Hypersensitivity pneumonitis (HP) is a complex, immüne mediated interstitial lung disease in which accurate diagnosis and long term management require integration of clinical, radiologic, and exposure-related information. Patients increasingly use artificial intelligence (AI) based chatbots to obtain disease related information; however, the quality, readability, and patient usability of such content remain unclear. This study aimed to evaluate the quality, reliability, readability, and patient-centered usability of AI chatbot generated information on HP. Using Google Trends, we identified four of the most frequently searched patient-oriented questions regarding HP: (1) What is HP and what causes it? (2) What are the clinical features of HP? (3) How is HP treated? (4) How is HP diagnosed? These questions were submitted verbatim to eight AI chatbots (ChatGPT-5.1, Claude 3, Microsoft Copilot, DeepSeek V3, Gemini Pro, Grok 4, Kimi K2, Perplexity AI). A total of 32 responses were independently evaluated in a blinded fashion by four pulmonology professors specializing in interstitial lung diseases. Content quality and reliability were assessed using DISCERN; understandability and actionability with PEMAT-P; global written readability with the Written Readability Rating (WRR); and structural readability with the Flesch-Kincaid Grade Level (FKGL). All chatbot outputs required advanced literacy, with FKGL scores ranging from 20.17 to 29.07 and a mean of approximately 24-25, indicating college or postgraduate reading level. No chatbot produced content within the recommended patient-appropriate range (FKGL ≤ 8). WRR scores declined with increasing clinical complexity, from 67.85 for definitional content (Q1) to 51.227 for diagnostic explanations (Q4). DISCERN scores varied substantially across models (35.001-57.103), with most chatbots falling into the "fair-good" range, reflecting partially reliable but incomplete information. [..] Conclusion: AI chatbots can generate clinically rich explanations of HP but currently produce content that is too complex and insufficiently actionable for most patients. [..].

BackgroundAlthough large language models (LLMs) show potential for patient education, their accuracy, usability, and comprehensibility lack validation in high-risk pediatric anesthesia. Rigorous evaluation is therefore essential prior to widespread clinical use in perioperative parental anesthesia education.ObjectiveThis study aims to evaluate the accuracy, reliability, and readability of responses generated by 5 LLMs to parental inquiries regarding pediatric anesthesia, and to assess their suitability for clinical use in perioperative caregiver education.MethodsTwo expert anesthesiologists identified 33 parental questions on pediatric anesthesia by screening authoritative resources and Google Trends. On December 14, 2025, these questions were submitted to 5 LLMs (DeepSeek-V3.2, ChatGPT-5, Gemini 2.5 Flash, Copilot, and Perplexity) via official web interfaces with default settings and zero-shot prompting, with each query in a separate conversation. Responses were standardized for blinded assessment. Two pediatric anesthesiologists with ≥10 years of clinical experience independently evaluated accuracy and reliability using the 4-point Likert accuracy scale, DISCERN, Ensuring Quality Information for Patients (EQIP), Journal of the American Medical Association (JAMA) benchmark, and Global Quality Score (GQS). After text preprocessing, readability was evaluated using 6 algorithms (Automated Readability Index [ARI], Flesch Reading Ease Score [FRES], Gunning Fog Index [GFI], Flesch-Kincaid Grade Level [FKGL], Coleman-Liau Index [CL], and the Simple Measure of Gobbledygook [SMOG]) via an online calculator. Interrater reliability was analyzed using the intraclass correlation coefficient (ICC); differences across models were assessed with the Kruskal-Wallis H test; and deviations from the sixth-grade benchmark were evaluated using 1-sample Wilcoxon signed-rank tests (P<.05 considered significant).ResultsAll 5 LLMs demonstrated high clinical accuracy (>90%; P=.12), with Gemini reaching 100%. Nevertheless, safety risks and content hallucinations were still observed. Excluding Gemini and Copilot, the remaining 3 models (ChatGPT, DeepSeek, and Perplexity) each produced unsafe content in 3.03% (n=1) of the 33 queries. Hallucinations were detected in all models except Gemini, with DeepSeek and Perplexity showing the highest hallucination rate (3/33, 9.09%). Furthermore, Perplexity showed superior reliability on DISCERN (median 41; P<.05), yet no model achieved a “good” rating. Gemini achieved the highest EQIP (median 66.67%; P<.05) despite lower GQS (median 3). Transparency was universally poor (JAMA median ≤1), with DeepSeek and ChatGPT showing a “floor effect.” ChatGPT had superior readability, but all models exceeded the recommended 6-grade complexity level.ConclusionsIn this study, 5 LLMs generally provided clinically accurate information when responding to parental questions about pediatric anesthesia. However, limitations were also identified, including hallucinated content, safety-related deficiencies, limited source transparency, and readability levels exceeding recommended standards. Therefore, LLM-generated information should be interpreted with caution and should not replace clinician guidance.

ABSTRACT The rapid rise of generative artificial intelligence (GenAI) is transforming how consumers access information, creating new challenges for marketing education. While Search Engine Optimization (SEO) is well established in curricula, the emergence of Generative Engine Optimization (GEO), optimizing content for AI-driven platforms such as ChatGPT, represents an urgent but underexplored pedagogical frontier. This study draws on interviews with 16 marketing practitioners across diverse sectors and triangulates their insights with Google Trends and job market data to examine GEO adoption patterns, the evolution of terminology, and implications for marketing education. Four themes emerged: (1) terminology convergence amid conceptual fluidity, (2) industry recognition of student readiness gaps, (3) implementation challenges and measurement uncertainty, and (4) specific recommendations for curriculum integration. Guided by the Transformative Marketing Education framework and the principles of curriculum agility, the study proposes a three-tier integration model to embed GEO concepts across digital, performance, and AI marketing courses. Findings indicate that GEO should complement rather than replace SEO. To support rapid classroom adoption, Appendix B provides ready-to-use assignments aligned with each tier.

COVID-19, caused by the SARS-CoV-2 virus, has become a global public health crisis with diverse clinical manifestations affecting multiple organ systems, including the integumentary system. One notable cutaneous manifestation, referred to as "COVID toes," involves the development of pernio-like chilblains, characterized by red-to-violet macules, plaques, or nodules, primarily on toes and fingers. This characteristic clinical feature gained significant attention due to its apparent association with COVID-19, especially during the early stages of the pandemic when individuals with mild or asymptomatic cases exhibited these symptoms. Concurrently, digital platforms such as Google Trends have emerged as tools for tracking public interest in health-related topics, offering insights into real-time patterns of disease awareness. Previous research has demonstrated that Google Trends data may correlate with the incidence of infectious diseases, suggesting that search interest can be a proxy for disease outbreaks. In this study, we sought to explore the potential relationship between public interest in COVID toes, as reflected in Google Trends, and the incidence and mortality rates of COVID-19. Specifically, we examined whether peaks in search interest for "COVID toes" corresponded with surges in COVID-19 cases and deaths. By analyzing trends in search data, we aimed to assess the utility of digital platforms as an epidemiological tool for monitoring disease progression and public awareness. Our findings provide insights into the potential role of digital search data in forecasting outbreaks and highlight the interplay between public perception and the clinical burden of COVID-19, emphasizing the importance of real-time data in public health surveillance and response.

Finerenone, a nonsteroidal mineralocorticoid receptor antagonist, has demonstrated greater receptor selectivity and fewer adverse effects compared with older mineralocorticoid receptor antagonists. Clinical trials support its efficacy in reducing kidney disease progression, cardiovascular events, and heart failure outcomes across various patient populations; however, anecdotally, the drug appears infrequently used in clinical practice. A serial, cross-sectional analysis of IQVIA's National Prescription Audit, which covers >70% of US outpatient prescription activity, was conducted from July 2021 to December 2024 to examine finerenone prescribing trends, focusing on cardiologists and nephrologists. For added context, prescription rates of finerenone were compared with those of 2 other medications with related mechanisms or indications commonly used in cardiovascular-kidney-metabolic syndrome, spironolactone and empagliflozin. Prescribing trends were correlated with total search activity using Google Trends. We found that finerenone was prescribed at a rate of 30 180 prescriptions/month in December 2022, increasing to 45 420 prescriptions/month in December 2023, and to 60 756 prescriptions/month in December 2024. This modest increase correlated with changes in Google search activity, with mild inflections after the release of major clinical trial data. The ratio of total prescriptions from nephrologists (15.81 prescriptions per nephrologist)/cardiologists (0.70 prescriptions per cardiologist) in 2024 was ≈23:1. The proportion of finerenone prescriptions to other cardiovascular-kidney-metabolic therapies remained low, with finerenone prescriptions numerically representing 3.0% of empagliflozin prescriptions and 2.6% of spironolactone prescriptions in 2024. The adoption of finerenone has been modest, especially among cardiologists compared with nephrologists, and lags behind other cardiovascular-kidney-metabolic therapies. However, recent trends show an upward shift in its use that correlates with public interest related to clinical trial results.

PurposeTo conduct an exploratory, descriptive trends analysis related to The Pitt's Freedom House Ambulance Service (FHAS) storyline.DesignMulti-method examination of Google Trends and Reddit data.SettingGoogle Trends and the subreddit r/ThePittTVShow.SampleWe extracted relative search volume (RSV) data from Google Trends 2weeks before the episode aired on February 20, 2025 to 2weeks after. We also searched r/ThePittTVShow for "Freedom House." We retrieved posts published prior to September 15, 2025 using RedditExtractoR, which were coded by 2 trained research assistants (RAs).MeasuresRSV for Google Trends. RAs coded Reddit comments for the presence or absence of specific content.AnalysisCalculation of percent change for Google Trends. For Reddit comments, calculation of descriptive statistics and cross-tabulations.ResultsRSV for "freedom house" increased 170% the day after the episode aired and remained elevated until 1week later. Of the 196 relevant posts, 36.9% (n = 72) provided information about FHAS. Posts coded as emotional engagement (27.6%, n = 54) were often also coded as not knowing about FHAS (29.6%), n = 16) or reflection (24.1%, n = 13).ConclusionsThere is a temporal association between online searches about FHAS and The Pitt storyline as well as online information sharing about FHAS via social media.

This study aimed to evaluate and compare the responses of different artificial intelligence-based large language models (AI LLMs) to frequently asked patient questions regarding post-operative care following total hip arthroplasty (THA) across four domains: reliability, quality, accuracy, and readability. Twenty-seven commonly asked questions were identified through Google Trends and expert consensus, covering exercises, activities of daily living, and dislocation precautions. Responses generated by the three AI models (May 2-5, 2025) were assessed using the modified DISCERN scale, Global Quality Scale (GQS), Accuracy Scale, and Flesch Reading Ease Score (FRES). Inter-rater reliability was highest for Gemini (ICC = 0.71), while ChatGPT models demonstrated moderate-to-good reliability (ICC = 0.67-0.71). Gemini scored significantly higher in reliability (P < 0.001), whereas ChatGPT 3.5 achieved the greatest readability (median FRES = 55). Significant differences were observed among models across reliability, quality, and readability metrics. AI LLMs can serve as supplementary tools for patient education following THA; however, their use requires clinician oversight to ensure accuracy and safety. Future studies should explore patient-centered evaluations and hybrid approaches combining AI guidance with professional supervision in orthopedic rehabilitation.

Allergic Rhinitis (AR) affects over 500 million people globally, posing a significant public health burden. Video-sharing platforms like YouTube and Bilibili have become primary sources of health information. This study aimed to compare content quality, user engagement, and alignment with seasonal search trends of AR-related videos on these two culturally distinct platforms. We retrieved the top 200 AR-related videos from each platform (keywords: "allergic rhinitis" for YouTube, "" for Bilibili) published between January 2022 and January 2025. After applying predefined exclusion criteria (irrelevance, non-English YouTube videos, out-of-timeframe, advertisements), 240 videos (91 YouTube, 149 Bilibili) were retained. Quality was assessed using the Patient Education Material Assessment Tool (PEMAT), Video Information Quality Index (VIQI), Global Quality Scale (GQS), and the Modified DISCERN Scale (mDISCERN). We also integrated Google Trends and Baidu Index data to analyze video characteristics, engagement, and correlations with seasonal search trends. AR-related video quality was generally low, though YouTube scored significantly higher than Bilibili in median PEMAT-Total (76.5 vs. 71.4), GQS (4 vs. 3), and mDISCERN (3 vs. 1) (all P < 0.001). YouTube featured more content from medical professionals (33.3%, n = 30/91), whereas Bilibili's was predominantly from non-professionals (61.4%, n = 92/149). Bilibili showed higher user engagement, with greater interactions through comments and donations. Both platforms showed spring and autumn search peaks, coinciding with allergen seasons. A moderate positive correlation emerged between Bilibili videos and Baidu Index (r = 0.37; P = 0.03), while YouTube videos correlated strongly with Google Trends (r = 0.63; P = 0.03). Professional content scored higher on both platforms, but treatment-related Bilibili videos correlated negatively with search volume (r = - 0.69; P = 0.01), signaling potential misinformation risks during peak periods. For AR-related videos, YouTube offers better content quality, while Bilibili excels in user engagement. Both platforms need improved content quality and coverage. A seasonal "demand-content" loop exists between AR search trends and video content, carrying misinformation risks during peak periods. We recommend year-round promotion of evidence-based content, adding medical warnings to misleading information, and encouraging collaboration between medical professionals and social media creators.

Bipolar disorder is a clinically sensitive and diagnostically complex condition in which unclear or incomplete psychoeducational information may contribute to misunderstanding of symptoms, delayed help-seeking, and unsafe interpretation of treatment options. Large language models are increasingly used as on-demand sources of mental health information, yet comparative evidence on the quality and readability of AI-generated information about bipolar disorder remains limited. This cross-sectional content analysis evaluated 180 responses generated by ChatGPT, Gemini, and DeepSeek to 20 bipolar disorder-related questions derived from Google Trends. Each question was asked in three independent new sessions for each model. Information quality was assessed using the 20-item EQIP instrument, and readability was evaluated using Flesch-Kincaid Grade Level, Flesch Reading Ease, and word count. To address the non-independence of repeated responses nested within prompts, a linear mixed-effects model was used with AI model and question category as fixed effects and question ID as a random intercept. In the mixed-effects analysis, AI model significantly predicted EQIP scores. Compared with ChatGPT, Gemini and DeepSeek generated higher EQIP scores, with DeepSeek showing the largest estimated difference. Question category also contributed to information quality, although category-level pairwise comparisons did not remain significant after Bonferroni adjustment. Higher EQIP scores were moderately associated with longer responses and more favorable readability indices. Inter-rater analyses showed moderate absolute agreement for total EQIP scores and variable item-level agreement. Within the specific models, access conditions, prompts, date, and settings tested in this study, AI-generated bipolar disorder information differed across models in EQIP-rated quality and readability. These findings should be interpreted as content-quality findings rather than evidence of clinical accuracy, safety, or patient benefit. AI-generated psychoeducation should therefore be treated as a supplementary information source requiring expert review rather than a replacement for clinician-guided education.

To evaluate changes in public interest in calcitonin gene-related peptide therapies associated with major product lifecycle milestones using online search data from the United States and Europe. Calcitonin gene-related peptide therapies have transformed migraine treatment, but the impact of key product lifecycle milestones on public awareness remains unclear. As patients increasingly turn to online platforms for health information, events such as trial publications, regulatory approvals, commercial launches, and advertising campaigns may drive search activity and signal broader awareness. We conducted a retrospective ecological infodemiology study using a quasi-experimental interrupted time-series design with monthly Google Trends data (January 2017-July 2025). Search volume index (0-100 scale) was assessed for seven CGRP-therapies. Milestone events (trial publications, regulatory approvals, commercial launches, and media exposure) were defined a priori as potential drivers of digital impact. We evaluated immediate and sustained changes in search interest following major milestones over time. The primary analysis included all therapies in the United States; secondary analyses evaluated erenumab across five European countries (Germany, France, Italy, Spain, and United Kingdom). In the United States, commercial launch was associated with significant immediate increases in search interest for erenumab (β = 102.54; 95% confidence interval [CI], 17.05-188.02, p = 0.021) and galcanezumab (brand search term) (β = 42.36; 95% CI, 15.80-68.91, p = 0.002). Sustained post-launch growth was observed for fremanezumab (brand search term) (β = +0.50/month; 95% CI, 0.27-0.73, p < 0.001), eptinezumab (β = +0.45/month; 95% CI, 0.26-0.64, p < 0.001), rimegepant (brand search term) (β = +0.97/month; 95% CI, 0.47-1.47, p < 0.001), atogepant (brand search term) (β = +1.28/month; 95% CI, 0.70-1.85, p < 0.001), and ubrogepant (brand search term) (β = +0.59/month; 95% CI, 0.42-0.77, p < 0.001). Media exposure was associated with significant immediate increases for erenumab (β = 94.33; 95% CI, 11.17-177.50, p = 0.029) and ubrogepant (β = 29.77; 95% CI, 11.12-48.41, p = 0.003). Regulatory approvals and trial publications were not consistently associated with significant changes in search interest. In Europe, commercial launch was associated with significant immediate increases for erenumab (generic search term) in Italy (β = 126.82; 95% CI, 62.12-191.52, p < 0.001), erenumab (brand search term) in Italy (β = 142.98; 95% CI, 23.88-262.08, p = 0.021) and the United Kingdom (β = 69.44; 95% CI, 6.71-132.16, p = 0.033). Post-launch trends were generally negative across countries, indicating gradual declines after initial spikes. Commercial launch was the most consistent driver of public search interest in CGRP-targeting therapies. Media exposure produced additional increases in selected cases, whereas regulatory approvals and clinical trial publications had limited measurable impact. These findings underscore the dominant role of commercial and consumer-facing communication in shaping public awareness of new migraine treatments.

Dengue transmission in inland Southeast Asia shows strong seasonality and short-term surges that challenge timely public-health response. We assessed whether province-level Google search activity can support short-horizon dengue monitoring in Dong Nai, Vietnam. Monthly reported dengue cases from July 2013 to April 2021 were aligned with the Google Trends index (GTI) for "Sốt xuất huyết". Models were evaluated using leakage-free rolling-origin expanding-window cross-validation with 3-month validation blocks. We compared a negative binomial (NB) autoregressive baseline, NB models incorporating GTI, tree-based machine-learning models using the same covariates, and a simple NB-random-forest ensemble. Predictive performance was assessed using root mean squared error (RMSE), mean absolute error (MAE), R2, and discrimination for 95th-percentile outbreak exceedance. Cross-correlation peaked at lag 0, indicating that GTI functioned primarily as a contemporaneous nowcasting signal rather than a long-lead predictor. NB models incorporating GTI showed competitive calibration, while boosted tree models achieved the lowest point-estimate errors; XGBoost achieved RMSE ≈ 40.95 and R2 ≈ 0.861 compared with RMSE ≈ 45.23 and R2 ≈ 0.823 for NB with GTI. Adding GTI to the autoregressive NB baseline reduced RMSE by 11.77%, but the 95% confidence interval crossed zero, indicating modest and statistically non-significant incremental gain. Search-signal anomaly screening suggested that GTI spikes largely coincided with epidemic peaks and did not clearly inflate out-of-sample errors. Overall, GTI provides operational value for dengue nowcasting and alert triage in inland provinces, while future systems should integrate climate, vector, mobility, and media-monitoring data.

Patients seeking information about Myofascial Pain Syndrome (MPS), which affects a large segment of the population, are increasingly turning to AI-based chatbots as an alternative to traditional methods. However, the medical accuracy of the content offered by these digital platforms, as well as its suitability to the “grade 6 reading level” standard, which determines its comprehensibility by patients, is a critical point of uncertainty. This study aims to fill this significant gap in the literature by systematically comparing MPS content generated by different AI models using readability indices, reliability, and quality metrics. The 18 most relevant keywords, derived from 25 keywords identified via Google Trends data, were queried using ChatGPT (GPT-5.2), Gemini 3 Flash, and Perplexity (Sonar-4 Large) models. The readability of the generated responses was analyzed using six different indices (FRES, FKGL, GFOG, CLI, ARI, SMOG), while content quality was assessed using GQS and EQIP scales, and reliability using DISCERN and JAMA scales by two independent observers. The responses generated by all AI models examined were found to be statistically significantly more complex than the suggested 6th-grade reading level (p < 0.001). In inter-model comparisons, ChatGPT exhibited the easiest readability [lowest linguistic difficulty] scores, while Perplexity scored significantly higher than both ChatGPT and Gemini in content quality and reliability metrics (JAMA, DISCERN, GQS, EQIP) (p < 0.05). Correlation analysis revealed a strong and positive relationship between quality and reliability parameters. Artificial intelligence platforms have been observed to exhibit high potential in the production of medical information. However, linguistic barriers exceeding sixth-grade reading comprehension, along with reliability limitations of current models, prevent them from replacing professional medical consultation. Perplexity has been found superior in terms of academic quality, while ChatGPT has been found superior in terms of readability. Nevertheless, positioning these systems as complementary “secondary consultation mechanisms” supporting physician oversight in clinical decision-making processes is critically important for patient safety.

Breast Cancer necessitates effective patient education. Large language models (LLMs) facilitate patient health consultation, yet their generated medical content may contain misleading and unsafe information. Systematic evaluations of mainstream LLMs for breast cancer health guidance are currently lacking. This study evaluated six LLMs' (ChatGPT-5.4-thinking, Claude-4.6-sonnet, Gemini-3.1-Pro, DeepSeek-V3.2, Doubao-2.2-thinking, and ERNIE 4.5 Turbo) performance in breast cancer consultation via a structured checklist. A set of 61 standardized questions regarding breast cancer was developed based on Google Trends, clinical guidelines, practical experiences, and expert reviews. Responses from each LLM were independently evaluated by three breast cancer experts focusing on quality, accuracy, comprehensiveness, and safety. Besides, four patients independently evaluated the satisfaction and understandability of their selected three questions of interest. This study utilized Bernard's Global Quality Score (GQS) tool to assess quality. Readability was assessed using the Chinese Resource Platform (CRP). Other indicators were evaluated using self-designed questionnaires. Statistical analyses were performed using RStudio. In expert evaluations, ERNIE 4.5 Turbo had the highest descriptive quality score and was among the top-performing models in safety (Bonferroni-adjusted P < 0.05), while several models performed comparably in comprehensiveness. There was no significant difference in accuracy among the models. ChatGPT-5.4-thinking scored significantly lower in safety, and Doubao-2.2-thinking had significantly lower reading difficulty, required age, and Chinese character count (adjusted P < 0.05). In patient evaluations, ERNIE 4.5 Turbo showed the highest descriptive satisfaction and understandability ratings. Six large language models performed strongly in breast cancer question-answering, with ERNIE 4.5 Turbo ranking highest. However, issues like poor readability and unsafe recommendations remain in answers. Future research should prioritize enhancing patient readability to facilitate AI's application in precision cancer health education.

Rhegmatogenous retinal detachment (RD) is a vision-threatening condition requiring timely diagnosis and treatment, yet population-level public interest and information-seeking behavior is difficult to assess using traditional utilization data alone. This study evaluated temporal and geographic patterns of public interest in retinal detachment in the United States and contextualized this interest relative to retina specialist availability. In this retrospective observational study, we queried Google Trends for RD-related search terms in the United States from 2008 to 2025. Google Trends reports relative search volume (RSV) on a normalized scale from 0 to 100. We extracted annual and state-level values using a composite of diagnostic and surgical RD terms. We obtained state-level counts of vitreoretinal surgeons from the American Society of Retina Specialists database and calculated retina surgeon density per 100,000 population using 2025 U.S. Census estimates. We also calculated the Search-to-Surgeon Index as an exploratory ratio of state-level relative search volume to retina surgeon density. We evaluated associations between RSV, Search-to-Surgeon Index, population age structure, and time using the using the Mann-Kendall trend test with Sen's slope estimator and the Spearman correlation test. From 2008 to 2025, retinal detachment-related relative search volume increased by 178%, with a significant temporal increasing trend (τ = 0.745, P < 0.001). Relative search volume declined sharply in April 2020. In 2025, state-level relative search volume ranged from 61 to 100 (median, 84). States with a higher proportion of residents aged 50 years and older demonstrated higher relative search volume (ρ = 0.56, P < 0.001). Retina surgeon density varied more than tenfold across states, ranging from 0.17 to 2.09 per 100,000 residents. Search-to-Surgeon index values ranged from 40 to 406 (median, 155), with the highest values observed in states with limited retina surgeon availability. Population age distribution was not significantly associated with Search-to-Surgeon index (ρ = -0.06, P > 0.10). Public interest in retinal detachment has increased substantially over time and varies widely across U.S. states. Integrating Google Trends data with retina surgeon distribution highlights geographic mismatches between public interest and specialist availability. This exploratory framework may help identify regions with disproportionate public concern relative to local workforce capacity.

This study analyzes U.S. public engagement with environmental finance from 2015–2023 using Google Trends data for four key terms: “green bonds,” “sustainable finance,” “climate finance,” and “ESG.” Our analysis reveals a notable rise in engagement with environmental finance topics, with Environmental, Social, and Governance (ESG) investing generating the highest levels of interest. Geographically, the District of Columbia exhibited disproportionate search activity relative to other regions. While public awareness of environmental finance has grown, we highlight disparities in knowledge and accessibility of information. These findings carry significant policy implications, particularly in ensuring equitable access to sustainable financial instruments. We underscore the need for environmental justice considerations in sustainable finance policy design to ensure benefits are distributed to those disproportionately affected by environmental degradation, highlighting the potential role of big data in informing these processes.

The temporal dynamics of public attention to aviation safety events.

Cancer awareness months in Mexico drive online search spikes but are not associated with short-term increases in breast cancer surveillance reports.

*BackgroundTuberculosis (TB) is a major health challenge in Indonesia, which ranks second globally in 2024. As the 2030 elimination target approaches, gaps in early detection and public education persist. The public’s tendency to seek health information online before consulting professionals presents an opportunity to leverage infodemiology for public health surveillance. Therefore, this study aimed to assess the relationship between multi-term Google search trends and annual TB report data in Indonesia to disseminate the potential use of digital search data as a complementary indicator for epidemiological surveillance.MethodsA cross-sectional design was adopted to analyze the relationship between search volumes for 53 TB-related terms on Google Trends and official Indonesia Health Profile data from 2019 to 2023 across 34 provinces. Case data were normalized (0–100) to reflect the Relative Search Volume (RSV). Statistical analysis was performed using the Spearman correlation test to assess the relationship between digital searches and actual cases.ResultsA consistently strong and positive correlation between TB search terms and case numbers across 34 Indonesian provinces (p < 0.001). Key correlations included “Characteristics of Pulmonary TBC (Ciri TBC paru)” (r = 0.722) and “Pulmonary TBC Medicine (Obat TBC paru)” (r = 0.739) in 2019, “Characteristics of Pulmonary TB (Ciri TB Paru)” and “TB Prevention (Pencegahan TB)” (r = 0.704) in 2020, “Characteristics of Pulmonary TB (Ciri TBC Paru)” (r = 0.731) and “Childhood Pulmonary TB (TB Paru anak)” (r = 0.707) in 2021, “Pulmonary TB Drugs (Obat TBC Paru)” (r = 0.782) and “Characteristics of Tuberculosis (Ciri Tuberkulosis)” (r = 0.709) in 2022, as well as “Characteristics of Tuberculosis (Ciri Tuberkulosis)” (r = 0.731) in 2023.ConclusionGoogle Trends data correlated strongly with official TB epidemiological data in Indonesia. These results suggest digital search trends can serve as complementary indicators to conventional surveillance and early warning systems.

BackgroundClimate and weather factors of temperature and humidity are widely reported to be associated with xerosis (dry skin), a common inflammatory skin condition and frequent driver of pruritus (itchy skin) and reduced quality of life. Growing evidence supports links between environmental conditions and skin barrier function, with extreme climates associated with increased atopic dermatitis–related clinical visits. Mechanistically, temperature and humidity affect the stratum corneum, the skin’s primary permeability barrier, with low humidity and high temperature increasing transepidermal water loss and promoting cutaneous inflammation.ObjectiveThis study examines the relationship between climate, namely temperature and humidity, and the general public’s experience in dry skin and moisturizing products, throughout the United States. This study sought to address gaps in traditional epidemiologic approaches by linking climate conditions with population-level online search behavior related to dry skin and moisturizer use across the United States.MethodsPublicly available climate data were obtained from the National Oceanic and Atmospheric Administration (NOAA), including average temperature and dew point by state over a recent nine-year period (2016‐2025). Dew point served as a proxy for ambient humidity. Google Trends was used to assess relative search interest for five dry skin– and moisturizer-related terms by state during the same period. Search interest was normalized per million residents, and associations between climate variables and search interest were evaluated using linear regression analyses. Statistical analyses were conducted using R.ResultsLower average temperatures and lower dew points were associated with higher dry skin–related search interest, while warmer, more humid states showed lower interest. Both temperature and dew point demonstrated significant negative associations with Google search interest. This work was not funded and data collection was performed using publicly available, free databases.ConclusionsPopulation-level search behavior related to xerosis reflects national patterns of climate-associated dermatologic burden.

The rapid growth of social media has transformed digital journalism practices, especially regarding the use of viral content as an initial source of information. Popmama.com as a parenting media platform requires a selective editorial strategy to ensure viral issues remain relevant to the needs of Millennial and Gen Z mothers. This study aims to analyze the use of viral social media news as news material at Popmama.com. A descriptive qualitative approach with a case study method was employed to understand the editorial process carried out by the newsroom. Data collection used in-depth interviews, article observation, documentation, and literature study. Research informants consisted of Senior Editors, Editors, Senior Creative Writers, and Creative Writers at Popmama.com. Findings show that the newsroom utilizes TikTok, Instagram, Google Trends, and other analytical tools to monitor viral issues. News processing includes issue selection, fact verification, contextual information enrichment, educational article writing, and editing based on journalistic principles to maintain information quality and the characteristics of digital parenting media.