METHOD/PROCESS: We study SNS' effects on well-being by accounting for users' personal (i.e., self-disclosure) and situational (i.e., social networks) attributes, using a mixed design of content analysis and social network analysis.
RESULT/CONCLUSION: We compare users' within-person changes in self-disclosure and social networks in two phases (over half a year), drawing on Weibo Depression SuperTalk, an online community for depression, and find: ① Several network attributes strengthen social support, including network connectivity, global efficiency, degree centralization, hubs of communities, and reciprocal interactions. ② Users' self-disclosure attributes reflect positive changes in mental well-being and increased attachment to the community. ③ Correlations exist between users' topological and self-disclosure attributes. ④ A Poisson regression model extracts self-disclosure attributes that may affect users' received social support, including the writing length, number of active days, informal words, adverbs, negative emotion words, biological process words, and first-person singular forms.
INNOVATION: We combine social network analysis with content analysis, highlighting the need to understand SNS' effects on well-being by accounting for users' self-disclosure (content) and communication partners (social networks).
IMPLICATION/CONTRIBUTION: Authentic user data helps to avoid recall bias commonly found in self-reported data. A longitudinal within-person analysis of SNS' effects on well-being is helpful for policymakers in public health intervention, community managers for group organizations, and users in online community engagement.
METHODS/PROCESS: We used a web crawler to obtain a corpus of an online depression community. We introduced a three-stage procedure for metaphor identification in a Chinese Corpus: (1) combine MIPVU to identify metaphorical expressions (ME) bottom-up and formulate preliminary working hypotheses; (2) collect more ME top-down in the corpus by performing semantic domain analysis on identified ME; and (3) analyze ME and categorize conceptual metaphors using a reference list. In this way, we have gained a greater understanding of how depression sufferers conceptualize their experience metaphorically in an under-represented language in the literature (Chinese) of a new genre (online health community).
RESULTS/CONCLUSION: Main conceptual metaphors for depression are classified into PERSONAL LIFE, INTERPERSONAL RELATIONSHIP, TIME, and CYBERCULTURE metaphors. Identifying depression metaphors in the Chinese corpus pinpoints the sociocultural environment people with depression are experiencing: lack of offline support, social stigmatization, and substitutability of offline support with online support. We confirm a number of depression metaphors found in other languages, providing a theoretical basis for researching, identifying, and treating depression in multilingual settings. Our study also identifies new metaphors with source-target connections based on embodied, sociocultural, and idiosyncratic levels. From these three levels, we analyze metaphor research's theoretical and practical implications, finding ways to emphasize its inherent cross-disciplinarity meaningfully.
METHODS AND RESULTS: A total of 16 cannabinoids are determined in optimized microwave pretreatment of hemp oil using the developed approach. Untargeted metabolomics analysis reveals that cannabinoid extract (CE) and its major constituent (cannabidiol, CBD), can alleviate high glucose-induced increases in lipids and carbohydrates, and decreases in amino acid and nucleic acid. Moreover, CE and CBD are also found to suppress the expression levels of mdt-15, sbp-1, fat-5, fat-6, fat-7, daf-2, and elevate the expression level of daf-1, daf-7, daf-16, sod-3, gst-4, lipl-4, resulting in the decrease of lipid synthesis and the enhance of kinetism. Canonical correspondence analysis (CCA) uncovers strong associations between specific metabolic alterations and gene expression levels.
CONCLUSION: These findings from this exploratory study offer a new insight into the roles of cannabinoids in the treatment of obesity and related complications.
OBJECTIVE: Employing the extended theory of social normative behavior, this study examines the influence of individual and collective norms on COVID-19 vaccination intention across eight Asian countries. We examine how cultural tightness-looseness, defined as the degree of a culture's emphasis on norms and tolerance of deviant behavior, shapes normative social influence on COVID-19 vaccination intention.
METHODS: We conducted a multicountry online survey (N = 2676) of unvaccinated individuals in China, Indonesia, Japan, Malaysia, Singapore, South Korea, Thailand, and Vietnam in May and June 2021, when COVID-19 vaccination mandates had not yet been implemented in those countries. We conducted hierarchical regression analyses with interaction terms for the total sample and then re-categorizied the eight countries as either "tight" (n = 1102) or "loose" (n = 1574) to examine three-way interactions between individual norms, collective norms, and cultural tightness-looseness.
RESULTS: Perceived injunctive norms exerted the strongest impact of all normative factors on vaccination intention. Collective injunctive norms' influence depended on both perceived injunctive and descriptive norms, which was larger when norms were lower (vs. higher). The interactive pattern between perceived and collective norms was more pronounced in countries with greater cultural tightness.
CONCLUSION: Our findings reveal nuanced patterns of how individual and collective social norms influence health behavioral decisions, depending on the degree of cultural tightness-looseness.
PURPOSE: To explore the underlying mechanism of AP in exerting anti-fatigue effects.
METHODS: In this study, we developed a chronic sleep deprivation-induced fatigue model and used physiological, hematological, and biochemical indicators to evaluate the anti- fatigue efficacy of AP. Additionally, a multi-omics approach was employed to reveal the anti-fatigue mechanisms of AP from the perspective of microbiome, metabolome, and proteome.
RESULTS: The detection of physiology, hematology and biochemistry index indicated that AP markedly alleviate mice fatigue state induced by sleep deprivation. The 16S rRNA sequencing showed the AP promoted the abundance of probiotics (Odoribacter, Dubosiella, Marvinbryantia, and Eubacterium) and suppressed harmful bacteria (Ruminococcus). On the other hand, AP was found to regulate the expression of colonic proteins, such as increases of adenosine triphosphate (ATP) synthesis and mitochondrial function related proteins, including ATP5A1, ATP5O, ATP5L, ATP5H, NDUFA, NDUFB, NDUFS, and NDUFV. Serum metabolomic analysis revealed AP upregulated the levels of anti-fatigue amino acids, such as taurine, leucine, arginine, glutamine, lysine, and l-proline. Hepatic proteins express levels, especially tricarboxylic acid (TCA) cycle (CS, SDHB, MDH2, and DLST) and redox-related proteins (SOD1, SOD2, GPX4, and PRDX3), were significantly recovered by AP administration. Spearman correlation analysis uncovered the strong correlation between microbiome, metabolome and proteome, suggesting the anti-fatigue effects of AP is attribute to the energy homeostasis and redox balance through gut-liver axis.
CONCLUSION: AP increased colonic ATP production and improve mitochondrial function by regulating gut microbiota, and further upregulated anti-fatigue amino acid levels in the blood. Based on the gut-liver axis, AP upregulated the hepatic tricarboxylic acid cycle and oxidoreductase-related protein expression, regulating energy homeostasis and redox balance, and ultimately exerting anti-fatigue effects. This study provides insights into the anti-fatigue mechanisms of AP, highlighting its potential as a therapeutic agent.