A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models.

Introduction: Ovarian cancer (OC) is one of the most frequent gynecologic cancers among women, and high-accuracy risk prediction techniques are essential to effectively select the best intervention strategies and clinical management for OC patients at different risk levels. Current risk prediction models used in OC have low sensitivity, and few of them are able to identify OC patients at high risk of mortality, which would both optimize the treatment of high-risk patients and prevent unnecessary medical intervention in those at low risk. Objectives: To this end, we have developed a bagging-based algorithm with GA-XGBoost models that predicts the risk of death from OC using gene expression profiles. Methods: Four gene expression datasets from public sources were used as training (n = 1) or validation (n = 3) sets. The performance of our proposed algorithm was compared with fine-tuning and other existing methods. Moreover, the biological function of selected genetic features was further interpreted, and the response to a panel of approved drugs was predicted for different risk levels. Results: The proposed algorithm showed good sensitivity (74-100%) in the validation sets, compared with two simple models whose sensitivity only reached 47% and 60%. The prognostic gene signature used in this study was highly connected to AKT, a key component of the PI3K/AKT/mTOR signaling pathway, which influences the tumorigenesis, proliferation, and progression of OC. Conclusion: These findings demonstrated an improvement in the sensitivity of risk classification of OC patients with our risk prediction models compared with other methods. Ongoing effort is needed to validate the outcomes of this approach for precise clinical treatment.

Potential Impact of a Paper About COVID-19 and Smoking on Twitter Users' Attitudes Toward Smoking: Observational Study.

BACKGROUND: A cross-sectional study (Miyara et al, 2020) conducted by French researchers showed that the rate of current daily smoking was significantly lower in patients with COVID-19 than in the French general population, implying a potentially protective effect of smoking. OBJECTIVE: We aimed to examine the dissemination of the Miyara et al study among Twitter users and whether a shift in their attitudes toward smoking occurred after its publication as preprint on April 21, 2020. METHODS: Twitter posts were crawled between April 14 and May 4, 2020, by the Tweepy stream application programming interface, using a COVID-19-related keyword query. After filtering, the final 1929 tweets were classified into three groups: (1) tweets that were not related to the Miyara et al study before it was published, (2) tweets that were not related to Miyara et al study after it was published, and (3) tweets that were related to Miyara et al study after it was published. The attitudes toward smoking, as expressed in the tweets, were compared among the above three groups using multinomial logistic regression models in the statistical analysis software R (The R Foundation). RESULTS: Temporal analysis showed a peak in the number of tweets discussing the results from the Miyara et al study right after its publication. Multinomial logistic regression models on sentiment scores showed that the proportion of negative attitudes toward smoking in tweets related to the Miyara et al study after it was published (17.07%) was significantly lower than the proportion in tweets that were not related to the Miyara et al study, either before (44/126, 34.9%; P<.001) or after the Miyara et al study was published (68/198, 34.3%; P<.001). CONCLUSIONS: The public's attitude toward smoking shifted in a positive direction after the Miyara et al study found a lower incidence of COVID-19 cases among daily smokers.

Promotion of Disposable Electronic Cigarette Flavors and Topics on Twitter.

Disposable electronic cigarettes (e-cigarettes) became popular among youth after the Food and Drug Administration (FDA) implemented an enforcement policy to restrict the sale of cartridge-based flavored e-cigarettes starting from February 2020 in the United States (US). We aimed to examine the flavors and topics related to disposable e-cigarettes on Twitter. The Twitter dataset, which includes 1489 tweets, was collected by the Tweepy streamapplication programming interface (API) using a keyword query from March to September 2020. The disposable e-cigarette flavors were curated from both online stores and collected tweets. Topics related to disposable e-cigarettes on Twitter were manually coded. Distributions of topics were compared between tweets from the US and tweets from non-US countries. The temporal analysis results showed a slight increase in the number of discussions over the study period. Strawberry, mango, watermelon, and mint were the most popular flavors of disposable e-cigarettes mentioned on Twitter. Almost all the tweets (97.11%) were commercial tweets, which were dominated by topics related to the product and flavor promotions. The US tweets focused more on product and flavor promotions and less on price promotions compared to non-US tweets. Our results suggest that companies exploited the limitations of legislation to promote flavors on Twitter, which could undermine public health and young people's finances if they get hooked on addictive products.