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At least 19 recordsLinked to original sources
PubMed2026

From population to individual: advocating personalised digital tools for heat-health early warning in a changing climate.

Escalating heat extremes under climate change are imposing substantial health burdens, with 2023 and 2024 consecutively breaking global temperature records. Mounting evidence suggests that heatwaves elevate the risks of hospitalisation and mortality across multiple disease categories, including ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and acute kidney injury. Nonetheless, most existing heat-health warning systems remain primarily reliant on population-level predictions, and considering individual differences and disease-specific considerations when defining warning levels would benefit the effectiveness of early prevention for high-risk groups. In this Viewpoint, which is based on the framework of precision public health-delivering the right intervention to the right population at the right time-we propose a framework for personalised digital heat-health early warning tools comprising three dimensions: individualised, risk-stratified prediction models that generate tiered early warnings; personalised health prompts coupled with theory-informed behavioural interventions; and adaptive, equity-oriented alert delivery mechanisms tailored to diverse populations. Such tools have the potential to bridge precision disease prevention and climate adaptation, thereby helping to mitigate heat exposure risks and disease burdens, particularly among high-risk populations. Future implementation research will be essential to address substantial challenges related to feasibility, validation, and equity.

Journal Article↗
PubMed2026

Fungal drivers of mycotoxin contamination in wheat: Early warning and plasma-based control.

Mycotoxin contamination in wheat is a major food safety concern; however, quantitative evidence linking fungal community signals, mycotoxin exceedance risk, and wheat quality traits in naturally contaminated wheat remains limited. In this study, wheat samples were collected from mycotoxin-prone monitoring sites under unusually rainy conditions in 2022 to explore early-warning indicators and post-harvest mitigation strategies. According to the National Food Safety Standard of China GB 2761-2017, aflatoxin B1 (AFB1), deoxynivalenol (DON), and zearalenone (ZEN) exceeded the maximum limits in 52.24, 47.76, and 23.88% of samples, respectively; 38.81% exceeded the reference EU threshold for T-2 toxin, and 46.27% showed co-contamination with at least two mycotoxins above their respective thresholds. Although Alternaria, Cladosporium, and Epicoccum dominated the fungal community, Fusarium abundance was significantly associated with DON contamination and Fusarium-damaged kernels (FDKs). Mediation analysis identified DON as a significant mediator linking Fusarium abundance to FDKs, accounting for 68.41% of the total effect. In addition, Fusarium abundance above 3.70% showed strong predictive performance for DON exceedance, with an area under the curve of 0.906, indicating its potential as an early-warning indicator. Culture-based assays confirmed the toxigenic potential of Aspergillus and Fusarium isolates under simulated temperature and moisture conditions. After optimization using a toxin-spiked wheat flour model, dielectric barrier discharge cold plasma degraded AFB1, DON, and ZEN by 29.30-35.68%, disrupted the morphology of toxigenic fungi, and did not significantly affect wheat quality. This study provides practical insights into mycotoxin risk warning and post-harvest mitigation in wheat.

Triticum↗
PubMed2024

Brood indicators are an early warning signal of honey bee colony loss-a simulation-based study.

Honey bees (Apis mellifera) are exposed to multiple stressors such as pesticides, lack of forage, and diseases. It is therefore a long-standing aim to develop robust and meaningful indicators of bee vitality to assist beekeepers While established indicators often focus on expected colony winter mortality based on adult bee abundance and honey reserves at the beginning of the winter, it would be useful to have indicators that allow detection of stress effects earlier in the year to allow for adaptive management. We used the established honey bee simulation model BEEHAVE to explore the potential of different indicators such as population size, number of capped brood cells, flight activity, abundance of Varroa mites, honey stores and a brood-bee ratio. We implemented two types of stressors in our simulations: 1) parasite pressure, i.e. sub-optimal Varroa treatment by the beekeeper (hereafter referred as Biotic stress) and 2) temporal forage gaps in spring and autumn (hereafter referred as Environmental stress). Neither stressor type could be detected by bee abundance or honey reserves at the end of the first year. However, all response variables used in this study did reveal early warning signals during the course of the year. The most reliable and useful measures seem to be related to brood and the abundance of Varroa mites at the end of the year. However, while in the model we have full access to time series of variables from stressed and unstressed colonies, knowledge of these variables in the field is challenging. We discuss how our findings can nevertheless be used to develop practical early warning indicators. As a next step in the interactive development of such indicators we suggest empirical studies on the importance of the number of capped brood cells at certain times of the year on bee population vitality.

Bees↗
PubMed2026

An RPA-assisted homogeneous electrochemical DNA sensor for on-site eDNA detection toward early warning of crown-of-thorns starfish outbreaks.

Crown-of-thorns starfish (COTS) outbreaks seriously threaten coral reef ecosystems, while conventional monitoring approaches are time-consuming and often lack sufficient sensitivity for early warning. Existing electrochemical DNA sensors usually require complex electrode-surface immobilization procedures, which can lead to uneven probe distribution, significant steric hindrance, and poor stability. Meanwhile, the low concentration of environmental DNA (eDNA) in marine environments further complicates detection. To overcome these challenges, this study developed a homogeneous electrochemical DNA sensor assisted by recombinase polymerase amplification (RPA) for COTS eDNA detection. Target DNA was first amplified by RPA, and the amplification products were then hybridized in solution with capture probe (CP)-modified magnetic beads (MB) and biotin-labeled signal probe (SP) to form sandwich-structured MB complexes. These complexes were subsequently magnetically enriched and immobilized on the electrode surface for electrochemical signal readout. Under optimized conditions, the sensor displayed a linear response to COTS genomic DNA from 3.77 fg/μL to 1 ng/μL, with an LOD of 2.02 fg/μL and an LOQ of 3.77 fg/μL. The sensor was applied to Xisha Islands samples, and the results agreed with droplet digital PCR (ddPCR) (P > 0.05), demonstrating its potential for sensitive and reliable on-site COTS eDNA detection.

Animals↗
PubMed2026

[Genomic evolution and epidemiological patterns of respiratory syncytial virus and their implications for surveillance and early warning].

Respiratory syncytial virus (RSV) is an important respiratory pathogen in infants, young children and older adults. Based on global RSV genomic surveillance data, this review systematically summarizes the geographic distribution, seasonal epidemic patterns, and long-term evolutionary trends of RSV, with particular emphasis on the sustained circulation and evolutionary mechanisms of dominant genotypes such as ON1 in RSV-A and BA9 in RSV-B. Current evidence indicates that RSV transmission dynamics are tightly coupled with viral evolution. The G gene evolves relatively rapidly and contains multiple positively selected sites, suggesting an important role in immune escape and population adaptation. In recent years, changes in social behavior patterns and population immunity have further disrupted the seasonal rhythm of RSV and may have influenced the spread of dominant genotypes. Under routine respiratory infectious disease surveillance, strengthened genomic monitoring and integration of multi-source data are needed to improve early warning of abnormal RSV epidemics and variant-associated risks, thereby providing prospective evidence for protecting high-risk populations and informing public health decision-making.

Humans↗
PubMed2026

Whole-genome sequencing of adenovirus 41 directly from wastewater using nested overlapping PCR and MinION.

Human adenovirus F41 (HAdV-F41) is one of the leading causes of children's acute gastroenteritis and was recently linked to an outbreak of severe acute hepatitis of unknown etiology among children during 2021 to 2022. While most evidence is based on clinical data, wastewater-based epidemiology offers a community-level approach to monitoring circulating strains and enhancing outbreak preparedness. In this study, we developed an overlapping amplicon-based whole-genome sequencing approach to directly detect HAdV-F41 from archived wastewater samples, using nested PCR with 13 primer sets. Archived wastewater samples were collected between 2021 and 2022 from three treatment plants in Seattle, USA. The viral load ranged from 1.2 × 103 to 8.4 × 103 genome copies per liter. The Oxford Nanopore platform was used for whole-genome sequencing. Complete or partial (>84%) HAdV-F41 genomes were recovered from wastewater samples, with mean coverage depths ranging from 10³ to 10⁵. The consensus sequences showed more than 99% similarity to reference genomes in the NCBI database. The phylogenetic analysis revealed that 2 sequences clustered within lineage 2a and 11 within lineage 2b, reflecting that at least two sub-lineages were circulating in the community at that time. Our results demonstrate that the overlapping amplicon-based whole-genome sequencing approach using the Oxford Nanopore platform reliably recovers HAdV-F41 genomes from wastewater. This method offers high-resolution genomic surveillance of circulating, clinically relevant HAdV-F41, supporting wastewater-based epidemiology as a valuable tool for detecting emerging variants and strengthening the early warning system for future disease outbreaks.IMPORTANCEHuman adenovirus F41 is a primary cause of childhood gastroenteritis and has been linked to recent outbreaks of severe acute hepatitis in children, yet community-level genomic surveillance of this virus remains limited. This study shows that wastewater can be used to recover nearly complete HAdV-F41 genomes through a targeted overlapping-amplicon sequencing strategy on the Oxford Nanopore platform. By applying this method to archived wastewater samples, we detected the simultaneous circulation of multiple viral lineages in a large city. These findings extend wastewater-based epidemiology beyond SARS-CoV-2 and emphasize its importance for monitoring clinically significant enteric viruses. The method described here offers a scalable tool for tracking viral evolution in communities and enhancing early warning systems for future outbreaks.

Wastewater↗
PubMed2026

Metaviromic profiling of mosquito excreta using superhydrophobic collection devices expands the known RNA virome of North America.

Nearly 30% of emerging infectious disease events worldwide are transmitted by arthropod vectors, and this proportion continues to rise. Rapid and accurate detection is critical for directing vector control interventions, thereby reducing the likelihood of widespread transmission. Surveillance of infected mosquitoes can provide an early warning of impending human infection; however, conventional virus testing relies on processing large pools of mosquitoes and requires labor-intensive pre-processing. During rapidly developing epidemic or panzootic events, these delays may limit the effectiveness of public health responses. Mosquito excreta has recently emerged as a promising alternative substrate for pathogen detection. Sugar-fed mosquitoes regularly excrete gut contents, offering a rich source of nucleic acids. In this study, we developed and applied custom superhydrophobic excreta-collection funnels that efficiently aggregate excreta produced by field-collected Culex mosquitoes into attached microcentrifuge tubes. Shotgun metagenomic sequencing of this material revealed a diverse RNA virome, including both globally distributed viruses and those reported here for the first time from the Americas. Beyond virus detection, additional analyses enabled confirmation of host mosquito species and identification of trypanosomatid parasites, demonstrating the broader utility of mosquito excreta for integrated surveillance. We anticipate that methods and devices of this type will become valuable components of vector surveillance programs, particularly in remote or resource-limited settings where repeated collections are challenging. Overall, our findings highlight the potential of excreta-based monitoring to improve early detection of emerging or unknown pathogens of One Health importance, refine our understanding of mosquito virome biogeography, and facilitate the discovery of previously undescribed viruses.IMPORTANCEMany infectious diseases that affect people and animals are spread by mosquitoes and other biting insects, and the number of these outbreaks is increasing. Detecting pathogens in mosquito populations early can provide a critical warning before human cases begin, allowing health officials to act quickly. However, traditional surveillance requires collecting and processing large numbers of mosquitoes, which can be slow and labor-intensive during fast-moving outbreaks. Here we demonstrate a simpler approach: testing mosquito waste. When mosquitoes feed on sugar, they excrete material that contains genetic traces of viruses and other organisms. Using specially designed collection devices and modern genetic sequencing, we show that mosquito excreta can reveal a wide range of viruses and parasites while also identifying the mosquito species present. This method could make disease surveillance faster and more practical in remote or resource-limited settings, improving our ability to detect emerging pathogens that threaten human, animal, and environmental health.

Animals↗
PubMed2026

Re-emerging Marburg virus disease in Africa: spillover ecology, geographic expansion, and surveillance vulnerabilities.

Marburg virus disease (MVD) is re-emerging across Africa as a high-consequence zoonosis shaped by expanding ecological suitability, repeated spillover, and uneven surveillance capacity. This review synthesizes current evidence on the ecological, epidemiological, and operational determinants of contemporary Marburg virus (MARV) emergence. We conceptualize MVD as an ecological-emergence system produced by interactions among reservoir-host biology, environmental change, human exposure, health-system readiness, and mobility, rather than as a series of isolated outbreaks. Recent detections in multiple African regions indicate wider enzootic circulation than previously recognized and support repeated, reservoir-associated introductions from distributed ecological foci. Spillover risk is heightened where mining, land-use change, agricultural encroachment, settlement growth, climate-sensitive habitat disruption, and population movement increase contact with Egyptian rousette bats (Rousettus aegyptiacus) and contaminated roost environments. Following primary spillover, diagnostic delays, fragmented surveillance, limited laboratory decentralization, healthcare-associated transmission, and mobility-linked exposure can enable outbreak amplification and delayed recognition. Serological findings further suggest possible "shadow epidemiology," with unrecognized or mild MARV infections occurring outside confirmed outbreak chains. Critical preparedness gaps persist in ecological risk mapping, longitudinal reservoir surveillance, decentralized molecular diagnostics, genomic sequencing, data integration, and cross-border early warning. Future preparedness should move beyond reactive containment toward integrated One Health approach combining predictive ecological surveillance, rapid community-level detection, real-time genomics, infection prevention, risk communication, and regional coordination to identify spillover early and prevent human transmission.

Animals↗
PubMed2026

Revealing potential biomarkers and metabolic mechanisms of ovarian aging in hens during late laying period based on machine learning and metabolomics.

Ovarian function decline during the late laying period represents a major bottleneck for the economic efficiency of the global poultry industry. However, the underlying metabolic mechanisms and reliable early-warning biomarkers for ovarian aging remain poorly understood. In this study, we performed the first untargeted LC-MS/MS metabolomics analysis of ovarian tissues from Taihe silky fowls at peak laying (30 weeks) and late laying (50 weeks) stages, and employed an ensemble machine learning strategy integrating LASSO, random forest, and support vector machine (SVM) algorithms to identify high-confidence core biomarkers of ovarian aging. Gene expression analysis was further conducted to validate the potential molecular mechanisms. Our results showed that the metabolic profiles of ovarian tissues differed significantly between the two groups. A total of 6 core biomarkers were identified, 4 of which were long-chain acylcarnitines. Mechanistic analysis revealed that downregulation of key genes in the carnitine shuttle system led to impaired mitochondrial fatty acid β-oxidation, which in turn triggered excessive oxidative stress and compromised ovarian endocrine function. In conclusion, this study identifies long-chain acylcarnitines as potential metabolic biomarkers for ovarian aging in Taihe silky fowls. These findings provide novel insights into the metabolic basis of poultry ovarian aging and lay a theoretical foundation for the precise regulation of reproductive performance in indigenous poultry breeds.

Animals↗
PubMed2026

Mapping Wastewater Pathogens and Their Associated Environmental and Public Health Risk Factors: A Systematic Review and Meta-Analysis.

BACKGROUND: Wastewater-based epidemiology (WBE) has emerged as a critical tool for public health surveillance, yet its application across diverse pathogens and geographical settings remains inconsistent. This systematic review synthesizes global evidence on wastewater surveillance to identify associated risk factors. METHODS: Following PRISMA 2020 guidelines (PROSPERO: CRD420261297382), a systematic search was conducted across PubMed, Scopus, Google Scholar, and Web of Science for studies published between 2000 and 2025. RESULTS: Thirty-nine peer-reviewed studies were included. The evidence base is geographically skewed toward the European Region (48.7%) and the Americas (23.1%), with significant underrepresentation in LMICs. Viruses were the primary biological target (89.7%), followed by bacteria (7.7%) and parasites (2.6%). A proportion meta-analysis of 31 eligible studies demonstrated a pooled wastewater pathogen detection prevalence of 62% (95% CI: 47.5-74.6%), with the European Region yielding the highest regional estimate (73%) and the African Region the lowest (8.3%). Conventional PCR and sequencing methods showed higher pooled detection rates (92.4% and 90.1%, respectively) than RT-qPCR (47.9%). CONCLUSION: WBE provides a robust early-warning system indicating a need for broader pathogen diversity, incorporating bacterial and parasitic surveillance and expansion into rural and resource-limited regions.

Contamination↗
PubMed2026

Respiratory pandemic risk in the Anthropocene: A One Health framework and GISRS+ agenda.

Recent epidemics and pandemics caused by respiratory viruses, alongside the animal panzootic spread of highly pathogenic avian influenza A(H5Nx), have become a structural feature of the Anthropocene, yet responses remain largely reactive. This review integrates findings from WHO's Global Influenza Surveillance and Response System (GISRS) and related surveillance data (2000-2024), epidemiological studies of influenza A virus, SARS-CoV, MERS-CoV, SARS-CoV-2, and H5Nx, and One Health literature. We examine major groups of respiratory viruses and identify mismatches between risk and surveillance by focusing on spillover potential from animal hosts, human-to-human transmission and its controllability, and Anthropocene characteristics that increase epidemic risk. The analysis indicated that SARS-related coronaviruses and influenza A viruses, particularly H5Nx, are among the leading candidates based on currently available evidence because they have large reservoirs in animal hosts and spillover to humans is highly probable. The previous presymptomatic spread of SARS-CoV-2 and recent mammalian adaptation in H5N1 clade 2.3.4.4b highlight limitations of the traditional symptom-based and pathogen-specific surveillance system. Spillover events tend to occur in tropical and subtropical regions in low- and middle-income countries, but most genomic surveillance is in high-income countries. We propose interventions that address the upstream, midstream, downstream processes of epidemics. Upstream interventions are primary prevention measures related to land use, livestock, wildlife, and urban environments; midstream interventions are GISRS+-based pathogen-agnostic genomic and metagenomic early warning systems triggered by One Health; and downstream interventions include vaccines, antivirals, non-pharmaceutical interventions, and engineering with equity-centred global governance and sustainable financing.

Anthropocene↗
PubMed2026

Herd-level heterogeneity of antimicrobial resistance in commensal Escherichia coli: A nationwide high-throughput survey of Australian pig herds.

Antimicrobial resistance in commensal Escherichia coli provides a useful indicator for overall antimicrobial resistance burden. We applied this approach to assess antimicrobial resistance within and between commercial pig herds across Australia. A high-throughput robotic workflow was used to isolate 2730 E. coli colonies from rectal contents collected in 2022 from healthy slaughter pigs (n = 300) representing 30 herds (∼70% of national production). Up to 94 isolates per herd underwent antimicrobial susceptibility testing using the Robotic Antimicrobial Susceptibility Platform. Isolate- and herd-level antimicrobial resistance indices were calculated, weighting antimicrobials by their human health importance. Resistance to first-line agents was widespread: ampicillin 77% and tetracycline 79%. By contrast, resistance to critically important antimicrobials was rare (ciprofloxacin 0.11%; extended-spectrum cephalosporins 0.04%), and no clinical resistance to carbapenems or colistin was detected. Overall, 56.9% of isolates were multi-class resistant. Herd-level antimicrobial resistance within indices ranged from 1.51 to 5.76, revealing substantial between-herd heterogeneity. Three herds carried critically important antimicrobials-resistant isolates that would likely have been missed using conventional, lower-density sampling approaches. Whole-genome sequencing identified fluoroquinolone-resistant isolates belonging to ST10 and ST69 (both qnrS1), and ST744 (Quinolone Resistance Determining Region mutations plus blaCTX-M-27). By testing approximately tenfold more isolates than conventional surveys, we uncovered considerable antimicrobial resistance with heterogeneity within and between animals and herds, including farm-specific variability. This expanded sampling also enabled detection of critically important antimicrobial resistance at very low prevalence. In conclusion, high-throughput, high-density testing offers a practical early-warning system and herd-level benchmark to inform surveillance and targeted interventions.

Animals↗
PubMed2026

Evaluating sampling strategies for the detection of avian influenza viruses in the environment.

Highly pathogenic avian influenza (HPAI) viruses pose an increasing threat to wildlife, livestock and human health, underscoring the need for scalable and early-warning surveillance systems. Environmental RNA (eRNA) monitoring offers a non-invasive, cost-effective alternative to traditional host-based sampling by detecting viral genetic material shed into the environment. Despite its utility, the relative performance of different environmental sampling approaches for avian influenza virus (AIV) detection remains poorly resolved. Here, we conducted a longitudinal study with monthly sampling over approximately one year across two urban waterfowl ponds in Aotearoa New Zealand to evaluate four eRNA sampling strategies - fresh faeces, sediment, active-filtered water and passive-filtered water - for their ability to detect AIV. Using a combination of metagenomic sequencing and RT-qPCR, we show that all sample types can detect AIV, although detections were highly inconsistent across sampling methods, locations and time points. While metagenomic sequencing provided valuable genomic data, including subtype identification and phylogenetic context, RT-qPCR exhibited greater sensitivity, with active-filtered water yielding the highest detection rates, and is currently the more cost-effective approach for large-scale surveillance. Notably, AIV detections were asynchronous among sample types and frequently lacked temporal concordance, suggesting that environmental heterogeneity, RNA persistence, and methodological detection limits strongly influence surveillance outcomes. Despite these inconsistencies, phylogenetic analyses revealed that detected viruses belong to established Australasian lineages, highlighting the ability of environmental surveillance to capture ecologically relevant viral diversity. Our findings demonstrate that while eRNA-based surveillance holds substantial promise as a complementary tool for AIV monitoring, its effectiveness is highly dependent on the environmental sampling strategies and laboratory detection methods used.

Ducks↗
PubMed1977

Fatal dextropropoxyphene poisoning in Northern Ireland. Review of 30 cases.

Data from 30 fatal cases of dextropropoxphene poisoning occurring in Northern Ireland over three years have been studied. All the victims had ingested more than therapeutic amounts, and many had also taken alcohol or other drugs. Most of the deaths were probably suicides. Death occurred very rapidly suggesting that the narcotic effects of dextropropoxyphene predominated, and this may explain the scarcity of clinical reports, It is suggested that many doctors are unaware of the danger of dextropropoxyphene in overdosage and that the problem of dextropropoxyphene poisoning in the United Kingdom has not been fully appreciated. This supposition, if correct, highlights the absence of a satisfactory early-warning system for serious drug effects including death.

Acetaminophen↗
PubMed1976

Murray Valley encephalitis virus infection in mosquitoes and domestic fowls in Queensland, 1974.

Field studies during an epidemic of Murray Valley encephalitis (MVE) led to the isolation of MVE virus from a pool of mosquitoes (Culex annulirostris) and a sentinel chicken from Charleville, south-west Queensland. A high proportion of domestic fowls at Charleville had antibody to MVE virus at the beginning of February 1974, in advance of the first case recognized in Queensland and allowing early warning from health authorities. A survey of antibody in domestic fowls in mid-1974 suggested widespread activity of MVE virus in western and east-central Queensland. Virus isolation and serological studies showed activity in south-west Queensland of three other viruses known to infect man, Ross River, Sindbis and Kunjin viruses.

Animals↗