ObjectiveTo explore the listed varieties and prescription characteristics of Chinese patent medicines for stroke in China， explore the medication rules of Chinese medicine for stroke， and provide guidance for further clinical research and development of Chinese patent medicines. MethodsExcel 2021 and the Ancient and Modern Medical Record Cloud Platform （V2.3.5） were used to systematically mine and analyze the varieties and prescriptions of Chinese patent medicines for stroke in China. ResultsA total of 244 Chinese patent medicines （two for different dosage forms of the same prescription）， 1 736 approval documents for Chinese patent medicines， 792 manufacturers， and 83 varieties of protected Chinese patent medicines were finally included in the database. The top three dosage forms were capsules （75）， pills （53）， and tablets （42）. There were 28 Chinese patent medicines for stroke in the National Essential Drug Catalogue （2018）， 129 in the National Essential Medical Insurance， Industrial Injury Insurance and Maternity Insurance Drug Catalogue （2023）， and 4 in the National Non-prescription Drug Catalogue. Among the 138 prescriptions screened out， Chinese patent medicines mainly treated stroke patients with the syndrome of Qi deficiency and blood stasis. The top three most frequent medicinal herbs were Chuanxiong Rhizoma （63）， Pheretima （47）， and Salviae Miltiorrhizae Radix et Rhizoma （47）. The medicinal herbs used were mainly warm， pungent， with the meridian tropism to the liver meridian. The correlation analysis showed that the herb pair with the highest support was Astragali Radix-Chuanxiong Rhizoma， and that with the highest confidence was Carthami Flos-Chuanxiong Rhizoma. Five herb combinations were identified based on the cluster analysis. ConclusionThe Chinese patent medicines for stroke mainly treat patients with the syndrome of Qi deficiency and blood stasis. The medicinal herbs used in the prescriptions mainly have the functions of activating blood and resolving stasis， extinguishing wind and stopping convulsions. Drug compatibility usually focuses on activating blood and resolving stasis， as well as expelling phlegm and opening orifices. This review of the varieties and prescription characteristics of Chinese patent medicines for stroke helps optimize clinical decision-making， guide drug research and development， promote medical research and scientific progress， and provide more effective support and guarantee for the treatment of stroke patients.

[Objective] To analyze the mediating pathways of autonomy perception on the intrinsic motivation for blood donation and the moderating mechanism of satisfaction, and to explore the factors that enhance the intrinsic motivation of blood donation in order to promote sustained donor engagement and effectiveness. [Methods] This study, grounded in Self-Determination Theory (SDT), recruited 1 293 employees from private enterprises in Zhejiang Province with prior blood donation experience as the research subjects. Data on demographic characteristics, intrinsic motivation for blood donation, and influencing factors were collected through a voluntary blood donation questionnaire. Spearman correlation analysis and the PROCESS macro were employed to examine the relationships among variables and their underlying mechanisms. [Results] In the multiple linear regression analysis with autonomy perception, life balance perception, and satisfaction as independent variables, 60% of the variance in intrinsic motivation for blood donation was explained by these predictors (adjusted R²=0.60). In the moderated mediation model test, life balance perception mediated the effect of autonomy perception on intrinsic motivation for blood donation. Furthermore, blood donation satisfaction positively moderated the direct path (β=0.06, P<0.01) while negatively moderating the second half of the mediation path (β=-0.04, P<0.01), revealing a dual moderating effect of satisfaction. [Conclusion] Autonomy perception and blood donation satisfaction jointly drive intrinsic motivation for blood donation. Strategies for optimizing blood donation satisfaction, which take into account both the direct path effect and the indirect mediation effect, can offer valuable insights for addressing the discrepancy between high first-time donation rates and low donor retention rates.

ObjectiveTo analyze the distribution characteristics of traditional Chinese medicine （TCM） syndrome elements in different risk populations of heart failure complicated with type 2 diabetes. MethodsClinical data of 675 type 2 diabetes patients were retrospectively collected. Lasso-multivariate Logistic regression was used to construct a clinical prediction nomogram model. Based on this， 441 non-heart failure patients were divided into a low-risk group （325 cases） and a high-risk group （116 cases） according to the median risk score of heart failure complicated with type 2 diabetes. TCM diagnostic information （four diagnostic methods） was collected for both groups， and factor analysis was applied to summarize the distribution of TCM syndrome elements in different risk populations. ResultsLasso-multivariate Logistic regression analysis identified age， disease duration， coronary heart disease， old myocardial infarction， arrhythmia， absolute neutrophil count， activated partial thromboplastin time， and α-hydroxybutyrate dehydrogenase as independent risk factors for heart failure complicated with type 2 diabetes. These were used as final predictive factors to construct the nomogram model. Model validation results showed that the area under the curve （AUC） of the receiver operating characteristic （ROC） curve for the modeling group and validation group were 0.934 and 0.935， respectively. The Hosmer-Lemeshow test （modeling group P = 0.996， validation group P = 0.121） indicated good model discrimination. Decision curve analysis showed that the curves for All and None crossed in the upper right corner， indicating high clinical utility. The low-risk and high-risk groups each obtained 14 common factors. Preliminary analysis revealed that the main disease elements in the low-risk group were qi deficiency （175 cases， 53.85%）， dampness （118 cases， 36.31%）， and heat （118 cases， 36.31%）， with the primary locations in the spleen （125 cases， 38.46%） and lungs （99 cases， 30.46%）. In the high-risk group， the main disease elements were yang deficiency （73 cases， 62.93%）， blood stasis （68 cases， 58.62%）， and heat （49 cases， 42.24%）， with the primary locations in the kidney （84 cases， 72.41%） and heart （70 cases， 60.34%）. ConclusionThe overall disease characteristics in different risk populations of type 2 diabetes patients with heart failure are a combination of deficiency and excess， with deficiency being predominant. Deficiency and heat are present throughout. The low-risk population mainly shows qi deficiency with dampness and heat， related to the spleen and lungs. The high-risk population shows yang deficiency with blood stasis and heat， related to the kidneys and heart.

OBJECTIVE: To construct a predictive model for the reduction in methadone maintenance treatment (MMT) and evaluate the effects of different interventions and other clinical factors on methadone reduction using Shapley additive explanations (SHAP). METHODS: Two clinical trials of acupuncture for methadone reduction in MMT patients were analyzed, and the baseline data, MMT related information, intervention measures, the data related to dose-reduction outcomes were collected. The predictive model was constructed by means of 6 machine learning algorithms including support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), Naive Bayes (NB), random forest (RF) and categorical-boosting (CatBoost), and 2 integration methods, blending-ensemble method (Blending) and Stacking-ensemble method (Stacking). SHAP was employed for the interpretability analysis of the optimal model. RESULTS: A total of 251 MMT patients were included, 128 cases in the acupuncture group and 123 cases in the non-acupuncture group. CatBoost and Stacking performed optimally in the test set. CatBoost obtained an accuracy of 0.780 0±0.060 8, a precision of 0.500 0±0.120 0, a recall of 0.818 2±0.140 2, F1 score of 0.620 7±0.114 0, and receiver operating characteristic-area under curve (ROC-AUC) of 0.857 8±0.140 2 for the subjects. In MMT patients with acupuncture as an adjunctive therapy, the top 5 important features for methadone reduction, included intervention measures, body mass index (BMI), the duration of MMT, the history of opioid use and occupation; and SHAP values were 1.25, 0.36, 0.21, 0.19 and 0.12, respectively. The SHAP feature dependence plot showed that BMI, MMT duration and the history of opioid use presented a nonlinear negative correlation with the reduction effect. CONCLUSION In acupuncture as adjunctive therapy for methadone reduction, the clinical factors should be considered comprehensively; and the interpretable predictive model provides a scientific basis for it, which is conducive to the improvement of clinical strategy of acupuncture for methadone reduction and the development of personalized reduction scheme.
Humans ; Methadone/therapeutic use* ; Acupuncture Therapy ; Adult ; Female ; Male ; Machine Learning ; Middle Aged ; Opiate Substitution Treatment ; Young Adult

BACKGROUND: The optimal antidepressant dosages remain controversial. This study aimed to analyze the efficacy of antidepressants and characterize their dose-response relationships in the treatments of major depressive disorders (MDD). METHODS: We searched multiple databases, including the Embase, Cochrane Central Register of Controlled Trials, PubMed, and Web of Science, for the studies that were conducted between January 8, 2016, and April 30, 2023. The studies are double-blinded, randomized controlled trials (RCTs) involving the adults (≥18 years) with MDD. The primary outcomes were efficacy of antidepressant and the dose-response relationships. A frequentist network meta-analysis was conducted, treating participants with various dosages of the same antidepressant as a single therapy. We also implemented the model-based meta-analysis (MBMA) using a Bayesian method to explore the dose-response relationships. RESULTS: The network meta-analysis comprised 135,180 participants from 602 studies. All the antidepressants were more effective than the placebo; toludesvenlafaxine had the highest odds ratio (OR) of 4.52 (95% confidence interval [CI]: 2.65-7.72), and reboxetine had the lowest OR of 1.34 (95%CI: 1.14-1.57). Moreover, amitriptyline, clomipramine, and reboxetine showed a linear increase in effect size from low to high doses. The effect size of toludesvenlafaxine increased significantly up to 80 mg/day and subsequently maintained the maximal dose up to 160 mg/day while the predictive curves of nefazodone were fairly flat in different dosages. CONCLUSIONS: Although most antidepressants were more efficacious than placebo in treating MDD, no consistent dose-response relationship between any antidepressants was observed. For most antidepressants, the maximum efficacy was achieved at lower or middle prescribed doses, rather than at the upper limit. REGISTRATION No. CRD42023427480; https://www.crd.york.ac.uk/prospero/display_record.php?
Humans ; Antidepressive Agents/therapeutic use* ; Depressive Disorder, Major/drug therapy* ; Dose-Response Relationship, Drug ; Randomized Controlled Trials as Topic

EGFR-mutant non-small cell lung cancer (NSCLC) is a common type of lung cancer, with EGFR tyrosine kinase inhibitors (EGFR-TKIs) being the standard first-line treatment. However, most patients with NSCLC eventually develop resistance to EGFR-TKIs. Studies on the mechanism underlying EGFR-TKI resistance have driven the development of personalized and precision medicine. Current strategies to address resistance include targeted therapy, immunotherapy, and novel drug treatments. Selecting the appropriate personalized treatment plan is crucial for improving the survival rate and quality of life of patients with EGFR-mutant NSCLC. Thus, this study provides a brief review of the current status and future perspectives in the treatment of EGFR-mutant NSCLC after progression on EGFR-TKI therapy.

Background A diverse cohort of patients and susceptible individuals congregate in healthcare facilities, where exposure to pathogenic microorganisms associated with respiratory infectious diseases constitutes a significant risk factor for cross-infection. Central air-conditioning ventilation systems improve some indoor environment indicators while exacerbating the risk of transmission of respiratory infectious diseases. Objective To investigate the distribution characteristics of microbial communities in the central air-conditioning ventilation systems of hospitals, providing a scientific basis for the selection of microbial indicators in hygiene standards for hospital central air-conditioning ventilation systems and for hospital risk early warning systems. Methods In October 2023, two central air-conditioning ventilation systems were selected from a Grade 3A hospital in Shanghai: one was an all-air air-conditioning system serving the waiting area on the ground floor, and the other was a fan coil plus fresh air system serving the outpatient area on the third floor. Samples from four different components of the ventilation systems—air outlets, filters, surface coolers, and condensate trays—were collected for high-throughput sequencing of the 16S rRNA gene to analyze bacterial communities. Alpha-diversity and beta-diversity analyses were performed to investigate the microbial community composition and diversity characteristics of the hospital central air-conditioning ventilation systems. Functional analysis was conducted to determine the relative abundance of bacterial functions in these systems.Results A total of 528 operational taxonomic units (OTUs) were identified, encompassing 20 bacterial phyla, 37 classes, 79 orders, 123 families, and 240 genera. The analysis revealed that the bacterial community was predominantly composed of Proteobacteria, Gemmatimonadates, Bacteroidetes, and Actinobacteria. The diversity analysis indicated that bacterial community richness and diversity were highest in the condensate trays, while no statistically significant differences (P > 0.05) were observed in the bacterial community composition among the air outlets, filters, and surface coolers. The functional analysis showed that the bacterial communities in the central air-conditioning ventilation systems primarily exhibited chemoheterotrophic, oxidative energy-dependent heterotrophic, and ureolytic functional characteristics. Conclusion The dominance of Proteobacteria suggests that this phylum exhibits strong adaptability in the central air-conditioning ventilation systems, possibly related to its ability to survive and reproduce under varying environmental conditions. The diversity analysis indicates that the condensate tray is a critical area for bacterial proliferation in the central air-conditioning ventilation systems. The similarity in environmental conditions among the air outlets, filters, and surface coolers result in similar bacterial community structures. The functional analysis reveals that the bacterial communities possess robust energy conversion and metabolic capabilities, potentially contributing to processes such as organic matter decomposition and nitrogen cycling within the central air-conditioning ventilation systems.

Animal models combining disease and syndrome are important research tools to explore the nature of traditional Chinese medicine (TCM) syndromes. At present, the construction and evaluation methods of animal models have preliminarily established the foundation for standardized development. Qi-yin deficiency syndrome is a common type of TCM syndrome in cardiovascular diseases. It is an important pathogenic factor causing the onset, pathological damage, and chronic nature of cardiovascular diseases, as well as triggering other illnesses. The establishment of an animal model of cardiovascular disease with the characteristics of Qi-yin deficiency, along with an objective and standardized evaluation system, has become an important part of modern cardiovascular disease research. In recent years, research on the construction and evaluation of animal models of heart Qi-yin deficiency syndrome has increased, but the construction methods and evaluation criteria vary. Compared with other animal models, the literature is limited, lacking statistics and overall analysis. Therefore, based on the scientific connotation of heart Qi-yin deficiency syndrome, this article systematically reviews the evaluation system of its animal model, covering multidimensional methods such as macroscopic characterization assessment, physicochemical indicators and objective evaluation, and syndrome differentiation based on prescriptions. The specific model construction strategies are described, including single-factor induction methods (sleep deprivation, chronic intermittent hypoxia, arterial occlusion, high-salt feeding) and the compound-factor induction methods (sleep deprivation combined with drug method, chronic intermittent hypoxia combined with drug method, exhaustive swimming combined with drug method). Meanwhile, application examples of each model in the research are listed, the existing problems in the current model construction and evaluation are analyzed, and optimization directions are proposed, such as promoting the compound factor induction strategy and improving the objectivity of the evaluation criteria. This article aims to provide theoretical references for constructing an animal model of heart Qi-yin deficiency syndrome that conforms to TCM characteristics, and thereby laying a scientific foundation for the prevention and treatment of cardiovascular diseases with TCM.

Objective To evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.