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Background: Recent withdrawals of major drugs have highlighted the critical importance of drug safety surveillance in the postmarketing phase. Limitations of spontaneous report data have led drug safety professionals to pursue alternative postmarketing surveillance approaches based on healthcare administrative claims data. These data are typically analysed by comparing the adverse event rates associated with a drug of interest to those of a single comparable reference drug.Objective: The aim of this study was to determine whether adverse event detection can be improved by incorporating information from multiple reference drugs. We developed a pharmacological network model that implemented this approach and evaluated its performance.Methods: We studied whether adverse event detection can be improved by incorporating information from multiple reference drugs, and describe two approaches for doing so. The first, reported previously, combines a set of related drugs into a single reference cohort. The second is a novel pharmacoepidemiological network model, which integrates multiple pair-wise comparisons across an entire set of related drugs into a unified consensus safety score for each drug. We also implemented a single reference drug approach for comparison with both multi-drug approaches. All approaches were applied within a sequential analysis framework, incorporating new information as it became available and addressing the issue of multiple testing over time. We evaluated all these approaches using statin (HMG-CoA reductase inhibitors) safety data from a large healthcare insurer in the US covering April 2000 through March 2005.Results: We found that both multiple reference drug approaches offer earlier detection (6-13 months) than the single reference drug approach, without triggering additional false positives.Conclusions: Such combined approaches have the potential to be used with existing healthcare databases to improve the surveillance of therapeutics in the postmarketing phase over single-comparator methods. The proposed network approach also provides an integrated visualization framework enabling decision makers to understand the key high-level safety relationships amongst a group of related drugs. [ABSTRACT FROM AUTHOR]

The current dengue epidemic in Latin America represents a major threat to health. However, surveillance of affected regions lacks timeliness and precision. We investigated real-time electronic sources for monitoring spread of dengue into new regions. This approach could provide timely estimates of changes in distribution of dengue, a critical component of prevention and control efforts. [ABSTRACT FROM AUTHOR]

The International Health Regulations (2005) [IHR(2005)] represent a potentially revolutionary change in global health governance. The use of the regulations by the World Health Organization (WHO) to respond to the outbreak of pandemic influenza A 2009-H1N1 highlights the importance of the regulations to protecting global health security. As the 2009-H1N1 pandemic illustrated, the IHR(2005) have provided a more robust framework for responding to public health emergencies of international concern (PHEICs), through requiring reporting of serious disease events, strengthening how countries and WHO communicate concerning health threats, empowering the WHO Director-General to declare the existence of PHEICs and to issue temporary recommendations for responding to them, and requiring countries not to implement measures that unnecessarily restrict trade and travel or infringe on human rights. However, limitations to the effectiveness of the IHR(2005) revealed in the 2009-H1N1 pandemic include continuing inadequacies in surveillance and response capacities within some countries, violations of IHR(2005) rules and a potentially narrowing scope of application only to influenza-like pandemic events. These limitations could undermine the IHR(2005)'s potential to contribute to national and global efforts to detect and mitigate future public health emergencies. Support for the IHR(2005) should be broadened and deepened to improve their utility as a tool to strengthen global health security. [ABSTRACT FROM AUTHOR]

Objective: Unstructured electronic information sources, such as news reports, are proving to be valuable inputs for public health surveillance. However, staying abreast of current disease outbreaks requires scouring a continually growing number of disparate news sources and alert services, resulting in information overload. Our objective is to address this challenge through the HealthMap.org Web application, an automated system for querying, filtering, integrating and visualizing unstructured reports on disease outbreaks.Design: This report describes the design principles, software architecture and implementation of HealthMap and discusses key challenges and future plans.Measurements: We describe the process by which HealthMap collects and integrates outbreak data from a variety of sources, including news media (e.g., Google News), expert-curated accounts (e.g., ProMED Mail), and validated official alerts. Through the use of text processing algorithms, the system classifies alerts by location and disease and then overlays them on an interactive geographic map. We measure the accuracy of the classification algorithms based on the level of human curation necessary to correct misclassifications, and examine geographic coverage.Results: As part of the evaluation of the system, we analyzed 778 reports with HealthMap, representing 87 disease categories and 89 countries. The automated classifier performed with 84% accuracy, demonstrating significant usefulness in managing the large volume of information processed by the system. Accuracy for ProMED alerts is 91% compared to Google News reports at 81%, as ProMED messages follow a more regular structure.Conclusion: HealthMap is a useful free and open resource employing text-processing algorithms to identify important disease outbreak information through a user-friendly interface. [ABSTRACT FROM AUTHOR]

The Zika virus epidemic of 2015–16, which caused over 1 million confirmed or suspected human cases in the Caribbean and Latin America, was driven by a combination of movement of infected humans and availability of suitable habitat for mosquito species that are key disease vectors. Both human mobility and mosquito vector abundances vary seasonally, and the goal of our research was to analyze the interacting effects of disease vector densities and human movement across metapopulations on disease transmission intensity and the probability of super-spreader events. Our research uses the novel approach of combining geographical modeling of mosquito presence with network modeling of human mobility to offer a comprehensive simulation environment for Zika virus epidemics that considers a substantial number of spatial and temporal factors compared to the literature. Specifically, we tested the hypotheses that 1) regions with the highest probability of mosquito presence will have more super-spreader events during dry months, when mosquitoes are predicted to be more abundant, 2) regions reliant on tourism industries will have more super-spreader events during wet months, when they are more likely to contribute to network-level pathogen spread due to increased travel. We used the case study of Colombia, a country with a population of about 50 million people, with an annual calendar that can be partitioned into overlapping cycles of wet and dry seasons and peak tourism and off tourism seasons that drive distinct cyclical patterns of mosquito abundance and human movement. Our results show that whether the first infected human was introduced to the network during the wet versus dry season and during the tourism versus off tourism season profoundly affects the severity and trajectory of the epidemic. For example, Zika virus was first detected in Colombia in October of 2015. Had it originated in January, a dry season month with high rates of tourism, it likely could have infected up to 60% more individuals and up to 40% more super-spreader events may have occurred. In addition, popular tourism destinations such as Barranquilla and Cartagena have the highest risk of super-spreader events during the winter, whereas densely populated areas such as Medellín and Bogotá are at higher risk of sustained transmission during dry months in the summer. Our research demonstrates that public health planning and response to vector-borne disease outbreaks requires a thorough understanding of how vector and host patterns vary due to seasonality in environmental conditions and human mobility dynamics. This research also has strong implications for tourism policy and the potential response strategies in case of an emergent epidemic. Author summary: The Zika virus pandemic of 2015–16, which caused over 1 million human cases in the Caribbean and Latin America, was driven by a combination of movement of infected humans and presence of mosquito species, such as Aedes aegypti, that carry the disease. Both human mobility and mosquito vector densities vary seasonally, and the goal of our research was to analyze the interacting effects of disease vector presence and human movement across metapopulations on disease transmission intensity and the probability of super-spreader events. Specifically, we tested the hypotheses that 1) regions with the highest probability of mosquito presence will have more super-spreader events during dry months, when mosquitoes are predicted to be more abundant, 2) regions reliant on tourism industries will have more super-spreader events during wet months, as they are more likely to contribute to network-level pathogen spread due to increased travel. We used the case study of Colombia, a nation with a population of about 50 million people, whose annual calendar can be partitioned into overlapping cycles of wet and dry seasons and tourism and off-tourism seasons that drive distinct cyclical patterns of mosquito occurrence and human movement. Our approach combined spatial modeling of mosquito presence with network modeling of human mobility. We used a Maximum Entropy model to estimate the spatially and temporally varying habitat suitability for Aedes aegypti, the primary mosquito vector of Zika virus in Colombia, in terms of environmental factors such as temperature, precipitation, and vegetation coverage and human factors indicating land use and development, socioeconomic status, and prevalence of standing water that provides key habitat for juvenile mosquitoes. We also used airline travel data to develop metapopulation patch network models of the 1,123 municipalities of Colombia for each month of 2015 and 2016. We combined our vector habitat suitability and human mobility network models to simulate the Zika virus epidemic if it had begun in each month of 2015. Our results show that whether the first human case was introduced to the network during the wet vs. dry season and during the tourism vs. off-tourism season profoundly affects the severity and trajectory of the epidemic. For example, Zika virus was first detected in Colombia in October of 2015. Had it originated in January, a dry season month with high rates of tourism, it likely could have infected up to 30% more individuals and up to 40% more super-spreader events may have occurred. In addition, popular vacation spots such as Barranquilla and Cartageña have the highest risk of super-spreader events during the winter, whereas densely populated areas such as Medellín and Bogotá are at higher risk of sustained transmission during dry months in the summer. Our research demonstrates that public health planning and response to vector-borne disease outbreaks requires a thorough understanding of how vector and host patterns vary due to seasonality in environmental conditions and human mobility dynamics. [ABSTRACT FROM AUTHOR]

Aedes aegypti, the vector for dengue, chikungunya, yellow fever, and Zika, poses a growing global epidemiological risk. Despite extensive research on Ae. aegypti's life history traits and behavior, critical knowledge gaps persist, particularly in integrating these findings across varied experimental contexts. The plasticity of Ae. aegypti's traits throughout its life cycle allows dynamic responses to environmental changes, yet understanding these variations within heterogeneous study designs remains challenging. A critical aspect often overlooked is the impact of using lab-adapted lines of Ae. aegypti, which may have evolved under laboratory conditions, potentially altering their life history traits and behavioral responses compared to wild populations. Therefore, incorporating field-derived populations in experimental designs is essential to capture the natural variability and adaptability of Ae. aegypti. The relationship between larval growing conditions and adult traits and behavior is significantly influenced by the specific context in which mosquitoes are studied. Laboratory conditions may not replicate the ecological complexities faced by wild populations, leading to discrepancies in observed traits and behavior. These discrepancies highlight the need for ecologically relevant experimental conditions, allowing mosquito traits and behavior to reflect field distributions. One effective approach is semi-field studies involving field-collected mosquitoes housed for fewer generations in the lab under ecologically relevant conditions. This growing trend provides researchers with the desired control over experimental conditions while maintaining the genetic diversity of field populations. By focusing on variations in life history traits and behavioral plasticity within these varied contexts, this review highlights the intricate relationship between larval growing conditions and adult traits and behavior. It underscores the significance of transstadial effects and the necessity of adopting study designs and reporting practices that acknowledge plasticity in adult traits and behavior, considering variations due to larval rearing conditions. Embracing such approaches paves the way for a comprehensive understanding of contextual variations in mosquito life history traits and behavior. This integrated perspective enables the synthesis of research findings across laboratory, semi-field, and field-based investigations, which is crucial for devising targeted intervention strategies tailored to specific ecological contexts to combat the health threat posed by this formidable disease vector effectively. [ABSTRACT FROM AUTHOR]

Objectives. To assess multilevel factors associated with variation in COVID-19 vaccination rates in a US network of community health centers. Methods. Using multilevel logistic regression with electronic health record data from ADVANCE (Accelerating Data Value Across a National Community Health Center Network; January 1, 2022–December 31, 2022), we assessed associations between health care delivery site–level (n = 1219) and patient-level (n = 1 864 007) characteristics and COVID-19 primary vaccine series uptake. Results. A total of 1 337 440 patients completed the COVID-19 primary vaccine series. Health care delivery site characteristics were significantly associated with lower series completion rates, including being located in non-Medicaid expansion states and isolated or rural communities and serving fewer patients. Patient characteristics associated with significantly lower likelihood of completing the vaccine series included being Black/African American or American Indian/Alaska Native (vs White), younger age, lower income, being uninsured or publicly insured (vs using private insurance), and having fewer visits. Conclusions. Both health care delivery site– and patient-level factors were significantly associated with lower COVID-19 vaccine uptake. Community health centers have been a critical resource for vaccination during the pandemic. (Am J Public Health. 2024;114(11):1242–1251. https://doi.org/10.2105/AJPH.2024.307773) [ABSTRACT FROM AUTHOR]

Seasonal influenza causes significant annual morbidity and mortality worldwide. In France, it is estimated that, on average, 2 million individuals consult their GP for influenza-like-illness (ILI) every year. Traditionally, mathematical models used for epidemic forecasting can either include parameters capturing the infection process (mechanistic or compartmental models) or rely on time series analysis approaches that do not make mechanistic assumptions (statistical or phenomenological models). While the latter make extensive use of past epidemic data, mechanistic models are usually independently initialized in each season. As a result, forecasts from such models can contain trajectories that are vastly different from past epidemics. We developed a mechanistic model that takes into account epidemic data from training seasons when producing forecasts. The parameters of the model are estimated via a first particle filter running on the observed data. A second particle filter is then used to produce forecasts compatible with epidemic trajectories from the training set. The model was calibrated and tested on 35 years' worth of surveillance data from the French Sentinelles Network, representing the weekly number of patients consulting for ILI over the period 1985–2019. Our results show that the new method improves upon standard mechanistic approaches. In particular, when retrospectively tested on the available data, our model provides increased accuracy for short-term forecasts (from one to four weeks into the future) and peak timing and intensity. Our new approach for epidemic forecasting allows the integration of key strengths of the statistical approach into the mechanistic modelling framework and represents an attempt to provide accurate forecasts by making full use of the rich surveillance dataset collected in France since 1985. Author summary: Seasonal influenza causes significant morbidity and mortality worldwide. In France, on average, 2 million individuals consult their GP for influenza-like-illness (ILI) every year. Forecasting the future trajectory of an epidemic in real-time can inform public health responses. Traditionally, two types of mathematical models are used to forecast infectious diseases outbreaks: (1) mechanistic models that explicitly capture transmission mechanisms and (2) statistical models that rely on the similarity between epidemic dynamics over the years. In contrast to statistical models, mechanistic models usually do not use information on past seasons to inform forecasts. This may result in poor performance when forecasted trajectories are very different from past epidemics. Here, we propose a framework that combines these two approaches by allowing mechanistic models to learn from trends observed in past epidemics. We evaluate this approach in the context of seasonal influenza in France. Our results show that the new method improves upon standard mechanistic approaches. In particular, our model provides increased accuracy for predicting the epidemic trajectory one to four weeks into the future, as well as the timing and size of the peak. Our new approach for epidemic forecasting allows combining statistical and mechanistic models to improve forecasts performance. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) are undergoing intensive research for various healthcare domains. This systematic review and meta-analysis assesses current applications, methodologies, and the performance of LLMs in clinical oncology. A mixed-methods approach was used to extract, summarize, and compare methodological approaches and outcomes. This review includes 34 studies. LLMs are primarily evaluated on their ability to answer oncologic questions across various domains. The meta-analysis highlights a significant performance variance, influenced by diverse methodologies and evaluation criteria. Furthermore, differences in inherent model capabilities, prompting strategies, and oncological subdomains contribute to heterogeneity. The lack of use of standardized and LLM-specific reporting protocols leads to methodological disparities, which must be addressed to ensure comparability in LLM research and ultimately leverage the reliable integration of LLM technologies into clinical practice. [ABSTRACT FROM AUTHOR]

Background: Cabbage (Brassica oleracea L. var. capitata) is an important crop within the Brassica oleracea species and is extensively cultivated worldwide. In recent years, outbreaks of downy mildew caused by Hyaloperonospora parasitica have resulted in substantial losses in cabbage production. Despite this, there have been limited studies on genes associated with resistance to downy mildew in cabbage. Results: This study identified sister lines exhibiting significant differences in disease resistance and susceptibility. Using bulked segregant analysis followed by sequencing (BSA-seq) and linkage analysis, the cabbage resistance locus BoDMR2 was accurately mapped to an approximately 300 kb interval on chromosome 7. Among the candidate genes identified, several single nucleotide polymorphisms (SNPs) and a 3-bp insertion were found within the conserved domain of the Bo7g117810 gene, encoding a leucine-rich repeat domain protein, in susceptible genotypes. Additionally, real-time quantitative polymerase chain reaction (RT‒qPCR) analysis revealed that the expression level of Bo7g117810 in resistant specimens was 2.5-fold higher than that in susceptible specimens. An insertion‒deletion (InDel) marker was designed based on the identified insertion in susceptible materials, facilitating the identification and selection of downy mildew-resistant cabbage cultivars. Conclusions: This study identifies Bo7g117810 as a potential candidate gene associated with adult-stage resistance to downy mildew in cabbage, supported by observed differences in gene sequence and expression levels. Furthermore, the development of an InDel marker I1-3, based on its mutation, provides valuable resources for breeding resistant cabbage cultivars. [ABSTRACT FROM AUTHOR]

Maternal use of e-cigarette (e-cig) aerosols poses significant risks to fetal brain development, potentially increasing susceptibility to neurodevelopmental disorders in later life. However, the underlying mechanisms remain incompletely understood. This study aimed to understand the effects of fetal e-cig exposure on DNA methylome and transcriptomic changes in the neonatal brain. Pregnant rats were exposed to e-cig aerosols, and neonatal brains (5 males and 5 females/group) from both control and e-cig-exposed groups were used for experimental analysis. Results indicated that prenatal e-cig exposure altered site-specific DNA methylation patterns at both CpG and CH (non-CpG) sites, predominantly in intergenic and intronic regions, with sex dimorphism in methylation and gene expression changes. Gene ontology analysis revealed that e-cig exposure not only affected neuron projection development and axonogenesis but also altered pathways related to neurodegeneration and long-term depression. These findings provide novel insights into the dynamic changes of CpG and CH methylation induced by e-cig exposure, underscoring the susceptibility of the developing brain to maternal e-cig exposure and its potential implications for developmental disorders and neurodegenerative diseases later in life. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance (AMR) poses a significant global health threat, primarily stemming from its misuse and overuse in both veterinary and public healthcare systems. The consequences of AMR are severe, leading to more severe infections, increased health protection costs, prolonged hospital stays, unresponsive treatments, and elevated fatality rates. The impact of AMR is direct and far-reaching, particularly affecting the Sustainable Development Goals (SDGs), underscoring the urgency for concerted global actions to achieve these objectives. Disproportionately affecting underprivileged populations, AMR compounds their vulnerabilities, pushing them further into poverty. Moreover, AMR has ramifications for food production, jeopardizing sustainable agriculture and diminishing the livelihoods of farmers. The emergence of antibiotic-resistant bacteria in underprivileged areas heightens the risk of complications and mortality. Climate change further contributes to AMR, as evidenced by increased instances of foodborne salmonellosis and the development of antibiotic resistance, resulting in substantial healthcare costs. Effectively addressing AMR demands collaboration among governments, entrepreneurs, and the public sector to establish institutions and policies across all regulatory levels. Expanding SDG 17, which focuses on partnerships for sustainable development, would facilitate global antimicrobial stewardship initiatives, technology transfer, surveillance systems, and investment in vaccine and drug research. The World Bank's SDG database, tracking progress towards sustainable development, reveals a concerning picture with only a 15% success rate till 2023 and 48% showing deviation, underscoring a global gap exacerbated by the COVID-19 pandemic. Tackling AMR's global impact necessitates international cooperation, robust monitoring, and evaluation methods. The five priorities outlined guide SDG implementation, while impoverished countries must address specific challenges in their implementation efforts. Addressing AMR and its impact on the SDGs is a multifaceted challenge that demands comprehensive and collaborative solutions on a global scale. [ABSTRACT FROM AUTHOR]

Background: In September 2022, Uganda experienced an outbreak of Sudan virus disease (SVD), mainly in central Uganda. As a result of enhanced surveillance activities for Ebola disease, samples from several patients with suspected viral hemorrhagic fever (VHF) were sent to the VHF Program at Uganda Virus Research Institute (UVRI), Entebbe, Uganda, and identified with infections caused by other viral etiologies. Herein, we report the epidemiologic and laboratory findings of Crimean-Congo hemorrhagic fever (CCHF) cases that were detected during the SVD outbreak response. Methodology: Whole blood samples from VHF suspected cases were tested for Sudan virus (SUDV) by real-time reverse transcription–polymerase chain reaction (RT-PCR); and if negative, were tested for CCHF virus (CCHFV) by RT-PCR. CCHFV genomic sequences generated by metagenomic next generation sequencing were analyzed to ascertain strain relationships. Principal findings: Between September 2022 and January 2023, a total of 2,626 samples were submitted for VHF testing at UVRI. Overall, 13 CCHF cases (including 7 deaths; case fatality rate of 53.8%), aged 4 to 60 years, were identified from 10 districts, including several districts affected by the SVD outbreak. Four cases were identified within the Ebola Treatment Unit (ETU) at Mubende Hospital. Most CCHF cases were males engaged in livestock farming or had exposure to wildlife (n = 8; 61.5%). Among confirmed cases, the most common clinical symptoms were hemorrhage (n = 12; 92.3%), fever (n = 11; 84.6%), anorexia (n = 10; 76.9%), fatigue (n = 9; 69.2%), abdominal pain (n = 9; 69.2%) and vomiting (n = 9; 69.2%). Sequencing analysis showed that the majority of identified CCHFV strains belonged to the Africa II clade previously identified in Uganda. Two samples, however, were identified with greater similarity to a CCHFV strain that was last reported in Uganda in 1958, suggesting possible reemergence. Conclusions/Significance: Identifying CCHFV from individuals initially suspected to be infected with SUDV emphasizes the need for comprehensive VHF testing during filovirus outbreak responses in VHF endemic countries. Without expanded testing, CCHFV-infected patients would have posed a risk to health care workers and others while receiving treatment after a negative filovirus diagnosis, thereby complicating response dynamics. Additionally, CCHFV-infected cases could acquire an Ebola infection while in the ETU, and upon release because of a negative Ebola virus result, have the potential to spread these infections in the community. Author summary: Crimean-Congo hemorrhagic fever (CCHF) is a severe hemorrhagic disease whose symptoms mimic those of other viral hemorrhagic fevers (VHFs), significantly challenging its differential diagnosis. Herein, we report on CCHF cases that were detected during the nationwide response activities against an outbreak of Sudan virus (SUDV) in Uganda, from September 2022 to January 2023. Confirmed CCHF cases, mainly males presenting with a range of clinical signs and symptoms, were identified in districts with circulating SUDV, including 4 cases identified within an Ebola treatment center at Mubende hospital, thus complicating patient management and treatment. Our findings highlight the need for broad testing for VHFs and other high-consequence pathogens, even during known VHF outbreaks, in order to identify high risk pathogens with outbreak potential, especially in countries with a broad range of endemic zoonotic viral pathogens. This article also adds to the known molecular epidemiology of CCHF virus in Uganda. [ABSTRACT FROM AUTHOR]

Author affiliations are essential in bibliometric studies, requiring relevant information extraction from free-text affiliations. Precisely determining an author's location from their affiliation is crucial for understanding research networks, collaborations, and geographic distribution. Existing geoparsing tools using regular expressions have limitations due to unstructured and ambiguous affiliations, resulting in erroneous location identification, especially for unconventional variations or misspellings. Moreover, their inefficient handling of big datasets hampers large-scale bibliometric studies. Though machine learning-based geoparsers exist, they depend on explicit location information, creating challenges when detailed geographic data is absent. To address these issues, we developed and evaluated a natural language processing model to predict the city, state, and country from an author's free-text affiliation. Our model automates location inference, overcoming drawbacks of existing methods. Trained and tested with MapAffil, a publicly available geoparsed dataset of PubMed affiliations up to 2018, our model accurately retrieves high-resolution locations, even without explicit mentions of a city, state, or even country. Leveraging NLP techniques and the LinearSVC algorithm, our machine learning model achieves superior accuracy based on several validation datasets. This research demonstrates a practical application of text classification for inferring specific geographical locations from free-text affiliations, benefiting researchers and institutions in analyzing research output distribution. [ABSTRACT FROM AUTHOR]

There are a limited number of effective vaccines against dengue virus (DENV) and significant efforts are being made to develop potent anti-virals. Previously, we described that platelet-chemokine CXCL4 negatively regulates interferon (IFN)-α/β synthesis and promotes DENV2 replication. An antagonist to CXCR3 (CXCL4 receptor) reversed it and inhibited viral replication. In a concurrent search, we identified CXCR3-antagonist from our compound library, namely 7D, which inhibited all serotypes of DENV in vitro. With a half-life of ~2.85 h in plasma and no significant toxicity, 7D supplementation (8 mg/kg-body-weight) to DENV2-infected IFNα/β/γR−/−AG129 or wild-type C57BL6 mice increased synthesis of IFN-α/β and IFN-λ, and rescued disease symptoms like thrombocytopenia, leukopenia and vascular-leakage, with improved survival. 7D, having the property to inhibit Sirt-1 deacetylase, promoted acetylation and phosphorylation of STAT3, which in-turn increased plasmablast proliferation, germinal-center maturation and synthesis of neutralizing-antibodies against DENV2 in mice. A STAT3-inhibitor successfully inhibited these effects of 7D. Together, these observations identify compound 7D as a stimulator of IFN-α/β/λ synthesis via CXCL4:CXCR3:p38:IRF3 signaling, and a booster for neutralizing-antibody generation by promoting STAT3-acetylation in plasmablasts, capable of protecting dengue infection. Synopsis: Effective vaccines and anti-viral drugs against dengue are limited. Our study identifies a small molecule, namely 7D as a stimulator of IFNα/β/λ synthesis, and also booster for neutralizing-antibody generation, capable of protecting dengue infection in 3 different mice models. Schematic describes the CXCL4-mediated activation of CXCR3:p38:IRF3 signaling, in turn suppression of IRF3 phosphorylation and IFNα/β/λ synthesis in monocytes and macrophages. Conversely, 7D supplementation reverses the above signaling and improves IFNα/β/λ synthesis. Besides, 7D increases acetylation and phosphorylation of STAT3, in turn promotes proliferation of plasmablasts and plasma cells, in turn increases IgG synthesis via suppression of deacetylase activity of Sirt-1. 7D is a potent anti-viral drug against dengue. Effective vaccines and anti-viral drugs against dengue are limited. Our study identifies a small molecule, namely 7D as a stimulator of IFNα/β/λ synthesis, and also booster for neutralizing-antibody generation, capable of protecting dengue infection in 3 different mice models. [ABSTRACT FROM AUTHOR]

Background: Under Sect. 564 of the Federal Food, Drug, and Cosmetic Act, when the US Department of Health and Human Services Secretary issues a declaration based on one of four types of determinations, FDA may authorize an unapproved product or unapproved uses of an approved product for emergency use. It is unclear how much the public and health care providers understand about emergency use authorizations and how that understanding influences perceptions or behavioral intentions. The current study aimed to explore consumer and health care provider perceptions of emergency use authorizations and how promotion of emergency use authorizations has been investigated in the literature. Methods: We conducted a scoping review via PubMed, Embase, Web of Science, and CINAHL for original research published in English from January 1, 2012, through November 10, 2022. We conducted surveillance of the literature through March 2023. Results: We identified 13 studies reported in 14 publications that addressed public and health care provider attitudes, understanding, and behaviors with regard to products authorized under an emergency use authorization. We did not identify any articles describing research regarding the promotion of emergency use authorization products. Findings suggest gaps in understanding of emergency use authorizations, both for the public and health care providers. Findings also suggest the public and health care providers have concerns about safety or the lack of full FDA approval that may contribute to decreased willingness to receive treatments and vaccines under an emergency use authorization. Conclusions: Future research should explore how emergency use authorizations have been promoted and how that promotion impacts public and health care provider perceptions. [ABSTRACT FROM AUTHOR]

Wastewater monitoring is a valuable public health tool that can track a variety of health markers. The strong correlations between trends in wastewater viral concentrations and county-level COVID-19 case counts point to the ability of wastewater data to represent changes in a community's disease burden. However, studies are lacking on whether the populations sampled through wastewater monitoring represent the characteristics of the broader community and the implications on health equity. We conducted a geospatial analysis to examine the extent to which populations contributing to wastewater collected through the North Carolina Wastewater Monitoring Network as of June 2022 represent the broader countywide and statewide populations. After intersecting sewershed boundary polygons for 38 wastewater treatment plants across 18 counties with census block and tract polygons, we compared the demographics and social vulnerability of (1) people residing in monitored sewersheds with countywide and statewide populations, and (2) sewered residents, regardless of inclusion in wastewater monitoring, with unsewered residents. We flagged as meaningful any differences greater than +/- 5 percentage points or 5 percent (for categorical and continuous variables, respectively) and noted statistically significant differences (p < 0.05). We found that residents within monitored sewersheds largely resembled the broader community on most variables analyzed, with only a few exceptions. We also observed that when multiple sewersheds were monitored within a county, their combined service populations resembled the county population, although individual sewershed and county populations sometimes differed. When we contrasted sewered and unsewered populations within a given county, we found that sewered populations were more vulnerable than unsewered populations, suggesting that wastewater monitoring may fill in the data gaps needed to improve health equity. The approach we present here can be used to characterize sewershed populations nationwide to ensure that wastewater monitoring is implemented in a manner that informs equitable public health decision-making. [ABSTRACT FROM AUTHOR]

Background: Dengue fever is one of the most prevalent mosquito-borne diseases in Cambodia. Until now, no specific vaccine nor antiviral treatment exists the virus causing Dengue fever. Consequently, its prevention relies only on vector control strategies. However, efficient vector control in turn relies on a good knowledge of the biology of the vector species. Therefore, this study aims to provide the first review of the distribution, ecology, meteorological impacts, trophic behavior, vector competence, vector control and insecticide resistance of dengue vector species in Cambodia. Methods: A systematic search of the Google Scholar and PubMed databases was conducted for relevant published articles. Of the 610 published articles originally identified, 70 articles were ultimately selected for inclusion in this review. We also included new data from unpublished research conducted in Cambodia between 2017 and 2023 related to dengue vector bionomics. Results: Eleven Aedes (Stegomyia) mosquito species have been recorded in Cambodia, including a new species described in 2024. Four species are associated with dengue virus transmission, among which Aedes aegypti and Ae. albopictus are the main vectors and Ae. malayensis and Ae. scutellaris are considered to be potential vectors. Aedes aegypti and Ae. albopictus are present in all provinces of Cambodia. Aedes albopictus shows a preference for forest, rural and suburban areas, while Ae. aegypti is mostly found in urban and suburban areas. The distribution of these two species is also influenced by meteorological factors, seasonality and the availability of breeding habitats and blood meals. Both species are predominant during the rainy season, and their respective density is impacted by precipitation and temperature. Aedes aegypti is characterized as anthropophilic, while Ae. albopictus exhibits zooanthropophilic behavior, and both species have been observed to be predominantly diurnal. In addition, they were found to be highly resistant to the insecticides used in Cambodia for their control, such as temephos for larvae and deltamethrin and permethrin for adult mosquitoes. Conclusions: This review provides extensive and important knowledge on dengue vectors in Cambodia. This knowledge is derived not only from published research articles but also from many recent studies in Cambodia on the bionomics of dengue vector species. The review provides valuable information for use by public health authorities on dengue virus transmission and to develop better vector control strategies in the country. [ABSTRACT FROM AUTHOR]

Evolutionary rescue occurs when populations survive lethal environmental stresses through the rising and fixation of tolerant genotypes. Temperature has long been believed to determine the evolutionary speed of populations and species. Here, we suggest that warmer temperatures can facilitate evolutionary rescue. Moreover, with dispersal among habitats, the advantage in evolutionary rescue for warmer populations may cause a bias in habitat colonization dynamics towards the warm-to-cold direction. We experimentally tested these hypotheses with a model microbial system. Our first experiment showed that bacterial populations at warmer temperatures had a greater chance to evolve resistance and escape the fate of extinction under an antibiotic treatment. In the second experiment, metapopulations that consisted of warm and cold habitats were exposed to the antibiotic stress; local populations that went extinct might be recolonized, and such recolonization events were biased to the warm-to-cold direction. We also examined possible mechanisms underlying the temperature effect on the rapid evolution of resistance in our study system. Our results may help to understand the mechanisms of maintenance of biodiversity and patterns of gene flow among climatic regions, particularly in pest species subject to chemical control treatments. [ABSTRACT FROM AUTHOR]

Background: Forecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses. Methods: The dataset used for this exploration results from active hospital surveillance, in which the World Health Organization Severe Acute Respiratory Infection (SARI) case definition was consistently used. This research nurse-led surveillance has been implemented in two public hospitals in Auckland and provides a systematic laboratory testing of SARI patients for nine respiratory viruses, including influenza, respiratory syncytial virus, and rhinovirus. The forecasting strategies used comprise automatic machine learning, one of the most recent generative pre-trained transformers, and established artificial neural network algorithms capable of univariate and multivariate forecasting. Results: We found that machine learning models compute more accurate forecasts in comparison to naïve seasonal models. Furthermore, we analyzed the impact of reducing the temporal resolution of forecasts, which decreased the model error of point forecasts and made probabilistic forecasting more reliable. An additional analysis that used the laboratory data revealed strong season-to-season variations in the incidence of respiratory viruses and how this correlates with total hospitalization cases. These variations could explain why it was not possible to improve forecasts by integrating this data. Conclusions: Active SARI surveillance and consistent data collection over time enable these data to be used to predict hospital bed utilization. These findings show the potential of machine learning as support for informing systems for proactive hospital management. [ABSTRACT FROM AUTHOR]

Background: Understanding the level of exposure to Lassa virus (LASV) in at-risk communities allows for the administration of effective preventive interventions to mitigate epidemics of Lassa fever. We assessed the seroprevalence of LASV antibodies in rural and semiurban communities of two cosmopolitan cities in Nigeria with poorly understood Lassa epidemiology. Methods: A cross-sectional study was conducted in ten communities located in the Abuja Municipal Area Council (AMAC), Abuja, and Ikorodu Local Government Area (LGA), Lagos, from February 2nd to July 5th, 2022. Serum samples collected from participants were analyzed for IgG and IgM antibodies using a ReLASV® Pan-Lassa NP IgG/IgM enzyme-linked immunosorbent assay (ELISA) kit. A questionnaire administered to participants collected self-reported sociodemographic and LASV exposure information. Seroprevalence of LASV IgG/IgM was estimated overall, and by study site. Univariate and multivariate log-binomial models estimated unadjusted and adjusted prevalence ratios (aPRs) and 95% confidence intervals (CI) for site-specific risk factors for LASV seropositivity. Grouped Least Absolute Shrinkage and Selection Operator (LASSO) was used for variable selection for multivariate analysis. Results: A total of 628 participants with serum samples were included in the study. Most participants were female (434, 69%), married (459, 73%), and had a median age of 38 years (interquartile range 28–50). The overall seroprevalence was 27% (171/628), with a prevalence of 33% (126/376) in Abuja and 18% (45/252) in Lagos. Based on site-specific grouped LASSO selection, enrollment in the dry season (vs. wet; aPR, 95% CI: 1.73, 1.33–2.24), reported inconsistent washing of fruits and vegetables (aPR, 95% CI: 1.45, 1.10–1.92), and a positive malaria rapid test (aPR, 95% CI: 1.48, 1.09-2.00) were independently associated with LASV seropositivity in Abuja, whereas, only a self-reported history of rhinorrhea (PR, 95% CI: 2.21, 1.31–3.72) was independently associated with Lassa seropositivity in Lagos. Conclusions: The LASV seroprevalence was comparable to that in other areas in Nigeria. Our findings corroborate those from other studies on the importance of limiting human exposure to rodents and focusing on behavioral factors such as poor hygiene practices to reduce exposure to LASV. [ABSTRACT FROM AUTHOR]

Precision medicine is an approach to provide individualized treatment to the patient for a specific disease based on the genotypic, phenotypic and also psychological factors which in turn enhances the patient care to several folds. Apart from providing personalized treatment, it can also be used to accurately diagnose a disease state in a patient. Precision medicine can be employed to improve the health status of both individual patients and general population at high risk. However, several ethical considerations have to be looked upon while employing precision medicine at the same time it required more cutting-edge methodologies for the accurate execution of the same. [ABSTRACT FROM AUTHOR]

Primates live in complex social systems, and social contact and disease interact to shape the evolution of animal (including human) sociality. Researchers use social network analysis (SNA), a method of mapping and measuring contact patterns within a network of individuals, to understand the role that social interactions play in disease transmission. Here, we review lessons learned from SNA of humans and nonhuman primates (NHPs) and explore how they can inform health and wildlife conservation. Utilizing the breadth of knowledge in human systems and outlining how we can integrate that knowledge into our understanding of NHP sociality will add to our comprehension of disease transmission in NHP social networks and, in turn, will reveal more about human disease and well-being. [ABSTRACT FROM AUTHOR]

The prevalence of mosquito-borne diseases is increasing due to urbanization and climate change. Synthetic insecticides have resulted in resistance, and alternative control methods are needed. This study evaluated hexane, chloroform, and methanol extracts from Sphaeranthus indicus leaves for their efficacy against the larvae, pupae and eggs of Aedes aegypti and Culex quinquefasciatus mosquitoes. The plant materials were coarsely powdered and sequentially extracted in hexane, chloroform, and methanol using a Soxhlet apparatus, followed by filtration and concentration under reduced pressure with a rotary vacuum evaporator. In the case of Ae. aegypti, the hexane extract exhibited notable toxicity against all three examined stages. The LC50 values for larval mortality, pupal lethality, and ovicidal activity were determined to be 81.83 ppm, 86.13 ppm, and 63.2 ppm, respectively. Similarly, for Cx. quinquefasciatus, the hexane extract showed efficacy with LC50 values of 81.23 ppm for larval mortality, 93.24 ppm for pupal lethality, and 75.2 ppm for ovicidal activity. Following the hexane extract, the chloroform and methanol extracts demonstrated moderate larvicidal and pupicidal activity against both mosquito species, but they exhibited limited effectiveness against ovicidal activity. These findings suggest the potential use of natural extracts as supplementary control measures against mosquito vectors. [ABSTRACT FROM AUTHOR]

Codon optimality refers to the effect that codon composition has on messenger RNA (mRNA) stability and translation level and implies that synonymous codons are not silent from a regulatory point of view. Here, we investigated the adaptation of virus genomes to the host optimality code using mosquito-borne dengue virus (DENV) as a model. We demonstrated that codon optimality exists in mosquito cells and showed that DENV preferentially uses nonoptimal (destabilizing) codons and avoids codons that are defined as optimal (stabilizing) in either human or mosquito cells. Human genes enriched in the codons preferentially and frequently used by DENV are upregulated during infection, and so is the tRNA decoding the nonoptimal and DENV preferentially used codon for arginine. We found that adaptation during single-host passaging in human or mosquito cells results in the selection of synonymous mutations towards DENV's preferred nonoptimal codons that increase virus fitness. Finally, our analyses revealed that hundreds of viruses preferentially use nonoptimal codons, with those infecting a single host displaying an even stronger bias, suggesting that host–pathogen interaction shapes virus-synonymous codon choice. Synopsis: Analyses of viral genome adaptation to the host optimality code, reveal that hundreds of human-infecting viruses preferentially use non-optimal codons and suggest that host-pathogen interactions can shape virus synonymous codon choice. Dengue virus (DENV) preferentially uses non-optimal codons and avoids codons that are defined as optimal in either human or mosquito cells. Codon optimality mechanism exists in mosquito cells. Human genes enriched in the codons preferentially and frequently used by DENV are up-regulated during DENV infection, as well as tRNAs decoding the non-optimal codon that DENV preferentially uses. Hundreds of human-infecting viruses preferentially use non-optimal codons suggesting that host-pathogen interaction shapes virus synonymous codon choice. Analyses of viral genome adaptation to the host optimality code, reveal that hundreds of human-infecting viruses preferentially use non-optimal codons and suggest that host-pathogen interactions can shape virus synonymous codon choice. [ABSTRACT FROM AUTHOR]

The unprecedented COVID-19 outbreak has significantly influenced our daily life, and COVID-19's spread is inevitably associated with human mobility. Given the pandemic's severity and extent of spread, a timely and comprehensive synthesis of the current state of research is needed to understand the pandemic's impact on human mobility and corresponding government measures. This study examined the relevant literature published to the present (March 2023), identified research trends, and conducted a systematic review of evidence regarding transport's response to COVID-19. We identified key research agendas and synthesized the results, examining: (1) mobility changes by transport modes analyzed regardless of government policy implementation, using empirical data and survey data; (2) the effect of diverse government interventions to reduce mobility and limit COVID-19 spread, and controversial issues on travel restriction policy effects; and (3) future research issues. The findings showed a strong relationship between the pandemic and mobility, with significant impacts on decreased overall mobility, a remarkable drop in transit ridership, changes in travel behavior, and improved traffic safety. Government implemented various non-pharmaceutical countermeasures, such as city lockdowns, travel restrictions, and social distancing. Many studies showed such interventions were effective. However, some researchers reported inconsistent outcomes. This review provides urban and transport planners with valuable insights to facilitate better preparation for future health emergencies that affect transportation. [ABSTRACT FROM AUTHOR]

With the advances in artificial intelligence (AI), data‐driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision‐making by such algorithms is not trustworthy for clinicians and is considered a black‐box process. Hence, the scientific community has introduced explainable artificial intelligence (XAI) to remedy the problem. This systematic scoping review investigates the application of XAI in breast cancer detection and risk prediction. We conducted a comprehensive search on Scopus, IEEE Explore, PubMed, and Google Scholar (first 50 citations) using a systematic search strategy. The search spanned from January 2017 to July 2023, focusing on peer‐reviewed studies implementing XAI methods in breast cancer datasets. Thirty studies met our inclusion criteria and were included in the analysis. The results revealed that SHapley Additive exPlanations (SHAP) is the top model‐agnostic XAI technique in breast cancer research in terms of usage, explaining the model prediction results, diagnosis and classification of biomarkers, and prognosis and survival analysis. Additionally, the SHAP model primarily explained tree‐based ensemble machine learning models. The most common reason is that SHAP is model agnostic, which makes it both popular and useful for explaining any model prediction. Additionally, it is relatively easy to implement effectively and completely suits performant models, such as tree‐based models. Explainable AI improves the transparency, interpretability, fairness, and trustworthiness of AI‐enabled health systems and medical devices and, ultimately, the quality of care and outcomes. [ABSTRACT FROM AUTHOR]

Anomaly detection of three-dimensional (3D) topographic data is a challenging problem in spatial data analysis. In this paper, we investigate spatial patterns of 3D surface data that exhibit multiple in-control modes. In complex manufacturing processes, surfaces of final products could contain different topographic features from one in-control surface to another, thus making it difficult to monitor the surface with existing approaches, which rely on the assumption of the presence of single mode surface topography. We propose a novel anomaly detection approach for monitoring local topographic variations in the presence of multimode surface topography. We present a binarization model to capture the generic behavior of the multimode surfaces and enhance the representation of the surface. To systematically monitor the surface, we introduce a new probabilistic distance measure (PDM) that quantifies the similarity of spatial patterns between two binarized surfaces. The proposed PDM takes advantage of identifying local variations by utilizing the order neighbor statistics, which captures the local property on the surface. Experimental results with numerical simulation data and real-life paper surface data are provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Deep phenotyping describes the use of standardised terminologies to create comprehensive phenotypic descriptions of biomedical phenomena. These characterisations facilitate secondary analysis, evidence synthesis, and practitioner awareness, thereby guiding patient care. The vast majority of this knowledge is derived from sources that describe an academic understanding of disease, including academic literature and experimental databases. Previous work indicates a gulf between the priorities, perspectives, and perceptions held by different healthcare stakeholders. Using social media data, we develop a phenotype model that represents a public perspective on disease and compare this with a model derived from a combination of existing academic phenotype databases. We identified 52,198 positive disease-phenotype associations from social media across 311 diseases. We further identified 24,618 novel phenotype associations not shared by the biomedical and literature-derived phenotype model across 304 diseases, of which we considered 14,531 significant. Manifestations of disease affecting quality of life, and concerning endocrine, digestive, and reproductive diseases were over-represented in the social media phenotype model. An expert clinical review found that social media-derived associations were considered similarly well-established to those derived from literature, and were seen significantly more in patient clinical encounters. The phenotype model recovered from social media presents a significantly different perspective than existing resources derived from biomedical databases and literature, providing a large number of associations novel to the latter dataset. We propose that the integration and interrogation of these public perspectives on the disease can inform clinical awareness, improve secondary analysis, and bridge understanding and priorities across healthcare stakeholders. [ABSTRACT FROM AUTHOR]

Background: A significant portion of the Muslim community participates in the Arba'een pilgrimage, with participant numbers increasing each year. There have been relatively few studies on the health of Arba'een Mass gathering (MG). Researching the prevalence and distribution of diseases among Arba'een pilgrims is essential to recognize any outbreaks and take timely responses to contain them. The current study aimed to identify exposures and risk factors for diarrheal disease in pilgrims who referred to the clinics located in Iraq among the Arba'een MG in 2023. Methods: This case-control study randomly selected 200 outpatients (100 cases and 100 controls) who were referred to Iraq clinics. Cases were patients with gastrointestinal symptoms (diarrhea), while controls were randomly selected from unaffected pilgrims at the same time for the cases. The study groups matched for age, and sex. Face-to-face interviews using a reliable field based checklist of the Center for Disease Control and Prevention of Iran's Ministry of Health to collect potential exposures and risk factors for diarrheal disease. Multiple logistic regression was used to estimate the crude and adjusted odds ratio (AOR) for the risk of diarrhea with a 95% confidence interval (CI). Results: The average age of the participants was 38.6 years. Diarrhea in 100% and fever in 81% were the most common clinical symptoms in patients. Having underlying diseases was not associated with an increased risk of diarrhea disease (P > 0.05). Regarding risk factors for diarrheal, the final analysis after adjusting for potential confounders indicated that consumption of insanitary (unpackaged) drinking water (AOR = 1.95; 95% CI: 1.05–3.6; P = 0.024), inappropriate hand washing (AOR = 3.82; 95% CI: 1.7–8.6; P = 0.001), ritual foods (AOR = 2.56; 95% CI: 1.3–5.2; P = 0.004), and public toilets (AOR = 1.46; 95% CI: 1.04–4.3; P = 0.038) were significantly increased the likelihood of diarrheal disease. Conclusions: Contamination of water sources, food, inadequate and poor hand washing were the most common sources of diarrheal diseases among Arba'een pilgrims. The results indicate that the potential occurrence of outbreaks, especially water- and foodborne diseases, threatens participants in the Arba'een MG. It is recommended to provide risk assessment, improve pilgrims' awareness, pre-and post-screening, vaccination, compliance with personal hygiene, improvement of the environment, provision of sanitary water and food sources and hygienic disposal of sewage, laboratory diagnosis to identify the common types of pathogens in Arba'een MG. [ABSTRACT FROM AUTHOR]

In part two of this mini-series, we evaluate the range of machine-learning tools now available for application to veterinary clinical text-mining. These tools will be vital to automate extraction of information from large datasets of veterinary clinical narratives curated by projects such as the Small Animal Veterinary Surveillance Network (SAVSNET) and VetCompass, where volumes of millions of records preclude reading records and the complexities of clinical notes limit usefulness of more "traditional" text-mining approaches. We discuss the application of various machine learning techniques ranging from simple models for identifying words and phrases with similar meanings to expand lexicons for keyword searching, to the use of more complex language models. Specifically, we describe the use of language models for record annotation, unsupervised approaches for identifying topics within large datasets, and discuss more recent developments in the area of generative models (such as ChatGPT). As these models become increasingly complex it is pertinent that researchers and clinicians work together to ensure that the outputs of these models are explainable in order to instill confidence in any conclusions drawn from them. [ABSTRACT FROM AUTHOR]

Dengue virus (DENV) is the most prevalent mosquito-borne Flavivirus that affects humans worldwide. Aedes albopictus, which is naturally infected with the bacteria Wolbachia, is considered to be a secondary vector of DENV. However, it was responsible for a recent DENV outbreak of unprecedented magnitude in Reunion Island, a French island in the South West Indian Ocean. Moreover, the distribution of the cases during this epidemic showed a spatially heterogeneous pattern across the island, leading to questions about the differential vector competence of mosquito populations from different geographic areas. The aim of this study was to gain a better understanding of the vector competence of the Ae. albopictus populations from Reunion Island for local DENV epidemic strains, while considering their infection by Wolbachia. Experimental infections were conducted using ten populations of Ae. albopictus sampled across Reunion Island and exposed to three DENV strains: one strain of DENV serotype 1 (DENV-1) and two strains of DENV serotype 2 (DENV-2). We analyzed three vector competence parameters including infection rate, dissemination efficiency and transmission efficiency, at different days post-exposition (dpe). We also assessed whether there was a correlation between the density of Wolbachia and viral load/vector competence parameters. Our results show that the Ae. albopictus populations tested were not able to transmit the two DENV-2 strains, while transmission efficiencies up to 40.79% were observed for the DENV-1 strain, probably due to difference in viral titres. Statistical analyses showed that the parameters mosquito population, generation, dpe and area of sampling significantly affect the transmission efficiencies of DENV-1. Although the density of Wolbachia varied according to mosquito population, no significant correlation was found between Wolbachia density and either viral load or vector competence parameters for DENV-1. Our results highlight the importance of using natural mosquito populations for a better understanding of transmission patterns of dengue. [ABSTRACT FROM AUTHOR]

The uncertainty-threat model of conservatism posits that people turn to political conservatism to protect themselves from perceived threats; indeed, studies show increases in conservative ideology and outgroup bias following threat priming. The COVID-19 pandemic is an unprecedented threat that has had devastating effects on the health and economic lives of Americans. Concerns surrounding the threat of COVID-19 may have secondary effects on other aspects of American life, such as political and anti-Asian racial bias. The current studies explored the effects of COVID-19 related threats on expressed political conservatism, xenophobia, and racial bias under the uncertainty-threat model. Study 1 assessed the effects of priming health or economic risks of COVID-19 (vs. control), and found that economic threat led to increased xenophobia, but had no effects on overall conservatism. Study 2 then investigated whether the effects of COVID-related economic threat prime extended to racial bias, and explored moderators and mediators of effects. Results showed that the economic threat prime increased perceived group-status threat, and indirectly increased conservatism, xenophobia, and racial bias through the mechanism of perceived group-status threat. Effects were greatest for those impacted financially by the pandemic. In general, our studies provide support for the uncertainty-threat model with the novel threat of the COVID-19 pandemic. Implications for understanding potential shifts in conservatism and bias in response to future threats in the United States are discussed. [ABSTRACT FROM AUTHOR]

Mosquito vectors of pathogens (e.g., Aedes, Anopheles, and Culex spp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.87). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming. Our results indicate that lab-based and field-based studies are highly complementary; performing the analyses in concert can help to more comprehensively understand vector response to climate change. Author summary: Mosquito vectors are strongly affected by temperature, and their distributions are likely to shift under climate change. Lab studies show that mosquito abundance has a unimodal response to temperature with thermal optima, upper and lower thermal limits. However, it remains unknown how mosquito laboratory-derived thermal responses relate to the thermal responses of mosquitoes in nature. We used a global database of field-collected mosquito occurrences, geospatial environmental covariates, and species distribution models to estimate the relationship between temperature and probability of mosquito occurrence. We found that thermal minima (r = 0.87) and, to a lesser degree, thermal optima (r = 0.69) estimated from laboratory studies were correlated with those from the species distribution models. For most species, we did not detect thermal maxima. These results suggest that laboratory studies and field-based machine learning studies are complementary. Together, they can help to better understand vector response to climate change. [ABSTRACT FROM AUTHOR]

Key Points: Question: Is medication abortion provided using no-test eligibility screening and mailing of medications as effective as in-person care with ultrasonography and safe overall? Findings: This prospective, observational study found that medication abortion obtained following no-test telehealth screening and mailing of medications was associated with similar rates of complete abortion compared with in-person care with ultrasonography (94.4% vs 93.3%, respectively) and met the 5% noninferiority margin, with a low prevalence of adverse events. Meaning: The findings suggest that models of medication abortion care that rely on no-test eligibility assessment and mailing medications to patients are an option for individuals considering medication abortion. Importance: In the US, access to medication abortion using history-based (no-test) eligibility assessment, including through telehealth and mailing of mifepristone, has grown rapidly. Additional evidence on the effectiveness and safety of these models is needed. Objective: To evaluate whether medication abortion with no-test eligibility assessment and mailing of medications is as effective as in-person care with ultrasonography and safe overall. Design, Setting, and Participants: Prospective, observational study with noninferiority analysis. Sites included 4 abortion-providing organizations in Colorado, Illinois, Maryland, Minnesota, Virginia, and Washington from May 2021 to March 2023. Eligible patients were seeking medication abortion up to and including 70 days' gestation, spoke English or Spanish, and were aged 15 years or older. Exposure: Study groups reflected the model of care selected by the patient and clinicians and included: (1) no-test (telehealth) eligibility assessment and mailing of medications (no-test + mail) (n = 228); (2) no-test eligibility assessment and pickup of medications (no-test + pickup) (n = 119); or (3) in-person with ultrasonography (n = 238). Main Outcomes and Measures: Effectiveness, defined as a complete abortion without the need for repeating the mifepristone and misoprostol regimen or a follow-up procedure, and safety, defined as an abortion-related serious adverse event, including overnight hospital admission, surgery, or blood transfusion. Outcomes were derived from patient surveys and medical records. Primary analysis focused on the comparison of the no-test + mail group with the in-person with ultrasonography group. Results: The mean age of the participants (N = 585) was 27.3 years; most identified as non-Hispanic White (48.6%) or non-Hispanic Black (28.1%). Median (IQR) gestational duration was 45 days (39-53) and comparable between study groups (P =.30). Outcome data were available for 91.8% of participants. Overall effectiveness was 94.4% (95% CI, 90.7%-99.2%) in the no-test + mail group and 93.3% (95% CI, 88.3%-98.2%) in the in-person with ultrasonography group in adjusted models (adjusted risk difference, 1.2 [95% CI, −4.1 to 6.4]), meeting the prespecified 5% noninferiority margin. Serious adverse events included overnight hospitalization (n = 4), blood transfusion (n = 2), and emergency surgery (n = 1) and were reported by 1.1% (95% CI, 0.4%-2.4%) of participants, with 3 in the no-test + mail group, 3 in the in-person with ultrasonography group, and none in the no-test + pickup group. Conclusions and Relevance: This prospective, observational study found that medication abortion obtained following no-test telehealth screening and mailing of medications was associated with similar rates of complete abortion compared with in-person care with ultrasonography and met prespecified criteria for noninferiority, with a low prevalence of adverse events. This prospective, observational study investigates the efficacy and safety of medication abortion provided using no-test telehealth eligibility screening and mailing of medications compared with in-person care with ultrasonography in the US. [ABSTRACT FROM AUTHOR]

Vaccine hesitancy was a major challenge during the COVID-19 pandemic. A common but sometimes ineffective intervention to reduce vaccine hesitancy involves providing information on vaccine effectiveness, side effects, and related probabilities. Could biased processing of this information contribute to vaccine refusal? We examined the information inspection of 1200 U.S. participants with anti-vaccination, neutral, or pro-vaccination attitudes before they stated their willingness to accept eight different COVID-19 vaccines. All participants—particularly those who were anti-vaccination—frequently ignored some of the information. This deliberate ignorance, especially toward probabilities of extreme side effects, was a stronger predictor of vaccine refusal than typically investigated demographic variables. Computational modeling suggested that vaccine refusals among anti-vaccination participants were driven by ignoring even inspected information. In the neutral and pro-vaccination groups, vaccine refusal was driven by distorted processing of side effects and their probabilities. Our findings highlight the necessity for interventions tailored to individual information-processing tendencies. [ABSTRACT FROM AUTHOR]

Sex and gender differences play a crucial role in health and disease outcomes. This study used data from the National Health and Nutrition Examination Survey to explore how environmental exposures affect health-related traits differently in males and females. We utilized a sex-stratified phenomic environment-wide association study (PheEWAS), which allowed the identification of associations across a wide range of phenotypes and environmental exposures. We examined associations between 272 environmental exposures, including smoking-related exposures such as cotinine levels and smoking habits, and 58 clinically relevant blood phenotypes, such as serum albumin and homocysteine levels. Our analysis identified 119 sex-specific associations. For example, smoking-related exposures had a stronger impact on increasing homocysteine, hemoglobin, and hematocrit levels in females while reducing serum albumin and bilirubin levels and increasing c-reactive protein levels more significantly in males. These findings suggest mechanisms by which smoking exposure may pose higher cardiovascular risks and greater induced hypoxia for women, and greater inflammatory and immune responses in men. The results highlight the importance of considering sex differences in biomedical research. Understanding these differences can help develop more personalized and effective health interventions and improve clinical outcomes for both men and women. [ABSTRACT FROM AUTHOR]

Background: Seasonal fluctuations in weather are recognized as factors that affect both Aedes (Ae.) aegypti mosquitoes and the diseases they carry, such as dengue fever. The El Niño-Southern Oscillation (ENSO) is widely regarded as one of the most impactful atmospheric phenomena on Earth, characterized by the interplay of shifting ocean temperatures, trade wind intensity, and atmospheric pressure, resulting in extensive alterations in climate conditions. In this study, we investigate the influence of ENSO and local weather conditions on the spatio-temporal variability of Ae. aegypti infestation index. Methods: We collected seasonal entomological survey data of immature forms of Ae. aegypti mosquitoes (Breteau index), as well as data on temperature, rainfall and the Oceanic Niño Index (ONI) for the period 2008–2018 over the 645 municipalities of the subtropical State of São Paulo (Brazil). We grounded our analytical approach on a Bayesian framework and we used a hierarchical spatio-temporal model to study the relationship between ENSO tracked by ONI, seasonal weather fluctuations and the larval index, while adjusting for population density and wealth inequalities. Results: Our results showed a relevant positive effect for El Niño on the Ae. aegypti larval index. In particular, we found that the number of positive containers would be expected to increase by 1.30-unit (95% Credible Intervals (CI): 1.23 to 1.37) with El Niño events (i.e., ≥ 1°C, moderate to strong) respect to neutral (and weak) events. We also found that seasonal rainfall exceeding 153.12 mm appears to have a notable impact on vector index, leading potentially to the accumulation of ample water in outdoor discarded receptacles, supporting the aquatic phase of mosquito development. Additionally, seasonal temperature above 23.30°C was found positively associated to the larval index. Although the State of São Paulo as a whole has characteristics favourable to proliferation of the vector, there were specific areas with a greater tendency for mosquito infestation, since the most vulnerable areas are predominantly situated in the central and northern regions of the state, with hot spots of abundance in the south, especially during El Niño events. Our findings also indicate that social disparities present in the municipalities contributes to Ae. aegypti proliferation. Conclusions: Considering the anticipated rise in both the frequency and intensity of El Niño events in the forthcoming decades as a consequence of climate change, the urgency to enhance our ability to track and diminish arbovirus outbreaks is crucial. Author summary: Dengue fever is a dramatically expanding disease transmitted by mosquitoes. Earlier research has demonstrated the susceptibility of dengue-carrying mosquitoes to weather conditions, with rainfall and temperature significantly impacting dengue transmission. While there is evidence of a connection between weather factors, the El Niño-Southern Oscillation (ENSO) and the risk of dengue, their impact on the behaviour of Aedes (Ae.) aegypti mosquitoes, which is the main disease vectors, remains poorly comprehended. In this study we analyse municipality-specific time series of a larval index of Ae. aegypti population in the State of São Paulo (southern Brazil) from 2008 to 2018. The main goal was to explore how intra- and inter-annual changes in weather conditions and ENSO periodic fluctuations affected mosquito abundance. We found a strong and significant coherence between the temporal pattern of mosquito abundance and El Niño phases, rainfall and temperature, showing the role that climate plays in driving infestation periodicity. Specifically, we identified a relationship between Ae. aegypti infestation and the strength of the ENSO phenomenon: periods characterized by El Niño phase (moderate to strong) were associated with a higher abundance of Ae. aegypti mosquito larvae. Additionally, we found that wealth inequalities increase opportunities for Ae. aegypti proliferation. Our discoveries carry significant implications for vector control strategies, as they offer valuable insights for categorizing areas based on spatial risk, enabling the prioritization of intensified vector control measures. Given the anticipated rise in the frequency of El Niño events due to climate change, these findings suggest that arbovirus outbreaks could also see an increase, and that El Niño occurrences might serve as a predictive tool for such outbreaks. [ABSTRACT FROM AUTHOR]

In pandemic mitigation, strategies such as social distancing and mask-wearing are vital to prevent disease resurgence. Yet, monitoring adherence is challenging, as individuals might be reluctant to share behavioral data with public health authorities. To address this challenge and demonstrate a framework for conducting observational research with sensitive data in a privacy-conscious manner, we employ a privacy-centric epidemiological study design: the federated cohort. This approach leverages recent computational advances to allow for distributed participants to contribute to a prospective, observational research study while maintaining full control of their data. We apply this strategy here to explore pandemic intervention adherence patterns. Participants (n = 3808) were enrolled in our federated cohort via the "Google Health Studies" mobile application. Participants completed weekly surveys and contributed empirically measured mobility data from their Android devices between November 2020 to August 2021. Using federated analytics, differential privacy, and secure aggregation, we analyzed data in five 6-week periods, encompassing the pre- and post-vaccination phases. Our results showed that participants largely utilized non-pharmaceutical intervention strategies until they were fully vaccinated against COVID-19, except for individuals without plans to become vaccinated. Furthermore, this project offers a blueprint for conducting a federated cohort study and engaging in privacy-preserving research during a public health emergency. [ABSTRACT FROM AUTHOR]

Background: While Human Factors (HF) methods have been applied to the design of decision support systems (DSS) to aid clinical decision-making, the role of HF to improve decision-support for population health outcomes is less understood. We sought to comprehensively understand how HF methods have been used in designing digital population health DSS. Materials and methods: We searched English documents published in health sciences and engineering databases (Medline, Embase, PsychINFO, Scopus, Comendex, Inspec, IEEE Xplore) between January 1990 and September 2023 describing the development, validation or application of HF principles to decision support tools in population health. Results: We identified 21,581 unique records and included 153 studies for data extraction and synthesis. We included research articles that had a target end-user in population health and that used HF. HF methods were applied throughout the design lifecycle. Users were engaged early in the design lifecycle in the needs assessment and requirements gathering phase and design and prototyping phase with qualitative methods such as interviews. In later stages in the lifecycle, during user testing and evaluation, and post deployment evaluation, quantitative methods were more frequently used. However, only three studies used an experimental framework or conducted A/B testing. Conclusions: While HF have been applied in a variety of contexts in the design of data-driven DSSs for population health, few have used Human Factors to its full potential. We offer recommendations for how HF can be leveraged throughout the design lifecycle. Most crucially, system designers should engage with users early on and throughout the design process. Our findings can support stakeholders to further empower public health systems. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate the relationship between parent and child COVID-19 immunization series completion using vaccine records. Methods: A cross-sectional chart review was performed on parent and child patients at the Albany Medical Center Internal Medicine and Pediatrics practice. Patient and parent demographic data, COVID-19 immunization status, and health care worker status was collected. Patient data was further separated into 2 cohorts to account for vaccine eligibility among child age groups, with 5–11 years and 6 months-4 years cohorts. Chi square or Fisher's exact test was used where appropriate. Results: Across both cohorts, 371 child patients were identified and cross-matched with corresponding parents. Neither cohort offered evidence linking child immunization series completion with the child's race, ethnicity, or county of residence. However, rates of series completion were higher for children with private insurance versus public options in both the 5–11 years and 6 months-4 years cohorts (both p < 0.001). Children were more likely to be immunized against COVID-19 if their parents were fully immunized against COVID-19 (both p < 0.05). Children aged 6 months-4 years were more likely to be immunized against COVID-19 if their parent was a health care worker (p = 0.038). Conclusions: This study demonstrates a significance between child insurance status, as well as between parental vaccination status and child vaccination status. Only children under 5 years were more likely to be vaccinated against COVID-19 if their parent was a health care worker. [ABSTRACT FROM AUTHOR]

Background: While dengue NS1 antigen has been shown to be associated with disease pathogenesis in some studies, it has not been linked in other studies, with the reasons remaining unclear. NS1 antigen levels in acute dengue are often associated with increased disease severity, but there has been a wide variation in results based on past dengue infection and infecting dengue virus (DENV) serotype. As NS1 engages with many host lipids, we hypothesize that the type of NS1-lipid interactions alters its pathogenicity. Methods: Primary human monocyte derived macrophages (MDMs) were co-cultured with NS1 alone or with HDL, LDL, LPS and/or platelet activating factor (PAF) from individuals with a history of past dengue fever (DF = 8) or dengue haemorrhagic fever (DHF = 8). IL-1β levels were measured in culture supernatants, and gene expression analysis carried out in MDMs. Monocyte subpopulations were assessed by flow cytometry. Hierarchical cluster analysis with Euclidean distance calculations were used to differentiate clusters. Differentially expressed variables were extracted and a classifier model was developed to differentiate between past DF and DHF. Results: Significantly higher levels of IL-1β were seen in culture supernatants when NS1 was co-cultured with LDL (p = 0.01, median = 45.69 pg/ml), but lower levels when NS1 was co-cultured with HDL (p = 0.05, median = 4.617 pg/ml). MDMs of those with past DHF produced higher levels of IL-1β when NS1 was co-cultured with PAF (p = 0.02). MDMs of individuals with past DHF, were significantly more likely to down-regulate RPLP2 gene expression when macrophages were co-cultured with either PAF alone, or NS1 combined with PAF, or NS1 combined with LDL. When NS1 was co-cultured with PAF, HDL or LDL two clusters were detected based on IL10 expression, but these did not differentiate those with past DF or DHF. Conclusions: As RPLP2 is important in DENV replication, regulating cellular stress responses and immune responses and IL-10 is associated with severe disease, it would be important to further explore how differential expression of RPLP2 and IL-10 could lead to disease pathogenesis based on NS1 and lipid interactions. [ABSTRACT FROM AUTHOR]

Bats (Chiroptera), the second largest group of mammals, are known for their unique immune system and their ability to act as vectors for various zoonoses. Bats also act as important carriers of fungi, which include plant, animal, and human pathogens. Their roosting areas, foraging behaviors, and even migration routes make bats ideal vectors for fungi. We isolated 75 culturable fungal species from bats in Yunnan Province, China, with 36 species representing known pathogens of plants, animals, and humans, while 39 species are non-pathogenic fungi. Among these species, 77% (58 species) belonged to Ascomycota, 9% (seven species) belonged to Basidiomycota, and 13% (10 species) belonged to Mucoromycota. Even though several taxonomic studies on fungi associated with bats have been published, studies exploring the role of bats as fungal vectors are lacking. This study discusses the fungi host-specific traits and pathogenicity and the impact and ecological significance of bats as fungal vectors. [ABSTRACT FROM AUTHOR]

The Zika virus (ZIKV) is a serious global public health crisis. A major control challenge is its multiple transmission modes. This paper aims to simulate the transmission patterns of ZIKV using a dynamic process-based epidemiological model written in ordinary differential equations, which incorporates the human-to-mosquito infection by bites and sewage, mosquito-to-human infection by bites, and human-to-human infection by sex. Mathematical analyses are carried out to calculate the basic reproduction number and backward bifurcation, and prove the existence and stability of the equilibria. The model is validated with infection data by applying it to the 2015–2016 ZIKV epidemic in Brazil. The results indicate that the reproduction number is estimated to be 2.13, in which the contributions by mosquito bite, sex and sewage account for 85.7%, 3.5% and 10.8%, respectively. This number and the morbidity rate are most sensitive to parameters related to mosquito ecology, rather than asymptomatic or human-to-human transmission. Multiple transmission routes and suitable temperature exacerbate ZIKV infection in Brazil, and the vast majority of human infection cases were prevented by the intervention implemented. These findings may provide new insights to improve the risk assessment of ZIKV infection. [ABSTRACT FROM AUTHOR]

The last decade has seen major advances and growth in internet-based surveillance for infectious diseases through advanced computational capacity, growing adoption of smart devices, increased availability of Artificial Intelligence (AI), alongside environmental pressures including climate and land use change contributing to increased threat and spread of pandemics and emerging infectious diseases. With the increasing burden of infectious diseases and the COVID-19 pandemic, the need for developing novel technologies and integrating internet-based data approaches to improving infectious disease surveillance is greater than ever. In this systematic review, we searched the scientific literature for research on internet-based or digital surveillance for influenza, dengue fever and COVID-19 from 2013 to 2023. We have provided an overview of recent internet-based surveillance research for emerging infectious diseases (EID), describing changes in the digital landscape, with recommendations for future research directed at public health policymakers, healthcare providers, and government health departments to enhance traditional surveillance for detecting, monitoring, reporting, and responding to influenza, dengue, and COVID-19. [ABSTRACT FROM AUTHOR]