RESUMO
BACKGROUND: Although Singapore has completely banned vaping, it is heavily promoted on social media. This study explored vaping-related social media content that Singaporeans are exposed to, and how it shapes vaping-related perceptions and experiences in the context of Singapore's strict regulations. METHODS: We held 10 focus group discussions with 63 Singaporeans aged 21-40 years, with diversity by sociodemographics, smoking history, vaping history and self-reported exposure to vaping-related social media content. Participants provided screenshots of any vaping-related content they encountered on their social media. Subsequently, in focus groups, they were shown a variety of this content and asked to discuss. We coded transcripts using inductive methods. RESULTS: Participants had encountered vape advertisements from neighbouring countries featuring attractive products, flavours, celebrity endorsements and entertainment shows, which they found highly appealing. Participants encountered posts that did not overtly advertise vaping but depicted people vaping in social settings, thereby normalising vaping despite its illegal status. They perceived government campaigns to deter vaping as biased and agenda driven, calling for a more nuanced message and use of local influencers and personal stories to communicate the rationale of the vaping regulations to the public. CONCLUSION: Having a law that bans vaping may not be enough; it needs to be complemented with more comprehensive marketing restrictions on social media platforms and effective enforcement of bans on social media promotions from overseas.
RESUMO
BACKGROUND: Liver cancer is one of the most common malignant tumors, and ranks as the fourth leading cause of cancer death worldwide. Microvascular invasion (MVI) is considered one of the most important factors for recurrence and poor prognosis of liver cancer. Thus, accurately identifying MVI before surgery is of great importance in making treatment strategies and predicting the prognosis of patients with hepatocellular carcinoma (HCC). Radiomics as an emerging field, aims to utilize artificial intelligence software to develop methods that may contribute to cancer diagnosis, treatment improvement and evaluation, and better prediction. AIM: To investigate the predictive value of computed tomography radiomics for MVI in solitary HCC ≤ 5 cm. METHODS: A total of 185 HCC patients, including 122 MVI negative and 63 MVI positive patients, were retrospectively analyzed. All patients were randomly assigned to the training group (n = 124) and validation group (n = 61). A total of 1351 radiomic features were extracted based on three-dimensional images. The diagnostic performance of the radiomics model was verified in the validation group, and the Delong test was applied to compare the radiomics and MVI-related imaging features (two-trait predictor of venous invasion and radiogenomic invasion). RESULTS: A total of ten radiomics features were finally obtained after screening 1531 features. According to the weighting coefficient that corresponded to the features, the radiomics score (RS) calculation formula was obtained, and the RS score of each patient was calculated. The radiomics model exhibited a better correction and identification ability in the training and validation groups [area under the curve: 0.72 (95% confidence interval: 0.58-0.86) and 0.74 (95% confidence interval: 0.66-0.83), respectively]. Its prediction performance was significantly higher than that of the image features (P < 0.05). CONCLUSION: Computed tomography radiomics has certain predictive value for MVI in solitary HCC ≤ 5 cm, and the predictive ability is higher than that of image features.