BACKGROUND: Wikipedia is a vital open educational resource in computational biology. The quality of computational biology coverage in English-language Wikipedia has improved steadily in recent years. However, there is an increasingly large \'knowledge gap\' between computational biology resources in English-language Wikipedia, and Wikipedias in non-English languages. Reducing this knowledge gap by providing educational resources in non-English languages would reduce language barriers which disadvantage non-native English speaking learners across multiple dimensions in computational biology.
RESULTS: Here, we provide a comprehensive assessment of computational biology coverage in Spanish-language Wikipedia, the second most accessed Wikipedia worldwide. Using Spanish-language Wikipedia as a case study, we generate quantitative and qualitative data before and after a targeted educational event, specifically, a Spanish-focused student editing competition. Our data demonstrates how such events and activities can narrow the knowledge gap between English and non-English educational resources, by improving existing articles and creating new articles. Finally, based on our analysis, we suggest ways to prioritize future initiatives to improve open educational resources in other languages.
METHODS: Scripts for data analysis are available at: https://github.com/ISCBWikiTeam/spanish.

BACKGROUND: To mitigate safety concerns, regulatory agencies must make informed decisions regarding drug usage and adverse drug events (ADEs). The primary pharmacovigilance data stem from spontaneous reports by health care professionals. However, underreporting poses a notable challenge within the current system. Explorations into alternative sources, including electronic patient records and social media, have been undertaken. Nevertheless, social media\'s potential remains largely untapped in real-world scenarios.
OBJECTIVE: The challenge faced by regulatory agencies in using social media is primarily attributed to the absence of suitable tools to support decision makers. An effective tool should enable access to information via a graphical user interface, presenting data in a user-friendly manner rather than in their raw form. This interface should offer various visualization options, empowering users to choose representations that best convey the data and facilitate informed decision-making. Thus, this study aims to assess the potential of integrating social media into pharmacovigilance and enhancing decision-making with this novel data source. To achieve this, our objective was to develop and assess a pipeline that processes data from the extraction of web forum posts to the generation of indicators and alerts within a visual and interactive environment. The goal was to create a user-friendly tool that enables regulatory authorities to make better-informed decisions effectively.
METHODS: To enhance pharmacovigilance efforts, we have devised a pipeline comprising 4 distinct modules, each independently editable, aimed at efficiently analyzing health-related French web forums. These modules were (1) web forums\' posts extraction, (2) web forums\' posts annotation, (3) statistics and signal detection algorithm, and (4) a graphical user interface (GUI). We showcase the efficacy of the GUI through an illustrative case study involving the introduction of the new formula of Levothyrox in France. This event led to a surge in reports to the French regulatory authority.
RESULTS: Between January 1, 2017, and February 28, 2021, a total of 2,081,296 posts were extracted from 23 French web forums. These posts contained 437,192 normalized drug-ADE couples, annotated with the Anatomical Therapeutic Chemical (ATC) Classification and Medical Dictionary for Regulatory Activities (MedDRA). The analysis of the Levothyrox new formula revealed a notable pattern. In August 2017, there was a sharp increase in posts related to this medication on social media platforms, which coincided with a substantial uptick in reports submitted by patients to the national regulatory authority during the same period.
CONCLUSIONS: We demonstrated that conducting quantitative analysis using the GUI is straightforward and requires no coding. The results aligned with prior research and also offered potential insights into drug-related matters. Our hypothesis received partial confirmation because the final users were not involved in the evaluation process. Further studies, concentrating on ergonomics and the impact on professionals within regulatory agencies, are imperative for future research endeavors. We emphasized the versatility of our approach and the seamless interoperability between different modules over the performance of individual modules. Specifically, the annotation module was integrated early in the development process and could undergo substantial enhancement by leveraging contemporary techniques rooted in the Transformers architecture. Our pipeline holds potential applications in health surveillance by regulatory agencies or pharmaceutical companies, aiding in the identification of safety concerns. Moreover, it could be used by research teams for retrospective analysis of events.

BACKGROUND: In the digital age, search engines and social media platforms are primary sources for health information, yet their commercial interests-focused algorithms often prioritize irrelevant content. Web-based health applications by reputable sources offer a solution to circumvent these biased algorithms. Despite this advantage, there remains a significant gap in research on the effective integration of content-ranking algorithms within these specialized health applications to ensure the delivery of personalized and relevant health information.
OBJECTIVE: This study introduces a generic methodology designed to facilitate the development and implementation of health information recommendation features within web-based health applications.
METHODS: We detail our proposed methodology, covering conceptual foundation and practical considerations through the stages of design, development, operation, review, and optimization in the software development life cycle. Using a case study, we demonstrate the practical application of the proposed methodology through the implementation of recommendation functionalities in the EndoZone platform, a platform dedicated to providing targeted health information on endometriosis.
RESULTS: Application of the proposed methodology in the EndoZone platform led to the creation of a tailored health information recommendation system known as EndoZone Informatics. Feedback from EndoZone stakeholders as well as insights from the implementation process validate the methodology\'s utility in enabling advanced recommendation features in health information applications. Preliminary assessments indicate that the system successfully delivers personalized content, adeptly incorporates user feedback, and exhibits considerable flexibility in adjusting its recommendation logic. While certain project-specific design flaws were not caught in the initial stages, these issues were subsequently identified and rectified in the review and optimization stages.
CONCLUSIONS: We propose a generic methodology to guide the design and implementation of health information recommendation functionality within web-based health information applications. By harnessing user characteristics and feedback for content ranking, this methodology enables the creation of personalized recommendations that align with individual user needs within trusted health applications. The successful application of our methodology in the development of EndoZone Informatics marks a significant progress toward personalized health information delivery at scale, tailored to the specific needs of users.

Due to the constraints of the COVID-19 pandemic, conducting sensory evaluations requiring direct interactions became challenging. In response, researchers have been motivated to devise non-face-to-face testing methods as alternatives. This study aimed to compare two non-face-to-face home-use tests (HUT) with the traditional face-to-face central location test (CLT). Both HUTs involved online recruitment and sample delivery to participants\' homes. One HUT provided a written protocol with no direct interaction (contactless HUT; C-HUT), whereas the other included an online meeting with a researcher for live guidance (online HUT; O-HUT). Four coffee samples were evaluated on the basis of liking and sensory and emotional attributes. The comparison between CLT and O-HUT showed RV coefficients of 0.92, 0.93, and 0.98 (P < 0.05) for liking and sensory and emotional attributes, respectively. In addition, based on the RV coefficient, the CLT results showed a significantly greater similarity to those of O-HUT compared to those of C-HUT. The O-HUT also outperformed the C-HUT in its ability to significantly discriminate between samples. Hence, real-time interactions between researchers and participants, as facilitated by O-HUT, may be more suitable in certain scenarios compared to C-HUT, which relies solely on a written protocol. Overall, these findings suggest that C-HUT and O-HUT are suitable methods for collecting sensory data and overcoming geographic and face-to-face contact limitations, providing greater flexibility, and reducing the time and cost associated with traditional sensory evaluations.

Which kinds of grievances garner support from the public on online platforms? Focusing on national online petitioning, one of the forms of direct democracy in contemporary politics, we examine the content and characteristics of petitions that succeeded in attracting public attention and support. Using our comprehensive data on online petitions that were submitted to the executive office between 2017 and 2022 in South Korea, our analysis yields three important findings. First, a mix of post-materialist topics such as human rights and gender equality and materialist topics such as safety and environment turn out to be salient among petitions that meet the signature threshold. Second, online petitions the contents of which reveal either moral emotions or Confucian attitudes are more likely to gain public support compared to others. Third, keywords that are related to moral claims asking for the apprehension of perpetrators on behalf of victims, such as \'victim,\' \'perpetrator,\' \'kid,\' and \'punishment,\' appear most frequently inside the petitions that cross the signature threshold. Such findings provide implications for understanding both the potentials and limitations of national online petitioning in contemporary democracies.

The leading role of the media is very important in the new media era to build the reporting system and framework of sexual violence crimes, guide people\'s awareness and public opinion, and improve society\'s vigilance on sexual crimes. This study took People\'s Daily Online, a representative online media in China, as a research object to analyse the reporting of sexual crimes over the past 15 years. We conducted relevant searches for specific keywords set in the Python crawler and used IBM SPSS Statistics 19 software to analyse the frequency of relevant content. The results of the research show that, firstly, there have been significant changes in the number of news stories about sexual crimes. Second, the majority of sexual crime news stories are from mainland China. Third, the focus of the news stories and people is relatively concentrated on the perpetrators. Fourth, the People\'s Daily Online\'s coverage of sexual crimes focuses on blaming the perpetrators. Fifth, sexual crimes show that the framework is more episodic. This paper examines changes in the coverage of sexual crimes in China and captures how the media cover socially relevant issues, providing important insights for future social health, psychological awareness and diversion, and media policy.

BACKGROUND: The prevalence of type 2 diabetes mellitus (DM) and pre-diabetes mellitus (pre-DM) has been increasing among youth in recent decades in the United States, prompting an urgent need for understanding and identifying their associated risk factors. Such efforts, however, have been hindered by the lack of easily accessible youth pre-DM/DM data.
OBJECTIVE: We aimed to first build a high-quality, comprehensive epidemiological data set focused on youth pre-DM/DM. Subsequently, we aimed to make these data accessible by creating a user-friendly web portal to share them and the corresponding codes. Through this, we hope to address this significant gap and facilitate youth pre-DM/DM research.
METHODS: Building on data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018, we cleaned and harmonized hundreds of variables relevant to pre-DM/DM (fasting plasma glucose level ≥100 mg/dL or glycated hemoglobin  ≥5.7%) for youth aged 12-19 years (N=15,149). We identified individual factors associated with pre-DM/DM risk using bivariate statistical analyses and predicted pre-DM/DM status using our Ensemble Integration (EI) framework for multidomain machine learning. We then developed a user-friendly web portal named Prediabetes/diabetes in youth Online Dashboard (POND) to share the data and codes.
RESULTS: We extracted 95 variables potentially relevant to pre-DM/DM risk organized into 4 domains (sociodemographic, health status, diet, and other lifestyle behaviors). The bivariate analyses identified 27 significant correlates of pre-DM/DM (P<.001, Bonferroni adjusted), including race or ethnicity, health insurance, BMI, added sugar intake, and screen time. Among these factors, 16 factors were also identified based on the EI methodology (Fisher P of overlap=7.06×106). In addition to those, the EI approach identified 11 additional predictive variables, including some known (eg, meat and fruit intake and family income) and less recognized factors (eg, number of rooms in homes). The factors identified in both analyses spanned across all 4 of the domains mentioned. These data and results, as well as other exploratory tools, can be accessed on POND.
CONCLUSIONS: Using NHANES data, we built one of the largest public epidemiological data sets for studying youth pre-DM/DM and identified potential risk factors using complementary analytical approaches. Our results align with the multifactorial nature of pre-DM/DM with correlates across several domains. Also, our data-sharing platform, POND, facilitates a wide range of applications to inform future youth pre-DM/DM studies.

Measles remains a significant threat to children worldwide despite the availability of effective vaccines. The COVID-19 pandemic exacerbated the situation by leading to the postponement of supplementary measles immunization activities. Along with this postponement, measles surveillance also deteriorated, with the lowest number of submitted specimens in over a decade. In this study, we focus on measles as a challenging case study due to its high vaccination coverage, which leads to smaller outbreaks and potentially weaker signals on Google Trends. Our research aimed to explore the feasibility of using Google Trends for real-time monitoring of infectious disease outbreaks. We evaluated the correlation between Google Trends searches and clinical case data using the Pearson correlation coefficient and Spearman\'s rank correlation coefficient across 30 European countries and Japan. The results revealed that Google Trends was most suitable for monitoring acute disease outbreaks at the regional level in high-income countries, even when there are only a few weekly cases. For example, from 2017 to 2019, the Pearson correlation coefficient was 0.86 (p-value< 0.05) at the prefecture level for Okinawa, Japan, versus 0.33 (p-value< 0.05) at the national level for Japan. Furthermore, we found that the Pearson correlation coefficient may be more suitable than Spearman\'s rank correlation coefficient for evaluating the correlations between Google Trends search data and clinical case data. This study highlighted the potential of utilizing Google Trends as a valuable tool for timely public health interventions to respond to infectious disease outbreaks, even in the context of diseases with high vaccine coverage.

OBJECTIVE: The coronavirus disease 2019 (COVID-19) public health emergency has had a huge impact worldwide. We analyzed news headlines and keywords from the initial period of COVID-19, and explored the dissemination timeline of news related to the epidemic, and the impact of Internet-based media on the public using lifecycle theory and agenda-setting theory. We aimed to explore the impact of Baidu news headlines on public attention during the first wave of COVID-19, as well as the management mechanism of regulatory departments for social public opinion.
METHODS: We searched Baidu News using the keywords \"Novel Coronavirus\" and \"COVID-19\" from 8 January to 21 February 2020, a total of 45 days, and used Python V3.6 to extract news samples during the first wave of the epidemic. We used text analysis software to structurally process captured news topics and content summaries, applied VOSviewer V6.19 and Ucinet V6.0 to examine key aspects of the data.
RESULTS: We analyzed the impact of Baidu News headlines on social opinion during the first wave of COVID-19 in the budding, spread, and outbreak stage of the information lifecycle. From clustering visualization and social network analysis perspectives, we explored the characteristics of Baidu News during the initial stage of the COVID-19. The results indicated that agenda-setting coverage through online media helped to mitigate the negative impact of COVID-19. The findings revealed that news reporting generated a high level of public attention toward a specific emergency event.
CONCLUSIONS: The public requires accurate and objective information on the progress of COVID-19 through Baidu News headlines to inform their planning for the epidemic. Meanwhile, government can enhance the management mechanism of news dissemination, correct false and inaccurate news, and guide public opinion in a positive direction. In addition, timely official announcements on the progress of the COVID-19 outbreak and responses to matters of public concern can help calm tensions and maintain social stability.

This study aims to evaluate the correctness of the generated answers by Google Bard, GPT-3.5, GPT-4, Claude-Instant, and Bing chatbots to decision-making clinical questions in the oral and maxillofacial surgery (OMFS) area.
A group of 3 board-certified oral and maxillofacial surgeons designed a questionnaire with 50 case-based questions in multiple-choice and open-ended formats. Answers of chatbots to multiple-choice questions were examined against the chosen option by 3 referees. The chatbots\' answers to the open-ended questions were evaluated based on the modified global quality scale. A P-value under .05 was considered significant.
Bard, GPT-3.5, GPT-4, Claude-Instant, and Bing answered 34%, 36%, 38%, 38%, and 26% of the questions correctly, respectively. In open-ended questions, GPT-4 scored the most answers evaluated as grades \"4\" or \"5,\" and Bing scored the most answers evaluated as grades \"1\" or \"2.\" There were no statistically significant differences between the 5 chatbots in responding to the open-ended (P = .275) and multiple-choice (P = .699) questions.
Considering the major inaccuracies in the responses of chatbots, despite their relatively good performance in answering open-ended questions, this technology yet cannot be trusted as a consultant for clinicians in decision-making situations.