Search Results (1,062)

Listeria monocytogenes is a significant foodborne pathogen associated with high rates of hospitalization and death, especially among vulnerable populations. Despite established regulatory standards and available antimicrobial intervention strategies, L. monocytogenes remains as a pathogen of concern in ready-to-eat (RTE) foods. This ultimately can lead to food recalls or listeriosis outbreak, highlighting its ongoing risks to food safety and public health. This review consolidates publicly accessible surveillance case counts and recall data of L. monocytogenes contamination from Australia, Europe, Canada, and the United States to assess the contamination trends in the RTE food supply chain. It also evaluates the effectiveness of antimicrobial intervention strategies, including both those currently implemented in industry and those that have been studied as potential interventions but are not yet widely adopted. Key factors affecting the efficiency of those strategies are identified, including food matrix composition, water activity (a_w), fat content, and strain variability. Emerging multi-hurdle technology that integrates physical, chemical, and biological antimicrobial interventions are highlighted as promising approaches for maintaining both food safety and product quality. It also outlines the role of quantitative microbial risk assessment (QMRA) as a decision-support tool to select appropriate control strategies, predict recall risk and guide evidence-based risk management. Future research directions are proposed to expand the application of QMRA in managing recall risks throughout the RTE food supply chain due to L. monocytogenes. Full article

Background. West Nile Virus (WNV) represents a significant public health concern in Europe, with Italy—particularly its northern regions—experiencing recurrent outbreaks. Climate variables and vector dynamics are known to significantly influence transmission patterns, highlighting the need for reliable predictive models to enable timely outbreak detection and response. Methods. We integrated epidemiological data on human WNV infections in Italy (2012–2024) with high-resolution climate variables (temperature, humidity, and precipitation). Using advanced feature engineering and a gradient boosting framework (XGBoost), we developed a predictive model optimized through time-series cross-validation. Results. The model achieved high predictive accuracy at the national level (R² = 0.994, MAPE = 5.16%) and maintained robust performance across the five most affected provinces, with R² values ranging from 0.896 to 0.996. SHAP analysis identified minimum temperature as the most influential climate predictor, while maximum temperature and rainfall demonstrated considerably weaker associations with case incidence. Conclusions. This machine learning approach provides a reliable framework for forecasting WNV outbreaks and supports evidence-based public health responses. The integration of climate and epidemiological data enhances surveillance capabilities and enables informed decision-making at regional and local levels. Full article

Acinetobacter baumannii is a multidrug-resistant opportunistic pathogen that poses critical challenges in hospital settings due to its environmental resilience and high resistance to antibiotics. Genomic surveillance has become essential for identifying transmission patterns, guiding antimicrobial stewardship, and informing infection control policies. We conducted whole-genome sequencing on 44 A. baumannii isolates collected between 2022 and 2023 from diverse wards in an Italian hospital. Illumina-based sequencing was followed by a comprehensive bioinformatics pipeline, including genome assembly, taxonomic validation, MLST, SNP-based phylogeny, pan-genome analysis, antimicrobial resistance (AMR) gene profiling, and virulence factor prediction. Most isolates were classified as ST2; SAMPLE-34 was ST1 and genetically distinct. Phylogenetic analysis revealed four clonal clusters with cluster-specific AMR and accessory gene content. The pan-genome included 5050 genes, with notable variation linked to hospital ward origin. ICU and internal medicine strains carried higher loads of AMR genes, especially against aminoglycosides, β-lactams, and quinolones. Virulence profiling highlighted widespread immune evasion mechanisms; “Acenovactin” was predominant, while some isolates lacked key adhesion or toxin factors. Our findings underscore the clinical relevance of integrating genomic epidemiology into routine hospital surveillance. Identifying clonal clusters and resistance signatures supports real-time outbreak detection, risk stratification, and targeted infection prevention strategies. Full article

Despite the availability of effective vaccines, yellow fever outbreaks persist, highlighting the need for antiviral drugs. Background/Objectives: This study investigated Hippeastrum puniceum (Amaryllidaceae) as a potential source of antiviral compounds against wild-type yellow fever virus (wt-YFV). Methods/Results: The crude bulb extract of H. puniceum exhibited 58% protection against wt-YFV. Bioassay-guided fractionation of the extract by UHPLC-HRMS led to the annotation of six alkaloids (bulbisine, cathinone, trigonelline, tetrahydroharman-3-carboxylic acid, and 2,7-dimethoxyhomolycorine or 3-O-acetylnarcissidine) in active fractions, along with the amino acids arginine, asparagine, tryptophan, and glutamic acid. In silico ADMET analyses predicted favorable pharmacokinetic and toxicological profiles, supporting their potential as drug candidates. Six of the annotated compounds were evaluated in vitro for cytotoxicity and antiviral activity against wt-YFV. However, none showed significant antiviral activity when tested individually, suggesting that the observed antiviral effect may result from synergistic interactions between two or more compounds within active fractions. Conclusions: Our results underscore the importance of further investigations in vitro, particularly assays exploring the synergy among the annotated compounds against YFV. The integration of bioassay-guided fractionation of active plant extracts with computational analyses emerges as a promising strategy for the discovery of natural products with therapeutic potential against yellow fever, a reemerging disease. Full article

Climate change and the El Niño Southern Oscillation (ENSO) events have been increasingly linked to infectious disease outbreaks. While growing evidence has connected climate variability with systemic illnesses, the ocular implications remain underexplored. This study aimed to assess the relationships between ENSO-driven climate events and infectious diseases with ophthalmic consequences. A narrative review of 255 articles was conducted, focusing on infectious diseases influenced by ENSO and their associated ocular findings. 39 articles met criteria for full review, covering diseases such as dengue, zika, chikungunya, malaria, leishmaniasis, leptospirosis, and Rift Valley fever. Warmer temperatures, increased rainfall, and humidity associated with ENSO events were found to enhance vector activity and disease transmission. Ocular complications included uveitis, retinopathy, and optic neuropathy, but the specific disease findings varied by infectious disease syndrome. The climactic variable changes in response to ENSO events differed across diseases and regions and were influenced by geography, local infrastructure, and socioeconomic factors. ENSO event-related climate shifts significantly impact the spread of infectious diseases with ocular symptoms. These findings highlight the need for region-specific surveillance and predictive models that may provide insight related to the risk of ophthalmic disease during ENSO events. Further research is needed to clarify long-term ENSO effects and develop integrated strategies for systemic and eye disease detection, prevention, and management. Full article

Monkeypox (Mpox) has re-emerged as a global public health threat, with recent outbreaks linked to novel mutations that enhance viral transmissibility and immune evasion. The Mpox virus (MPXV), a double-stranded deoxyribonucleic acid (DNA) orthopoxvirus, shares high structural and enzymatic similarity with the variola virus, underscoring the need for urgent therapeutic interventions. While conventional antiviral development is time-intensive and costly, drug repurposing offers a rapid and cost-effective strategy by leveraging the established safety and pharmacological profiles of existing medications. This is a narrative integrative review synthesizing published evidence on drug repurposing strategies against MPXV. To address these issues, this review explores MPXV molecular targets critical for genome replication, transcription, and viral assembly, highlighting how the Food and Drug Administration (FDA)-approved antivirals (cidofovir, tecovirimat), antibiotics (minocycline, nitroxoline), antimalarials (atovaquone, mefloquine), immunomodulators (infliximab, adalimumab), and chemotherapeutics (doxorubicin) have demonstrated inhibitory activity against the virus using computational or experimental approaches. This review further evaluates advances in computational methodologies that have accelerated the identification of host-directed and viral-directed therapeutic candidates. Nonetheless, translational challenges persist, including pharmacokinetic limitations, toxicity concerns, and the limited efficacy of current antivirals such as tecovirimat in severe Mpox cases. Future research should integrate computational predictions with high-throughput screening, organ-on-chip technologies, and clinical pipelines, while using real-time genomic surveillance to track viral evolution. These strategies establish a scalable and sustainable framework for the MPXV drug discovery. Full article

Foot-and-mouth disease (FMD) remains a devastating threat to livestock health and food security in the Middle East and North Africa (MENA), where complex interactions among host, environmental, and anthropogenic factors constitute an optimal endemic landscape for virus circulation. Here, we applied an interpretable machine learning (ML) statistical framework to model the epidemiological landscape of FMD between 2005 and 2025. Furthermore, we compared the ecological niche of serotypes O and A in the MENA region. Our ML algorithms demonstrated high predictive performance (accuracies > 85%) in identifying the geographical extent of high-risk areas, including under-reported regions such as the Southern and Northeastern Arabian Peninsula. Sheep density emerged as the dominant predictor for all FMD outbreaks and serotype O, with significant non-linear relationships with wind, temperature, and human population density. In contrast, serotype A risk was primarily influenced by buffalo density and proximity to roads and cropland. Our in-depth interaction and Shapley value analyses provided fine-scale interpretability by interrogating the threshold effects of each feature in shaping the spatial risk of FMD. Further implementation of our analytical pipeline to guide risk-based surveillance programs and intervention efforts will help reduce the economic and public health impacts of this devastating animal pathogen. Full article

Artificial intelligence (AI), as an emerging interdisciplinary field dedicated to simulating and extending human intelligence, is increasingly integrating into the domain of infectious disease medicine with unprecedented depth and breadth. This narrative review is based on a systematic literature search in databases such as PubMed and Web of Science for relevant studies published between 2018 and 2025, with the aim of synthesizing the current landscape. It demonstrates transformative potential, particularly in the realm of diagnostic assistance. Confronting global challenges such as pandemic control, emerging infectious diseases, and antimicrobial resistance, AI technologies offer innovative solutions to these pressing issues. Leveraging its robust capabilities in data mining, pattern recognition, and predictive analytics, AI enhances diagnostic efficiency and accuracy, enables real-time monitoring, and facilitates the early detection and intervention of outbreaks. This narrative review systematically examines the application scenarios of AI within infectious disease diagnostics, based on an analysis of recent literature. It highlights significant technological advances and demonstrated practical outcomes related to high-throughput sequencing (HTS) for pathogen surveillance, AI-driven analysis of digital and radiological images, and AI-enhanced point-of-care testing (POCT). Simultaneously, the review critically analyzes the key challenges and limitations hindering the clinical translation of current AI-based diagnostic technologies. These obstacles include data scarcity and quality constraints, limitations in model generalizability, economic and administrative burdens, as well as regulatory and integration barriers. By synthesizing existing research findings and cataloging essential data resources, this review aims to establish a valuable reference framework to guide future in-depth research, from model development and data sourcing to clinical validation and standardization of AI-assisted infectious disease diagnostics. Full article

Background/Objectives: Influenza continues to cause high morbidity and mortality rates worldwide, inflicting a major burden on the public health system. There is little data available on the 2024–2025 seasonal outbreak. Moreover, biomarkers for rapidly identifying subjects at higher risk for severe forms are needed. Methods: We retrospectively collected hospitalization data for influenza in Cannes, France, during the 2024–2025 seasonal outbreak. Severe forms were defined as cases either requiring admission to the Intensive Care Unit (ICU) or resulting in death. They were compared to uncomplicated forms. Main demographic, clinical, radiological, and laboratory characteristics were collected for each patient. Results: From October 2024 to May 2025, 59 patients were admitted to either the Infectious Diseases Department or the ICU (56% male, age 72 years, 27% vaccinated, influenza type A 93%, symptom duration 3.5 days prior to hospitalization, 31% admissions to ICU, 14% deaths). Vaccination status did not differ between severe and uncomplicated forms. In the univariate analysis, severe forms had higher neutrophil/lymphocyte and platelet/lymphocyte ratios upon admission and included more cases of acute hepatitis, pneumonia, and oseltamivir use than uncomplicated forms. A neutrophil/lymphocyte ratio > 15 was independently associated with severity (OR_adj 8.79, 95% CI: 1.34–57.6, p = 0.023), with 40.9% sensitivity, 94.6% specificity, 81.8% positive predictive value, and 72.3% negative predictive value for predicting a severe form. Conclusions: The N/L ratio was an easy-to-perform predictive marker for influenza severity during the 2024–2025 seasonal outbreak, warranting further prospective studies Full article

Walnut (Juglans regia L.) is a significant nut crop in the southern regions of Kazakhstan; however, its productivity is substantially limited by fungal and bacterial diseases. Therefore, a phytopathological investigation was conducted in 2023–2024 in the Almaty and Turkestan regions, including field monitoring, pathogen isolation, molecular identification, and pathogenicity testing. Field monitoring revealed that symptoms of brown spot, walnut canker, walnut blight, bacterial blight, and crown gall were widespread. The overall disease incidence ranged from 8% to 30%, while the disease severity index varied from 15% to 70% across the surveyed sites. Pure cultures of pathogens were isolated from 69 samples, and their morphology was characterized. Molecular identification through sequencing of universal genetic loci (the internal transcribed spacer for fungi and 16S ribosomal RNA for bacteria) revealed the presence of the fungal species Alternaria alternata and Fusarium incarnatum, as well as the bacterial species Pantoea agglomerans and Xanthomonas arboricola pv. juglandis. Pathogenicity testing confirmed the virulence of the identified pathogens, which induced characteristic symptoms of brown spot, walnut canker, and walnut blight, consistent with those observed in the field. These findings have considerable practical significance for improving phytosanitary monitoring and protection systems in walnut plantations, thereby facilitating disease outbreak prediction and the development of effective quarantine measures. Full article

African swine fever (ASF) is a highly contagious viral disease with significant impacts on domestic pigs and wild boar populations. This study applies GIS-based spatial analysis to monitor ASF outbreaks in northwestern Italy (Piedmont and Liguria) and identify areas at increased risk. Key factors considered include pig density, wildlife proximity, and environmental conditions. The spatial analysis revealed that central–western municipalities exhibited higher risk due to favorable environmental conditions and dense wild boar populations, while peripheral areas showed a temporal delay in outbreak emergence. Mapping the spreading rate and habitat interfaces allowed the development of a spatial risk model, which was further analyzed using geostatistical techniques to understand disease dynamics. The results demonstrate the effectiveness of geospatial modeling in identifying high-risk zones, characterizing spatio-temporal patterns, and supporting targeted prevention and surveillance strategies. These findings provide actionable insights for ASF management and resource allocation. Future studies may refine these models by integrating additional datasets and environmental variables, enhancing predictive capacity and applicability across different regions. Full article

Stomoxys calcitrans (Linnaeus, 1758) is a hematophagous fly species of veterinary importance, known for its negative effects on animal health and productivity. The stress caused by their painful bites results in losses in milk and meat production. Despite its impact, data on its ecology and activity in Algeria are lacking. Such knowledge is needed to evaluate its potential effects on livestock production and rural health, and to support surveillance, outbreak prediction, and control strategies. This study aimed to investigate the monthly and daily dynamics of S. calcitrans in livestock farms in the Batna region and evaluate the influence of climatic factors on its abundance. From July 2022 to July 2023, Vavoua traps were placed monthly from 7 a.m. to 6 p.m. on four farms in the Batna region, representing different livestock types. Captured flies were identified, sexed, and counted every two hours. Climatic data were collected both in situ and from NASA POWER datasets. Fly abundance was analyzed using non-parametric statistics, Spearman’s correlation, and multiple regression analysis. A total of 1244 S. calcitrans were captured, mainly from cattle farms. Activity occurred from August to December, with a peak in September. Males were more abundant and exhibited a bimodal activity in September. Fly abundance was positively correlated with temperature and precipitation and negatively correlated with wind speed and humidity. This study presents the first ecological data on S. calcitrans in northeastern Algeria, highlighting its seasonal dynamics and the climatic drivers that influence it. The results highlight the species’ preference for cattle and indicate that temperature and rainfall are key factors influencing its abundance. These findings lay the groundwork for targeted control strategies against this neglected pest in Algeria. Full article

Colibacillosis, caused by avian pathogenic Escherichia coli (APEC), represents a major threat to poultry production, leading to significant mortality and economic losses. This study aimed to characterize an APEC strain, HPVN24, isolated from diarrheic chickens at a farm in Hai Phong, Vietnam. The strain was investigated through phenotypic assays, antibiotic susceptibility profiling, and whole-genome sequencing using the Illumina platform. HPVN24 exhibited β-hemolytic activity and resistance to trimethoprim, ampicillin, and ciprofloxacin. Whole-genome analysis identified the strain as serotype O78:H9 and sequence type ST23, with a genome size of 5.05 Mb and a GC content of 50.57%. Genome annotation revealed a wide repertoire of genes involved in metabolism, secretion systems, virulence, and biofilm formation. Virulence-associated genes included those related to adhesion, iron acquisition, hemolysin production, and stress response. Analysis predicted multidrug resistance to 18 antibiotic classes, with particularly strong resistance to fluoroquinolones. Phylogenetic comparison demonstrated that HPVN24 clustered closely with O78:H9 strains isolated from poultry in other regions, suggesting potential transmission across populations. These findings indicate that HPVN24 is a multidrug-resistant and highly virulent APEC strain linked to colibacillosis outbreaks in Vietnam and highlight the need for ongoing surveillance, judicious antibiotic usage, and alternative strategies to ensure poultry health and food safety. Full article

Locusts represent a persistent global agricultural pest, responsible for significant crop losses and socio-economic repercussions. The initiation of chemical control measures dates back to the late 19th century, with the use of poisoned baits, before advancing in the mid-20th century with the introduction of organochlorines, such as dieldrin. Despite their efficacy, the associated environmental, ecological, and human health risks led to the prohibition of dieldrin by the United States and the FAO by 1988. The demand for insecticides with reduced persistence and toxicity prompted the establishment of international organizations to coordinate locust research and management. In recent decades, chemical control has transitioned towards compounds with diminished persistence and selective agents. Concurrently, research has progressed in the development of bioinsecticides, notably Metarhizium acridum, and has reinforced preventive strategies. Emerging technologies, including remote sensing and machine learning, have facilitated early monitoring and predictive modeling, thereby enhancing outbreak forecasting. These tools support proactive, targeted interventions and are consistent with Integrated Pest Management principles, promoting more sustainable and ecologically responsible locust control strategies. Full article

We propose the Clustered Link-Prediction SEIRS model with Temporal Node Activation (CLP-SEIRS-T), a novel epidemiological framework that integrates community structure, link prediction, and temporal activation schedules to simulate malware propagation in urban communication networks. Unlike traditional static or homogeneous models, our approach captures the heterogeneous community structure of the network (modular connectivity), along with evolving connectivity (emergent links) and periodic device-usage patterns (online/offline cycles), providing a more realistic portrayal of how computer viruses spread. Simulation results demonstrate that strong community modularity and intermittent connectivity significantly slow and localize outbreaks. For instance, when devices operate on staggered duty cycles (asynchronous online schedules), malware transmission is fragmented into multiple smaller waves with lower peaks, often confining infections to isolated communities. In contrast, near-continuous and synchronized connectivity produces rapid, widespread contagion akin to classic epidemic models, overcoming community boundaries and infecting the majority of nodes in a single wave. Furthermore, by incorporating a common-neighbor link-prediction mechanism, CLP-SEIRS-T accounts for future connections that can bridge otherwise disconnected clusters. This inclusion significantly increases the reach and persistence of malware spread, suggesting that ignoring evolving network topology may underestimate outbreak risk. Our findings underscore the importance of considering temporal usage patterns and network evolution in malware epidemiology. The proposed model not only elucidates how timing and community structure can flatten or exacerbate infection curves, but also offers practical insights for enhancing the resilience of urban communication networks—such as staggering device online schedules, limiting inter-community links, and anticipating new connections—to better contain fast-spreading cyber threats. Full article