performance, which provides new ideas and methods for the clinical early identification of hip fracture patients with high transfusion risk and the implementation of targeted interventions.

ObjectiveTo observe the effects of Dihuang Yinzi （DY） on motor dysfunction in rats with advanced Parkinson's disease （PD） and to investigate the mechanisms by which DY improves advanced PD symptoms through the "gut microbiota-short-chain fatty acids （SCFAs）-inflammation-neuroprotection pathway". MethodsAn advanced PD rat model was induced by rotenone. Rats were divided into a normal group， model group， positive drug group （levodopa， 50 mg·kg^-1）， and DY low-， medium-， and high-dose groups （5.2， 10.4， 20.8 g·kg^-1）. After 7 days of administration， motor function was evaluated using the open-field， pole-climbing， and inclined plate tests. Hematoxylin-eosin （HE） staining was used to observe pathological changes in the substantia nigra and colon， and immunohistochemistry was performed to detect α-Synuclein （α-Syn） and tyrosine hydroxylase （TH） expression in the substantia nigra. Enzyme-linked immunosorbent assay （ELISA） was used to measure levels of dopamine （DA）， 5-hydroxytryptamine （5-HT）， 3，4-dihydroxyphenylacetic acid （DOPAC）， Levodopa， homovanillic acid （HVA）， tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， and interleukin-1β （IL-1β）. Western blot analysis was used to detect the expression of zonula occludens-1 （ZO-1） and occludin. Gut microbiota diversity was analyzed by 16S rRNA sequencing， and gas chromatography （GC） was used to determine the content of SCFAs in colonic contents. ResultsCompared with the normal group， the model group showed significantly decreased movement speed and distance in the open-field test， prolonged pole-climbing time， and reduced retention angle on the inclined plate （P<0.01）， accompanied by increased α-Syn expression （P<0.01） and decreased TH expression （P<0.01） in the brain. Compared with the model group， all DY dose groups improved motor dysfunction in advanced PD rats to varying degrees （P<0.05， P<0.01） and alleviated pathological damage in the brain and colon. High-dose DY significantly reduced α-Syn aggregation in the substantia nigra （P<0.01） and increased TH expression （P<0.01）. ELISA and Western blot results showed that， compared with the normal group， the model group exhibited decreased levels of DA， 5-HT， DOPAC， Levodopa， and HVA in the striatum （P<0.01）， increased levels of TNF-α， IL-6， and IL-1β in the colon and striatum （P<0.01）， and significantly reduced expression of ZO-1 （P<0.05） and occludin in the colon （P<0.01）. Compared with the model group， all DY dose groups increased the levels of DA， 5-HT， DOPAC， Levodopa， and HVA in the striatum to varying degrees （P<0.05， P<0.01）. In the high-dose DY group， the levels of TNF-α， IL-6， and IL-1β in the colon and striatum were reduced （P<0.01）， while the expression of ZO-1 （P<0.05） and occludin in the intestine was increased. The 16S rRNA sequencing results indicated that the relative abundances of Actinobacteriota， Enterobacteriaceae， and Erysipelotrichaceae were increased in the model group， whereas the relative abundances of Bacteroidota， class Clostridia， Lachnospiraceae， and Akkermansia muciniphila were decreased. These changes were effectively reversed after high-dose DY intervention. GC analysis showed that the content of SCFAs in the colonic contents of rats in the model group was decreased （P<0.05， P<0.01）， while after high-dose DY intervention， the levels of acetate， propionate， isobutyrate， and butyrate were significantly increased （P<0.05， P<0.01）. ConclusionDY may exert therapeutic effects in advanced PD by regulating the gut microbiota-SCFAs-inflammation pathway.

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.

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.

Background With China's socioeconomic development, significant lifestyle changes have occurred among occupational groups, leading to alterations in cardiovascular metabolic risk factors. However, few studies have examined the secular trends of these risk factors in China's working population. Objective To analyze the trends in cardiovascular metabolic risk factors among the occupational population in nine provinces of China from 2009 to 2018, and to explore the associations between different occupational types and these risk factors, along with their clustering patterns, thereby providing evidence for targeted interventions. Methods This study utilized data from the China Health and Nutrition Survey (CHNS) in 2009, 2015, and 2018. The dataset covered 13274 employed individuals aged 18-59 years. Only participants with complete blood test results and physical measurement data were included in the final analysis. The average annual percentage change (AAPC) in the positive rates of risk factors was calculated using Joinpoint regression to analyze temporal trends. Multinomial logistic regression and binary logistic regression models were employed to examine the cross-sectional associations between occupational types (categorized as white-collar workers, agricultural workers, blue-collar workers, service workers, and others) and nine cardiovascular metabolic risk factors and their clustering (defined as having ≥2 risk factors) in 2018. Results A total of 8451 participants were included. From 2009 to 2018, significant increasing trends (AAPC>0, P<0.05) were observed for overweight, obesity, central obesity, borderline elevated triglycerides (TG), borderline elevated to elevated total cholesterol (TC), borderline elevated low-density lipoprotein cholesterol (LDL-C), elevated blood pressure and hypertension, diabetes. The most rapid increases were found for obesity (AAPC=6.07%, 95%CI: 3.42%, 8.88%), hypertension (AAPC=4.10%, 95%CI: 0.62%, 7.77%), and central obesity (AAPC=3.27%, 95%CI: 1.24%, 5.36%). The clustering rate of risk factors increased from 62.3% in 2009 to 73.2% in 2018 (AAPC=1.87%, 95%CI: 1.02%, 2.73%). The cross-sectional analysis in 2018, after adjusting for confounders, showed that compared with white-collar workers, blue-collar workers had a significantly lower risk of overweight (OR=0.67, 95%CI: 0.48, 0.95), central obesity (OR=0.65, 95%CI: 0.45, 0.92), and borderline high TG (OR=0.54, 95%CI: 0.35, 0.82). In contrast, agricultural workers had a higher risk of hypertension (OR=1.76, 95%CI: 1.14, 2.72). Conclusion Between 2009 and 2015, the burden of most cardiometabolic risk factors increases significantly among the Chinese occupational population, with the rate of increase slowing by 2018. Risks vary across occupational types, among which blue- collar workers report lower target outcomes. Targeted interventions focusing on the rapidly increasing risk factors and high-risk occupational groups are recommended.