Functional integrity of visual coding following advanced photoreceptor degeneration.

E-cigarettes are devices that produce an aerosol often containing nicotine and flavorings. While linked to health risks, some evidence suggests e-cigarettes may be an effective smoking cessation tool sparking social media debates []. Currently, there is limited knowledge on how users engage with scientific information, nudge peers, and resist/accept misinformation. This study aims to (a) explore how peers embed and use science-based evidence in their interactions and (b) assess public attitudes towards vaping-related science topics. We extracted 26,598 vaping-related comments from the subreddit r/Science using the Pushshift API and key terms like “vape”, “e-cigarettes”, or “vaping” []. We removed weblinks, symbols, duplicated words, and comments shorter than 50 characters. We used stemming and lemmatization to address redundant word forms prevalent in social media. We used BERTopic to generate interpretable topic representations []. Topics were manually labeled using grounded theory analysis []. Sentiment and psycholinguistic features were measured using SEANCE []. Differences in component scores by the parent topics were assessed by one-way ANOVA with post-hoc tests (α = 0.05). Of the 25,127 comments, 6,102 comments were clustered into 25 topics with 19,025 comments placed in an outlier cluster to maintain interpretability. Topics were grouped into three parent topics: positive effects of vaping (PEV), negative effects of vaping (NEV), and vaping-related news & information (VNI). VADER sentiment, fear/disgust, and trust component scores were significantly different across parent topics whereas certainty scores were not. Post-hoc tests showed significant differences in fear/disgust between all pairs and in trust scores between NEV and PEV.