BackgroundObstructive sleep apnea （OSA） represents a prevalent sleep disordered breathing condition characterized by complex pathophysiology. The arousal threshold （ArTH）， a core non-anatomical contributor to OSA pathogenesis， is intimately tied to the disease severity and clinical phenotypes. To date， research regarding factors associated with ArTH has yielded inconsistent findings， and ArTH profiles and disparities across populations with different genetic backgrounds are not fully elucidated. ObjectiveTo explore the differences of ArTH in OSA patients with different genetic backgrounds and analyze the key factors affecting ArTH， thereby providing evidence for understanding OSA pathophysiology and formulating targeted treatment regimens. MethodsA total of 285 patients who met the diagnostic criteria for OSA， as defined by the Multidisciplinary Diagnosis and Treatment Guidelines for Adult Obstructive Sleep Apnea， were retrospectively enrolled in this study. All participants underwent overnight polysomnography （PSG） at the Sleep Medicine Center of Inner Mongolia Mental Health Center from December 2022 to May 2024. Based on the study design， the cohort was stratified into two distinct genetic background subgroups （group A and group B）. Demographic and clinical characteristics， the Epworth Sleepiness Scale （ESS） score， and overnight PSG data were collected. The apnea hypopnea index （AHI）， the lowest pulse oxygen saturation （LSpO₂）， and fraction of hypopneas （F_Hypopneas） were utilized as surrogate indicators to estimate ArTH in OSA patients. The influencing factors of low ArTH were tested by binary Logistic regression analysis. ResultsAmong the 285 OSA patients， there were 227 cases （79.65%） in group A and 58 cases （20.35%） in group B. Comparisons between the two genetic background subgroups revealed no statistically significant differences in the proportion of low ArTH， ESS score， PSG parameters， and the three markers for low ArTH （AHI<30 events/h， LSpO₂>82.5%， F_Hypopneas>58.3%） （P>0.05）. Binary Logistic regression analysis identified sex （OR=2.421， 95% CI： 1.070–5.478）， BMI （OR=0.847， 95% CI： 0.770–0.932）， N1 sleep duration （OR=0.974， 95% CI： 0.963–0.985）， and hypertension （OR=0.348， 95% CI： 0.143–0.848） as independent factors associated with low ArTH in the total cohort. Stratified analysis by genetic backgrounds revealed that sex（OR=3.799， 95% CI： 1.389–10.392）， BMI（OR=0.819， 95% CI：0.723–0.929）， and N1 sleep duration（OR=0.973， 95% CI： 0.961–0.986） were independent factors of low ArTH in group A. In contrast， only N1 sleep duration （OR=0.951， 95% CI： 0.911–0.993） remained a significant factor in group B. ConclusionArthur may have conservative characteristics in patients with OSA with different genetic backgrounds， but the pathophysiological mechanism of OSA may have population heterogeneity. ［Funded by Inner Mongolia Autonomous Region Natural Science Fundation Project （number， 2024QN08050）； Intra-institutional Scientific Research Project of Inner Mongolia Mental Health Center （number， 2022QNWN0010）］

Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder affecting multiple systems, characterized by the development of harmful autoantibodies and immune complexes that lead to damage in organs and tissues. Chinese medicine (CM) plays a role in mitigating complications, enhancing treatment effectiveness, and reducing toxicity of concurrent medications, and ensuring a safe pregnancy. However, CM mainly solves the disease comprehensively through multi-target and multi-channel regulation process, therefore, its treatment mechanism is often complicated, involving many molecular links. This review introduces the research progress of pathogenesis of SLE from the aspects of genetics, epigenetics, innate immunity and acquired immunity, and then discusses the molecular mechanism and target of single Chinese herbal medicine and prescription that are commonly used and effective in clinic to treat SLE.
Lupus Erythematosus, Systemic/immunology* ; Humans ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology* ; Animals

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BACKGROUND: The use of inserted sham acupuncture as a placebo in randomized controlled trials (RCTs) is controversial, because it may produce specific effects that cause an underestimation of the effect of acupuncture treatment. OBJECTIVE: This systematic survey investigates the magnitude of insert-specific effects of sham acupuncture and whether they affect the estimation of acupuncture treatment effects. SEARCH STRATEGY: PubMed, Embase and Cochrane Central Register of Controlled Trials were searched to identify acupuncture RCTs from their inception until December 2022. INCLUSION CRITERIA: RCTs that evaluated the effects of acupuncture compared to sham acupuncture and no treatment. DATA EXTRACTION AND ANALYSIS: The total effect measured for an acupuncture treatment group in RCTs were divided into three components, including the natural history and/or regression to the mean effect (controlled for no-treatment group), the placebo effect, and the specific effect of acupuncture. The first two constituted the contextual effect of acupuncture, which is mimicked by a sham acupuncture treatment group. The proportion of acupuncture total effect size was considered to be 1. The proportion of natural history and/or regression to the mean effect (PNE) and proportional contextual effect (PCE) of included RCTs were pooled using meta-analyses with a random-effect model. The proportion of acupuncture placebo effect was the difference between PCE and PNE in RCTs with non-inserted sham acupuncture. The proportion of insert-specific effect of sham acupuncture (PIES) was obtained by subtracting the proportion of acupuncture placebo effect and PNE from PCE in RCTs with inserted sham acupuncture. The impact of PIES on the estimation of acupuncture's treatment effect was evaluated by quantifying the percentage of RCTs that the effect of outcome changed from no statistical difference to statistical difference after removing PIES in the included studies, and the impact of PIES was externally validated in other acupuncture RCTs with an inserted sham acupuncture group that were not used to calculate PIES. RESULTS: This analysis included 32 studies with 5492 patients. The overall PNE was 0.335 (95% confidence interval [CI], 0.255-0.415) and the PCE of acupuncture was 0.639 (95% CI, 0.567-0.710) of acupuncture's total effect. The proportional contribution of the placebo effect to acupuncture's total effect was 0.191, and the PIES was 0.189. When we modeled the exclusion of the insert-specific effect of sham acupuncture, the acupuncture treatment effect changed from no difference to a significant difference in 45.45% of the included RCTs, and in 40.91% of the external validated RCTs. CONCLUSION The insert-specific effect of sham acupuncture in RCTs represents 18.90% of acupuncture's total effect and significantly affects the evaluation of the acupuncture treatment effect. More than 40% of RCTs that used inserted sham acupuncture would draw different conclusions if the PIES had been controlled for. Considering the impact of the insert-specific effect of sham acupuncture, caution should be taken when using inserted sham acupuncture placebos in RCTs. Please cite this article as: Luo XC, Liu JL, Yao MH, Chen YM, Fan AY, Liang FR, Zhao JP, Zhao L, Zhou X, Zhong XY, Yang JH, Li B, Zhang Y, Sun X, Li L. Specific effect of inserted sham acupuncture and its impact on the estimation of acupuncture treatment effect in randomized controlled trials: A systematic survey. J Integr Med. 2025; 23(6):630-640.
Acupuncture Therapy/methods* ; Humans ; Randomized Controlled Trials as Topic ; Placebo Effect ; Placebos ; Treatment Outcome

This study aimed to characterize and identify the non-volatile components in aqueous and ethanolic extracts of the stems and leaves of Rhododendron tomentosum by using sensitive and efficient ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry(UHPLC-Q-TOF-MS) combined with a self-built information database. By comparing with reference compounds, analyzing fragment ion information, searching relevant literature, and using a self-built information database, 118 compounds were identified from the aqueous and ethanolic extracts of R. tomentosum, including 35 flavonoid glycosides, 15 phenolic glycosides, 12 flavonoids, 7 phenolic acids, 7 phenylethanol glycosides, 6 tannins, 6 phospholipids, 5 coumarins, 5 monoterpene glycosides, 6 triterpenes, 3 fatty acids, and 11 other types of compounds. Among them, 102 compounds were reported in R. tomentosum for the first time, and 36 compounds were identified by comparing them with reference compounds. The chemical components in the ethanolic and aqueous extracts of R. tomentosum leaves and stems showed slight differences, with 84 common chemical components accounting for 71.2% of the total 118 compounds. This study systematically characterized and identified the non-volatile chemical components in the ethanolic and aqueous extracts of R. tomentosum for the first time. The findings provide a reference for active ingredient research, quality control, and product development of R. tomentosum.
Rhododendron/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Mass Spectrometry/methods* ; Plant Leaves/chemistry*

Panax ginseng as a perennial herb of Araliaceae, exhibits pharmacological effects such as central nervous system stimulation, anti-tumor properties, and cardiovascular and cerebrovascular protection. The B3 gene family plays a crucial role in growth and development, antioxidant activity, stress resistance, and secondary metabolism regulation of plants and has been extensively studied in various plants. However, the identification and analysis of the B3 gene family in P. ginseng have not been reported. In this study, a total of 145 B3 genes(PgB3s) with complete open reading frames(ORF) were identified from P. ginseng and classified into five subfamilies based on domain types. Through correlation analysis with ginsenoside content, SNP/InDels analysis, and interaction analysis with key enzyme genes, 15 PgB3 transcripts were found to be significantly correlated with ginsenoside content and exhibited a close interaction network with key enzyme genes involved in ginsenoside biosynthesis, which indicated that these genes may participate in the regulation of ginsenoside biosynthesis. Additionally, this study found that PgB3 genes exhibited induced expression in response to methyl jasmonate(MeJA) stress, which aligned with the presence of abundant stress response elements in their promoters, confirming the important role of the B3 gene family in P. ginseng in stress resistance. The results of this study revealed the potential functions of PgB3 genes in ginsenoside biosynthesis and stress response, providing a significant theoretical basis for further research on the functions of PgB3 genes and their regulatory mechanisms.
Panax/metabolism* ; Gene Expression Regulation, Plant ; Plant Proteins/metabolism* ; Ginsenosides/biosynthesis* ; Multigene Family ; Phylogeny

Objective To construct a diagnostic and prognostic model for malignant pleural effusion (MPE) in patients with non-M1b stage (AJCC 7th edition) non-small cell lung cancer (NSCLC) by machine learning. Methods Retrospective analysis was conducted on patients diagnosed with NSCLC in the Surveillance, Epidemiology, and End Results database from 2010 to 2015, excluding those in the M1b stage. Two sets of data were collected: data 1 (patients with non-M1b stage NSCLC, n=47 392) was used to construct the MPE diagnostic model; and data 2 (patients with M1a stage NSCLC and MPE, n=2 422) was used to construct a prognostic model. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to screen feature variables, with a training set and validation set ratio of 7:3. Models were built using eight machine learning algorithms, with evaluation metrics including accuracy, precision, recall, F1 score, area under the ROC curve (AUC), decision curve, calibration curve, and precision recall curve (PR), with ROC-AUC as the main evaluation metric. Results The incidence of MPE in patients with non-M1b stage NSCLC was 5.12%, and the 1-year survival rate of patients with MPE was 32.5%. LASSO regression identified nine diagnostic-related variables and 12 prognostic-related variables. The AUC values of the models constructed by eight machine learning algorithms all exceeded 0.70. The random forest model performed the best in the diagnostic model (training set AUC=0.908, validation set AUC=0.897), and the XGBoost model showed the best performance in the prognostic model (training set AUC=0.905, validation set AUC=0.875). Other evaluation indicators showed good results and balanced distribution. SHAP feature importance analysis showed that tumor size, lymph node metastasis, and histological type were important influencing factors for the occurrence of MPE, and chemotherapy intervention was the most remarkably prognostic factor. Conclusion The random forest diagnostic model constructed in this study can effectively predict the risk of MPE in patients with non-M1b stage NSCLC, and the XGBoost prognostic model can predict the prognosis of M1a-stage NSCLC patients with concurrent MPE.

To evaluate the diagnostic performance of a three-gene (GBP5, DUSP3, and TBP) tuberculosis (TB) score in bacteriologically-negative pulmonary tuberculosis, and to develop and validate a discriminative diagnostic model by integrating inflammatory cytokines (IL-2, IL-5, IL-17, and IFN-γ). A prospective cohort study was conducted, a total of 238 patients admitted to the Affiliated Infectious Disease Hospital of Soochow University from May 2023 to May 2024 were enrolled, including 119 pathogen-negative pulmonary tuberculosis patients and 119 patients with other pulmonary diseases (OPD). The GeneXpert MTB-HR kit was used to detect the three-gene TB scores from residual blood routine samples. The diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis. Concurrent data on 12 inflammatory cytokines were collected from patients. Potential biomarkers were screened using univariate analysis and multivariate logistic regression, and selected features were incorporated into the construction of four machine learning models: logistic regression, support vector machine (SVM), K-nearest neighbors (KNN), and adaptive boosting (AdaBoost). The samples were randomly split into a training set (85%) and a test set (15%). The models were trained on the training set, and their diagnostic performance was validated using the test set. The predictive ability of each model was evaluated based on ROC curve parameters. The results showed that the three-gene TB score alone yielded an AUC of 0.539 (sensitivity: 50.94%, specificity: 60.50%) in distinguishing pathogen-negative pulmonary tuberculosis from OPD. Four non-col-linear inflammatory factors (IL-2, IL-5, IL-17, and IFN-γ) were selected and combined with the three-gene TB score to construct machine learning models. The AdaBoost model demonstrated the best performance, achieving an AUC of 0.893 (sensitivity: 85.4%, specificity: 73.0%) in the training set and an AUC of 0.873 (sensitivity: 88.2%, specificity: 72.2%) in the test set. In conclusion,the AdaBoost diagnostic model integrating the three-gene TB score with inflammatory factors (IL-2, IL-5, IL-17, and IFN-γ) exhibits superior discriminating performance for pathogen-negative pulmonary tuberculosis compared to OPD, significantly outperforming the three-gene TB score alone.

OBJECTIVE To explore the diagnostic value of the guanylate binding protein 5(GBP5)gene in pa-tients infected with Mycobacterium tuberculosis(MTB).METHODS Patients who visited the Fifth People's Hos-pital of Suzhou from Jan.2024 to May 2024,as well as individuals undergoing health examinations during the same period,were selected as the study subjects.Based on the diagnostic criteria,the subjects were divided into active tuberculosis(ATB)group(230 cases),pneumonia group(207 cases),latent tuberculosis infection(LTBI)group(48 cases)and healthy control(HC)group(122 cases).Real-time quantitative reverse transcription poly-merase chain reaction was used to detect the expression level of the GBP 5 gene in the whole blood of the subjects,and receiver operating characteristic(ROC)curves were employed to analyze its diagnostic performance.RESULTS There were statistically significant differences in GBP5 gene expression among the four groups(P＜0.001).The area under the ROC curve(AUC)for GBP5 in distinguishing the ATB group from the HC group,LTBI group and pneumonia group was 0.902,0.813 and 0.745,respectively.In the ATB group,the positive de-tection rate of the GBP5 method was 67.39％,higher than that of Gene Xpert MTB/RIF(62.20％),tuberculosis culture(60.00％)and smear acid-fast staining(35.20％)(P＜0.05).When distinguishing between ATB and LTBI patients,the positive prediction value of GBP5 was 95.09％,higher than that of IGRA testing(79.92％)(P＜0.05).The expression level of GBP5 was negatively correlated with the MTB bacterial load(P＜0.001).CONCLUSIONS The detection of the GBP5 gene has important clinical significance in the auxiliary diagno-sis of tuberculosis and can serve as an effective supplement to existing diagnostic methods for tuberculosis.