Background:Candidozyma auris, first recognized in 2009, has emerged as a pathogen of major global concern, particularly in association with health care-associated infections in long-term care facilities. Methods:In a study of infected and colonized patients conducted between 1/17/2023 and 11/30/2023 at our medical center in North-Central Florida, whole-genome sequence data were obtained for 43 C. auris isolates from 36 patients. Results:Thirteen (30%) of the 43 isolates were from cultures collected as part of an investigation of a possible infection, with blood (7 isolates) being the most common source; 8 (62%) of the 13 patients with clinical infections died. Isolates were within either a Clade I monophyletic subclade associated with European and Middle Eastern strains (n = 27) or were from Florida subclades within C. auris Clade III (n = 16). In 7 instances, multiple isolates with virtually identical genetic profiles were isolated from the same patient at time intervals that ranged from 3 weeks to 7 months, with, in some instances, intervening negative cultures. All isolates were resistant to triazoles, albeit with resistance mutations at different nucleotide positions and within different genes for Clade I and Clade III isolates. One Clade I isolate was resistant to echinocandins. Conclusions:Data are consistent with a point-source C. auris outbreak involving a Clade I subclade of possible European origin, combined with multiple introductions and transmission of Clade III isolates from Florida. Strains were able to persist for extended periods of time in colonized/infected patients.

Hospital outbreak identification often lacks standardization and uses time-intensive surveillance, potentially delaying intervention against hospital-acquired pathogens. We combined space-time permutation analysis with machine learning to statistically identify hospital-onset Pseudomonas aeruginosa outbreaks and contributing etiologies. We retrospectively analyzed hospital-onset P. aeruginosa isolates (2016-2024) using electronic health records (EHR) at a single tertiary care center. Hospital-onset infections were defined as cultures collected more than two days after admission. Susceptibility profiles were standardized by imputing intrinsic resistance and classifying extensive (XDR) or multidrug-resistance (MDR). Space-time permutation analysis (modified WHONET-SatScan) identified resistance-based clusters, validated by whole-genome sequencing (WGS). For each cluster, we conducted case-control studies comparing identified cases with non-case P. aeruginosa during the same period. Feature selection for temporally scaled risk factors was performed using elastic net regularization. Among 19,055 hospitalizations (11,112 patients) with P. aeruginosa, 6,557 (34.4%) were hospital-onset. Hospital-onset isolates were 1.9 (95% CI: 1.8–2.1) times more likely to be MDR and 2.2 (95% CI: 1.9–2.6) times more likely to be XDR compared to community-onset. Of 615 unique resistance profiles identified, 53% were susceptible to all antipseudomonal classes. Resistance to antipseudomonal cephalosporins was most common (30.2%). Space-time permutation analysis detected 12 unique clusters, none matching WGS-identified clusters. One cluster defined by cephalosporin resistance (n=17) was further examined. Final elastic net model identified 32 features associated with cluster membership compared to controls (n=213), including open excision 5–9 days prior (OR: 28.1, p< 0.001) and skin graft 2 days prior (OR: 45.2, p< 0.001). We demonstrate the use of EHRs to statistically detect related hospital-onset P. aeruginosa and suggest biologically plausible etiologies. Resistance-based models did not align with clusters confirmed by WGS. Future research will establish causal links and extend to other hospital-acquired pathogens. Kathryn DeSear, PharmD, Abbvie: Advisor/Consultant|Biomerieux: Advisor/Consultant|Cormedix: Speaking|GSK: Advisor/Consultant|Shionogi: Speaking

Nurses represent the largest segment of the United States healthcare workforce and played an instrumental role in the country’s response to the COVID-19 pandemic. Yet, little attention has been given to the contribution of this component of the U.S. medical personnel in the nation’s ability to face public health crisis. We present a cross-sectional, ecological analysis using cumulative annual reports from different national databases to assess the relationship between registered nurse (RN) density at a state level and age-adjusted COVID-19 mortality within the state, using data from 2021 when mortality rates were peaking in the U.S. At the state level, an increase of 1,000 RNs per 100,000 people, was associated with an estimated 24 to 44 fewer COVID-19 deaths per 100,000 residents (B= -0.024, [[EQUATION]]= -0.146, 95% CI: -0.044 to -0.003, p = .024). In this multivariate analysis including medical co-morbidities, vaccination, health insurance, and poverty level, RN density explained nearly 11% of the variability in COVID-19 mortality among states. Our findings underscore the critical role played by nurses in responding to the COVID-19 pandemic, and the importance of incorporating nursing workforce data into planning for future public health emergencies. ### Competing Interest Statement The authors declare no competing interests. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used ONLY openly available human data, information regarding data sources can be found here: https://github.com/lrigan2025/Data\_and\_Script.git I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online at [https://github.com/lrigan2025/Data\_and\_Script.git][1] [1]: https://github.com/lrigan2025/Data_and_Script.git

Mosquito-borne viruses pose a significant global health challenge, particularly in resource-limited settings where multiple viruses often cause illnesses with similar symptoms that require different treatment. We introduce the first 7-plex reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay in a hand-held device capable of detecting the presence of Chikungunya virus (CHIKV), dengue virus serotypes (DENV 1-4), Mayaro virus (MAYV), and Zika virus (ZIKV) in a single test. If the result is positive from the single-plex device for the 7-plex assay, 3-plex and 4-plex devices are then used to identify the exact virus within a specimen. In-situ detection is achieved by integrating valve-enabled, paper-based sample preparation with fluorescence detection using a blue LED flashlight as a light source and a yellow plastic film as a filter, allowing visual discrimination between positive and negative samples by the naked eye or by recording images using a smart phone. The detection limits ranged from 2 genome equivalents (GE)/reaction (for ZIKV) to 92 GE/reaction (for DENV-3) across 7 types of viruses when 1 μL of viral RNA was used. We observed 90% overall agreement between the point-of-care (POC) device and lab-based reverse transcription polymerase chain reactions (RT-PCR) when blinded clinical specimens were tested. This assay and device have a potential to address critical surveillance gaps in endemic regions, enabling timely detection of multiple mosquito-borne viruses to guide appropriate clinical management and public health countermeasures in settings where laboratory resources are scarce.

Bird flu is caused by the H5N1 subtype of influenza A virus. This publication aims to help consumers better understand bird flu and ways bird flu may impact consumable products, such as eggs or milk. Written by A. McLeod-Morin, B. D. Anderson, G. Morris, C. Sanders, C. Larson, R. Telg, and A. Henson, and published by the UF/IFAS Department of Animal Sciences, September 2025.

A day care teacher presented with complaints of headache, neck stiffness, and fever. Because of initial concerns about meningococcal meningitis, families of day care attendees were notified, and 10 children from the day care presented for evaluation. Cerebrospinal fluid from the teacher and nasal swabs from 4 children who were febrile were positive for enterovirus on reverse transcription polymerase chain reaction. A novel recombinant enterovirus was cultured from the teacher's cerebrospinal fluid and from 2 of the nasal swabs. The amino-terminal portion of the recombinant virus was derived from echovirus 6, with the carboxy-terminal portion originating from coxsackievirus B1; recombinant segments were most closely related to similar segments from strains isolated in France. Recombination occurred within the C2 gene associated with virus replication and virion morphogenesis. Structural modeling predicted that the recombinant protein was capable of forming hexameric and heptameric assemblies. Our data highlight the potential for recombination among enteroviruses, leading to modifications within viral proteins that may affect virulence.

Background Widespread usage of glyphosate has resulted in contamination of water, air, and food products. The objective of this study was to examine the prevalence and magnitude of environmental exposure to glyphosate among subjects from the general population, as well as assess the potential risk of renal toxicity associated with these exposures. Methods Concentrations of glyphosate and its environmental breakdown product aminomethylphosphonic acid (AMPA) were measured in the urine of 168 random, de-identified subjects using liquid chromatograph-tandem mass spectrometry (LC-MS/MS) to determine the frequency of detection. Glyphosate doses producing the observed glyphosate in urine were estimated for comparison with regulatory safe exposure limits. Forty-eight of these samples were further screened for biomarkers of renal injury, with urinary extracellular vesicles prepared from a subset of 12 of these samples for miRNA analysis. Results Glyphosate was detected in the urine of 40% of all subjects with a maximum concentration of 25.3 µg/L, and AMPA was detected in 6.5% of all subjects with a maximum concentration of 0.86 µg/L. Estimated glyphosate doses corresponding to urinary levels were 1–2 orders of magnitude below U.S. and European exposure limits. There was no relationship between glyphosate or glyphosate plus AMPA concentrations in urine and any of 21 urinary biomarkers of renal injury, and no concentration related increase in urinary extracellular vesicular miRNAs previously associated with a high acute glyphosate exposure in humans. Conclusions Exposure to glyphosate, as reflected in urinary glyphosate levels, is common among members of the general public in the United States. Observed levels were below established exposure limits, and our data did not document adverse effects of glyphosate on the kidneys at doses at which the general population is exposed. However, more work is needed in subjects with known chronic glyphosate exposure before potential effects from prevalent low doses can be ruled out.

Interpretation of seroepidemiology studies of cholera relies on knowledge of antibody kinetics, which are not well known in African populations. We performed vibriocidal antibody assays on 212 serum samples from 115 patients with culture-positive cholera (median age, 8 years) in Goma, Democratic Republic of Congo, which were collected at enrollment and 3 to 449 days after. Vibriocidal responses peaked at 7 to 40 days after symptom onset, with 89.5% waning to a titer ≤160 by 180 days. We used a bayesian exponential decay model to show an 88% probability of the posterior distribution supporting a faster decay in children ≤5 years of age.

In this paper we describe the spatial data challenges faced in terms of providing accurate and timely analysis for a clinic during a cholera epidemic that spread through Port au Prince, Haiti in late 2022. This "triage" spatial epidemiology involved developing a bespoke geocoder that allowed for weekly maps of spread to be created in near real time. Resulting case data were also analyzed using a novel grid heatmapping approach which considers the epidemiological curve for each neighborhood. Adding further complexity during this period to both the data generation, and explaining cholera amplification and spread patterns, was a rising gang presence in the Port au Prince neighborhoods. Results identify a coastal pattern of amplification, which is expected given the informal settlement style living environments found in many of these neighborhoods. A second pattern then emerges of spread along a western and southern axis, which is far better captured in the grid heat mapping approach because of the lower numbers of patients seeking care at the clinic. The combination of traditional cartography and grid heat mapping help reveal the overall pattern of the epidemic, while also identifying key neighborhoods that require additional epidemiological investigation. Knowing why these neighborhoods played such an important role, possibly due to specific gang activity, is important in terms of understanding future disease spread in and around Port au Prince. Indeed, results presented can help contextualize official cholera reporting in 2025 where data availability is still hampered by ongoing gang rule.

In 2021, we screened 91 children in Haiti with acute undifferentiated febrile illness for arbovirus infections. We identified a major outbreak of dengue virus type 2, with 67% of the children testing positive. Two others were positive for chikungunya East/Central/South African IIa subclade, and 2 were positive for Zika virus.

Background:Although methicillin-resistant Staphylococcus aureus (MRSA) transmission has traditionally been viewed separately in hospital and community settings, this distinction is increasingly blurred. We used whole-genome sequencing and epidemiologic analyses to characterize the movement of MRSA across these interfaces in a rural-urban population. Methods:Serial cross-sectional sampling of MRSA isolates occurred at a tertiary care hospital between 2010 and 2019. Community-onset MRSA was prospectively isolated from patients presenting to the emergency department with acute skin and soft tissue infections (SSTIs), while hospital-onset MRSA was sampled before (2010), during (2015-2017), and after (2019) this community collection period. MRSA transmission was assessed using a joint application of epidemiological approaches and phylodynamic analysis of whole-genome sequences. Results:After whole-genome sequencing on community and hospital MRSA isolates, phylogenetic analysis revealed 2 major clades distinguished by clonal complex (CC) CC8/t008 and CC5/t002 spa types. Multiple independent introductions of MRSA lineages from the community to the hospital were observed. Geographic clustering of community-onset MRSA was uniquely present outside of the urban center. Subjects with rural residence or livestock exposure were more likely to have community-onset MRSA SSTI compared with those with non-MRSA SSTI. Conclusions:MRSA transmission in hospital settings was introduced from strains with ancestral origins in community settings. Although community-onset MRSA transmission appears sustained with limited influence from hospital strains, more comprehensive surveillance is required to quantify this relationship. Nosocomial MRSA outbreak prevention strategies should target unique aspects of the community in addition to the hospital, particularly hot spots, risk behaviors, and strain reservoirs.

Cholera remains a major and increasing global public health problem for all people without adequate access to safe water. Goma, in the eastern Democratic Republic of Congo (DRC), has been a major cholera hotspot in Africa since 1994 and is currently experiencing one of the largest outbreaks in the world. This article contributes to the existing scholarship on cholera risk by utilizing a variety of qualitative research methods. Goma offers several advantages to a study of cholera as a city on the shores of Kivu Lake, but where the majority of population does not have access to clean water and experiences recurrent cholera epidemic outbreaks. Two local members of our research team are experts in public health and conducted all the interviews in Swahili and French. They also led transect walks and a participatory mapping workshop. Data were collected between 2021 and 2022 in six areas of Goma. Data were analysed using a qualitative software Open code 4.03 to generate codes for a thematic purpose. Our results show that the lack of water infrastructure was the main issue with cholera risk in Goma as it prompted use of unsafe drinking water from Lake Kivu, the small Lake vert and Mubambiro River. Additionally, there were specific social groups with an increased risk based on age and gender, health status, some occupational risks, and socio-economic status. Cholera risks were framed in relation to broader life-threatening events, such as natural disasters, that occurred in the city. Cholera risk was also ascribed to challenges with care seeking and treatment, and issues with implementation of prevention strategies. Finally, the lack of empowerment of local communities in cholera prevention measures was considered a secondary source of risk due to the emphasis on the public health outreach practices and short-term emergency responses. This work broadens our understanding of factors that contribute to cholera risk in Goma. These factors should be addressed by implementing diverse strategies that involve the affected communities rather than focusing on rapid public health outreach response interventions. In addition, the, development and the maintenance of a safe and reliable water infrastructures in the city is essential to reduce the chronic nature of cholera infection in the city of Goma.

Abstract Background Antimicrobial resistance (AMR) is a major cause of treatment failure in hospital-onset sepsis. Approaches to guide empiric antimicrobial therapy are urgently needed. We utilized machine learning to predict empiric treatment failure and AMR patterns within a high-risk hospital-onset sepsis cohort using electronic health records (EHRs). Methods We examined hospitalizations with documented bacterial infection from 2010 to 2023 at a tertiary-care academic hospital system. Hospital-onset sepsis was defined using CDC Adult Sepsis Event criteria. Empiric therapy was considered adequate if all pathogens isolated were susceptible to antimicrobials administered. AMR patterns included methicillin (MRSA), extended-spectrum beta-lactamase (ESBL), vancomycin (VRE), ceftriaxone, and carbapenem (CRE). Features for prediction were patient characteristics, hospitalization details, and clinical severity metrics measured within 24 hours prior to sepsis onset. Linear and non-linear machine learning models were evaluated using bootstrap validation and out-of-bag area under the receiver operating characteristic curve (AUC). Results Among 49,581 hospitalizations, 2019 (4%) hospital-onset sepsis encounters were identified. Mean (SD) age was 65 (16.4) years and 900 (45%) were female. At 24 hours after sepsis onset, 377 (19%) received inadequate empiric treatment and 911 (45%) infections expressed AMR. Those who received inadequate empiric treatment were 8.2 (95%CI: 6.2-10.9) times more likely to have an AMR infection. Random forest model was best-performing, with AUC (95%CI) for any AMR pattern, VRE, and CRE at 0.64 (0.63-0.64), 0.71 (0.69-0.72), and 0.70 (0.67-0.73), respectively. The AUC (95%CI) for predicting inadequate empiric antimicrobial treatment was 0.65 (0.64-0.66) with a sensitivity of 64% (58-69%) and specificity of 52% (46-68%) at optimal probability thresholds. Conclusion In a hospital-onset sepsis cohort with prevalent AMR, machine learning approaches utilizing limited EHR data accessible at initial sepsis recognition had moderate, but clinically insufficient, discriminatory ability in predicting AMR and empiric antimicrobial treatment failure. Future research will evaluate the utilization of large language models using unstructured EHRs. Disclosures All Authors: No reported disclosures

Bird flu is caused by the H5N1 subtype of influenza A virus. Among migratory birds and poultry, this virus can be highly contagious and cause severe disease and death. Biosecurity and proper hygiene significantly reduce this risk. This publication aims to help owners of backyard flocks protect their birds and identify possible signs of influenza H5N1 infection. Written by A. McLeod-Morin, B. D. Anderson, G. Morris, C. Sanders, C. Larson, R. Telg, and A. Henson, and published by the UF/IFAS Department of Animal Sciences, September 2025.

BackgroundThe creation of relief camps following a disaster, conflict or other form of externality often generates additional health problems. The density of people in a highly stressed environment with questionable safe food and water access presents the potential for infectious disease outbreaks. These camps are also not static data events but rather fluctuate in size, composition, and level and quality of service provision. While contextualized geospatial data collection and mapping are vital for understanding the nature of these camps, various challenges, including a lack of data at the required spatial or temporal granularity, as well as the issue of sustainability, can act as major impediments. Here, we present the first steps toward a deep learning-based solution for dynamic mapping using spatial video (SV).MethodsWe trained a convolutional neural network (CNN) model on a SV dataset collected from Goma, Democratic Republic of Congo (DRC) to identify relief camps from video imagery. We developed a spatial filtering approach to tackle the challenges associated with spatially tagging objects such as the accuracy of global positioning system and positioning of camera. The spatial filtering approach generates smooth surfaces of detection, which can further be used to capture changes in microenvironments by applying techniques such as raster math.ResultsThe initial results suggest that our model can detect temporary physical dwellings from SV imagery with a high level of precision, recall, and object localization. The spatial filtering approach helps to identify areas with higher concentrations of camps and the web-based tool helps to explore these areas. The longitudinal analysis based on applying raster math on the detection surfaces revealed locations, which had a considerable change in the distribution of tents over space and time.ConclusionsThe results lay the groundwork for automated mapping of spatial features from imagery data. We anticipate that this work is the building block for a future combination of SV, object identification and automatic mapping that could provide sustainable data generation possibilities for challenging environments such as relief camps or other informal settlements.

We monitored severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants in Haiti from 2020 to 2023. Despite Haitian coronavirus disease 2019 (COVID-19) travel restrictions and in the setting of a vaccination rate of 2.7%, the timing and lineage evolution of the Haiti epidemic mirrored what was occurring in the rest of the world. Sources for importation of lineages into Haiti were the United States, the Dominican Republic, Europe, and Brazil, with exportation of lineages to the United States, the Dominican Republic, Europe, and Asia. Viral loads in patients infected by the Delta and Omicron BA.1 variants were correlated along the phylogenies, suggesting that higher viral loads have facilitated strain transmission and evolution.

ABSTRACTA daycare teacher presented to a local Emergency Department (ED) with complaints of headache, neck stiffness, and fever. Preliminary analysis of cerebral spinal fluid (CSF) raised concerns about meningococcal meningitis and prompted notification of the county health department, which then notified the daycare. Ten children presented to a University referral hospital for evaluation, four of whom were febrile. CSF from the teacher and nasal swabs from the febrile children were all RT-PCR positive for enterovirus. A novel recombinant enterovirus was cultured from the teacher’s CSF and from two of the nasal swabs. The amino-terminal portion of the recombinant virus was derived from an Echovirus E6 with the carboxy-terminal portion originating from a Coxsackievirus B1; recombinant segments were most closely related to similar segments from strains isolated in France. Recombination occurred within the C-2 gene which encodes a multifunctional protein that functions as an RNA-stimulated ATPase associated with virus replication and virion morphogenesis. Structural modeling predicted that the recombinant protein was capable of forming hexameric and heptameric assemblies. Our data highlight the ongoing potential for recombination among enteroviruses groups, leading to modifications within viral proteins which may impact virulence.Brief Article SummaryAfter a daycare teacher was diagnosed with meningitis, ten children were referred by the county health department to a University Hospital for evaluation. Cerebral Spinal Fluid (CSF) from the teacher and nasal swabs from symptomatic children were positive for a novel recombinant enterovirus, with the amino-terminal portion of the virus derived from Echovirus E6 and the carboxy-terminal portion originating from a Coxsackievirus B1.

A novel jeilongvirus was identified through next-generation sequencing in cell cultures inoculated with spleen and kidney extracts. The spleen and kidney were obtained from a Peromyscus gossypinus rodent (cotton mouse) found dead in the city of Gainesville, in North-Central Florida, USA. Jeilongviruses are paramyxoviruses of the subfamily Orthoparamyxovirinae that have been found in bats, cats, and rodents. We designated the virus we discovered as Gainesville rodent jeilong virus 1 (GRJV1). Preliminary results indicate that GRJV1 can complete its life cycle in various human, non-human primate, and rodent cell lines, suggesting that the virus has a generalist nature with the potential for a spillover event. The early detection of endemic viruses circulating within hosts in North-Central Florida can significantly enhance surveillance efforts, thereby bolstering our ability to monitor and respond to potential outbreaks effectively.

We screened 65 longitudinally collected nasal swab samples from 31 children aged 0-16 years who were positive for severe acute respiratory syndrome coronavirus 2 Omicron BA.1. By day 7 after onset of symptoms, 48% of children remained positive by rapid antigen test. In a sample subset, we found 100% correlation between antigen test results and virus culture.