The Impact of Metabolic Syndrome on the Prognosis of High-Risk Alcoholic Hepatitis Patients: Redefining Alcoholic Hepatitis.

Background: Alcoholic hepatitis (AH) is characterized by acute symptomatic hepatitis associated with heavy alcohol use. This study was designed to assess the impact of metabolic syndrome on high-risk patients with AH with discriminant function (DF) score ≥ 32 and its effect on mortality. Methods: We searched the hospital database for ICD-9 diagnosis codes of acute AH, alcoholic liver cirrhosis, and alcoholic liver damage. The entire cohort was categorized into two groups: AH and AH with metabolic syndrome. The effect of metabolic syndrome on mortality was evaluated. Also, an exploratory analysis was used to create a novel risk measure score to assess mortality. Results: A large proportion (75.5%) of the patients identified in the database who had been treated as AH had other etiologies and did not meet the American College of Gastroenterology (ACG)-defined diagnosis of acute AH, thus had been misdiagnosed as AH. Such patients were excluded from analysis. The mean body mass index (BMI), hemoglobin (Hb), hematocrit (HCT), and alcoholic liver disease/non-alcoholic fatty liver disease index (ANI) were significantly different between two groups (P < 0.05). The results of a univariate Cox regression model showed that age, BMI, white blood cells (WBCs), creatinine (Cr), international normalized ratio (INR), prothrombin time (PT), albumin levels, albumin < 3.5, total bilirubin, Na, Child-Turcotte-Pugh (CTP), model for end-stage liver disease (MELD), MELD ≥ 21, MELD ≥ 18, DF score, and DF ≥ 32 had a significant effect on mortality. Patients with a MELD greater than 21 had a hazard ratio (HR) (95% confidence interval (CI) of 5.81 (2.74 - 12.30) (P < 0.001). The adjusted Cox regression model results showed that age, Hb, Cr, INR, Na, MELD score, DF score, and metabolic syndrome were independently associated with high patient mortality. However, the increase in BMI and mean corpuscular volume (MCV) and sodium significantly reduced the risk of death. We found that a model including age, MELD ≥ 21, and albumin < 3.5 was the best model in identifying patient mortality. Our study showed that patients admitted with a diagnosis of alcoholic liver disease with metabolic syndrome had an increased mortality risk compared to patients without metabolic syndrome, in high-risk patients with DF ≥ 32 and MELD ≥ 21. A bivariate correlation analysis revealed that patients with AH with metabolic syndrome were more likely to have infection (43%) compared to AH (26%) with correlation coefficient of 0.176 (P = 0.03, CI: 0.018 - 1.0). Conclusion: In clinical practice, the diagnosis of AH is inaccurately applied. Metabolic syndrome significantly increases the mortality risk in high-risk AH. It signifies that the presence of features of metabolic syndrome modifies the behavior of AH in acute settings, warranting different therapeutic strategies. We propose that in defining AH, patients overlapping with metabolic syndrome may need to be excluded as their outcome is different with regard to risk of renal dysfunctions, infections and death.

Social Media Images as an Emerging Tool to Monitor Adherence to COVID-19 Public Health Guidelines: Content Analysis.

BACKGROUND: Innovative surveillance methods are needed to assess adherence to COVID-19 recommendations, especially methods that can provide near real-time or highly geographically targeted data. Use of location-based social media image data (eg, Instagram images) is one possible approach that could be explored to address this problem. OBJECTIVE: We seek to evaluate whether publicly available near real-time social media images might be used to monitor COVID-19 health policy adherence. METHODS: We collected a sample of 43,487 Instagram images in New York from February 7 to April 11, 2020, from the following location hashtags: #Centralpark (n=20,937), #Brooklyn Bridge (n=14,875), and #Timesquare (n=7675). After manually reviewing images for accuracy, we counted and recorded the frequency of valid daily posts at each of these hashtag locations over time, as well as rated and counted whether the individuals in the pictures at these location hashtags were social distancing (ie, whether the individuals in the images appeared to be distanced from others vs next to or touching each other). We analyzed the number of images posted over time and the correlation between trends among hashtag locations. RESULTS: We found a statistically significant decline in the number of posts over time across all regions, with an approximate decline of 17% across each site (P<.001). We found a positive correlation between hashtags (#Centralpark and #Brooklynbridge: r=0.40; #BrooklynBridge and #Timesquare: r=0.41; and #Timesquare and #Centralpark: r=0.33; P<.001 for all correlations). The logistic regression analysis showed a mild statistically significant increase in the proportion of posts over time with people appearing to be social distancing at Central Park (P=.004) and Brooklyn Bridge (P=.02) but not for Times Square (P=.16). CONCLUSIONS: Results suggest the potential of using location-based social media image data as a method for surveillance of COVID-19 health policy adherence. Future studies should further explore the implementation and ethical issues associated with this approach.

Characterizing Social Media Messages Related to Underage JUUL E-Cigarette Buying and Selling: Cross-Sectional Analysis of Reddit Subreddits.

BACKGROUND: Stopping the epidemic of e-cigarette use among youth has become the common goal of both regulatory authorities and health departments. JUUL is currently the most popular e-cigarette brand on the market. Young people usually obtain and exchange information about JUUL with the help of social media platforms. Along with the rising prevalence of JUUL, posts about underage JUUL buying and selling have appeared on social media platforms such as Reddit, which sharply increase the risk of minors being exposed to JUUL. OBJECTIVE: This study aims to analyze Reddit messages about JUUL buying and selling among the users of the UnderageJuul subreddit, and to further summarize the characteristics of those messages. The findings and insights can contribute to a better understanding of the patterns of underage JUUL use, and help public health officials provide timely education and guidance to minors who have intentions of accessing JUUL. METHODS: We used a novel cross-subreddit method to analyze the Reddit messages on 2 subreddits. From July 9, 2017, to January 7, 2018, we collected data from the UnderageJuul subreddit, which was created for underage JUUL use discussion. The data set included 716 threads, 2935 comments, and 844 Reddit users (ie, Redditors). We collected our second data set, comprising 23,840 threads and 162,106 comments posted between July 9, 2017, and January 8, 2019, from the JUUL subreddit. We conducted analyses including the following: (1) annotation of users with buying/selling intention, (2) posting patterns discovery and topic comparison, and (3) posting activeness observation of discovered Redditors. Term frequency-inverse document frequency and regular expression-enhanced keyword search methods were applied during the content analysis to extract the posting patterns. The public posting records of the discovered users on the JUUL subreddit during the year after the UnderageJuul subreddit was shut down were analyzed to determine whether they were still active and interested in obtaining JUUL. RESULTS: Our study revealed the following: (1) Among the 716 threads on the UnderageJuul subreddit, there were 214 threads related to JUUL sale and 168 threads related to JUUL purchase, which accounted for 53.5% (382/714) of threads. (2) Among the 844 Redditors of the UnderageJuul subreddit, 23.82% (201/844) of users were annotated with buying intention, and 21.10% (178/844) of users were annotated with selling intention. There were 34 users with buying/selling intention that self-reported as being <21 years old. (3) The most common key phrases used in selling threads were "WTS," "want to sell," "for sale," and "selling" (154/214, 72.0%). The most common key phrases used in buying threads were "look for/get JUUL/pods" (58/168, 34.5%) and "WTB" (53/168, 31.5%). (4) The most important concern that UnderageJuul Redditors had in obtaining JUULs was the price (311/1306, 23.81%), followed by the delivery service (68/1306, 5.21%). (5) The most popular flavors among the users with buying/selling intention were mango, cucumber, and mint. The flavor preferences remained consistent on both subreddits. Adverse symptoms related to the mango flavor were reported by 3 users on the JUUL subreddit. (6) In total, 24.4% (49/201) of users wanted to buy JUULs and 46.6% (83/178) of users wanted to sell JUULs, including 11 self-reported underage users, who also participated in the discussions on the JUUL subreddit. (7) Within one year of the UnderageJuul subreddit shutting down, there were 40 users who continued to post 186 threads on the JUUL subreddit, including 10 threads indicating buying/selling willingness that were posted shortly after the UnderageJuul subreddit was closed. CONCLUSIONS: There were overlapping users active in the JUUL and UnderageJuul subreddits. The buying/selling-related content appeared in multiple venues with certain posting patterns from July 9, 2017, to January 7, 2018. Such content might lead to a high risk of health problems for minors, such as nicotine addiction. Based on these findings, this study provided some insights and suggestions that might contribute to the decision-making processes of regulators and public health officials.

Public Reactions to the Cigarette Control Regulation on a Chinese Microblogging Platform: Empirical Analysis.

BACKGROUND: On January 1, 2019, a new regulation on the control of smoking in public places was officially implemented in Hangzhou, China. On the day of the implementation, a large number of Chinese media reported the contents of the regulation on the microblog platform Weibo, causing a strong response from and heated discussion among netizens. OBJECTIVE: This study aimed to conduct a content and network analysis to examine topics and patterns in the social media response to the new regulation. METHODS: We analyzed all microblogs on Weibo that mentioned and explained the regulation in the first 8 days following the implementation. We conducted a content analysis on these microblogs and used social network visualization and descriptive statistics to identify key users and key microblogs. RESULTS: Of 7924 microblogs, 12.85% (1018/7924) were in support of the smoking control regulation, 84.12% (6666/7924) were neutral, and 1.31% (104/7924) were opposed to the smoking regulation control. For the negative posts, the public had doubts about the intentions of the policy, its implementation, and the regulations on electronic cigarettes. In addition, 1.72% (136/7924) were irrelevant to the smoking regulation control. Among the 1043 users who explicitly expressed their positive or negative attitude toward the policy, a large proportion of users showed supportive attitudes (956/1043, 91.66%). A total of 5 topics and 11 subtopics were identified. CONCLUSIONS: This study used a content and network analysis to examine topics and patterns in the social media response to the new smoking regulation. We found that the number of posts with a positive attitude toward the regulation was considerably higher than that of the posts with a negative attitude toward the regulation. Our findings may assist public health policy makers to better understand the policy's intentions, scope, and potential effects on public interest and support evidence-based public health regulations in the future.

Underage JUUL Use Patterns: Content Analysis of Reddit Messages.

BACKGROUND: The popularity of JUUL (an e-cigarette brand) among youth has recently been reported in news media and academic papers, which has raised great public health concerns. Little research has been conducted on the age distribution, geographic distribution, approaches to buying JUUL, and flavor preferences pertaining to underage JUUL users. OBJECTIVE: The aim of this study was to analyze social media data related to demographics, methods of access, product characteristics, and use patterns of underage JUUL use. METHODS: We collected publicly available JUUL-related data from Reddit. We extracted and summarized the age, location, and flavor preference of subreddit UnderageJuul users. We also compared common and unique users between subreddit UnderageJuul and subreddit JUUL. The methods of purchasing JUULs were analyzed by manually examining the content of the Reddit threads. RESULTS: A total of 716 threads and 2935 comments were collected from the subreddit UnderageJuul before it was shut down. Most threads did not mention a specific age, but ages ranged from 13 years to greater than 21 years in those that did. Mango, mint, and cucumber were the most popular among the 7 flavors listed on JUUL's official website, and 336 subreddit UnderageJuul threads mentioned 7 discreet approaches to circumvent relevant legal regulations to get JUUL products, the most common of which was purchasing JUUL from other Reddit users (n=181). Almost half of the UnderageJuul users (389/844, 46.1%) also participated in discussions on the main JUUL subreddit and sought information across multiple Reddit forums. Most (64/74, 86%) posters were from large metropolitan areas. CONCLUSIONS: The subreddit UnderageJuul functioned as a forum to explore methods of obtaining JUUL and to discuss and recommend specific flavors before it was shut down. About half of those using UnderageJuul also used the more general JUUL subreddit, so a forum still exists where youths can attempt to share information on how to obtain JUUL and other products. Exploration of such social media data in real time for rapid public health surveillance could provide early warning for significant health risks before they become major public health threats.

Electronic cigarette usage patterns: a case study combining survey and social media data.

Objective: To identify who were social media active e-cigarette users, to compare the use patterns from both survey and social media data for data triangulation, and to jointly use both datasets to conduct a comprehensive analysis on e-cigarette future use intentions. Materials and Methods: We jointly used an e-cigarette use online survey (n = 5132) and a social media dataset. We conducted analysis from 3 different perspectives. We analyzed online forum participation patterns using survey data. We compared e-cigarette use patterns, including brand and flavor types, ratings, and purchase approaches, between the 2 datasets. We used logistic regression to study intentions to use e-cigarettes using both datasets. Results: Male and younger e-cigarette users were the most likely to participate in e-cigarette-related discussion forums. Forum active survey participants were hardcore vapers. The e-cigarette use patterns were similar in the online survey data and the social media data. Intention to use e-cigarettes was positively related to e-liquid ratings and flavor ratings. Social media provided a valuable source of information on users' ratings of e-cigarette refill liquids. Discussion: For hardcore vapers, social media data were consistent with online survey data, which suggests that social media may be useful to study e-cigarette use behaviors and can serve as a useful complement to online survey research. We proposed an innovative framework for social media data triangulation in public health studies. Conclusion: We illustrated how social media data, combined with online survey data, can serve as a new and rich information source for public health research.

Identifying Topics for E-Cigarette User-Generated Contents: A Case Study From Multiple Social Media Platforms.

BACKGROUND: Electronic cigarette (e-cigarette) is an emerging product with a rapid-growth market in recent years. Social media has become an important platform for information seeking and sharing. We aim to mine hidden topics from e-cigarette datasets collected from different social media platforms. OBJECTIVE: This paper aims to gain a systematic understanding of the characteristics of various types of social media, which will provide deep insights into how consumers and policy makers effectively use social media to track e-cigarette-related content and adjust their decisions and policies. METHODS: We collected data from Reddit (27,638 e-cigarette flavor-related posts from January 1, 2011, to June 30, 2015), JuiceDB (14,433 e-juice reviews from June 26, 2013 to November 12, 2015), and Twitter (13,356 "e-cig ban"-related tweets from January, 1, 2010 to June 30, 2015). Latent Dirichlet Allocation, a generative model for topic modeling, was used to analyze the topics from these data. RESULTS: We found four types of topics across the platforms: (1) promotions, (2) flavor discussions, (3) experience sharing, and (4) regulation debates. Promotions included sales from vendors to users, as well as trades among users. A total of 10.72% (2,962/27,638) of the posts from Reddit were related to trading. Promotion links were found between social media platforms. Most of the links (87.30%) in JuiceDB were related to Reddit posts. JuiceDB and Reddit identified consistent flavor categories. E-cigarette vaping methods and features such as steeping, throat hit, and vapor production were broadly discussed both on Reddit and on JuiceDB. Reddit provided space for policy discussions and majority of the posts (60.7%) holding a negative attitude toward regulations, whereas Twitter was used to launch campaigns using certain hashtags. Our findings are based on data across different platforms. The topic distribution between Reddit and JuiceDB was significantly different (P<.001), which indicated that the user discussions focused on different perspectives across the platforms. CONCLUSIONS: This study examined Reddit, JuiceDB, and Twitter as social media data sources for e-cigarette research. These mined findings could be further used by other researchers and policy makers. By utilizing the automatic topic-modeling method, the proposed unified feedback model could be a useful tool for policy makers to comprehensively consider how to collect valuable feedback from social media.

Analysis of symptoms and their potential associations with e-liquids' components: a social media study.

BACKGROUND: The electronic cigarette (e-cigarette) market has grown rapidly in recent years. However, causes of e-cigarette related symptoms among users and their impact on health remain uncertain. This research aims to mine the potential relationships between symptoms and e-liquid components, such as propylene glycol (PG), vegetable glycerine (VG), flavor extracts, and nicotine, using user-generated data collected from Reddit. METHODS: A total of 3605 e-liquid related posts from January 1st, 2011 to June 30th, 2015 were collected from Reddit. Then the patterns of VG/PG distribution among different flavors were analyzed. Next, the relationship between throat hit, which was a typical symptom of e-cigarette use, and e-liquid components was studied. Finally, other symptoms were examined based on e-liquid components and user sentiment. RESULTS: We discovered 3 main sets of findings: 1) We identified three groups of flavors in terms of VG/PG ratios. Fruits, cream, and nuts flavors were similar. Sweet, menthol, and seasonings flavors were classified into one group. Tobacco and beverages flavors were the third group. 2) Throat hit was analyzed and we found that menthol and tobacco flavors, as well as high ratios of PG and nicotine level, could produce more throat hit. 3) A total of 9 systems of 25 symptoms were identified and analyzed. Components including VG/PG ratio, flavor, and nicotine could be possible reasons for these symptoms. CONCLUSIONS: E-liquid components shown to be associated with e-cigarette use symptomology were VG/PG ratios, flavors, and nicotine levels. Future analysis could be conducted based on the structure of e-liquid components categories built in this study. Information revealed in this study could be utilized by e-cigarette users to understand the relationship between e-liquid type and symptoms experienced, by vendors to choose appropriate recipes of e-liquid, and by policy makers to develop new regulations.

An Examination of Electronic Cigarette Content on Social Media: Analysis of E-Cigarette Flavor Content on Reddit.

In recent years, the emerging electronic cigarette (e-cigarette) marketplace has shown great development prospects all over the world. Reddit, one of the most popular forums in the world, has a very large user group and thus great influence. This study aims to gain a systematic understanding of e-cigarette flavors based on data collected from Reddit. Flavor popularity, mixing, characteristics, trends, and brands are analyzed. Fruit flavors were mentioned the most (n = 15,720) among all the posts and were among the most popular flavors (n = 2902) used in mixed blends. Strawberry and vanilla flavors were the most popular for e-juice mixing. The number of posts discussing e-cigarette flavors has increased sharply since 2014. Mt. Baker Vapor and Hangen were the most popular brands discussed among users. Information posted on Reddit about e-cigarette flavors reflected consumers' interest in a variety of flavors. Our findings suggest that Reddit could be used for data mining and analysis of e-cigarette-related content. Understanding how e-cigarette consumers' view and utilize flavors within their vaping experience and how producers and marketers use social media to promote flavors and sell products could provide valuable information for regulatory decision-makers.

Chemical fingerprinting of Shexiang Baoxin Pill and simultaneous determination of its major constituents by HPLC with evaporative light scattering detection and electrospray mass spectrometric detection.

High-performance Liquid Chromatography (HPLC) with evaporative light scattered detection (ELSD) and electrospray ionization mass spectrometric detection (ESI-MS) was employed to establish chemical fingerprint of Shexiang Baoxin Pill (SBP) and to simultaneously determinate its seven major constituents, including cholic acid, deoxycholic acid, ursodeoxycholic acid, chenodeoxycholic acid, cinobufagin, recibufogenin, and ginsenoside Rb1. The analysis was performed on a C18 column with water-acetonitrile gradient elution, and the investigated constituents were authenticated by comparing their retention times and mass spectra with those of reference compounds. The proposed method was applied to analyze nine SBP samples and produced data with acceptable linearity, precision, stability and accuracy. Both the chemical fingerprints and quantification data were used to evaluate the quality of various SBP products. The proposed method allows obtaining chemical fingerprint and quantification of multi-components in one run, and therefore can be readily utilized as a comprehensive quality control approach for traditional Chinese medicine.