Search Results (1,201)

Since its first appearance as a human pathogen in 1976, Legionella pneumophila has been identified as a causative agent of community-acquired pneumonia (CAP). It survives in rivers, bays, lakes, and water reservoirs, and it is categorized as the fourth most common causative agent of CAP leading to hospitalization. We aimed to investigate patterns in which environmental, seasonal and regional factors may affect the prevalence of Legionnaires’ disease in Crete during the last two decades (2000–2022).The data used originated from the national surveillance database and included any person reported with travel-associated Legionnaires’ disease (TALD) between January 2000 and December 2022. Meteorological data were collected from the National Weather Service. The meteorological variables included (max) temperature (in °C), cloudiness (in octas), wind speed (in knots), and relative humidity (RH) (%). The statistical analysis was based on a case-crossover design with 1:1 matching characteristic. We revealed both seasonal and regional effects on the incidence of Legionnaires’ disease. Cases are significantly more frequent in autumn, in comparison to the other three seasons, while Rethymnon is the prefecture with fewer cases in comparison to Chania or Heraklion. In addition, our research showed that the majority of cases occurred during the years 2017–2018. TALD in Crete is significantly associated with temperature in °C and wind speed in knots. Our research suggests that temporal and spatial factors significantly influence disease cases. These results are in line with studies from foreign countries. The study results aspire to expand our knowledge regarding the epidemiological characteristics of Legionnaires’ disease in relation to local, geographical and meteorological factors on the island of Crete. Full article

African swine fever (ASF) needs to be controlled, and prevention of the spread of African swine fever virus (ASFV) is dependent on enhanced surveillance and early disease detection. Commercial swine operations, especially in North America, Europe, and Asia, are characterized by comparatively large numbers of pigs, and sampling individual pigs, which represents the main strategy for current ASF surveillance, can be both costly and labor intensive. A study performed in Ghana was designed to estimate the diagnostic sensitivity of pen-based aggregate oral fluid testing for ASFV in infected pigs in a pen of 30 animals and to evaluate its utility as a tool to support surveillance of ASF in the US. This study was performed in three phases: (i) virus (Ghana ASFV24) amplification in a target host species to generate the challenge inoculum; (ii) titration of the inoculum (10% spleen homogenate) in target host species to determine the minimum dose inducing acute ASF in pigs with survival up to 5–6 days post-inoculation (dpi); and (iii) the main study, involving 186 pigs, consisting of 6 replicates of 30 pigs per pen and one seeder pig inoculated with wildtype ASFV (highly virulent genotype II) per pen. Daily sampling of aggregate oral fluids, uncoagulated blood, oropharyngeal swabs, fecal and water nipple swabs, and recording of rectal temperatures and clinical observations was carried out. The seeder pigs were each inoculated intramuscularly with 0.5 mL of the 10% spleen homogenate, which induced the desired clinical course of ASF in the pigs, with survival of up to 6 dpi. ASFV DNA was detected in the seeder pigs as early as 1 dpi and 2 dpi in the blood and oropharyngeal swabs, respectively. Transmission of ASFV from the seeder pigs to the contact pig population was detected via positive amplification of ASFV DNA in aggregate oral fluid samples at 3 days post-contact (dpc) in 4 out of 6 pens, and in all 6 pens, at 4 dpc. Testing of oropharyngeal swabs and blood samples from individual pigs revealed a variable number of ASFV-positive pigs between 3 and 5 dpc, with detection of 100% positivity between 6 and 18 dpc, the study endpoint. These findings demonstrate the potential utility of aggregate oral fluid sampling for sensitive and early detection of ASFV incursion into naïve swine herds. It also demonstrates that testing of environmental samples from the premises could further enhance overall ASF early detection and surveillance strategies. Full article

Background/Objectives: This study examines how seasonality, pollution, and sample type (water and sediment) influence the presence and distribution of antibiotic resistance genes (ARGs), with a focus on antibiotic resistance genes (ARGs) located on plasmids (the complete set of plasmid-derived sequences, including ARGs) in a tropical urban river. Methods: Samples were collected from three sites along a pollution gradient in the Virilla River, Costa Rica, during three seasonal campaigns (wet 2021, dry 2022, and wet 2022). ARGs in water and sediment were quantified by qPCR, and metagenomic sequencing was applied to analyze chromosomal and plasmid-associated resistance profiles in sediments. Tobit and linear regression models, along with multivariate ordination, were used to assess spatial and seasonal trends. Results: During the wet season of 2021, the abundance of antibiotic resistance genes (ARGs) such as sul-1, intI-1, and tetA in water samples decreased significantly, likely due to dilution, while intI-1 and tetQ increased in sediments, suggesting particle-bound accumulation. In the wet season 2022, intI-1 remained low in water, qnrS increased, and sediments showed significant increases in tetQ, tetA, and qnrS, along with decreases in sul-1 and sul-2. Metagenomic analysis revealed spatial differences in plasmid-associated ARGs, with the highest abundance at the most polluted site (Site 3). Bacterial taxa also showed spatial differences, with greater plasmidome diversity and a higher representation of potential pathogens in the most contaminated site. Conclusions: Seasonality and pollution gradients jointly shape ARG dynamics in this tropical river. Plasmid-mediated resistance responds rapidly to environmental change and is enriched at polluted sites, while sediments serve as long-term reservoirs. These findings support the use of plasmid-based monitoring for antimicrobial resistance surveillance in aquatic systems. Full article

Unmanned Aerial Vehicles (UAVs) have rapidly grown into different essential applications, including surveillance, disaster response, agriculture, and urban monitoring. However, for UAVS to steer safely and autonomously, the ability to detect obstacles and nearby aircraft remains crucial, especially under hard environmental conditions. This study comprehensively analyzes the recent landscape of obstacle and aircraft detection techniques tailored for UAVs acting in difficult scenarios such as fog, rain, smoke, low light, motion blur, and disorderly environments. It starts with a detailed discussion of key detection challenges and continues with an evaluation of different sensor types, from RGB and infrared cameras to LiDAR, radar, sonar, and event-based vision sensors. Both classical computer vision methods and deep learning-based detection techniques are examined in particular, highlighting their performance strengths and limitations under degraded sensing conditions. The paper additionally offers an overview of suitable UAV-specific datasets and the evaluation metrics generally used to evaluate detection systems. Finally, the paper examines open problems and coming research directions, emphasising the demand for lightweight, adaptive, and weather-resilient detection systems appropriate for real-time onboard processing. This study aims to guide students and engineers towards developing stronger and intelligent detection systems for next-generation UAV operations. Full article

Objectives: Norovirus outbreaks occur in densely populated environments, such as long-term care facilities, hospitals, and schools. On 22 October 2022, an outbreak of acute gastroenteritis was reported at a kindergarten in Korea. An epidemiologic investigation was conducted to identify the source of the infection and prevent further spread. Methods: Rectal swab and environmental samples were collected for bacterial and viral testing. A retrospective cohort study was conducted among 114 kindergarteners at the kindergarten. Relative risks (RRs) and 95% confidence intervals (CIs) were calculated to assess associations of contact with the primary case, as well as food and water consumption. Results: Of the kindergarteners, 28 out of 114 (24.6%) met the case definition. The primary case occurred on 19 October, and subsequent cases began on 21 October. Sharing the same four-year-old class as the primary case (RR, 2.56; 95% CI, 1.35–4.87), being in the same regular class (RR, 2.37; 95% CI, 1.27–4.41), being on the same floor during after-school class (RR, 3.49; 95% CI, 1.74–7.00), and attending the same English class (RR, 1.98; 95% CI, 1.05–3.72) were statistically significant. Consumption of drinking water on the third floor and fourth floor on 20 October had significantly higher and lower RRs, respectively. Norovirus was detected in 9 out of 18 rectal swab samples (50.0%). Conclusions: This norovirus outbreak at the kindergarten was presumed to have been caused by person-to-person transmission from the primary case. Isolation and restriction of symptomatic children in kindergartens should be thoroughly implemented. Additionally, enhanced surveillance among family members of affected individuals is necessary to prevent further outbreaks. Full article

The accelerated evolution of remote sensing technology has intensified the demand for real-time tile map generation, highlighting the limitations of conventional mapping approaches that rely on manual cartography and field surveys. To address the critical need for rapid cartographic updates, this study presents a novel multi-stage generative framework that synergistically integrates Generative Adversarial Networks (GANs) with Diffusion Denoising Models (DMs) for high-fidelity map generation from remote sensing imagery. Specifically, our proposed architecture first employs GANs for rapid preliminary map generation, followed by a cascaded diffusion process that progressively refines topological details and spatial accuracy through iterative denoising. Furthermore, we propose a hybrid attention mechanism that strategically combines channel-wise feature recalibration with coordinate-aware spatial modulation, enabling the enhanced discrimination of geographic features under challenging conditions involving edge ambiguity and environmental noise. Quantitative evaluations demonstrate that our method significantly surpasses established baselines in both structural consistency and geometric fidelity. This framework establishes an operational paradigm for automated, rapid-response cartography, demonstrating a particular utility in time-sensitive applications including disaster impact assessment, unmapped terrain documentation, and dynamic environmental surveillance. Full article

Invasive fungal infections (IFIs) represent a growing global health threat, particularly for immunocompromised populations, with mortality exceeding 1.5 million deaths annually. Despite their clinical and economic burden—costing billions in healthcare expenditures—fungal infections remain underprioritized in public health agendas. This review examines the current landscape of antifungal therapy, focusing on advances, challenges, and future directions. Key drug classes (polyenes, azoles, echinocandins, and novel agents) are analyzed for their mechanisms of action, pharmacokinetics, and clinical applications, alongside emerging resistance patterns in pathogens like Candida auris and azole-resistant Aspergillus fumigatus. The rise of resistance, driven by agricultural fungicide use and nosocomial transmission, underscores the need for innovative antifungals, rapid diagnostics, and stewardship programs. Promising developments include next-generation echinocandins (e.g., rezafungin), triterpenoids (ibrexafungerp), and orotomides (olorofim), which target resistant strains and offer improved safety profiles. The review also highlights the critical role of “One Health” strategies to mitigate environmental and clinical resistance. Future success hinges on multidisciplinary collaboration, enhanced surveillance, and accelerated drug development to address unmet needs in antifungal therapy. Full article

Environmental DNA (eDNA) analysis has emerged as a transformative tool in environmental monitoring, enabling non-invasive detection of species and microbial communities across diverse ecosystems. This study systematically reviews the role of bioinformation technology in eDNA analysis, focusing on methodologies and applications across air, soil, groundwater, sediment, and aquatic environments. Advances in molecular biology, high-throughput sequencing, bioinformatics tools, and field-deployable detection systems have significantly improved eDNA detection sensitivity, allowing for early identification of invasive species, monitoring ecosystem health, and tracking pollutant degradation processes. Airborne eDNA monitoring has demonstrated potential for assessing microbial shifts due to air pollution and tracking pathogen transmission. In terrestrial environments, eDNA facilitates soil and groundwater pollution assessments and enhances understanding of biodegradation processes. In aquatic ecosystems, eDNA serves as a powerful tool for biodiversity assessment, invasive species monitoring, and wastewater-based epidemiology. Despite its growing applicability, challenges remain, including DNA degradation, contamination risks, and standardization of sampling protocols. Future research should focus on integrating eDNA data with remote sensing, machine learning, and ecological modeling to enhance predictive environmental monitoring frameworks. As technological advancements continue, eDNA-based approaches are poised to revolutionize environmental assessment, conservation strategies, and public health surveillance. Full article

Non-polio enteroviruses continue to cause numerous epidemics world-wide that range from mild to severe disease, including acute flaccid paralysis, meningitis, severe respiratory infections and encephalitis. Using publicly available data we present a comprehensive global and regional temporal distribution of non-polio enteroviruses, with a focus on highly prevalent genotypes. We found that regional distribution did vary compared to global prevalence where the top prevalent genotypes included CVA6 and EV-A71 in Asia, EV-D68 in North America and CVA13 in Africa, while E-30 was prevalent in Europe, South America and Oceania. In 2020, the COVID-19 pandemic did interrupt non-polio enterovirus detections globally, and cases rebounded in subsequent years, albeit at lower prevalence and with decreased genotype diversity. Environmental surveillance for non-polio enteroviruses does occur and has been used in some regions as an early-warning system; however, further development is needed to effectively supplement potential gaps in clinical surveillance data. Overall, monitoring for non-polio enteroviruses is critical to identify true incidence, improve understanding of genotype circulation, provide an early warning system for emerging/re-emerging genotypes and allow for better outbreak control. Full article

Background: Patients diagnosed with melanoma are at increased risk of developing multiple primary melanomas (MPMs). Identifying clinical and genetic factors associated with MPM is critical for implementing personalized surveillance strategies. This study aims to describe the clinical, histopathological, and genetic characteristics of patients with MPM managed in a tertiary hospital and to contextualize findings within the current literature. Methods: We conducted a retrospective review of patients diagnosed with two or more primary melanomas between 2010 and 2023 at a tertiary dermatology unit. Demographic data, personal and family cancer history, phototype, melanoma characteristics, genetic testing, staging, treatments, and outcomes were collected. These data were compared with findings from the recent literature. Results: Thirteen patients (ten males, three females; median age: 59 years) were found to have a total of 33 melanomas. Most patients had Fitzpatrick phototype II and no immunosuppression. The number of melanomas per patient ranged from two to five. Synchronous lesions were observed in two patients. Common locations included the trunk and extremities. Histologically, 57% were in situ melanomas, and subsequent melanomas were generally thinner than the index lesion. Two patients showed progression to advanced disease. One patient was positive for MC1R mutation; the rest were negative or inconclusive. Additional phenotypic and environmental risk factors were extracted from patient records and are summarized as follows: Ten patients (76.9%) had Fitzpatrick skin phototype II, and three (23.1%) had phototype III. Chronic occupational sun exposure was reported in four patients (30.8%), while five (38.5%) recalled having suffered multiple sunburns during childhood or adolescence. Eight patients (61.5%) presented with a total nevus count exceeding 50, and five (38.5%) exhibited clinically atypical nevi. None of the patients reported use of tanning beds. Conclusions: Our findings are consistent with the existing literature indicating that patients with MPM often present with thinner subsequent melanomas and require long-term dermatologic follow-up. The inclusion of genetic testing and phenotypic risk factors enables stratified surveillance and supports the application of personalized medicine in melanoma management. Full article

Coastal ecosystems are increasingly threatened by climate change and anthropogenic pressures, which can disrupt microbial communities and favor the emergence of pathogenic organisms. In this study, we applied metagenomic analysis to characterize fungal communities in sediment samples from an urban mangrove subjected to environmental stress. The results revealed a fungal community with reduced richness—28% lower than expected for similar ecosystems—likely linked to physicochemical changes such as heavy metal accumulation, acidic pH, and eutrophication, all typical of urbanized coastal areas. Notably, we detected an increase in potentially pathogenic genera, including Candida, Aspergillus, and Pseudoascochyta, alongside a decrease in key saprotrophic genera such as Fusarium and Thelebolus, indicating a shift in ecological function. The fungal assemblage was dominated by the phyla Ascomycota and Basidiomycota, and despite adverse conditions, symbiotic mycorrhizal fungi remained present, suggesting partial resilience. A considerable fraction of unclassified fungal taxa also points to underexplored microbial diversity with potential ecological or health significance. Importantly, this study does not aim to compare pristine and contaminated environments, but rather to provide a sanitary alert by identifying the presence and potential proliferation of pathogenic fungi in a degraded mangrove system. These findings highlight the sensitivity of mangrove fungal communities to environmental disturbance and reinforce the value of metagenomic approaches for monitoring ecosystem health. Incorporating fungal metagenomic surveillance into environmental management strategies is essential to better understand biodiversity loss, ecological resilience, and potential public health risks in degraded coastal environments. Full article

The growth and sustainability of today’s global economy heavily relies on smooth maritime operations. The increasing security concerns to marine environments pose complex security challenges, such as smuggling, illegal fishing, human trafficking, and environmental threats, for traditional surveillance methods due to their limitations. Artificial intelligence (AI), particularly deep learning, has offered strong capabilities for automating object detection, anomaly identification, and situational awareness in maritime environments. In this paper, we have reviewed the state-of-the-art deep learning models mainly proposed in recent literature (2020–2025), including convolutional neural networks, recurrent neural networks, Transformers, and multimodal fusion architectures. We have highlighted their success in processing diverse data sources such as satellite imagery, AIS, SAR, radar, and sensor inputs from UxVs. Additionally, multimodal data fusion techniques enhance robustness by integrating complementary data, yielding more detection accuracy. There still exist challenges in detecting small or occluded objects, handling cluttered scenes, and interpreting unusual vessel behaviours, especially under adverse sea conditions. Additionally, explainability and real-time deployment of AI models in operational settings are open research areas. Overall, the review of existing maritime literature suggests that deep learning is rapidly transforming maritime domain awareness and response, with significant potential to improve global maritime security and operational efficiency. We have also provided key datasets for deep learning models in the maritime security domain. Full article

Surveillance of swine influenza A virus (swIAV) traditionally focuses on respiratory matrices, yet emerging evidence suggests that fecal shedding and secondary environmental contamination may also contribute to viral dissemination. In this study, we collected and analyzed nasal, rectal, environmental, milk, and colostrum samples from naturally infected pigs in a commercial farm in Minas Gerais, Brazil. IAV RNA was detected in 25% of samples, including 42% from asymptomatic animals, with nasal swabs showing higher detection rates (30%) than rectal swabs (20%), though rectal Ct values were consistently higher, indicative of lower viral loads. We successfully isolated viable viruses from feces and effluent samples. Whole-genome sequencing revealed co-circulation of enzootic pH1N1 clade #2 (HA) and pN1 clade #4 (NA), alongside human-origin H3N2 sequences clustering within clade 3C.2a1b.2a.2a.1, and N2 segments related to pre-3C human lineages from 2001 to 2002. Phylogenetic and p-distance analyses support both recent reverse zoonosis and historical transmission events. Detection of complete HA/NA sequences from rectal swabs and treated effluent further emphasizes the surveillance value of non-respiratory matrices. The integration of respiratory and fecal/environmental sampling appears important to achieve more comprehensive IAV monitoring in swine herds and may have significant implications for One Health strategies in Brazil and beyond. Full article

Unmanned aerial vehicles (UAVs) are widely used in surveillance and combat for their efficiency and autonomy, whilst complex, dynamic environments challenge the modeling of inter-agent relations and information transmission. This research proposes a novel UAV tactical choice-making algorithm utilizing graph neural networks to tackle these challenges. The proposed algorithm employs a graph neural network to process the observed state information, the convolved output of which is then fed into a reconstructed critic network incorporating a Laplacian convolution kernel. This research first enhances the accuracy of obtaining unstable state information in hostile environments. The proposed algorithm uses this information to train a more precise critic network. In turn, this improved critic network guides the actor network to make decisions that better meet the needs of the battlefield. Coupled with a policy transfer mechanism, this architecture significantly enhances the decision-making efficiency and environmental adaptability within the multi-agent system. Results from the experiments show that the average effectiveness of the proposed algorithm across the six planned scenarios is 97.4%, surpassing the baseline by 23.4%. In addition, the integration of transfer learning makes the network convergence speed three times faster than that of the baseline algorithm. This algorithm effectively improves the information transmission efficiency between the environment and the UAV and provides strong support for UAV formation combat. Full article

Cancer disparities in low- and middle-income countries (LMICs) arise from multifaceted interactions between environmental exposures, infectious agents, and systemic inequities, such as limited access to care. The exposome, a framework encompassing the totality of non-genetic exposures throughout life, offers a powerful lens for understanding these disparities. In LMICs, populations are disproportionately affected by air and water pollution, occupational hazards, and oncogenic infections, including human papillomavirus (HPV), hepatitis B virus (HBV), Helicobacter pylori (H. pylori), human immunodeficiency virus (HIV), and neglected tropical diseases, such as schistosomiasis. These infectious agents contribute to increased cancer susceptibility and poor outcomes, particularly in immunocompromised individuals. Moreover, climate change, food insecurity, and barriers to healthcare access exacerbate these risks. This review adopts a population-level exposome approach to explore how environmental and infectious exposures intersect with genetic, epigenetic, and immune mechanisms to influence cancer incidence and progression in LMICs. We highlight the critical pathways linking chronic exposure and inflammation to tumor development and evaluate strategies such as HPV and HBV vaccination, antiretroviral therapy, and environmental regulation. Special attention is given to tools such as exposome-wide association studies (ExWASs), which offer promise for exposure surveillance, early detection, and public health policy. By integrating exposomic insights into national health systems, especially in regions such as sub-Saharan Africa (SSA) and South Asia, LMICs can advance equitable cancer prevention and control strategies. A holistic, exposome-informed strategy is essential for reducing global cancer disparities and improving outcomes in vulnerable populations. Full article