Search Results (95)

This study evaluated the diffusion of Listeria monocytogenes (LM) in a beef steak tartare production chain, aiming to (1) evaluate Listeria spp. diffusion in finishing farms supplying beef cattle, (2) evaluate LM prevalence in carcasses, and (3) map LM diffusion in the production plant. A detection rate of 6/76 was observed in the farm, while carcasses after skinning and before refrigeration tested positive in 19/30 and 11/30, respectively. During tartare production, 57/154 meat and 35/191 environmental samples tested positive. A total of 114 LM isolates were characterized via a whole-genome sequencing approach. Five clonal complexes (CCs) and seven sequence types (STs) were identified, with CC9-ST580 being the most prevalent. Four clusters were identified from both the slaughtering and production phases. Genes related to resistance to fosfomycin, quinolones, sulfonamides, lincosamide, and tetracycline were detected. Two hypervirulent strains (CC6-ST6 and CC2-ST145), harboring a full-length inlA, several virulence genes, and stress islands, were detected. Stress Survival Islet 1 was found in almost all the isolates. The wide diffusion of LM in steak tartare requires the management of some critical phases of the production chain (mainly slaughtering); genomic methodologies could be useful in describing the circulation and virulence of LM strains. Full article

Listeria monocytogenes (L. monocytogenes) is an important foodborne pathogen that poses great risks to food safety and public health, and knowledge about its presence and diversity in potential sources is crucial for effectively tracking and controlling it in the food chain. In this study, we investigated the prevalence, antimicrobial susceptibility, and genomic characteristics of Listeria monocytogenes (L. monocytogenes) collected from retail chicken meat samples in Qingdao, China, in 2022. A total of 38 (10.6%, 38/360) L. monocytogenes isolates were recovered from 360 retail chickens. All 38 isolates were classified into two lineages (I and II), three serogroups (IIa, IIb, IIc), eight sequence types (STs), eight clonal complexes (CCs), eight Sublineages (SLs) and nine cgMLSTs (CTs). ST121 and ST9 were the most prevalent STs in this study. The ST121 strains from China had heterogeneity with those from other countries, while the Chinese ST9 strains had homogeneity with those from other countries. One resistance cassette tet(M)-entS-msr(D) was identified in eight L2-SL121-ST121-CT13265 isolates, the genetic structure of which was identical to that of three reference genomes. All isolates carried the L. monocytogenes pathogenic island (LIPI)-1, with only one carrying LIPI-3 and three carrying LIPI-4. In addition, 11 isolates subtyped as L2-SL121-ST121-CT13265 were found to have a premature stop codon (PMSC) in the inlA gene in this study. Our data revealed the antimicrobial susceptibility, genomic characteristics and evolutionary relationships of L. monocytogenes in retail chicken in Qingdao, China. The characterization of genotypes, virulence, stress and antimicrobial markers of strains circulating in retail chicken in Qingdao, as described in this study, provides the opportunity to improve risk assessments of L. monocytogenes exposure. Full article

Listeria monocytogenes is a Gram-positive bacterial species that causes listeriosis, a major foodborne disease worldwide. The virulence factor inlA facilitates the invasion of L. monocytogenes into intestinal epithelial cells expressing E-cadherin receptors. Naturally occurring premature stop codon (PMSC) mutations in inlA have been shown to result in the production of truncated proteins associated with attenuated virulence. Moreover, different L. monocytogenes strains contain distinct inlA variants. In this study, we first characterized inlA in 546 L. monocytogenes strains isolated from various foods in Shanghai. The results showed that 36.1% (95% Confidence Interval: 32.0~40.2%) of the food isolates harbored inlA with PMSC, which was found to be associated with clonal complex (CC) types, with the highest proportions observed in CC9 and CC121. To investigate the function of inlA, we first used the dominant CC87 isolated from patients as the test strain and constructed an inlA-deleted strain via homologous recombination. Resistance tests and virulence tests showed that while inlA did not affect the resistance of L. monocytogenes, it significantly influenced cell adhesion and invasiveness. To further explore the function of inlA, we performed virulence tests on five CC-type strains carrying inlA with PMSC and their corresponding strains with intact inlA. We found that the virulence of L. monocytogenes strains carrying inlA or inlA with PMSC was associated with their CC type. Our preliminary results showed that premature termination of inlA did not significantly affect the adhesion and invasion abilities of low-virulence CC-type L. monocytogenes strains in Caco-2 cells, but substantially promoted those of high-virulence strains such as CC8 and CC7. In summary, this study preliminarily evaluated the effects of inlA integrity and PMSC mutation variation on the virulence of L. monocytogenes, providing a foundation for further research on inlA-related pathogenic mechanisms. Full article

Listeria monocytogenes poses health threats due to its resilience and potential to cause severe infections, especially in vulnerable populations. Plant extracts and/or phytocomplexes have demonstrated the capability of natural compounds in mitigating L. monocytogenes virulence. Here we explored the suitability of a computational pipeline envisioned to identify the molecular determinants for the recognition between the bacterial protein internalin A (InlA) and the human E-cadherin (Ecad), which is the first step leading to internalization. This pipeline consists of molecular docking and extended atomistic molecular dynamics simulations to identify key interaction clusters between InlA and Ecad. It exploits this information in the screening of chemical libraries of natural compounds that might competitively interact with InIA and hence impede the formation of the InIA–Ecad complex. This strategy was effective in providing a molecular model for the anti-adhesive activity of punicalagin and disclosed two natural phenolic compounds with a similar interaction pattern. Besides elucidating key aspects of the mutual recognition between InIA and Ecad, this study provides a molecular basis about the mechanistic underpinnings of the anti-adhesive action of punicalagin that enable application against L. monocytogenes. Full article

Listeria monocytogenes causes listeriosis, a severe foodborne disease with high mortality. Contamination with it poses significant risks to food safety and public health. Notably, genetic characteristic differences exist between strains causing human infections and those found in routine food inspections. This study examined the genotypic factors influencing the pathogenicity of L. monocytogenes, focusing on virulence gene profiles and key integrity genes like inlA to explain these divergences. The dataset included 958 strains isolated from human, food, and environmental samples. Whole-genome sequencing identified virulence genes, and principal component analysis (PCA) examined 92 virulence genes and inlA integrity to uncover potentially pathogenic patterns. The results highlight differences in virulence characteristics between strains of different origins. The integrity of inlA and genes such as inlD, inlG, and inlL were pivotal to pathogenicity. Strains with premature stop codons (PMSCs) in inlA, associated with reduced virulence, accounted for a low percentage of human cases but over 30% of food isolates. Sequence types (STs) like ST121, ST580, and ST199 showed unique profiles, while ST9, dominant in food, occasionally caused human cases, posing risks to vulnerable individuals. This research highlights the complexity of the pathogenicity of L. monocytogenes and emphasizes the importance of genomic surveillance for effective risk assessment. Full article

HIV remains a major public health challenge in sub-Saharan Africa, with South Africa bearing the highest burden. This study confirms that KwaZulu-Natal (KZN) is a hotspot, with a high HIV prevalence of 47.4% (95% CI: 45.7–49.1) among females aged 15–34. We investigated the spatial distribution and key socio-demographic, behavioural, and economic factors associated with HIV prevalence in this group using a Bayesian spatial logistic regression model. Secondary data from 3324 females in the HIV Incidence Provincial Surveillance System (HIPSS) (2014–2015) in uMgungundlovu District, KZN, were analysed. Bayesian spatial models fitted using the Integrated Nested Laplace Approximation (INLA) identified key predictors and spatial clusters of HIV prevalence. The results showed that age, education, marital status, income, alcohol use, condom use, and number of sexual partners significantly influenced HIV prevalence. Older age groups (20–34 years), alcohol use, multiple partners, and STI/TB diagnosis increased HIV risk, while tertiary education and condom use were protective. Two HIV hotspots were identified, with one near Greater Edendale being statistically significant. The findings highlight the need for targeted, context-specific interventions to reduce HIV transmission among young females in KZN. Full article

Listeria monocytogenes is one of the most important foodborne pathogens that can cause invasive listeriosis. In this study, the virulence levels of 26 strains of L. monocytogenes isolated from food and clinical samples in Shanghai, China, between 2020 and 2022 were analyzed. There were significant differences among isolates in terms of their mortality rate in Galleria mellonella, cytotoxicity to JEG-3 cells, hemolytic activity, and expression of important virulence genes. Compared with other STs, both the ST121 (food source) and ST1930 (clinic source) strains exhibited higher G. mellonella mortality. The 48 h mortality in G. mellonella of lineage II strains was significantly higher than that in lineage I. Compared with other STs, ST1930, ST3, ST5, and ST1032 exhibited higher cytotoxicity to JEG-3 cells. Based on the classification of sources (food and clinical strains) and serogroups (II a, II b, and II c), there were no significant differences observed in terms of G. mellonella mortality, cytotoxicity, and hemolytic activity. In addition, ST121 exhibited significantly higher hly, inlA, inlB, prfA, plcA, and plcB gene expression compared with other STs. A gray relation analysis showed a high correlation between the toxicity of G. mellonella and the expression of the hly and inlB genes; in addition, L. monocytogenes may have a consistent virulence mechanism involving hemolysis activity and cytotoxicity. Through the integration of in vivo and in vitro infection models with information on the expression of virulence factor genes, the differences in virulence between strains or subtypes can be better understood. Full article

Background: Drug-resistant tuberculosis (DR-TB) remains a major public health challenge in China, with varying treatment outcomes across different regions. Understanding the spatial distribution of DR-TB treatment outcomes is crucial for targeted interventions to improve treatment success in high-burden areas such as Hunan Province. This study aimed to map the spatial distribution of DR-TB treatment outcomes at a local level and identify sociodemographic and environmental factors associated with poor treatment outcomes in Hunan Province, China. Methods: A spatial analysis was conducted using DR-TB data from the Tuberculosis Control Institute of Hunan Province, covering the years 2013 to 2018. The outcome variable, the proportion of poor treatment outcomes, was defined as a composite measure of treatment failure, death, and loss to follow-up. Sociodemographic, economic, healthcare, and environmental variables were obtained from various sources, including the WorldClim database, the Malaria Atlas Project, and the Hunan Bureau of Statistics. These covariates were linked to a map of Hunan Province and DR-TB notification data using R software version 4.4.0. The spatial clustering of poor treatment outcomes was analyzed using the local Moran’s I and Getis–Ord statistics. A Bayesian logistic regression model was fitted, with the posterior parameters estimated using integrated nested Laplace approximation (INLA). Results: In total, 1381 DR-TB patients were included in the analysis. An overall upward trend in poor DR-TB treatment outcomes was observed, peaking at 14.75% in 2018. Deaths and treatment failures fluctuated over the years, with a notable increase in deaths from 2016 to 2018, while the proportion of patients lost to follow-up significantly declined from 2014 to 2018. The overall proportion of poor treatment outcomes was 9.99% (95% credible interval (CI): 8.46% to 11.70%), with substantial spatial clustering, particularly in Anxiang (50%), Anren (50%), and Chaling (42.86%) counties. The proportion of city-level indicators was significantly associated with higher proportions of poor treatment outcomes (odds ratio (OR): 1.011; 95% CRI: 1.20 December 2024 001–1.035). Conclusions: This study found a concerning increase in poor DR-TB treatment outcomes in Hunan Province, particularly in certain high-risk areas. Targeted public health interventions, including enhanced surveillance, focused healthcare initiatives, and treatment programs, are essential to improve treatment success. Full article

We explore the application of integrated nested Laplace approximations for the Bayesian estimation of stochastic volatility models characterized by long memory. The logarithmic variance persistence in these models is represented by a Fractional Gaussian Noise process, which we approximate as a linear combination of independent first-order autoregressive processes, lending itself to a Gaussian Markov Random Field representation. Our results from Monte Carlo experiments indicate that this approach exhibits small sample properties akin to those of Markov Chain Monte Carlo estimators. Additionally, it offers the advantages of reduced computational complexity and the mitigation of posterior convergence issues. We employ this methodology to estimate volatility dependency patterns for both the SP&500 index and major cryptocurrencies. We thoroughly assess the in-sample fit and extend our analysis to the construction of out-of-sample forecasts. Furthermore, we propose multi-factor extensions and apply this method to estimate volatility measurements from high-frequency data, underscoring its exceptional computational efficiency. Our simulation results demonstrate that the INLA methodology achieves comparable accuracy to traditional MCMC methods for estimating latent parameters and volatilities in LMSV models. The proposed model extensions show strong in-sample fit and out-of-sample forecast performance, highlighting the versatility of the INLA approach. This method is particularly advantageous in high-frequency contexts, where the computational demands of traditional posterior simulations are often prohibitive. Full article

Traffic crashes have become one of the key public health issues, triggering significant apprehension among citizens and urban authorities. However, prior studies have often been limited by their inability to fully capture the dynamic and complex nature of spatiotemporal instability in urban traffic crashes, typically focusing on static or purely spatial effects. Addressing this gap, our study employs a novel methodological framework that integrates an Integrated Nested Laplace Approximation (INLA)-based Stochastic Partial Differential Equation (SPDE) model with spatially adaptive graph structures, which enables the effective handling of vast and intricate geospatial data while accounting for spatiotemporal instability. This approach represents a significant advancement over conventional models, which often fail to account for the fluid interplay between time-varying weather conditions, geographical attributes, and crash severity. We applied this methodology to analyze traffic crashes across three major U.S. cities—New York, Los Angeles, and Houston—using comprehensive crash data from 2016 to 2019. Our findings reveal city-specific disparities in the factors influencing severe traffic crashes, which are defined as incidents resulting in at least one person sustaining serious injury or death. Despite some universal trends, such as the risk-enhancing effect of cold weather and pedestrian crossings, we find marked differences across cities in relation to factors like temperature, precipitation, and the presence of certain traffic facilities. Additionally, the adjustment observed in the spatiotemporal standard deviations, with values such as 0.85 for New York and 0.471 for Los Angeles, underscores the varying levels of annual temporal instability across cities, indicating that the fluctuation in crash severity factors over time differs markedly among cities. These results underscore the limitations of traditional modeling approaches, demonstrating the superiority of our spatiotemporal method in capturing the heterogeneity of urban traffic crashes. This work has important policy implications, suggesting a need for tailored, location-specific strategies to improve traffic safety, thereby aiding authorities in better resource allocation and strategic planning. Full article

Heart failure is a major global health concern, especially in Ethiopia. Numerous studies have analyzed heart failure data to inform decision-making, but these often struggle with limitations to accurately capture death dynamics and account for within-cluster dependence and heterogeneity. Addressing these limitations, this study aims to incorporate dependence and analyze heart failure data to estimate survival time and identify risk factors affecting patient survival. The data, obtained from 497 patients at Jimma University Medical Center in Ethiopia were collected between July 2015 and January 2019. Residence was considered as the clustering factor in the analysis. We employed the Bayesian accelerated failure time (AFT), and Bayesian AFT shared gamma frailty models, comparing their performance using the Deviance Information Criterion (DIC) and Watanabe–Akaike Information Criterion (WAIC). The Bayesian log-normal AFT shared gamma frailty model had the lowest DIC and WAIC, with well-capturing cluster dependency that was attributed to unobserved heterogeneity between patient residences. Unlike other methods that use Markov-Chain Monte-Carlo (MCMC), we applied the Integrated Nested Laplace Approximation (INLA) to reduce computational load. The study found that 39.44% of patients died, while 60.56% were censored, with a median survival time of 34 months. Another interesting finding of this study is that adding frailty into the Bayesian AFT models boosted the performance in fitting the heart failure dataset. Significant factors reducing survival time included age, chronic kidney disease, heart failure history, diabetes, heart failure etiology, hypertension, anemia, smoking, and heart failure stage. Full article

Accurate forecasting of high-resolution particulate matter 2.5 (PM_2.5) levels is essential for the development of public health policy. However, datasets used for this purpose often contain missing observations. This study presents a two-stage approach to handle this problem. The first stage is a multivariate spatial time series (MSTS) model, used to generate forecasts for the sampled spatial units and to impute missing observations. The MSTS model utilizes the similarities between the temporal patterns of the time series of the spatial units to impute the missing data across space. The second stage is the high-resolution prediction model, which generates predictions that cover the entire study domain. The second stage faces the big N problem giving rise to complex memory and computational problems. As a solution to the big N problem, we propose a Gaussian Markov random field (GMRF) for innovations with the Matérn covariance matrix obtained from the corresponding Gaussian field (GF) matrix by means of the stochastic partial differential equation (SPDE) method and the finite element method (FEM). For inference, we propose Bayesian statistics and integrated nested Laplace approximation (INLA) in the R-INLA package. The above approach is demonstrated using daily data collected from 13 PM_2.5 monitoring stations in Jakarta Province, Indonesia, for 1 January–31 December 2022. The first stage of the model generates PM_2.5 forecasts for the 13 monitoring stations for the period 1–31 January 2023, imputing missing data by means of the MSTS model. To capture temporal trends in the PM_2.5 concentrations, the model applies a first-order autoregressive process and a seasonal process. The second stage involves creating a high-resolution map for the period 1–31 January 2023, for sampled and non-sampled spatiotemporal units. It uses the MSTS-generated PM_2.5 predictions for the sampled spatiotemporal units and observations of the covariate’s altitude, population density, and rainfall for sampled and non-samples spatiotemporal units. For the spatially correlated random effects, we apply a first-order random walk process. The validation of out-of-sample forecasts indicates a strong model fit with low mean squared error (0.001), mean absolute error (0.037), and mean absolute percentage error (0.041), and a high R² value (0.855). The analysis reveals that altitude and precipitation negatively impact PM_2.5 concentrations, while population density has a positive effect. Specifically, a one-meter increase in altitude is linked to a 7.8% decrease in PM_2.5, while a one-person increase in population density leads to a 7.0% rise in PM_2.5. Additionally, a one-millimeter increase in rainfall corresponds to a 3.9% decrease in PM_2.5. The paper makes a valuable contribution to the field of forecasting high-resolution PM_2.5 levels, which is essential for providing detailed, accurate information for public health policy. The approach presents a new and innovative method for addressing the problem of missing data and high-resolution forecasting. Full article

Listeriosis is one of the most serious foodborne diseases under surveillance, with an overall mortality rate in the EU currently being high at 18.1%. Therefore, this study aims to investigate Listeria monocytogenes strains isolated from clinical and food samples for susceptibility to antimicrobials, presence of virulence factors, and genetic diversity. Species were identified using the MALDI-TOF, resistance to 11 antibiotics was determined according to EUCAST guidelines, and multiplex PCR was used for serotyping and detecting virulence genes. Strains were genotyped using the PFGE method. Clinical strains showed full sensitivity to all tested antibiotics. In total, 33.3% of strains from food products were found to be resistant to ciprofloxacin and 4.2% to tetracycline. Most of the tested isolates (79.2%) belonged to serotype 1/2a-3a, and the rest (20.8%) belonged to serotype 4ab-4b,4d-4e. Five virulence genes (prfA, hlyA, plcB, inlA, and lmo2672) were detected in all strains studied. The llsX gene was the least common, in 37.5% of clinical strains and 18.75% of strains isolated from food products. Among the analyzed strains, 13 strains displayed unique PFGE profiles. The other 11 strains belong to 3 clusters of pulsotypes: cluster 1 (2 strains), cluster 2 (6 strains), and cluster 3 (2 strains). The percentage of hospitalizations and deaths of Polish patients with listeriosis indicates the seriousness of this disease, especially in an aging society, while the molecular testing of clinical strains has been rarely performed, which makes it difficult to determine the source of infection. Full article

Listeria monocytogenes is an important pathogen responsible for listeriosis, a serious foodborne illness associated with high mortality rates. Therefore, L. monocytogenes is considered a challenge for the food industry due to the ability of some strains to persist in food-associated environments. Biofilm production is presumed to contribute to increased L. monocytogenes resistance and persistence. The aims of this study were to (1) assess the biofilm formation of L. monocytogenes isolates from a meat processing facility and sheep farm previously characterized and subjected to whole-genome sequencing and (2) perform a comparative genomic analysis to compare the biofilm formation and the presence of a known set of biofilm-associated genes and related resistance or persistence markers. Among the 37 L. monocytogenes isolates of 15 sequence types and four serogroups involved in this study, 14%, 62%, and 24% resulted in the formation of weak, moderate, and strong biofilm, respectively. Increased biofilm-forming ability was associated with the presence of the stress survival islet 1 (SSI-1), inlL, and the truncated inlA genes. Combining the phenotypic and genotypic data may contribute to understanding the relationships between biofilm-associated genes and L. monocytogenes biofilm-forming ability, enabling improvement in the control of this foodborne pathogen. Full article

Our study examines how dengue fever incidence is associated with spatial (demographic and socioeconomic) alongside temporal (environmental) factors at multiple scales in the city of Ibagué, located in the Andean region of Colombia. We used the dengue incidence in Ibagué from 2013 to 2018 to examine the associations with climate, socioeconomic, and demographic factors from the national census and satellite imagery at four levels of local spatial aggregation. We used geographically weighted regression (GWR) to identify the relevant socioeconomic and demographic predictors, and we then integrated them with environmental variables into hierarchical models using integrated nested Laplace approximation (INLA) to analyze the spatio-temporal interactions. Our findings show a significant effect of spatial variables across the different levels of aggregation, including human population density, gas and sewage connection, percentage of woman and children, and percentage of population with a higher education degree. Lagged temporal variables displayed consistent patterns across all levels of spatial aggregation, with higher temperatures and lower precipitation at short lags showing an increase in the relative risk (RR). A comparative evaluation of the models at different levels of aggregation revealed that, while higher aggregation levels often yield a better overall model fit, finer levels offer more detailed insights into the localized impacts of socioeconomic and demographic variables on dengue incidence. Our results underscore the importance of considering macro and micro-level factors in epidemiological modeling, and they highlight the potential for targeted public health interventions based on localized risk factor analyses. Notably, the intermediate levels emerged as the most informative, thereby balancing spatial heterogeneity and case distribution density, as well as providing a robust framework for understanding the spatial determinants of dengue. Full article