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.

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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.

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BackgroundStemona 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.MethodsStemona 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.ResultsST 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.ConclusionsThese 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.