Interleukin-37 relieves PM2.5-triggered lung injury by inhibiting autophagy through the AKT/mTOR signaling pathway in vivo and in vitro.

Autophagy mediates PM2.5-related lung injury (LI) and is tightly linked to inflammation and apoptosis processes. IL-37 has been demonstrated to regulate autophagy. This research aimed to examine the involvement of IL-37 in the progression of PM2.5-related LI and assess whether autophagy serves as a mediator for its effects.To create a model of PM2.5-related LI, this research employed a nose-only PM2.5 exposure system and utilized both human IL-37 transgenic mice and wild-type mice. The hIL-37tg mice demonstrated remarkable reductions in pulmonary inflammation and pathological LI compared to the WT mice. Additionally, they exhibited activation of the AKT/mTOR signaling pathway, which served to regulate the levels of autophagy and apoptosis.Furthermore, in vitro experiments revealed a dose-dependent upregulation of autophagy and apoptotic proteins following exposure to PM2.5 DMSO extraction. Simultaneously, p-AKT and p-mTOR expression was found to decrease. However, pretreatment with IL-37 demonstrated a remarkable reduction in the levels of autophagy and apoptotic proteins, along with an elevation of p-AKT and p-mTOR. Interestingly, pretreatment with rapamycin, an autophagy inducer, weakened the therapeutic impact of IL-37. Conversely, the therapeutic impact of IL-37 was enhanced when treated with 3-MA, a potent autophagy inhibitor. Moreover, the inhibitory effect of IL-37 on autophagy was successfully reversed by administering AKT inhibitor MK2206. The findings suggest that IL-37 can inhibit both the inflammatory response and autophagy, leading to the alleviation of PM2.5-related LI. At the molecular level, IL-37 may exert its anti autophagy and anti apoptosis effects by activating the AKT/mTOR signaling pathway.

Understanding the Adoption of Dietary Interventions Within a Chinese Autism Online Community: A Diffusion of Innovations Perspective.

Dietary interventions are common but controversial treatments for autistic people. This study aims to understand the adoption of dietary interventions based on diffusion of innovations theory in the autism online community from four aspects: popularity, adoption process, the influence of opinion leaders, and post-adoption feedback. Our data was extracted from a Chinese autism community named Baidu Tieba autism forum. We applied a mixed-method including four analytical approaches: descriptive statistics for popularity analysis; machine learning models for automatic data classification and topic detection; social network analysis for exploring the influence of opinion leaders on the adoption phase; content analysis for revealing the family caregiver-reported feedback after adoption. Dietary interventions have become increasingly popular in the autism online community since 2018. Analysis of the adoption process revealed that family caregivers at different stages of adoption focused on different topics, and the number of interactions with opinion leaders had a significant effect on the highest level (p < .001) and stage span (p < .001) of health information adoption. According to findings from the feedback of family caregivers, the effects of dietary interventions varied from individuals with autism. Our study revealed the diffusion of unproven interventions, which is of great significance in promoting evidence-based practices.

Exploring the Incentive Function of Virtual Academic Degrees in a Chinese Online Smoking Cessation Community: Qualitative Content Analysis.

BACKGROUND: Previous studies on online smoking cessation communities (OSCCs) have shown how such networks contribute to members' health outcomes from behavior influence and social support perspectives. However, these studies rarely considered the incentive function of OSCCs. One of the ways OSCCs motivate smoking cessation behaviors is through digital incentives. OBJECTIVE: This study aims to explore the incentive function of a novel digital incentive in a Chinese OSCC-the awarding of academic degrees-to promote smoking cessation. It specifically focuses on "Smoking Cessation Bar," an OSCC in the popular web-based Chinese forum Baidu Tieba. METHODS: We collected discussions about the virtual academic degrees (N= 1193) from 540 members of the "Smoking Cessation Bar." The time frame of the data set was from November 15, 2012, to November 3, 2021. Drawing upon motivational affordances theory, 2 coders qualitatively coded the data. RESULTS: We identified five key topics of discussion, including members' (1) intention to get virtual academic degrees (n=38, 2.47%), (2) action to apply for the degrees (n=312, 20.27%), (3) feedback on the accomplishment of goals (n=203, 13.19%), (4) interpersonal interaction (n=794, 51.59%), and (5) expression of personal feelings (n=192, 12.48%). Most notably, the results identified underlying social and psychological motivations behind using the forum to discuss obtaining academic degrees for smoking cessation. Specifically, members were found to engage in sharing behavior (n=423, 27.49%) over other forms of interaction such as providing recommendations or encouragement. Moreover, expressions of personal feelings about achieving degrees were generally positive. It was possible that members hid their negative feelings (such as doubt, carelessness, and dislike) in the discussion. CONCLUSIONS: The virtual academic degrees in the OSCC created opportunities for self-presentation for participants. They also improved their self-efficacy to persist in smoking cessation by providing progressive challenges. They served as social bonds connecting different community members, triggering interpersonal interactions, and inducing positive feelings. They also helped realize members' desire to influence or to be influenced by others. Similar nonfinancial rewards could be adopted in various smoking cessation projects to enhance participation and sustainability.

Echo Chamber Effect in Rumor Rebuttal Discussions About COVID-19 in China: Social Media Content and Network Analysis Study.

BACKGROUND: The dissemination of rumor rebuttal content on social media is vital for rumor control and disease containment during public health crises. Previous research on the effectiveness of rumor rebuttal, to a certain extent, ignored or simplified the structure of dissemination networks and users' cognition as well as decision-making and interaction behaviors. OBJECTIVE: This study aimed to roughly evaluate the effectiveness of rumor rebuttal; dig deeply into the attitude-based echo chamber effect on users' responses to rumor rebuttal under multiple topics on Weibo, a Chinese social media platform, in the early stage of the COVID-19 epidemic; and evaluate the echo chamber's impact on the information characteristics of user interaction content. METHODS: We used Sina Weibo's application programming interface to crawl rumor rebuttal content related to COVID-19 from 10 AM on January 23, 2020, to midnight on April 8, 2020. Using content analysis, sentiment analysis, social network analysis, and statistical analysis, we first analyzed whether and to what extent there was an echo chamber effect on the shaping of individuals' attitudes when retweeting or commenting on others' tweets. Then, we tested the heterogeneity of attitude distribution within communities and the homophily of interactions between communities. Based on the results at user and community levels, we made comprehensive judgments. Finally, we examined users' interaction content from three dimensions-sentiment expression, information seeking and sharing, and civility-to test the impact of the echo chamber effect. RESULTS: Our results indicated that the retweeting mechanism played an essential role in promoting polarization, and the commenting mechanism played a role in consensus building. Our results showed that there might not be a significant echo chamber effect on community interactions and verified that, compared to like-minded interactions, cross-cutting interactions contained significantly more negative sentiment, information seeking and sharing, and incivility. We found that online users' information-seeking behavior was accompanied by incivility, and information-sharing behavior was accompanied by more negative sentiment, which was often accompanied by incivility. CONCLUSIONS: Our findings revealed the existence and degree of an echo chamber effect from multiple dimensions, such as topic, interaction mechanism, and interaction level, and its impact on interaction content. Based on these findings, we provide several suggestions for preventing or alleviating group polarization to achieve better rumor rebuttal.

Predicting Family Implementation of Complementary and Alternative Medicine in Autism Online Communities.

Complementary and alternative medicine (CAM) is widely adopted by families with autistic children. This study aims to predict family caregivers' CAM implementation in Autism online communities. Dietary interventions were reported as a case study. We extracted behavioral (degree and betweenness), environmental (positive feedback and social persuasion), and personal features (language style) of family caregivers in online communities. The results of the experiment showed that random forests performed well in predicting families' tendency to implement CAM (AUC=0.887). It is promising to use machine learning to predict and intervene in the CAM implementation by family caregivers.

The echo chamber effect of rumor rebuttal behavior of users in the early stage of COVID-19 epidemic in China.

During public health emergencies, as one of the most effective rumor management strategies, rumor rebuttals depend on users' cognition, decision-making and interactive behaviors. Taking the dissemination of rumor rebuttals related to COVID-19 epidemic in the early stage in China as an example, we firstly adapted network analysis to construct representative networks of information and communication flow networks of users based on users' retweeting and commenting behaviors. Then quantitative indicators and exponential random graph models were used to evaluate the level of homophily based on topic and veracity in information networks, identity and standpoint in user networks. Meanwhile, chi square tests were added to compare the degree of echo chamber effect in retweeting and commenting. Findings showed that, users did show significant echo chamber effect when retweeting or commenting on rumor rebuttal information with different veracity. They showed diversification when retweeting but a certain tendency and pertinence when commenting in topic selection. Weibo's direct and open platform for retweeting and commenting broke the boundaries between stakeholders from different professional fields. However, the retweeting mechanism promoted self-isolation of users' standpoints, while the commenting mechanism provided an understanding and integrating channel for groups with opposing standpoints.

Exploring features of social support in a Chinese online smoking cessation community: A multidimensional content analysis of user interaction data.

Due to the rapid development of information technology, an increasing number of smokers choose online smoking cessation communities to interact with other individuals to help themselves quit smoking. Though it is well known that social support plays a key role in the process of smoking cessation, the features of social support that one can get from online smoking cessation communities remain unclear. We collected user interaction data from the largest Chinese online smoking cessation community, the quit smoking forum of Baidu Tieba. We selected 2758 replies from 29 active repliers and 408 correlated posts as our data set. Multidimensional content analysis is carried out from three aspects: posting scenarios, user quitting behavior stages, and types of social support. This article also explores the co-occurrence relationships of different types of social support by social network analysis. Results showed that users receive different compositions of social support in various posting scenarios and behavior stages. In most cases, emotional support is the most typical support the community provides. The community will provide more informational support when needed. Besides, informational support, especially personal experience and perceptual knowledge, has more diverse combination patterns with other types of social support. "Gratitude-Mutual assistance" and "Encouragement-Mutual assistance" are the most frequent co-occurrence relationships. The online smoking cessation community brings people who quit smoking together, and users provide rich types of social support for each other. Users can effectively obtain expected social support in different posting scenarios and smoking cessation stages. Smoking cessation projects should be designed to promote user communication and interaction, which positively affects achieving users' smoking cessation goals.

Privacy Policy Compliance of Chronic Disease Management Apps in China: Scale Development and Content Evaluation.

BACKGROUND: With the development of mobile health (mHealth), chronic disease management apps have brought not only the possibility of reducing the burden of chronic diseases but also huge privacy risks to patients' health data. OBJECTIVE: The purpose of the study was to analyze the extent to which chronic disease management apps in China comply with the Personal Information Security Specification (PI Specification). METHODS: The compliance of 45 popular chronic disease management apps was evaluated from the perspective of the information life cycle. To conduct a fine-grained evaluation, a scale based on the PI Specification was developed. Finally, 6 level 1 indicators, 22 level 2 indicators, and 61 level 3 indicators were defined. RESULTS: There were 33/45 apps (73%) with a privacy policy, and the average score of these apps was 40.4 out of 100. Items of level 1 indicators with high scores included general characteristics (mean 51.9% [SD 28.1%]), information collection and use (mean 51.1% [SD 36.7%]), and information sharing and transfer (mean 50.3% [SD 33.5%]). Information storage and protection had the lowest compliance with PI Specification (mean 29.4% [SD 32.4%]). Few personal information (PI) controllers have stated how to handle security incidents, including security incident reporting (7/33, 21%), security incident notification (10/33, 30%), and commitment to bear corresponding legal responsibility for PI security incidents (1/33, 3%). The performance of apps in the stage of information destruction (mean 31.8% [SD 40.0%]) was poor, and only 21% (7/33) apps would notify third parties to promptly delete PI after individuals cancelled their accounts. Moreover, the scoring rate for rights of PI subjects is generally low (mean 31.2% [SD 35.5%]), especially for obtaining copies of PI (15%) and responding to requests (25%). CONCLUSIONS: Although most chronic disease management apps had a privacy policy, the total compliance rate of the policy content was low, especially in the stage of information storage and protection. Thus, the field has a long way to go with regard to compliance around personal privacy protection in China.

Comparative Analysis of Topical Evolution Patterns and Temporal Trends of Hypertension Research.

Exploring the topical evolution patterns and temporal trends of hypertension can promote knowledge communication among experts, and is of great significance to understand the profile and frontiers of chronic disease. Current popular topic detection mainly focuses on two directions: one is based on social network analysis (SNA), the other is based on the topic models. Aiming at distinguishing their similarities and differences, this paper adopts the community detection method and expanded topic model Dirichlet-multinomial regression (DMR) respectively to detect the topic distribution and evolution trends of hypertension research. A total of 26,717 articles in the PubMed database were used as examples to construct the MeSH Terms co-occurrence matrix. It is found that hypertension literature is mainly concentrated on three communities and five research topics. MeSH Terms obtained from SNA are more specific and clearer, while the DMR has an advantage in exploring the evolution patterns of various themes.

Comparing Information Needs of Diabetes Patients in Chinese and American Health Communities of Questions and Answers.

This study selects the users generated content in diabetes groups of ManYouBang and DailyStrength which are two representative HCQA (Health Communities of Questions and Answers). Theme coding was applied to identify information needs while social network analysis was used to compare Chinese and American HCQA. In theory, we combine Social Network Analysis and content analysis to capture and contrast the pattern of Q&A in HCQA. On the one hand, the core needs of HCQA are "how to treat the disease" and the "diet" theme is closely related to other themes. On the other hand, the Chinese diabetes communities have "question and answer attributes" while the American diabetes communities have both "question and answer attributes" and "social support attributes." In practice, the founding provides enlightenment for the development of HCQA including improving content quality and strengthen emotional connection and disease treatment infrastructure.