UNASSIGNED: Artificial intelligence (AI)-based text generators, such as ChatGPT (OpenAI) and Google Bard (now Google Gemini), have demonstrated proficiency in predicting words and providing responses to various questions. However, their performance in answering clinical queries has not been well assessed. This comparative analysis aimed to assess the capabilities of ChatGPT and Google Gemini in addressing clinical questions.
UNASSIGNED: Separate interactions with ChatGPT and Google Gemini were conducted to obtain responses to the clinical question, posed in a PICOT (patient, intervention, comparison, outcome, time) format. To ascertain the accuracy of the information provided by the AI chat bots, a thorough examination of full-text articles was conducted.
UNASSIGNED: Although ChatGPT exhibited relative accuracy in generating bibliographic information, it displayed some inconsistencies in clinical content. Conversely, Google Gemini generated citations and summaries that were entirely fabricated.
UNASSIGNED: Despite generating responses that may appear credible, both AI-based tools exhibited factual inaccuracies, raising substantial concerns about their reliability as potential sources of clinical information. [J Nurs Educ. 2024;63(8):556-559.].

BACKGROUND: Tuberculosis (TB) burden and the underreporting of TB remain major health challenges in Indonesia. Interest in the internet is growing extensively, and the introduction of the TB mandatory electronic notification system in 2017 engaged the public\'s interest to leverage digital traces regarding TB information in Indonesia.
OBJECTIVE: To quantify the correlation between Google Trends data and Indonesian TB surveillance data before and after the implementation of a mandatory TB notification system.
METHODS: Google Trends searches on TB information were used. We used two sets of time series data, including before and after the launch of the TB notification system. Pearson\'s correlation was used to measure the correlation between TB search terms and official TB reports.
RESULTS: The moving average graph showed a linear pattern of TB information with TB reports after 2017. Pearson\'s correlation estimated a high correlation for TB definition, TB symptoms, and official TB reports with an R-value range of 0.97 to -1.00 (p ≤ 0.05) and showed an increasing trend in TB information searching after 2016.
CONCLUSIONS: Google Trends data can depict public interest in the TB epidemic. Validation of information-searching behavior is required to advocate the implementation of Google Trends for TB digital surveillance in Indonesia.

To design effective vaccine policies, policymakers need detailed data about who has been vaccinated, who is holding out, and why. However, existing data in the US are insufficient: reported vaccination rates are often delayed or not granular enough, and surveys of vaccine hesitancy are limited by high-level questions and self-report biases. Here we show how search engine logs and machine learning can help to fill these gaps, using anonymized Bing data from February to August 2021. First, we develop a vaccine intent classifier that accurately detects when a user is seeking the COVID-19 vaccine on Bing. Our classifier demonstrates strong agreement with CDC vaccination rates, while preceding CDC reporting by 1-2 weeks, and estimates more granular ZIP-level rates, revealing local heterogeneity in vaccine seeking. To study vaccine hesitancy, we use our classifier to identify two groups, vaccine early adopters and vaccine holdouts. We find that holdouts, compared to early adopters matched on covariates, are 67% likelier to click on untrusted news sites, and are much more concerned about vaccine requirements, development, and vaccine myths. Even within holdouts, clusters emerge with different concerns and openness to the vaccine. Finally, we explore the temporal dynamics of vaccine concerns and vaccine seeking, and find that key indicators predict when individuals convert from holding out to seeking the vaccine.

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BACKGROUND:  The rate of suicide death has been increasing, making understanding risk factors of growing importance. While exposure to explicit suicide-related media, such as description of means in news reports or sensationalized fictional portrayal, is known to increase population suicide rates, it is not known whether prosuicide website forums, which often promote or facilitate information about fatal suicide means, are related to change in suicide deaths overall or by specific means.
OBJECTIVE:  This study aimed to estimate the association of the frequency of Google searches of known prosuicide web forums and content with death by suicide over time in the United States, by age, sex, and means of death.
METHODS:  National monthly Google search data for names of common prosuicide websites between January 2010 and December 2021 were extracted from Google Health Trends API (application programming interface). Suicide deaths were identified using the CDC (Centers for Disease Control and Prevention) National Vital Statistics System (NVSS), and 3 primary means of death were identified (poisoning, suffocation, and firearm). Distributed lag nonlinear models (DLNMs) were then used to estimate the lagged association between the number of Google searches on suicide mortality, stratified by age, sex, and means, and adjusted for month. Sensitivity analyses, including using autoregressive integrated moving average (ARIMA) modeling approaches, were also conducted.
RESULTS:  Months in the United States in which search rates for prosuicide websites increased had more documented deaths by intentional poisoning and suffocation among both adolescents and adults. For example, the risk of poisoning suicide among youth and young adults (age 10-24 years) was 1.79 (95% CI 1.06-3.03) times higher in months with 22 searches per 10 million as compared to 0 searches. The risk of poisoning suicide among adults aged 25-64 was 1.10 (95% CI 1.03-1.16) times higher 1 month after searches reached 9 per 10 million compared with 0 searches. We also observed that increased search rates were associated with fewer youth suicide deaths by firearms with a 3-month time lag for adolescents. These models were robust to sensitivity tests.
CONCLUSIONS:  Although more analysis is needed, the findings are suggestive of an association between increased prosuicide website access and increased suicide deaths, specifically deaths by poisoning and suffocation. These findings emphasize the need to further investigate sites containing potentially dangerous information and their associations with deaths by suicide, as they may affect vulnerable individuals.

OBJECTIVE: To enhance and evaluate the quality of PubMed search results for Social Determinants of Health (SDoH) through the addition of new SDoH terms to Medical Subject Headings (MeSH).
METHODS: High priority SDoH terms and definitions were collated from authoritative sources, curated based on publication frequencies, and refined by subject matter experts. Descriptive analyses were used to investigate how PubMed search details and best match results were affected by the addition of SDoH concepts to MeSH. Three information retrieval metrics (Precision, Recall, and F measure) were used to quantitatively assess the accuracy of PubMed search results. Pre- and post-update documents were clustered into topic areas using a Natural Language Processing pipeline, and SDoH relevancy assessed.
RESULTS: Addition of 35 SDoH terms to MeSH resulted in more accurate algorithmic translations of search terms and more reliable best match results. The Precision, Recall, and F measures of post-update results were significantly higher than those of pre-update results. The percentage of retrieved publications belonging to SDoH clusters was significantly greater in the post- than pre-update searches.
CONCLUSIONS: This evaluation confirms that inclusion of new SDoH terms in MeSH can lead to qualitative and quantitative enhancements in PubMed search retrievals. It demonstrates the methodology for and impact of suggesting new terms for MeSH indexing. It provides a foundation for future efforts across behavioral and social science research (BSSR) domains.
CONCLUSIONS: Improving the representation of BSSR terminology in MeSH can improve PubMed search results, thereby enhancing the ability of investigators and clinicians to build and utilize a cumulative BSSR knowledge base.

Google Trends data were analyzed to assess search trends for pediatric ophthalmology and strabismus terms from 2010 to 2022. The highest average search volumes were \"lazy eye,\" \"strabismus,\" and \"vision therapy.\" \"Amblyopia\" had the lowest search volume. These data highlight the importance of understanding the utilization of online resources in health care and patient education. [J Pediatr Ophthalmol Strabismus. 2024;61(4):e39-e42.].

The demand for palliative care is increasing globally, yet a notable lack of awareness continues to present a significant obstacle to its widespread adoption. The use of digital tools like Google Trends can help gauging public interest in specific topics. We used Google Trends to conduct a systematic search of terms related to palliative care from January 1, 2010, to May 10, 2023. The results were filtered by location, including worldwide and Latin American countries. We found a global increase in searches for terms related to palliative care, with a peak in December 2022 associated with the death of Brazilian footballer Pelé. Countries like Brazil, Mexico, and Colombia mirrored this trend, while others like Argentina and Peru did not. Interest in palliative care is on the rise in Latin America, albeit with notable regional variations.

Proteomics, the study of proteins within biological systems, has seen remarkable advancements in recent years, with protein isoform detection emerging as one of the next major frontiers. One of the primary challenges is achieving the necessary peptide and protein coverage to confidently differentiate isoforms as a result of the protein inference problem and protein false discovery rate estimation challenge in large data. In this chapter, we describe the application of artificial intelligence-assisted peptide property prediction for database search engine rescoring by Oktoberfest, an approach that has proven effective, particularly for complex samples and extensive search spaces, which can greatly increase peptide coverage. Further, it illustrates a method for increasing isoform coverage by the PickedGroupFDR approach that is designed to excel when applied on large data. Real-world examples are provided to illustrate the utility of the tools in the context of rescoring, protein grouping, and false discovery rate estimation. By implementing these cutting-edge techniques, researchers can achieve a substantial increase in both peptide and isoform coverage, thus unlocking the potential of protein isoform detection in their studies and shedding light on their roles and functions in biological processes.

The increasing complexity and volume of mass spectrometry (MS) data have presented new challenges and opportunities for proteomics data analysis and interpretation. In this chapter, we provide a comprehensive guide to transforming MS data for machine learning (ML) training, inference, and applications. The chapter is organized into three parts. The first part describes the data analysis needed for MS-based experiments and a general introduction to our deep learning model SpeCollate-which we will use throughout the chapter for illustration. The second part of the chapter explores the transformation of MS data for inference, providing a step-by-step guide for users to deduce peptides from their MS data. This section aims to bridge the gap between data acquisition and practical applications by detailing the necessary steps for data preparation and interpretation. In the final part, we present a demonstrative example of SpeCollate, a deep learning-based peptide database search engine that overcomes the problems of simplistic simulation of theoretical spectra and heuristic scoring functions for peptide-spectrum matches by generating joint embeddings for spectra and peptides. SpeCollate is a user-friendly tool with an intuitive command-line interface to perform the search, showcasing the effectiveness of the techniques and methodologies discussed in the earlier sections and highlighting the potential of machine learning in the context of mass spectrometry data analysis. By offering a comprehensive overview of data transformation, inference, and ML model applications for mass spectrometry, this chapter aims to empower researchers and practitioners in leveraging the power of machine learning to unlock novel insights and drive innovation in the field of mass spectrometry-based omics.