PRECIS-2 analysis of pragmatic acupuncture trials: a systematic review.

BACKGROUND: Pragmatic acupuncture trials (PATs) are a research tool for assessing the effectiveness of acupuncture treatments in a real-world setting. This study aimed to provide a comprehensive methodological analysis of PATs using the PRECIS-2(PRagmatic Explanatory Continuum Indicator Summary-2) tool to determine their pragmatism. METHODS: The MEDLINE, EMBASE, Cochrane Central Register for Controlled Trials, CINAHL, Allied and Complementary Medicine Database, China National Knowledge Infrastructure, VIP, WANFANG, Taiwan Periodical Literature Database, KoreaMed, KMbase, Research Information Service System, Oriental Medicine Advanced Searching Integrated System, CiNii and ClinicalTrials.gov were searched. The search included randomised controlled trials (RCTs) and protocols of RCTs that investigated all types of acupuncture and used self-declared pragmatic design. Two authors independently collected the basic information and characteristics of the studies and assessed their pragmatism using the nine PRECIS-2 domains and the additional domain of control. RESULTS: A total of 93 studies were included. The means of eligibility, recruitment, organisation, primary outcome, primary analysis, and control domains were statistically larger than three and were shown to be pragmatic. The means of setting, flexibility:delivery, and follow-up domains were not greater than three and were shown to be non-pragmatic. For flexibility:adherence domain was inappropriate for assessment owing to insufficient information in the studies. CONCLUSIONS: PATs were pragmatic in the domain of eligibility, recruitment, organisation, primary outcome, primary analysis, and control and were not pragmatic in the domain of setting, flexibility:delivery, and follow-up. Future PATs need to strengthen the pragmatism in the setting, flexibility:delivery, and follow-up domains and to describe the flexibility:adherence domain in more detail. TRIAL REGISTRATION: CRD42021236975.

Multicentre validation of a machine learning model for predicting respiratory failure after noncardiac surgery.

BACKGROUND: Postoperative respiratory failure is a serious complication that could benefit from early accurate identification of high-risk patients. We developed and validated a machine learning model to predict postoperative respiratory failure, defined as prolonged (>48 h) mechanical ventilation or reintubation after surgery. METHODS: Easily extractable electronic health record (EHR) variables that do not require subjective assessment by clinicians were used. From EHR data of 307,333 noncardiac surgical cases, the model, trained with a gradient boosting algorithm, utilised a derivation cohort of 99,025 cases from Seoul National University Hospital (2013-9). External validation was performed using three separate cohorts A-C from different hospitals comprising 208,308 cases. Model performance was assessed by area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC), a measure of sensitivity and precision at different thresholds. RESULTS: The model included eight variables: serum albumin, age, duration of anaesthesia, serum glucose, prothrombin time, serum creatinine, white blood cell count, and body mass index. Internally, the model achieved an AUROC of 0.912 (95% confidence interval [CI], 0.908-0.915) and AUPRC of 0.113. In external validation cohorts A, B, and C, the model achieved AUROCs of 0.879 (95% CI, 0.876-0.882), 0.872 (95% CI, 0.870-0.874), and 0.931 (95% CI, 0.925-0.936), and AUPRCs of 0.029, 0.083, and 0.124, respectively. CONCLUSIONS: Utilising just eight easily extractable variables, this machine learning model demonstrated excellent discrimination in both internal and external validation for predicting postoperative respiratory failure. The model enables personalised risk stratification and facilitates data-driven clinical decision-making.

Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU.

Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-based heart rate variability (HRV) measures. The HRV measures, including time/frequency domains and nonlinear measures, were calculated from 5 min epochs of ECG signals from ICU patients. A light gradient boosting machine (LGBM) algorithm was used to develop the proposed model for predicting in-hospital cardiac arrest within 0.5-24 h. The LGBM model using 33 HRV measures achieved an area under the receiver operating characteristic curve of 0.881 (95% CI: 0.875-0.887) and an area under the precision-recall curve of 0.104 (95% CI: 0.093-0.116). The most important feature was the baseline width of the triangular interpolation of the RR interval histogram. As our model uses only ECG data, it can be easily applied in clinical practice.

Predicting optimal endotracheal tube size and depth in pediatric patients using demographic data and machine learning techniques.

BACKGROUND: Use of endotracheal tubes (ETTs) with appropriate size and depth can help minimize intubation-related complications in pediatric patients. Existing age-based formulae for selecting the optimal ETT size present several inaccuracies. We developed a machine learning model that predicts the optimal size and depth of ETTs in pediatric patients using demographic data, enabling clinical applications. METHODS: Data from 37,057 patients younger than 12 years who underwent general anesthesia with endotracheal intubation were retrospectively analyzed. Gradient boosted regression tree (GBRT) model was developed and compared with traditional age-based formulae. RESULTS: The GBRT model demonstrated the highest macro-averaged F1 scores of 0.502 (95% CI 0.486, 0.568) and 0.669 (95% CI 0.640, 0.694) for predicting the uncuffed and cuffed ETT size (internal diameter [ID]), outperforming the age-based formulae that yielded 0.163 (95% CI 0.140, 0.196, P < 0.001) and 0.392 (95% CI 0.378, 0.406, P < 0.001), respectively. In predicting the ETT depth (distance from tip to lip corner), the GBRT model showed the lowest mean absolute error (MAE) of 0.71 cm (95% CI 0.69, 0.72) and 0.72 cm (95% CI 0.70, 0.74) compared to the age-based formulae that showed an error of 1.18 cm (95% CI 1.16, 1.20, P < 0.001) and 1.34 cm (95% CI 1.31, 1.38, P < 0.001) for uncuffed and cuffed ETT, respectively. CONCLUSIONS: The GBRT model using only demographic data accurately predicted the ETT size and depth. If these results are validated, the model may be practical for predicting optimal ETT size and depth for pediatric patients.

Using ChatGPT as a Learning Tool in Acupuncture Education: Comparative Study.

BACKGROUND: ChatGPT (Open AI) is a state-of-the-art artificial intelligence model with potential applications in the medical fields of clinical practice, research, and education. OBJECTIVE: This study aimed to evaluate the potential of ChatGPT as an educational tool in college acupuncture programs, focusing on its ability to support students in learning acupuncture point selection, treatment planning, and decision-making. METHODS: We collected case studies published in Acupuncture in Medicine between June 2022 and May 2023. Both ChatGPT-3.5 and ChatGPT-4 were used to generate suggestions for acupuncture points based on case presentations. A Wilcoxon signed-rank test was conducted to compare the number of acupuncture points generated by ChatGPT-3.5 and ChatGPT-4, and the overlapping ratio of acupuncture points was calculated. RESULTS: Among the 21 case studies, 14 studies were included for analysis. ChatGPT-4 generated significantly more acupuncture points (9.0, SD 1.1) compared to ChatGPT-3.5 (5.6, SD 0.6; P<.001). The overlapping ratios of acupuncture points for ChatGPT-3.5 (0.40, SD 0.28) and ChatGPT-4 (0.34, SD 0.27; P=.67) were not significantly different. CONCLUSIONS: ChatGPT may be a useful educational tool for acupuncture students, providing valuable insights into personalized treatment plans. However, it cannot fully replace traditional diagnostic methods, and further studies are needed to ensure its safe and effective implementation in acupuncture education.

Development and validation of a reinforcement learning model for ventilation control during emergence from general anesthesia.

Ventilation should be assisted without asynchrony or cardiorespiratory instability during anesthesia emergence until sufficient spontaneous ventilation is recovered. In this multicenter cohort study, we develop and validate a reinforcement learning-based Artificial Intelligence model for Ventilation control during Emergence (AIVE) from general anesthesia. Ventilatory and hemodynamic parameters from 14,306 surgical cases at an academic hospital between 2016 and 2019 are used for training and internal testing of the model. The model's performance is also evaluated on the external validation cohort, which includes 406 cases from another academic hospital in 2022. The estimated reward of the model's policy is higher than that of the clinicians' policy in the internal (0.185, the 95% lower bound for best AIVE policy vs. -0.406, the 95% upper bound for clinicians' policy) and external validation (0.506, the 95% lower bound for best AIVE policy vs. 0.154, the 95% upper bound for clinicians' policy). Cardiorespiratory instability is minimized as the clinicians' ventilation matches the model's ventilation. Regarding feature importance, airway pressure is the most critical factor for ventilation control. In conclusion, the AIVE model achieves higher estimated rewards with fewer complications than clinicians' ventilation control policy during anesthesia emergence.

Validation of MRI-Based Models to Predict MGMT Promoter Methylation in Gliomas: BraTS 2021 Radiogenomics Challenge.

O6-methylguanine-DNA methyl transferase (MGMT) methylation prediction models were developed using only small datasets without proper external validation and achieved good diagnostic performance, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when using a larger dataset in the RSNA-MICCAI Brain Tumor Radiogenomic Classification 2021 challenge. To our knowledge, there has been no study regarding the external validation of MGMT prediction models using large-scale multicenter datasets. We tested recent CNN architectures via extensive experiments to investigate whether MGMT methylation in gliomas can be predicted using MR images. Specifically, prediction models were developed and validated with different training datasets: (1) the merged (SNUH + BraTS) (n = 985); (2) SNUH (n = 400); and (3) BraTS datasets (n = 585). A total of 420 training and validation experiments were performed on combinations of datasets, convolutional neural network (CNN) architectures, MRI sequences, and random seed numbers. The first-place solution of the RSNA-MICCAI radiogenomic challenge was also validated using the external test set (SNUH). For model evaluation, the area under the receiver operating characteristic curve (AUROC), accuracy, precision, and recall were obtained. With unexpected negative results, 80.2% (337/420) and 60.0% (252/420) of the 420 developed models showed no significant difference with a chance level of 50% in terms of test accuracy and test AUROC, respectively. The test AUROC and accuracy of the first-place solution of the BraTS 2021 challenge were 56.2% and 54.8%, respectively, as validated on the SNUH dataset. In conclusion, MGMT methylation status of gliomas may not be predictable with preoperative MR images even using deep learning.

Association of Dietary Behaviors with Poor Sleep Quality and Increased Risk of Obstructive Sleep Apnea in Korean Military Service Members.

Purpose: Sleep quality among military service members is important for enhancing their capabilities and preventing psychiatric problems. We aimed to explore the association of dietary behaviors with poor sleep quality and increased risk of obstructive sleep apnea (OSA) in military men on active duty. Patients and Methods: A large-scale multi-site cross-sectional survey was conducted in five units of the Republic of Korea's army. Poor sleep quality and increased risk of OSA were defined using the Pittsburgh sleep quality index (PSQI) and Berlin Questionnaire, respectively. Information on dietary behaviors, including the frequency of skipping breakfast, eating snacks, consuming a night meal, and overeating, were collected. Results: From August 2021 to September 2021, 4389 male respondents, mean age (20.8 ± 1.3 years), completed the survey; 2579 (58.8%) were assessed as having poor sleep quality, and 614 (14.0%) increased risk of OSA. After adjusting for lifestyle and occupational covariates, skipping breakfast 1-2 times weekly was associated with an increased likelihood of experiencing poor sleep quality, compared with not skipping breakfast (odds ratio: 1.23 [95% CI 1.02-1.47]). Eating night meals 5-6 times weekly was also associated with poor sleep quality (odds ratio: 5.54 [95% CI 2.49-14.18]). In addition, skipping breakfast daily (odds ratio: 2.28 [95% CI 1.27-4.03]) and eating night meals daily (odds ratio: 2.30 [95% CI 1.21-4.22]) were related to an increased risk of OSA. Conclusion: Dietary behaviors appear to be related to poor sleep quality and a high risk of OSA. To improve sleep quality, dietary factors could be considered when promoting health programs for military personnel in further research.

Deep autoencoder-powered pattern identification of sleep disturbance using multi-site cross-sectional survey data.

Pattern identification (PI) is a diagnostic method used in Traditional East Asian medicine (TEAM) to select appropriate and personalized acupuncture points and herbal medicines for individual patients. Developing a reproducible PI model using clinical information is important as it would reflect the actual clinical setting and improve the effectiveness of TEAM treatment. In this paper, we suggest a novel deep learning-based PI model with feature extraction using a deep autoencoder and k-means clustering through a cross-sectional study of sleep disturbance patient data. The data were obtained from an anonymous electronic survey in the Republic of Korea Army (ROKA) members from August 16, 2021, to September 20, 2021. The survey instrument consisted of six sections: demographics, medical history, military duty, sleep-related assessments (Pittsburgh sleep quality index (PSQI), Berlin questionnaire, and sleeping environment), diet/nutrition-related assessments [dietary habit survey questionnaire and nutrition quotient (NQ)], and gastrointestinal-related assessments [gastrointestinal symptom rating scale (GSRS) and Bristol stool scale]. Principal component analysis (PCA) and a deep autoencoder were used to extract features, which were then clustered using the k-means clustering method. The Calinski-Harabasz index, silhouette coefficient, and within-cluster sum of squares were used for internal cluster validation and the final PSQI, Berlin questionnaire, GSRS, and NQ scores were used for external cluster validation. One-way analysis of variance followed by the Tukey test and chi-squared test were used for between-cluster comparisons. Among 4,869 survey responders, 2,579 patients with sleep disturbances were obtained after filtering using a PSQI score of >5. When comparing clustering performance using raw data and extracted features by PCA and the deep autoencoder, the best feature extraction method for clustering was the deep autoencoder (16 nodes for the first and third hidden layers, and two nodes for the second hidden layer). Our model could cluster three different PI types because the optimal number of clusters was determined to be three via the elbow method. After external cluster validation, three PI types were differentiated by changes in sleep quality, dietary habits, and concomitant gastrointestinal symptoms. This model may be applied to the development of artificial intelligence-based clinical decision support systems through electronic medical records and clinical trial protocols for evaluating the effectiveness of TEAM treatment.

Applying the PRECIS-2 tool for self-declared 'pragmatic' acupuncture trials: protocol for a systematic review.

INTRODUCTION: The pragmatic design has received much attention in the field of acupuncture clinical trials because of insufficient information about the specific effects of acupuncture. However, pragmatism in pragmatic acupuncture trials has not been comprehensively investigated. The PRECIS-2 tool was developed and has been gradually used to design pragmatic trials; therefore, we will apply the PRECIS-2 tool to investigate the pragmatism of pragmatic acupuncture trials in this study. METHODS AND ANALYSIS: In this systematic review, self-declared 'pragmatic' randomised clinical trials (RCTs) or protocols of self-declared 'pragmatic' RCTs investigating acupuncture will be searched and included to be reviewed. MEDLINE, EMBASE, the Cochrane Central Register for Controlled Trials, CINAHL, Allied and Complementary Medicine Database (AMED), China National Knowledge Infrastructure, VIP, WANFANG, Taiwan Periodical Literature Database, KoreaMed, KMbase, Research Information Service System, Oriental Medicine Advanced Searching Integrated System, CiNii and ClinicalTrials.gov for registered trials will be electronically searched from inception to March 2022. Protocols of published RCTs or secondary analysis of RCTs will be excluded. Additionally, no language restriction will be applied. Two authors will independently extract descriptive information and assess the pragmatism of pragmatic acupuncture trials using nine domains of the PRECIS-2 tool and one additional domain-control. Descriptive statistics will be reported for each domain and the overall score, and a one-sample t-test will be used to statistically analyse whether the score is greater than 3 (equally pragmatic and explanatory). The wheel diagrams of the nine domains of the PRECIS-2 tool will be used to demonstrate the pragmatism of the included studies. ETHICS AND DISSEMINATION: Ethical approval is not warranted as this study will obtain data from previously reported articles. The results will be disseminated through peer-reviewed journals and conferences. PROSPERO REGISTRATION NUMBER: CRD42021236975.

Medical Specialty Recommendations by an Artificial Intelligence Chatbot on a Smartphone: Development and Deployment.

BACKGROUND: The COVID-19 pandemic has limited daily activities and even contact between patients and primary care providers. This makes it more difficult to provide adequate primary care services, which include connecting patients to an appropriate medical specialist. A smartphone-compatible artificial intelligence (AI) chatbot that classifies patients' symptoms and recommends the appropriate medical specialty could provide a valuable solution. OBJECTIVE: In order to establish a contactless method of recommending the appropriate medical specialty, this study aimed to construct a deep learning-based natural language processing (NLP) pipeline and to develop an AI chatbot that can be used on a smartphone. METHODS: We collected 118,008 sentences containing information on symptoms with labels (medical specialty), conducted data cleansing, and finally constructed a pipeline of 51,134 sentences for this study. Several deep learning models, including 4 different long short-term memory (LSTM) models with or without attention and with or without a pretrained FastText embedding layer, as well as bidirectional encoder representations from transformers for NLP, were trained and validated using a randomly selected test data set. The performance of the models was evaluated on the basis of the precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). An AI chatbot was also designed to make it easy for patients to use this specialty recommendation system. We used an open-source framework called "Alpha" to develop our AI chatbot. This takes the form of a web-based app with a frontend chat interface capable of conversing in text and a backend cloud-based server application to handle data collection, process the data with a deep learning model, and offer the medical specialty recommendation in a responsive web that is compatible with both desktops and smartphones. RESULTS: The bidirectional encoder representations from transformers model yielded the best performance, with an AUC of 0.964 and F1-score of 0.768, followed by LSTM model with embedding vectors, with an AUC of 0.965 and F1-score of 0.739. Considering the limitations of computing resources and the wide availability of smartphones, the LSTM model with embedding vectors trained on our data set was adopted for our AI chatbot service. We also deployed an Alpha version of the AI chatbot to be executed on both desktops and smartphones. CONCLUSIONS: With the increasing need for telemedicine during the current COVID-19 pandemic, an AI chatbot with a deep learning-based NLP model that can recommend a medical specialty to patients through their smartphones would be exceedingly useful. This chatbot allows patients to identify the proper medical specialist in a rapid and contactless manner, based on their symptoms, thus potentially supporting both patients and primary care providers.

The efficacy and safety of Hanslim for obese patients: Study protocol for a multicenter, randomized, double-blind, multi-dose, placebo-controlled, phase IIb clinical trial.

BACKGROUND: This study aims to evaluate the efficacy, safety, and appropriate dose of Hanslim, a Korean traditional herbal medicine, for obese patients, when compared to a placebo. METHODS/DESIGN: This study is a randomized, double-blinded, multicenter, multidose, placebo-controlled, phase IIb clinical trial. A total of 165 obese patients with a body mass index (BMI) of more than 30 kg/m or obese patients with a BMI of 27 to 29.9 kg/m and one or more risk factors such as hypertension, diabetes, or hyperlipidemia will be enrolled. Participants will be randomly assigned to 1 of 3 groups (high-dose, low-dose, or placebo) with a 1:1:1 allocation ratio and will have 4 scheduled visits during the 12-week treatment period. The participants will be administered 2 tablets of Hanslim or placebo, 2 times per day. The difference in the proportion of participants who lost weight by more than 5% from their baseline at 12 weeks compared to the placebo group will be examined as the primary efficacy outcome. Secondary efficacy outcomes include differences in body weight, BMI, body-fat percentage, fat mass, skeletal-muscle mass, edema index, waist circumference, hip circumference, waist-hip ratio, serum lipid, blood glucose, C-reactive protein, and total score of Korean version of obesity-related quality of life after 12 weeks of treatment. Adverse events, laboratory test results, vital sings, and electrocardiography will be recorded to evaluate safety. DISCUSSION: This is the first prospective clinical trial to explore the efficacy and safety of Hanslim for obese patients. If the results provide the appropriate dosage of Hanslim, this study would contribute to the confirmatory evidence for the use of Hanslim as a treatment for obesity needed to conduct a large-scale, phase III clinical trial. The results will be published in a peer-reviewed journal. TRIAL REGISTRATION: Clinical Research Information Service, ID: KCT0002193. Registered on January 6, 2017. https://cris.nih.go.kr/cris/search/search_result_st01_en.jsp?seq=7468.

Liver enzyme abnormalities of inpatients with rheumatic diseases: A 10-year retrospective study in a Korean medicine hospital.

Herbal medicines have been used as a treatment option for rheumatic disease (RD), but they often produce liver enzyme abnormality. This study examines the incidence of herb-induced liver injury (HILI) and the relationship between risk factors and liver enzyme abnormality (LEA) in inpatients with RD. HILI was analyzed using the Roussel Uclaf causality assessment method liver injury criteria and causality assessment. Multivariable analysis was performed to assess the relationship between patient characteristics and LEA in RD. The features of LEA were also examined in each RD. Among 352 patients included in this study, 105 patients showed LEA on admission, of which 6 had fulfilled the Roussel Uclaf causality assessment method criteria. The incidence risks of LEA and HILI were 12.55% and 0.58%, respectively. Multivariable analysis showed that LEA on admission and occasional use of alcohol could be risk factors for LEA on follow-up. In an additional analysis with each RD, all rheumatoid arthritis patients with LEA were taking nonsteroidal anti-inflammatory drugs, steroids, and disease-modifying antirheumatic drugs, and 4 out of 5 gout patients with LEA were taking steroids. The use of herbal medicine in RD is relatively safe. However, regular monitoring of liver enzyme tests and examination of alcohol consumption are required.

Curcumin attenuates the scurfy-induced immune disorder, a model of IPEX syndrome, with inhibiting Th1/Th2/Th17 responses in mice.

BACKGROUND: Immunodysregulation polyendocrinopathy enteropathy X-linked syndrome (IPEX) is a lethal autoimmune disease caused by mutations in the Foxp3 gene scurfin (scurfy). Immunosuppressive therapy for IPEX patients has been generally ineffective and has caused severe side effects, however curcumin has shown immune regulation properties for inflammatory diseases, such as rheumatoid arthritis, psoriasis, and inflammatory bowel diseases without side effects. OBJECTIVE: The aim of this study was to investigate whether curcumin would attenuate symptoms of IPEX in mouse model and would prolong its survival period. METHODS: C57BL/6 mice were separated into scurfy or wild-type litter mate groups by genotyping, and each group subsequently was separated into 2 subgroups that were fed a 1% curcumin containing or normal diet from the last day of breast-feeding. After weaning, pups were fed either a 1% curcumin containing or normal diet until all scurfy mice die for survival data. To elucidate immune cell proportions in spleen and lymph nodes, cells were analyzed by flowcytometry. Cellular cytokine production was accessed to investigate the effects of curcumin in T cell differentiation in vitro. RESULTS: Scurfy mice fed a 1% curcumin diet survived 4.0-fold longer compared to scurfy (92.5 days) mice fed a normal diet (23 days). A curcumin diet decreased all of the Th1/Th2/Th17 cell populations and attenuated diverse symptoms such as splenomegaly in scurfy mice. In vitro experiments showed that curcumin treatment directly decreased the Th1/Th2/Th17 cytokine production of IFN-γ, IL-4, and IL-17A in CD4+ T cells. CONCLUSIONS: Curcumin diet attenuated the scurfy-induced immune disorder, a model of IPEX syndrome, by inhibiting Th1/Th2/Th17 responses in mice. These results have implications for improving clinical therapy for patients with IPEX and other T cell related autoimmune diseases.

Casticin, an active compound isolated from Vitex Fructus, ameliorates the cigarette smoke-induced acute lung inflammatory response in a murine model.

The aim of this study was to determine of the effect of casticin, as an anti-inflammatory agent, on an acute lung inflammation in vivo model established through exposure to cigarette smoke (CS). Casticin is a phytochemical from Vitex species such as Vitex rotundifolia and Vitex agnus-castus that was recently shown to exert an anti-inflammatory effect in vivo. To demonstrate the effects of casticin, C57BL/6 mice were whole-body exposed to mainstream CS or fresh air for two weeks and treated with 1, 2, and 10mg/kg casticin via an i.p. injection. Immune cell infiltrations and cytokine productions were assessed from bronchoalveolar lavage Fluid (BALF), and lung histological analysis was performed. Treatment with casticin was observed to significantly inhibit the numbers of total cells, neutrophils, macrophages, and lymphocytes and reduce the levels of proinflammatory cytokines and chemokines in the BALF. In addition, casticin significantly decreased the infiltration of peribronchial and perivascular inflammatory cells and the epithelium thickness. The results of this study indicate that casticin has significant effects on the lung inflammation induced by CS in a mouse model. According to these outcomes, casticin may have therapeutic potential in inflammatory lung diseases, such as chronic obstructive pulmonary disease (COPD).

Bee venom phospholipase A2 suppresses allergic airway inflammation in an ovalbumin-induced asthma model through the induction of regulatory T cells.

Bee venom (BV) is one of the alternative medicines that have been widely used in the treatment of chronic inflammatory diseases. We previously demonstrated that BV induces immune tolerance by increasing the population of regulatory T cells (Tregs) in immune disorders. However, the major component and how it regulates the immune response have not been elucidated. We investigated whether bee venom phospholipase A2 (bvPLA2) exerts protective effects that are mediated via Tregs in OVA-induced asthma model. bvPLA2 was administered by intraperitoneal injection into control and OVA-challenged mice. The Treg population, total and differential bronchoalveolar lavage fluid (BALF) cell count, Th2 cytokines, and lung histological features were assessed. Treg depletion was used to determine the involvement of Treg migration and the reduction of asthmatic symptoms. The CD206-dependence of bvPLA2-treated suppression of airway inflammation was evaluated in OVA-challenged CD206(-/-) mice. The bvPLA2 treatment induced the Tregs and reduced the infiltration of inflammatory cells into the lung in the OVA-challenged mice. Th2 cytokines in the bronchoalveolar lavage fluid (BALF) were reduced in bvPLA2-treated mice. Although bvPLA2 suppressed the number of inflammatory cells after OVA challenge, these effects were not observed in Treg-depleted mice. In addition, we investigated the involvement of CD206 in bvPLA2-mediated immune tolerance in OVA-induced asthma model. We observed a significant reduction in the levels of Th2 cytokines and inflammatory cells in the BALF of bvPLA2-treated OVA-induced mice but not in bvPLA2-treated OVA-induced CD206(-/-) mice. These results demonstrated that bvPLA2 can mitigate airway inflammation by the induction of Tregs in an OVA-induced asthma model.

Inhibitory effects of Stemona tuberosa on lung inflammation in a subacute cigarette smoke-induced mouse model.

BACKGROUND: Stemona tuberosa has long been used in Korean and Chinese medicine to ameliorate various lung diseases such as pneumonia and bronchitis. However, it has not yet been proven that Stemona tuberosa has positive effects on lung inflammation. METHODS: Stemona tuberosa extract (ST) was orally administered to C57BL/6 mice 2 hr before exposure to CS for 2 weeks. Twenty-four hours after the last CS exposure, mice were sacrificed to investigate the changes in the expression of cytokines such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), chemokines such as keratinocyte-derived chemokine (KC) and inflammatory cells such as macrophages, neutrophils, and lymphocytes from bronchoalveolar lavage fluid (BALF). Furthermore, we compared the effect of ST on lung tissue morphology between the fresh air, CS exposure, and ST treatment groups. RESULTS: ST significantly decreased the numbers of total cells, macrophages, neutrophils, and lymphocytes in the BALF of mice that were exposed to CS. Additionally, ST reduced the levels of cytokines (TNF-α, IL-6) and the tested chemokine (KC) in BALF, as measured by enzyme-linked immunosorbent assay (ELISA). We also estimated the mean alveolar airspace (MAA) via morphometric analysis of lung tissues stained with hematoxylin and eosin (H&E). We found that ST inhibited the alveolar airspace enlargement induced by CS exposure. Furthermore, we observed that the lung tissues of mice treated with ST showed ameliorated epithelial hyperplasia of the bronchioles compared with those of mice exposed only to CS. CONCLUSIONS: These results indicate that Stemona tuberosa has significant effects on lung inflammation in a subacute CS-induced mouse model. According to these outcomes, Stemona tuberosa may represent a novel therapeutic herb for the treatment of lung diseases including COPD.