Background: and Purpose Live cell-based assays (LCBA) are increasingly used for serological antibody detection due to their ability to preserve antigen conformation, offering moderately higher sensitivity than fixed cell-based assays. However, the clinical necessity of prioritizing LCBA for the detection of aquaporin-4 immunoglobulin G (AQP4-IgG) in neuromyelitis optica spectrum disorder (NMOSD) remains unclear, especially when compared to its established role in diagnosing myelin oligodendrocyte glycoprotein antibody-associated disease. Methods: We compared the performance of live cell-based assays using immunofluorescence (LCBA-IF) and fixed cell-based assays using immunofluorescence (FCBA-IF) in detecting AQP4-IgG in 90 cases of NMOSD meeting 2015 International Panel for Neuromyelitis Optica Diagnosis criteria, alongside 40 controls. Additionally, we further investigated the relationship between AQP4-IgG titers as measured by LCBA-IF and FCBA-IF and clinical parameters in NMOSD patients. Results: Results showed 96.9% agreement between LCBA-IF and FCBA-IF (Cohen’s κ=0.935), with a strong Spearman correlation (0.977, p<0.001). Both methods demonstrated 100% specificity, with LCBA-IF showing slightly higher sensitivity compared to FCBA-IF. Within LCBAIF-tested groups, statistically significant differences in annualized relapse rates were observed across all pairwise comparisons (low-titer vs. moderate-titer, low-titer vs. high-titer, and moderate-titer vs. high-titer; all p<0.050). However, this association reached statistical significance in some FCBA-IF-tested groups. Conclusions Overall, there is a strong concordance between LCBA-IF and FCBA-IF in detecting AQP4-IgG, where LCBA-IF shows slightly higher sensitivity. Furthermore, there is a potential link between elevated AQP4-IgG titers and an increased risk of relapse, and this correlation may appear more pronounced when using LCBA-IF.

This study predicts the quality markers(Q-markers) for the cough-relieving and phlegm-expelling effects of Kening Granules based on pharmacodynamics, plasma drug chemistry, network pharmacology, and pharmacokinetics. Strong ammonia solution spray and phenol red secretion assays were employed to evaluate the cough-relieving and phlegm-expelling effects of Kening Granules. Twentysix absorbed prototype components of Kening Granules were identified by ultra high performance liquid chromatography coupled with QExactive Plus quadrupole/Orbitrap high resolution mass spectrometry(UHPLC-Q-Exactive Plus Orbitrap HRMS). Through network pharmacology, 11 potential active components were screened out for the cough-relieving and phlegm-expelling effects of Kening Granules. The 11 components acted on 40 common targets such as IL6, TLR4, and STAT3, which mainly participated in PI3K/Akt, HIF-1, and EGFR signaling pathways. Pharmacokinetic quantitative analysis was performed for 7 prototype components. Three compounds including azelaic acid, caffeic acid, and vanillin were identified as Q-markers for the cough-relieving and phlegm-expelling effects of Kening Granules based on their effectiveness, transmissibility, and measurability. The results of this study are of great significance for clarifying the pharmacological substance basis, optimizing the quality standards, and promoting the clinical application of Kening Granules.
Drugs, Chinese Herbal/administration & dosage* ; Network Pharmacology ; Cough/blood* ; Male ; Humans ; Animals ; Rats ; Rats, Sprague-Dawley ; Biomarkers/blood* ; Quality Control ; Chromatography, High Pressure Liquid ; Antitussive Agents/chemistry*

This study aims to explore the role and mechanism of berberine in promoting the expression of aquaporin 4(AQP4) in astrocytes by regulating the expression of miR-383-5p in HepG2 cell-derived exosomes under insulin resistance(IR). The IR-HepG2 cell model was established with 1×10~(-6) mol·L~(-1) insulin. With metformin as the positive control, the safe concentrations of berberine and metformin were screened by cell counting kit-8(CCK-8) and lactate dehydrogenase(LDH) leakage assays, and the effect of berberine on the IR of HepG2 cells was evaluated by glucose consumption. NanoSight was used to measure the particle size and concentration of exosomes secreted by HepG2 cells in each group. HepG2 cell-derived exosomes in each group were incubated with astrocytes for 24 h, and the protein and mRNA levels of AQP4 in HA1800 cells were determined by Western blot and qRT-PCR, respectively. qRT-PCR was performed to determine the expression of miR-383-5p in HepG2 cell-derived exosomes and HA1800 cells after co-incubation. Western blotting was employed to determine the expression levels of miRNAs and proteins associated with exosome production and release in HepG2 cells. The results showed that 10 μmol·L~(-1) berberine and 1 mmol·L~(-1) metformin significantly alleviated the IR of HepG2 cells and reduced the concentration of exosomes in HepG2 cells. The exosomes of HepG2 cells treated with berberine and metformin significantly up-regulated the protein and mRNA levels of AQP4 in HA1800 cells. The mRNA level of miR-383-5p in HepG2 cell exosomes and HA1800 cells co-incubated with berberine and metformin decreased significantly. The intervention with berberine and metformin significantly down-regulated the expression of proteins associated with the production of miRNAs(Dicer, Drosha) as well as the production(Alix, Vps4A) and release(Rab35, VAMP3) of exosomes in IR-HepG2 cells. In conclusion, berberine can promote the expression of AQP4 in astrocytes by inhibiting the production and release of miR-383-5p in HepG2-derived exosomes under IR.
Humans ; MicroRNAs/metabolism* ; Berberine/pharmacology* ; Hep G2 Cells ; Exosomes/genetics* ; Aquaporin 4/metabolism* ; Insulin Resistance ; Astrocytes/drug effects*

Wastewater-based epidemiology has emerged as a transformative surveillance tool for estimating substance consumption and monitoring disease prevalence, particularly during the COVID-19 pandemic. It enables the population-level monitoring of illicit drug use, pathogen prevalence, and environmental pollutant exposure. In this perspective, we summarize the key challenges specific to the Chinese context: (1) Sampling inconsistencies, necessitating standardized 24-hour composite protocols with high-frequency autosamplers (≤ 15 min/event) to improve the representativeness of samples; (2) Biomarker validation, requiring rigorous assessment of excretion profiles and in-sewer stability; (3) Analytical method disparities, demanding inter-laboratory proficiency testing and the development of automated pretreatment instruments; (4) Catchment population dynamics, reducing estimation uncertainties through mobile phone data, flow-based models, or hydrochemical parameters; and (5) Ethical and data management concerns, including privacy risks for small communities, mitigated through data de-identification and tiered reporting platforms. To address these challenges, we propose an integrated framework that features adaptive sampling networks, multi-scale wastewater sample banks, biomarker databases with multidimensional metadata, and intelligent data dashboards. In summary, wastewater-based epidemiology offers unparalleled scalability for equitable health surveillance and can improve the health of the entire population by providing timely and objective information to guide the development of targeted policies.
China/epidemiology* ; Humans ; Wastewater/analysis* ; COVID-19/epidemiology* ; Public Health ; Wastewater-Based Epidemiological Monitoring ; SARS-CoV-2

6-Shogaol is an active component of gingerol in zingiber,which can be converted from 6-Gingerol under acidic and heating conditions.Modern research shows that 6-Shogaol has rich pharmacological activities,and it is found that 6-Shogaol has stronger anti-inflammatory,anti-tumor and antioxidant activities than 6-Gingerol.In this article,the preparation technology and pharmacological effects of 6-Shogaol were reviewed,and the extraction and separation methods of 6-Shogaol,as well as the targets and pathways involved in the process of exerting its pharmacological effects,were summarized,which could lay the foundation for the comprehensive development and clinical application of 6-Shogaol.

Objective To evaluate the clinical outcome of neurosurgical robot-assisted stereotactic puncture and drainage for brain abscess.Methods A retrospective analysis was conducted on the clinical data of 53 patients with brain abscess admitted to our hospital from January 2018 to December 2024.Among them,29 cases underwent craniotomy for abscess resection(craniotomy group),while 24 cases received neurosurgical robot-assisted stereotactic puncture and drainage(robot-assisted group).The operation time,intraoperative blood loss,decompressive craniectomy rate,proportion of postoperative antibiotic regimen adjustment,postoperative hospital stay,incidence of postoperative complications,mortality rate and Glasgow outcome scale(GOS)scores 6 months after surgery of patients were compared between the two groups.Results Compared with the craniotomy group,the robot-assisted group demonstrated significantly shorter operation time,less intraoperative blood loss,and lower incidence of postoperative complication,the differences were all statistically significant(P<0.05).However,there were no statistically significant differences in terms of decompressive craniectomy rate,postoperative hospital stay,mortality rate,GOS score,or proportion of the postoperative antibiotic regimen adjustment between the two groups(P>0.05).Conclusion As a precise and minimally invasive surgical method,neurosurgical robot-assisted stereotactic puncture and drainage for patients with brain abscess can effectively improve the operational efficiency,shorten the operation time,reduce intraoperative injury,and lower the risk of postoperative complications.It has high clinical application value and potential for widespread adoption.

AIM To establish the quantitative models for gallic acid,mononucleoside,loganin,resveratrol,and rhein in Yishen Xiezhuo Mixture.METHODS HPLC was adopted in the content determination of various effective components,after which the near-infrared spectroscopy(NIRS)data were collected in 128 batches of samples and pretreatment was conducted,competitive adaptive reweighting sampling(CARS)algorithm was used for screening wavelength,partial least square method(PLS)regression analysis was performed.RESULTS There were no significant differences between the predicted values obtained by PLS models and measured values obtained by HPLC for various effective components(P＞0.05).CONCLUSION The quantitative models established by NIRS combined with chemometrics display good predictive performance,which can be used for the rapid determination of effective components in Yishen Xiezhuo Mixture,and provide a reference for the rapid monitoring of other traditional Chinese medicine preparations in production processes.

Objective:To assess the efficacy and safety of Gongxuening Capsules in reducing post-abortion bleeding following artificial abortion.Methods:A multicenter, randomized, double-blind study was conducted. From May 31, 2022 to March 31, 2023, 484 women who underwent vacuum aspiration abortion for early intrauterine pregnancy were enrolled in 11 centers and randomly assigned to control group and the study group at a 1∶1 ratio using a center-block randomization method. Control group were administered a placebo of Gongxuening Capsules for 9 d, while the study group received the actual Gongxuening Capsules for the same duration. The outcomes measured included vaginal bleeding volume, duration of vaginal bleeding, endometrial thickness, time to menstrual recovery, and complications.Results:1) A total of 484 subjects were enrolled, and 472 completed the study. Totally 450 subjects were included in the efficacy analysis set, with 224 in control group and 226 in the study group; 468 subjects were included in the safety analysis set, with 236 in control group and 232 in the study group. The baseline characteristics of the two groups were comparable (all P>0.05). 2) The vaginal bleeding volume was lower in the study group [(13.30±12.14) mL] than in control group [(19.00±17.67) mL, P<0.001]. The proportion of subjects in the study group with bleeding days less than 4 d [29.65% (67/226)] was higher than that in control group [19.20% (43/224), P=0.010]. 3) No significant differences were observed between the two groups in terms of time to menstrual recovery and endometrial thickness (all P>0.05). 4) In the study group, 3 subjects experienced non-therapeutic-related complications, while 11 subjects in control group. The incidence of complications was lower in the study group [1.29% (3/232)] than in control group [4.66% (11/236), P=0.033]. Conclusion:The administration of Gongxuening Capsules to women following artificial abortion significantly reduced vaginal bleeding volume and was associated with good safety, with the treatment being well-tolerated by the subjects.

Objective To construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.

PURPOSE: Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy. Although traditional statistical methods or machine learning (ML) algorithms have been proposed to predict septic shock, these potential approaches have never been systematically compared. The present work aimed to develop and compare models to predict septic shock among patients with sepsis. METHODS: It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022. Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training (n=311) and validation (n=173) sets. All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected. We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort. The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots. Decision curve analysis was used to evaluate the net benefit of the models. The validation set was applied to verify the predictive accuracy of the models. This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm. RESULTS: Among all patients, 37.2% experienced septic shock. The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets, respectively. The model with the best prediction performance was based on the support vector machine (SVM) algorithm, which was constructed by age, tissue plasminogen activator-inhibitor complex, prothrombin time, international normalized ratio, white blood cells, and platelet counts. The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis. CONCLUSION The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
Humans ; Shock, Septic/blood* ; Machine Learning ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Sepsis/complications* ; ROC Curve ; Cohort Studies ; Adult ; Intensive Care Units ; Algorithms ; Blood Coagulation ; Critical Illness