RESUMO
INTRODUCTION: Instagram and TikTok, video-based social media platforms popular among adolescents, contain tobacco-related content despite the platforms' policies prohibiting substance-related posts. Prior research identified themes in e-cigarette-related social media posts using qualitative or text-based machine learning methods. We developed an image-based computer vision model to identify e-cigarette products in social media images and videos. METHODS: We created a dataset of 6,999 Instagram images labeled for 8 object classes: mod or pod devices, e-juice containers, packaging boxes, nicotine warning labels, e-juice flavors, e-cigarette brand names, and smoke clouds. We trained a DyHead object detection model using a Swin-Large backbone, evaluated the model's performance on 20 Instagram and TikTok videos, and applied the model to 14,072 e-cigarette-related promotional TikTok videos (2019-2022; 10,276,485 frames). RESULTS: The model achieved the following mean average precision scores on the image test set: e-juice container: 0.89; pod device: 0.67; mod device: 0.54; packaging box: 0.84; nicotine warning label: 0.86; e-cigarette brand name: 0.71; e-juice flavor name: 0.89; and smoke cloud: 0.46. The largest number of TikTok videos - 9,091 (65%) - contained smoke clouds, followed by mod and pod devices detected in 6,667 (47%) and 5,949 (42%) videos respectively. Prevalence of nicotine warning labels was the lowest, detected in 980 videos (7%). CONCLUSIONS: Deep learning-based object detection technology enables automated analysis of visual posts on social media. Our computer vision model can detect the presence of e-cigarettes products in images and videos, providing valuable surveillance data for tobacco regulatory science. IMPLICATIONS: Prior research identified themes in e-cigarette-related social media posts using qualitative or text-based machine learning methods. We developed an image-based computer vision model to identify e-cigarette products in social media images and videos.We trained a DyHead object detection model using a Swin-Large backbone, evaluated the model's performance on 20 Instagram and TikTok videos featuring at least two e-cigarette objects, and applied the model to 14,072 e-cigarette-related promotional TikTok videos (2019-2022; 10,276,485 frames).The deep learning model can be used for automated, scalable surveillance of image- and video-based e-cigarette-related promotional content on social media, providing valuable data for tobacco regulatory science. Social media platforms could use computer vision to identify tobacco-related imagery and remove it promptly, which could reduce adolescents' exposure to tobacco content online.
RESUMO
Drug-loaded microbubbles have been proven to be an effective strategy for non-invasive and local drug delivery when combined with ultrasound excitation for targeted drug release. Inertial cavitation is speculated to be a major mechanism for releasing drugs from drug-loaded microbubbles, but it results in lethal cellular pore damage that greatly limits its application. Thus, we investigated the cellular vesicle attachment and uptake to evaluate the efficiency of drug delivery by modulating the behaviors of targeted microbubble oscillation. The efficiency of vesicle attachment on the targeted cell membrane was 36.5 ± 15.9% and 3.8 ± 2.3% under stable and inertial cavitation, respectively. Further, stable cavitation enhanced cell permeability (26.8 ± 3.2%), maintained cell viability (90.8 ± 2.1%), and showed 7.9 ± 1.9-fold enhancement of in vivo vesicle release on tumor vessels. Therefore, our results reveal the ability to improve drug delivery via stable cavitation induced by targeted microbubbles. We propose that this strategy might be suitable for tissue repair or neuromodulation.