Impact of Wildfires on Cardiovascular Health.

Wildfire smoke (WFS) is a mixture of respirable particulate matter, environmental gases, and other hazardous pollutants that originate from the unplanned burning of arid vegetation during wildfires. The increasing size and frequency of recent wildfires has escalated public and occupational health concerns regarding WFS inhalation, by either individuals living nearby and downstream an active fire or wildland firefighters and other workers that face unavoidable exposure because of their profession. In this review, we first synthesize current evidence from environmental, controlled, and interventional human exposure studies, to highlight positive associations between WFS inhalation and cardiovascular morbidity and mortality. Motivated by these findings, we discuss preventative measures and suggest interventions to mitigate the cardiovascular impact of wildfires. We then review animal and cell exposure studies to call attention on the pathophysiological processes that support the deterioration of cardiovascular tissues and organs in response to WFS inhalation. Acknowledging the challenges of integrating evidence across independent sources, we contextualize laboratory-scale exposure approaches according to the biological processes that they model and offer suggestions for ensuring relevance to the human condition. Noting that wildfires are significant contributors to ambient air pollution, we compare the biological responses triggered by WFS to those of other harmful pollutants. We also review evidence for how WFS inhalation may trigger mechanisms that have been proposed as mediators of adverse cardiovascular effects upon exposure to air pollution. We finally conclude by highlighting research areas that demand further consideration. Overall, we aspire for this work to serve as a catalyst for regulatory initiatives to mitigate the adverse cardiovascular effects of WFS inhalation in the community and alleviate the occupational risk in wildland firefighters.

Disentangling the causes of mumps reemergence in the United States.

Over the past two decades, multiple countries with high vaccine coverage have experienced resurgent outbreaks of mumps. Worryingly, in these countries, a high proportion of cases have been among those who have completed the recommended vaccination schedule, raising alarm about the effectiveness of existing vaccines. Two putative mechanisms of vaccine failure have been proposed as driving observed trends: 1) gradual waning of vaccine-derived immunity (necessitating additional booster doses) and 2) the introduction of novel viral genotypes capable of evading vaccinal immunity. Focusing on the United States, we conduct statistical likelihood-based hypothesis testing using a mechanistic transmission model on age-structured epidemiological, demographic, and vaccine uptake time series data. We find that the data are most consistent with the waning hypothesis and estimate that 32.8% (32%, 33.5%) of individuals lose vaccine-derived immunity by age 18 y. Furthermore, we show using our transmission model how waning vaccine immunity reproduces qualitative and quantitatively consistent features of epidemiological data, namely 1) the shift in mumps incidence toward older individuals, 2) the recent recurrence of mumps outbreaks, and 3) the high proportion of mumps cases among previously vaccinated individuals.

Dynamics in a behavioral-epidemiological model for individual adherence to a nonpharmaceutical intervention.

The SARS-CoV-2 pandemic has highlighted the importance of behavioral drivers in epidemic dynamics. With the relaxation of mandated nonpharmaceutical interventions (NPIs) formerly in place to decrease transmission, such as mask-wearing or social distancing, adherence to an NPI is now the result of individual decision-making. To study these coupled dynamics, we embed a game-theoretic model for individual NPI adherence within an epidemiological model. When the disease is endemic, we find that our model has multiple (but none concurrently stable) equilibria: one each with zero, complete, or partial NPI adherence. Surprisingly, for the equilibrium with partial NPI adherence, the number of infections is independent of the transmission rate. Therefore, in that regime, a change in the rate of pathogen transmission, e.g., due to another (mandated) NPI or a new variant, has no effect on endemic infection levels. On the other hand, we show that vaccination successfully decreases endemic infection levels, and, unexpectedly, also reduces the number of susceptibles at equilibrium when there is partial adherence. From a game-theoretic perspective, we find that highly effective NPIs lead at most to partial adherence. As this effectiveness decreases, partially effective NPIs initially lead to increases in population-level adherence, especially if the risk is high enough. However, a completely ineffective NPI results in no adherence. Furthermore, we identify parameter regions where the individual incentives may not align with those of society as a whole. Overall, our findings illustrate complexities that can arise due to behavioral-epidemiological feedback and suggest appropriate measures to avoid more pessimistic population-level outcomes.

Inferring the disruption of rabies circulation in vampire bat populations using a betaherpesvirus-vectored transmissible vaccine.

Transmissible vaccines are an emerging biotechnology that hold prospects to eliminate pathogens from wildlife populations. Such vaccines would genetically modify naturally occurring, nonpathogenic viruses ("viral vectors") to express pathogen antigens while retaining their capacity to transmit. The epidemiology of candidate viral vectors within the target wildlife population has been notoriously challenging to resolve but underpins the selection of effective vectors prior to major investments in vaccine development. Here, we used spatiotemporally replicated deep sequencing to parameterize competing epidemiological mechanistic models of Desmodus rotundus betaherpesvirus (DrBHV), a proposed vector for a transmissible vaccine targeting vampire bat-transmitted rabies. Using 36 strain- and location-specific time series of prevalence collected over 6 y, we found that lifelong infections with cycles of latency and reactivation, combined with a high R0 (6.9; CI: 4.39 to 7.85), are necessary to explain patterns of DrBHV infection observed in wild bats. These epidemiological properties suggest that DrBHV may be suited to vector a lifelong, self-boosting, and transmissible vaccine. Simulations showed that inoculating a single bat with a DrBHV-vectored rabies vaccine could immunize >80% of a bat population, reducing the size, frequency, and duration of rabies outbreaks by 50 to 95%. Gradual loss of infectious vaccine from vaccinated individuals is expected but can be countered by inoculating larger but practically achievable proportions of bat populations. Parameterizing epidemiological models using accessible genomic data brings transmissible vaccines one step closer to implementation.

Diagnostic Potential of Oscillometry: A Population-based Approach.

Rationale: Respiratory resistance (Rrs) and reactance (Xrs) as measured by oscillometry and their intrabreath changes have emerged as sensitive parameters for detecting early pathological impairments during tidal breathing. Objectives: This study evaluates the prevalence and association of abnormal oscillometry parameters with respiratory symptoms and respiratory diseases in a general adult population. Methods: A total of 7,560 subjects in the Austrian LEAD (Lung, hEart, sociAl, boDy) Study with oscillometry measurements (computed with the Resmon Pro FULL; Restech Srl) were included in this study. The presence of respiratory symptoms and doctor-diagnosed respiratory diseases was assessed using an interview-based questionnaire. Rrs and Xrs at 5 Hz, their inspiratory and expiratory components, the area above the Xrs curve, and the presence of tidal expiratory flow limitation were analyzed. Normality ranges for oscillometry parameters were defined. Measurements and Main Results: The overall prevalence of abnormal oscillometry parameters was 20%. The incidence of abnormal oscillometry increased in the presence of symptoms or diagnoses: 17% (16-18%) versus 27% (25-29%), P < 0.0001. All abnormal oscillometry parameters except Rrs at 5 Hz were significantly associated with respiratory symptoms/diseases. Significant associations were found, even in subjects with normal spirometry, with abnormal oscillometry incidence rates increasing by 6% (4-8%; P < 0.0001) in subjects with symptoms or diagnoses. Conclusions: Abnormal oscillometry parameters are present in one-fifth of this adult population and are significantly associated with respiratory symptoms and disease. Our findings underscore the potential of oscillometry as a tool for detecting and evaluating respiratory impairments, even in individuals with normal spirometry.

Modeling for COVID-19 college reopening decisions: Cornell, a case study.

We consider epidemiological modeling for the design of COVID-19 interventions in university populations, which have seen significant outbreaks during the pandemic. A central challenge is sensitivity of predictions to input parameters coupled with uncertainty about these parameters. Nearly 2 y into the pandemic, parameter uncertainty remains because of changes in vaccination efficacy, viral variants, and mask mandates, and because universities' unique characteristics hinder translation from the general population: a high fraction of young people, who have higher rates of asymptomatic infection and social contact, as well as an enhanced ability to implement behavioral and testing interventions. We describe an epidemiological model that formed the basis for Cornell University's decision to reopen for in-person instruction in fall 2020 and supported the design of an asymptomatic screening program instituted concurrently to prevent viral spread. We demonstrate how the structure of these decisions allowed risk to be minimized despite parameter uncertainty leading to an inability to make accurate point estimates and how this generalizes to other university settings. We find that once-per-week asymptomatic screening of vaccinated undergraduate students provides substantial value against the Delta variant, even if all students are vaccinated, and that more targeted testing of the most social vaccinated students provides further value.

Real-time pandemic surveillance using hospital admissions and mobility data.

Forecasting the burden of COVID-19 has been impeded by limitations in data, with case reporting biased by testing practices, death counts lagging far behind infections, and hospital census reflecting time-varying patient access, admission criteria, and demographics. Here, we show that hospital admissions coupled with mobility data can reliably predict severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission rates and healthcare demand. Using a forecasting model that has guided mitigation policies in Austin, TX, we estimate that the local reproduction number had an initial 7-d average of 5.8 (95% credible interval [CrI]: 3.6 to 7.9) and reached a low of 0.65 (95% CrI: 0.52 to 0.77) after the summer 2020 surge. Estimated case detection rates ranged from 17.2% (95% CrI: 11.8 to 22.1%) at the outset to a high of 70% (95% CrI: 64 to 80%) in January 2021, and infection prevalence remained above 0.1% between April 2020 and March 1, 2021, peaking at 0.8% (0.7-0.9%) in early January 2021. As precautionary behaviors increased safety in public spaces, the relationship between mobility and transmission weakened. We estimate that mobility-associated transmission was 62% (95% CrI: 52 to 68%) lower in February 2021 compared to March 2020. In a retrospective comparison, the 95% CrIs of our 1, 2, and 3 wk ahead forecasts contained 93.6%, 89.9%, and 87.7% of reported data, respectively. Developed by a task force including scientists, public health officials, policy makers, and hospital executives, this model can reliably project COVID-19 healthcare needs in US cities.

Infectious and clinical tuberculosis trajectories: Bayesian modeling with case finding implications.

The importance of finding people with undiagnosed tuberculosis (TB) hinges on their future disease trajectories. Assays for systematic screening should be optimized to find those whose TB will contribute most to future transmission or morbidity. In this study, we constructed a mathematical model that tracks the future trajectories of individuals with TB at a cross-sectional timepoint ("baseline"), classifying them by bacterial burden (smear positive/negative) and symptom status (symptomatic/subclinical). We used Bayesian methods to calibrate this model to targets derived from historical survival data and notification, mortality, and prevalence data from five countries. We combined resulting disease trajectories with evidence on infectiousness to estimate each baseline TB state's contribution to future transmission. For a person with smear-negative subclinical TB at baseline, the expected future duration of disease was short (mean 4.8 [95% uncertainty interval 3.3 to 8.4] mo); nearly all disease courses ended in spontaneous resolution, not treatment. In contrast, people with baseline smear-positive subclinical TB had longer undiagnosed disease durations (15.9 [11.1 to 23.5] mo); nearly all eventually developed symptoms and ended in treatment or death. Despite accounting for only 11 to 19% of prevalent disease, smear-positive subclinical TB accounted for 35 to 51% of future transmission-a greater contribution than symptomatic or smear-negative TB. Subclinical TB with a high bacterial burden accounts for a disproportionate share of future transmission. Priority should be given to developing inexpensive, easy-to-use assays for screening both symptomatic and asymptomatic individuals at scale-akin to rapid antigen tests for other diseases-even if these assays lack the sensitivity to detect paucibacillary disease.

Wild-type distributions of minimum inhibitory concentrations and epidemiological cut-off values-laboratory and clinical utility.

The characterization of wild-type minimum inhibitory concentration (MIC) and zone diameter distributions with the setting of epidemiological cut-off values (ECOFFs or ECVs) provides a reference for the otherwise relative MIC values in the international system for antimicrobial susceptibility testing. Distributions of MIC values for a species and an agent follow a log-normal distribution, which in the absence of resistance mechanisms is monomodal and designated wild type (WT). The upper end of the WT distribution, the ECOFF, can be identified with statistical methods. In the presence of phenotypically detectable resistance, the distribution has at least one more mode (the non-WT), but despite this, the WT is most often identifiable using the same methods. The ECOFF provides the most sensitive measure of resistance development in a species against an agent. The WT and non-WT modes are independent of the organism´s response to treatment, but when the European Committee on Antimicrobial Susceptibility Testing (EUCAST) determines the clinical breakpoints, the committee avoids breakpoints that split WT distributions of target species. This is to avoid the poorer reproducibility of susceptibility categorization when breakpoints split major populations but also because the EUCAST has failed to identify different clinical outcomes for isolates with different MIC values inside the wild-type distribution. In laboratory practice, the ECOFF is used to screen for and exclude resistance and allows the comparison of resistance between systems with different breakpoints from different breakpoint organizations, breakpoints evolving over time, and different breakpoints between human and animal medicine. The EUCAST actively encourages colleagues to question MIC distributions as presented on the website (https://www.eucast.org/mic_and_zone_distributions_and_ecoffs) and to contribute MIC and inhibition zone diameter data.

Tensor product algorithms for inference of contact network from epidemiological data.

We consider a problem of inferring contact network from nodal states observed during an epidemiological process. In a black-box Bayesian optimisation framework this problem reduces to a discrete likelihood optimisation over the set of possible networks. The cardinality of this set grows combinatorially with the number of network nodes, which makes this optimisation computationally challenging. For each network, its likelihood is the probability for the observed data to appear during the evolution of the epidemiological process on this network. This probability can be very small, particularly if the network is significantly different from the ground truth network, from which the observed data actually appear. A commonly used stochastic simulation algorithm struggles to recover rare events and hence to estimate small probabilities and likelihoods. In this paper we replace the stochastic simulation with solving the chemical master equation for the probabilities of all network states. Since this equation also suffers from the curse of dimensionality, we apply tensor train approximations to overcome it and enable fast and accurate computations. Numerical simulations demonstrate efficient black-box Bayesian inference of the network.

Epidemiological and clinical features of a large blastomycosis outbreak at a paper mill in Michigan.

BACKGROUND: Blastomycosis is an environmentally acquired fungal infection that can result in severe pulmonary illness and high hospitalization rates. In 2023, a blastomycosis outbreak was detected among workers at a paper mill in Delta County, Michigan. METHODS: We included patients with clinical and laboratory evidence of blastomycosis who had spent ≥40 hours in Delta County since September 1, 2022 and had illness onset December 1, 2022-July 1, 2023. We assessed epidemiological and clinical features of patients and evaluated factors associated with hospitalization. We performed whole-genome sequencing to characterize genetic relatedness of clinical isolates from eight patients. RESULTS: In total, 131 patients were identified; all had worked at or visited the mill. Sixteen patients (12%) were hospitalized; one died. Compared with non-hospitalized patients, more hospitalized patients had diabetes (p=0.03) and urine antigen titers above the lower limit of quantification (p<0.001). Hospitalized patients were also more likely to have had ≥1 healthcare visits before receiving a blastomycosis diagnostic test (p=0.02) and to have been treated with antibiotics prior to antifungal prescription (p=0.001). All sequenced isolates were identified as Blastomyces gilchristii and clustered into a distinct outbreak cluster. CONCLUSIONS: This was the largest documented blastomycosis outbreak in the United States. Epidemiologic evidence indicated exposures occurred at or near the mill, and genomic findings suggested a common exposure source. Patients with diabetes may have increased risk for hospitalization, and elevated urine antigen titers could indicate greater disease severity. Early suspicion of blastomycosis may prompt earlier diagnosis and treatment, potentially reducing unnecessary antibiotic prescriptions and improving patient outcomes.

Wastewater Surveillance for Poliovirus in Selected Jurisdictions, United States, 2022-2023.

Wastewater testing can inform public health action as a component of polio outbreak response. During 2022-2023, a total of 7 US jurisdictions (5 states and 2 cities) participated in prospective or retrospective testing of wastewater for poliovirus after a paralytic polio case was identified in New York state. Two distinct vaccine-derived poliovirus type 2 viruses were detected in wastewater from New York state and New York City during 2022, representing 2 separate importation events. Of those viruses, 1 resulted in persistent community transmission in multiple New York counties and 1 paralytic case. No poliovirus was detected in the other participating jurisdictions (Connecticut, New Jersey, Michigan, and Illinois and Chicago, IL). The value of routine wastewater surveillance for poliovirus apart from an outbreak is unclear. However, these results highlight the ongoing risk for poliovirus importations into the United States and the need to identify undervaccinated communities and increase vaccination coverage to prevent paralytic polio.

Wastewater Target Pathogens of Public Health Importance for Expanded Sampling, Houston, Texas, USA.

Building on the success of initiatives put forth during the COVID-19 pandemic response, US health officials are expanding wastewater surveillance programs to track other target pathogens and diseases of public health interest. The Houston Health Department in Houston, Texas, USA, conducted a hypothesis-generating study whereby infectious disease subject matter experts suggested potential targets. This study addressed 2 criteria recommended by the National Academies of Sciences, Engineering, and Medicine for selecting wastewater targets. Results can be used as a basis of a questionnaire for a future population-based study to recommend targets of highest priority to include for expanded wastewater sampling.

Prediagnostic whole blood cadmium and molybdenum associated with pancreatic cancer in an American cohort.

Environmental exposures such as cadmium might be contributing to the increasing incidence of pancreatic cancer. Few prospective studies have examined the association between trace elements and pancreatic ductal adenocarcinoma (PDAC). We conducted a nested case-control study in participants aged 55-74 years at baseline from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial cohort to examine the association between 12 trace elements measured in predignostic whole blood and PDAC. From May 1998 through December 2014, 318 incident PDAC cases were identified during follow-up to 16.7 years. Two controls (n = 636) alive when each case was diagnosed were selected and matched by age (+ 5 years), sex, calendar date of blood draw (2-month blocks), and race and ethnic group. We used multivariable adjusted conditional logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI). Cadmium and molybdenum were associated with PDAC [highest compared to lowest quintile: cadmium OR=1.81; 95% CI: 01.12, 2.95; P-trend = 0.03; molybdenum OR=0.50; 95% CI: 0.32, 0.80; P-trend = 0.02]. The inverse molybdenum association was only observed among ever smokers (OR=0.31, 95% CI: 0.17, 0.58, P-trend= 0.003, P-interaction=0.03) with no association in never smokers. Lead, arsenic, and other trace elements were not associated with PDAC. Our results support that increasing prediagnostic whole blood cadmium increases while molybdenum reduces PDAC risk.

Aspirin but not statins is inversely related to gastric cancer with a duration-risk effect: Results from the Stomach Cancer Pooling Project Consortium.

BACKGROUND: Aspirin and statins have been suggested to have potential chemopreventive effects against gastric cancer (GC), although the results of previous studies have been inconsistent. This study therefore aimed to investigate the association between the use of aspirin and statins and GC. METHODS: A pooled analysis of seven case-control studies within the Stomach Cancer Pooling Project, including 3220 cases and 9752 controls, was conducted. Two-stage modeling analyses were used to estimate the association between aspirin and statin use and GC after adjusting for potential confounders. RESULTS: The pooled odds ratio (OR) of GC for aspirin users versus nonusers was 0.72 (95% confidence interval [CI], 0.54-0.95). The protective effect of aspirin appeared stronger in individuals without a GC family history (OR, 0.60; 95% CI, 0.37-0.95), albeit with borderline heterogeneity between those with and without a family history (p = .064). The OR of GC decreased with increasing duration of aspirin use, with an OR of 0.41 (95% CI, 0.18-0.95) for durations of ≥15 years. An inverse, nonsignificant association with the risk of GC was observed for the use of statins alone (OR, 0.79; 95% CI, 0.52-1.18). CONCLUSIONS: These findings suggest that aspirin use, particularly long-term use, is associated with a reduced risk of GC, whereas a similar association was not observed with statins, possibly because of the low frequency of use.

Characterizing the polygenic overlap and shared loci between rheumatoid arthritis and cardiovascular diseases.

BACKGROUND: Despite substantial research revealing that patients with rheumatoid arthritis (RA) have excessive morbidity and mortality of cardiovascular disease (CVD), the mechanism underlying this association has not been fully known. This study aims to systematically investigate the phenotypic and genetic correlation between RA and CVD. METHODS: Based on UK Biobank, we conducted two cohort studies to evaluate the phenotypic relationships between RA and CVD, including atrial fibrillation (AF), coronary artery disease (CAD), heart failure (HF), and stroke. Next, we used linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and bivariate causal mixture model (MiXeR) methods to examine the genetic correlation and polygenic overlap between RA and CVD, using genome-wide association summary statistics. Furthermore, we explored specific shared genetic loci by conjunctional false discovery rate analysis and association analysis based on subsets. RESULTS: Compared with the general population, RA patients showed a higher incidence of CVD (hazard ratio [HR] = 1.21, 95% confidence interval [CI]: 1.15-1.28). We observed positive genetic correlations of RA with AF and stroke, and a mixture of negative and positive local genetic correlations underlying the global genetic correlation for CAD and HF, with 13 ~ 33% of shared genetic variants for these trait pairs. We further identified 23 pleiotropic loci associated with RA and at least one CVD, including one novel locus (rs7098414, TSPAN14, 10q23.1). Genes mapped to these shared loci were enriched in immune and inflammatory-related pathways, and modifiable risk factors, such as high diastolic blood pressure. CONCLUSIONS: This study revealed the shared genetic architecture of RA and CVD, which may facilitate drug target identification and improved clinical management.

Outbreak-associated Salmonella Baildon found in wastewater demonstrates how sewage monitoring can supplement traditional disease surveillance.

Non-typhoidal Salmonella is a common cause of gastroenteritis worldwide, but current non-typhoidal Salmonella surveillance is suboptimal. Here, we evaluated the utility of wastewater monitoring to enhance traditional surveillance for this foodborne pathogen. In June 2022, we tested raw sewage collected twice a week from two treatment plants in central Pennsylvania for non-typhoidal Salmonella and characterized isolates using whole-genome sequencing. We recovered 43 Salmonella isolates from wastewater samples, differentiated by genomic analysis into seven serovars: 16 Panama (37.2%), 9 Senftenberg (20.9%), 8 Baildon (18.6%), and 3 or fewer of four other serovars. We assessed genetic relatedness and epidemiologic links between these wastewater isolates with those from patients with salmonellosis. All S. Baildon isolates from wastewater were genetically similar to those associated with a known contemporaneous salmonellosis outbreak. S. Baildon from wastewater and 42 outbreak-related isolates in the national outbreak detection database had the same core genome multilocus sequence typing, and outbreak code differed by zero or one single polynucleotide polymorphism. One of the 42 outbreak-related isolates was obtained from a patient residing in the wastewater sample collection catchment area, which serves approximately 17000 people. S. Baildon is a rare serovar (reported in <1% cases nationally, over five years). Our study underscores the value of monitoring sewage from a defined population to supplement traditional surveillance methods for the evidence of Salmonella infections and to determine the extent of outbreaks.IMPORTANCEDuring the COVID-19 pandemic, monitoring for SARS-CoV-2 in wastewater was highly effective in identifying the variants of concern earlier than clinical surveillance methods. Here, we show that monitoring domestic sewage can also augment traditional reporting of foodborne illnesses to public health authorities. Our study detected multiple Salmonella enterica serovars in samples from two wastewater treatment plants in central Pennsylvania. Using whole-genome sequencing, we demonstrated that the isolates of variant S. Baildon clustered with those from a foodborne salmonellosis outbreak that occurred in a similar time frame. Cases were primarily from Pennsylvania, and one individual lived within the wastewater treatment catchment area. This study highlights the effectiveness of domestic sewage testing as a proactive public health strategy to track and respond to infectious disease outbreaks.

Hospital distribution, seasonality, time trends and antifungal susceptibility profiles of all Aspergillus species isolated from clinical samples from 2015 to 2022 in a tertiary care hospital.

BACKGROUND: Aspergillus species cause a variety of serious clinical conditions with increasing trend in antifungal resistance. The present study aimed at evaluating hospital epidemiology and antifungal susceptibility of all isolates recorded in our clinical database since its implementation. METHODS: Data on date of isolation, biological samples, patients' age and sex, clinical settings, and antifungal susceptibility tests for all Aspergillus spp. isolated from 2015 to 2022 were extracted from the clinical database. Score test for trend of odds, non-parametric Mann Kendall trend test and logistic regression analysis were used to analyze prevalence, incidence, and seasonality of Aspergillus spp. isolates. RESULTS: A total of 1126 Aspergillus spp. isolates were evaluated. A. fumigatus was the most prevalent (44.1%) followed by A. niger (22.3%), A. flavus (17.7%) and A. terreus (10.6%). A. niger prevalence increased over time in intensive care units (p-trend = 0.0051). Overall, 16 (1.5%) were not susceptible to one azole compound, and 108 (10.9%) to amphotericin B, with A. niger showing the highest percentage (21.9%). The risk of detecting A. fumigatus was higher in June, (OR = 2.14, 95% CI [1.16; 3.98] p = 0.016) and reduced during September (OR = 0.48, 95% CI [0.27; 0.87] p = 0.015) and October as compared to January (OR = 0.39, 95% CI [0.21; 0.70] p = 0.002. A. niger showed a reduced risk of isolation from all clinical samples in the month of June as compared to January (OR = 0.34, 95% CI [0.14; 0.79] p = 0.012). Seasonal trend for A. flavus showed a higher risk of detection in September (OR = 2.7, 95% CI [1.18; 6.18] p = 0.019), October (OR = 2.32, 95% CI [1.01; 5.35] p = 0.048) and November (OR = 2.42, 95% CI [1.01; 5.79] p = 0.047) as compared to January. CONCLUSIONS: This is the first study to analyze, at once, data regarding prevalence, time trends, seasonality, species distribution and antifungal susceptibility profiles of all Aspergillus spp. isolates over a 8-year period in a tertiary care center. Surprisingly no increase in azole resistance was observed over time.

Antimicrobial resistance and clonal relationships of Salmonella enterica Serovar Gallinarum biovar pullorum strains isolated in China based on whole genome sequencing.

BACKGROUND: Pullorum disease is a serious problem in many countries. Caused by Salmonella enterica serovar Gallinarum biovar Pullorum (S. Pullorum), it creates huge economic losses in the poultry industry. Although pullorum disease has been well-controlled in many developed countries, it is still a critical problem in developing countries. However, there is still a lack of information on S. Pullorum strains isolated from different regions and sources in China. The objective of this study was to supply the antimicrobial resistance patterns and clonal relationships of S. Pullorum from breeder chicken farms. METHODS: In this study, a total of 114 S. Pullorum strains recovered from 11 provinces and municipalities in China between 2020 and 2021 were selected. These 114 S. Pullorum strains were analyzed using whole genome sequencing (WGS). Antimicrobial resistance (AMR) was tested both by genotypic prediction using the WGS method and using disc diffusion to assess phenotypic AMR. RESULTS: These 114 sequenced S. Pullorum strains were divided into three sequence types (STs), the dominant STs was ST92 (104/114). Further core genome multi-locus sequence typing analysis indicated that 114 S. Pullorum strains may have a close relationship, which could be clonally transmitted among different provinces and municipalities. Our results showed a close relationship between the S. Pullorum strains found in different regions, indicating these strains may have been transmitted in China a long time ago. Nearly all S. Pullorum strains 94.74% (n = 108) were resistant to at least one antimicrobial class, and 35.96% of the examined Salmonella strains were considered multiple drug resistant. CONCLUSION: Overall, this study showed that S. Pullorum strains in China have a close genetic relationship in terms of antimicrobial resistance, suggesting widespread clonal transmission.

Epidemiological characterization of hand, foot, and mouth disease among hospitalized children from 2014 to 2023 in a hospital in Henan Province: Longitudinal surveillance study.

Hand, foot, and mouth disease (HFMD) is an acute infectious illness primarily caused by enteroviruses. The present study aimed to describe the epidemiological characteristics of hospitalized HFMD patients in a hospital in Henan Province (Zhengzhou, China), and to predict the future epidemiological parameters. In this study, we conducted a retrospective analysis of general demographic and clinical data on hospitalized children who were diagnosed with HFMD from 2014 to 2023. We used wavelet analysis to determine the periodicity of the disease. We also conducted an analysis of the impact of the COVID-19 epidemic on the detection ratio of severe illness. Additionally, we employed a Seasonal Difference Autoregressive Moving Average (SARIMA) model to forecast characteristics of future newly hospitalized HFMD children. A total of 19 487 HFMD cases were included in the dataset. Among these cases, 1515 (7.8%) were classified as severe. The peak incidence of HFMD typically fell between May and July, exhibiting pronounced seasonality. The emergence of COVID-19 pandemic changed the ratio of severe illness. In addition, the best-fitted seasonal ARIMA model was identified as (2,0,2)(1,0,1)12. The incidence of severe cases decreased significantly following the introduction of the vaccine to the market (χ2 = 109.9, p < 0.05). The number of hospitalized HFMD cases in Henan Province exhibited a seasonal and declining trend from 2014 to 2023. Non-pharmacological interventions implemented during the COVID-19 pandemic have led to a reduction in the incidence of severe illness.