Colistin is a polymyxin antimicrobic mainly used to treat infection caused by multi-drug resistant Gram-negative bacteria. Mechanisms of colistin resistance are linked to the mobile colistin resistance (mcr) genes, which are transferable within mobile plasmids. Currently, there is limited research on the environmental dissemination of these genes. The behavioural and morphological characteristics of Apis mellifera L. make honey bees effective environmental bioindicators for assessing the prevalence of antimicrobial-resistant bacteria. This study aims to evaluate the colistin phenotypic and genotypic resistance in environmental Gram-negative bacteria isolated from foraging honey bees, across a network of 33 colonies distributed across the Emilia-Romagna region in Italy. Phenotypic resistances were determined through a microdilution assay using the minimum inhibitory concentration (MIC) with dilutions ranging from 0.5 μg/ml to 256 μg/ml. Strains with MIC values gather than 2 μg/ml were classified as resistant. Also, the identification of the nine mcr genes was carried out using two separate multiplex PCR assays. The study found that 68.5% of isolates were resistant and the genus with the higher resistance rates observed in Enterobacter spp. (84.5%). At least one mcr gene was found in 137 strains (53.3%). The most detected gene was mcr5 (35.3%), which was the most frequently detected gene in the seven provinces, while the least observed was mcr4 (4.8%), detected only in two provinces. These results suggested the feasibility of detecting specific colistin resistance genes in environmentally spread bacteria and understanding their distribution at the environmental level, despite their restricted clinical use. In a One-Health approach, this capability enables valuable environmental monitoring, considering the significant role of colistin in the context of public health.

BACKGROUND: Flight can drastically enhance dispersal capacity and is a key trait defining the potential of exotic insect species to spread and invade new habitats. The phytophagous European spongy moths (ESM, Lymantria dispar dispar) and Asian spongy moths (ASM; a multi-species group represented here by L. d. asiatica and L. d. japonica), are globally invasive species that vary in adult female flight capability-female ASM are typically flight capable, whereas female ESM are typically flightless. Genetic markers of flight capability would supply a powerful tool for flight profiling of these species at any intercepted life stage. To assess the functional complexity of spongy moth flight and to identify potential markers of flight capability, we used multiple genetic approaches aimed at capturing complementary signals of putative flight-relevant genetic divergence between ESM and ASM: reduced representation genome-wide association studies, whole genome sequence comparisons, and developmental transcriptomics. We then judged the candidacy of flight-associated genes through functional analyses aimed at addressing the proximate demands of flight and salient features of the ecological context of spongy moth flight evolution.
RESULTS: Candidate gene sets were typically non-overlapping across different genetic approaches, with only nine gene annotations shared between any pair of approaches. We detected an array of flight-relevant functional themes across gene sets that collectively suggest divergence in flight capability between European and Asian spongy moth lineages has coincided with evolutionary differentiation in multiple aspects of flight development, execution, and surrounding life history. Overall, our results indicate that spongy moth flight evolution has shaped or been influenced by a large and functionally broad network of traits.
CONCLUSIONS: Our study identified a suite of flight-associated genes in spongy moths suited to exploration of the genetic architecture and evolution of flight, or validation for flight profiling purposes. This work illustrates how complementary genetic approaches combined with phenotypically targeted functional analyses can help to characterize genetically complex traits.

The unexpected foodborne outbreak in Singapore in 2015 has accentuated Group B Streptococcus (GBS, Streptococcus agalactiae) sequence type 283 as an emerging foodborne pathogen transmitted via the consumption of contaminated raw freshwater fish. Isolation-based workflows utilizing conventional microbiological and whole-genome sequencing methods are commonly used to support biosurveillance efforts critical for the control management of this emerging foodborne pathogen. However, these isolation-based workflows tend to have relatively long turnaround times that hamper a timely response for implementing risk mitigation. To address this gap, we have developed a metagenomics-based workflow for the simultaneous detection and genomic characterization of GBS in raw freshwater fish. Notably, our validation results showed that this metagenomics-based workflow could achieve comparable accuracy and potentially better detection limits while halving the turnaround time (from 2 weeks to 5 days) relative to an isolation-based workflow. The metagenomics-based workflow was also successfully adapted for use on a portable long-read nanopore sequencer, demonstrating its potential applicability for real-time point-of-need testing. Using GBS in freshwater fish as an example, this work represents a proof-of-concept study that supports the feasibility and validity of metagenomics as a rapid and accurate test methodology for the detection and genomic characterization of foodborne pathogens in complex food matrices.
OBJECTIVE: The need for a rapid and accurate food microbiological testing method is apparent for a timely and effective foodborne outbreak response. This is particularly relevant for emerging foodborne pathogens such as Group B Streptococcus (GBS) whose associated food safety risk might be undercharacterized. By using GBS in raw freshwater fish as a case example, this study describes the development of a metagenomics-based workflow for rapid food microbiological safety testing and surveillance. This study can inform as a working model for various foodborne pathogens in other complex food matrices, paving the way for future methodological development of metagenomics for food microbiological safety testing.

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RAND Europe was commissioned by the Novo Nordisk Foundation to conduct a study on pathogen surveillance and current initiatives. The study aims to provide an overview of the pathogen surveillance space internationally and the stakeholders involved, as well as to understand the strengths and weaknesses of different initiatives, the challenges of pathogen surveillance and how they have been addressed, and how data has been used to inform public health decision making. To do this, a scoping review of pathogen surveillance initiatives was conducted, and ten case studies were developed and selected for further review following a workshop attended by the Novo Nordisk Foundation and RAND Europe study team. Interviews were conducted with individuals involved in pathogen surveillance initiatives to gather additional information to develop case studies, and expert interviews addressed gaps in the pathogen surveillance space and models that would be helpful in filling these gaps.

BACKGROUND: Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records.
OBJECTIVE: This study sought to validate and test an artificial intelligence (AI)-based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak.
METHODS: Subjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children\'s hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement (F1-score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F1-score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras.
RESULTS: There were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms (F1-score=0.796) than ICD-10 codes (F1-score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F1-score=0.828 and ICD-10: F1-score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras.
CONCLUSIONS: This study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.

Analysis of environmental DNA (eDNA) enables indirect detection of species without the need to directly observe and sample them. For biosecurity and invasion biology, eDNA-based methods are useful to address biological invasions at all phases, from detecting arrivals to confirming eradication of past invasions. We conducted a systematic review of the literature and found that in biosecurity and invasion biology, eDNA has primarily been used to detect new incursions and monitor spread in marine and freshwater ecosystems, with much slower uptake in terrestrial ecosystems, reflecting a broader trend common to the usage of eDNA tools. In terrestrial ecosystems, eDNA research has mostly focussed on the use of eDNA metabarcoding to characterise biodiversity, rather than targeting biosecurity threats or non-native populations. We discuss how eDNA-based methods are being applied to terrestrial ecosystems for biosecurity and managing non-native populations at each phase of the invasion continuum: transport, introduction, establishment, and spread; across different management options: containment, control, and eradication; and for detecting the impact of non-native organisms. Finally, we address some of the current technical issues and caveats of eDNA-based methods, particularly for terrestrial ecosystems, and how these might be solved. As eDNA-based methods improve, they will play an increasingly important role in the early detection and adaptive management of biological invasions, and the implementation of effective biosecurity controls.

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Detecting and monitoring populations of the invasive emerald ash borer (EAB) is crucial to successful management of the pest and evaluation of its ecological impacts. However, the beetle\'s cryptic habit makes accurate monitoring costly and time-consuming. Biosurveillance takes advantage of the foraging effort of a predatory wasp Cerceris fumipennis (Hymenoptera: Crabronidae). This native, solitary, ground-nesting hunting wasp hunts adult buprestid beetles to provision its brood cells. By intercepting the hunting wasps, we can learn which species of buprestids are in the surrounding forest. The resulting data provides information on the presence and relative abundance of invasive buprestids like EAB which can supplement other monitoring efforts. In this paper we share results of ten years of biosurveillance surveys of the EAB in Connecticut. Among 112 sites, we observed EAB populations; from first detection, through the population peak and then through to the population crash, matching patterns observed in other regions of the United States. We also observed the spread of the EAB relative abundance as it moved through the state following an invasion front starting in New Haven, Co. The average time from first detection to population crash was nine years. On average, populations peaked three years after first detection, and remained at peak levels for three to four years. Population decline was gradual and took another three to four years. Notably, no evidence of a second introduction to Connecticut was seen with proportional abundance increasing over time after expanding outward from the introduction point. These results corroborate other traditional monitoring efforts in the eastern U.S. and provide independent validation of predicted population dynamics in ash stands.

The spotted lanternfly, Lycorma delicatula (Hemiptera: Fulgoridae), is an invasive planthopper from Asia that is estimated to have spread 17 km/yr since it\'s initial detection in Pennsylvania in 2014. Lycorma delicatula is a pest to the agricultural and forestry industries in the Mid-Atlantic region of the United States, in part due to its highly polyphagous nature. Current detection relies on visual observations, unbaited traps, or eDNA surveillance in its primary hosts, including grape and hardwoods. These approaches narrow the surveillance area by concentrating on known host plants but could be further refined to narrow the search parameters from the 100+ known host plants. Because L. delicatula appears to have a strong population buildup in wooded areas, we evaluated the relationship between egg mass presence and habitat characteristics in wooded habitats adjacent to vineyards in New Jersey at six farms within the first two years of L. delicatula detection. Habitat characteristics included distance from wood edge, and presence of a critical host plant Ailanthus altissima, and presence of Vitis spp. within 4.5 m. We identified a significant relationship between egg mass presence and Vitis spp. with an 88% probability of finding an egg mass close to a wild grapevine, dropping to 9% where grapes were absent. During the early invasion stages when this research was conducted, a two-year delay from initial detection in wooded habitats to nymphal presence in the vineyard was observed.