Vascular contribution to cognitive impairment and dementia (VCID) is a clinical label encompassing a wide range of cognitive disorders progressing from mild to major vascular cognitive impairment (VCI), which is also defined as vascular dementia (VaD). VaD diagnosis is mainly based on clinical and imaging findings. Earlier biomarkers are needed to identify subjects at risk to develop mild VCI and VaD. In the present meta-analysis, we comprehensively evaluated the role of inflammatory biomarkers in differential diagnosis between VaD and Alzheimer’s disease (AD), and assessed their prognostic value on predicting VaD incidence. We collected literature until January 31, 2021, assessing three inflammatory markers [interleukin(IL)-6, C-reactive protein (CRP), tumor necrosis factor (TNF)-α] from blood or cerebrospinal fluid (CSF) samples. Thirteen cross-sectional and seven prospective studies were included. Blood IL-6 levels were cross-sectionally significantly higher in people with VaD compared to AD patients (SMD: 0.40, 95% CI: 0.18 to 0.62) with low heterogeneity (I2: 41%, p = 0.13). Higher IL-6 levels were also associated to higher risk of incident VaD (relative risk: 1.28, 95% CI: 1.03 to 1.59, I2: 0%). IL-6 in CSF was significantly higher in people with VaD compared to healthy subjects (SMD: 0.77, 95% CI: 0.17 to 1.37, I2: 70%), and not compared to AD patients, but due to limited evidence and high inconsistency across studies, we could not draw definite conclusion. Higher blood IL-6 levels might represent a useful biomarker able to differentiate people with VaD from those with AD and might be correlated with higher risk of future VaD.

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IntroductionYouTube is a popular social media used by youth and has electronic cigarette (e-cigarette) content. We used machine learning to identify the content of e-cigarette videos, featured e-cigarette products, video uploaders, and marketing and sales of e-cigarette products.MethodsWe identified e-cigarette content using 18 search terms (eg, e-cig) using fictitious youth viewer profiles and predicted four models using the metadata as the input to supervised machine learning: (1) video themes, (2) featured e-cigarette products, (3) channel type (ie, video uploaders) and (4) discount/sales. We assessed the association between engagement data and the four models.Results3830 English videos were included in the supervised machine learning. The most common video theme was ‘product review’ (48.9%), followed by ‘instruction’ (eg, ‘how to’ use/modify e-cigarettes; 17.3%); diverse e-cigarette products were featured; ‘vape enthusiasts’ most frequently posted e-cigarette videos (54.0%), followed by retailers (20.3%); 43.2% of videos had discount/sales of e-cigarettes; and the most common sales strategy was external links for purchasing (34.1%). ‘Vape trick’ was the least common theme but had the highest engagement (eg, >2 million views). ‘Cannabis’ (53.9%) and ‘instruction’ (49.9%) themes were more likely to have external links for purchasing (p<0.001). The four models achieved an F1 score (a measure of model accuracy) of up to 0.87.DiscussionOur findings indicate that on YouTube videos accessible to youth, a variety of e-cigarette products are featured through diverse videos themes, with discount/sales. The findings highlight the need to regulate the promotion of e-cigarettes on social media platforms.

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What is already known on this topic?With the growing popularity of vaping and the proliferation of e-cigarette advertisements on social media, evidence has emerged about the association between social media exposure and vaping. What is added by this report?This study highlights the potentially stronger influence of Snapchat (daily and nondaily), Instagram (daily use), Facebook (nondaily use), and Twitter (nondaily use), on experimental and current vaping among adolescents in Florida.What are the implications for public health practice? Public health interventions are needed to promote education and brief counseling about social media use in schools, households, and clinics. In addition, it is important to develop counter-marketing campaigns targeting social media platforms that adolescents most frequently use (eg, Instagram, Snapchat), strict age restrictions, and regulatory actions at the policy level.