Search Results (6,134)

Determining the transmission routes of pathogens in indoor environments is challenging, with most studies limited to specific case analyses and pilot experiments. When pathogens are instantaneously released by a patient in an indoor environment, the peak infection risk may not occur immediately but may instead appear at a specific moment during the pathogen’s spread. We developed a concise model to describe the temporal crest of infection risk. The model incorporates the transmission and degradation characteristics of aerosols and surface particles to predict infection risks via air and surface routes. Only four real-world outbreaks met the criteria for validating this phenomenon. Based on the available data, norovirus is likely to transmit primarily via surface touch (i.e., the fomite route). In contrast, crests of infection risk were not observed in outbreaks of respiratory diseases (e.g., SARS-CoV-2), suggesting a minimal probability of surface transmission in such cases. The new model can serve as a preliminary indicator for identifying different indoor pathogen transmission routes (e.g., food, air, or fomite). Further analyses of pathogens’ transmission routes require additional evidence. Full article

Background: Concerns over women’s mental health have intensified globally, especially among mothers managing dual careers and family responsibilities. Positive Psychology Interventions (PPIs), such as gratitude journaling and well-being workshops, have demonstrated promise in enhancing mental health; however, their applicability in Arab contexts remains underexplored. This study aims to investigate the effectiveness of PPIs on mothers’ well-being, gratitude, depression, anxiety, and stress in Saudi Arabia. Methods: This quasi-experimental, one-group pretest–posttest study assessed the effects of a four-week PPI on 37 Saudi working mothers (aged 21–50 years) employed at a private school in Riyadh. The intervention included guided gratitude journaling thrice weekly and two workshops on positive psychology and gratitude. Pre- and post-intervention assessments used validated Arabic versions of the Depression Anxiety Stress Scale (DASS-21), WHO-5 Well-being Index, and Gratitude Questionnaire (GQ-6). Results: Significant improvements were found post-intervention: depression, anxiety, and stress scores decreased (p < 0.001), while well-being and gratitude increased (p = 0.001). However, participants with lower household income (<50,000 SAR) showed less improvement, indicating a potential moderating effect of socioeconomic status. Conclusions: The intervention demonstrated promising short-term improvements in mental health and well-being among Saudi mothers. The findings underscore the importance of culturally appropriate PPIs and highlight the need for further research using controlled, long-term designs. Limitations include the small, non-random sample, absence of a control group, and restriction to a single geographic region. Full article

Floods stand as one of the most harmful natural disasters, which have become more dangerous because of climate change effects on urban structures and agricultural fields. This research presents a comprehensive flood mapping approach that combines multi-sensor satellite data with a machine learning method to evaluate the July 2021 flood in the Netherlands. The research developed 25 different feature scenarios through the combination of Sentinel-1, Landsat-8, and Radarsat-2 imagery data by using backscattering coefficients together with optical Normalized Difference Water Index (NDWI) and Hue, Saturation, and Value (HSV) images and Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture features. The Random Forest (RF) classifier was optimized before its application based on two different flood-prone regions, which included Zutphen’s urban area and Heijen’s agricultural land. Results demonstrated that the multi-sensor fusion scenarios (S18, S20, and S25) achieved the highest classification performance, with overall accuracy reaching 96.4% (Kappa = 0.906–0.949) in Zutphen and 87.5% (Kappa = 0.754–0.833) in Heijen. For the flood class F1 scores of all scenarios, they varied from 0.742 to 0.969 in Zutphen and from 0.626 to 0.969 in Heijen. Eventually, the addition of SAR texture metrics enhanced flood boundary identification throughout both urban and agricultural settings. Radarsat-2 provided limited benefits to the overall results, since Sentinel-1 and Landsat-8 data proved more effective despite being freely available. This study demonstrates that using SAR and optical features together with texture information creates a powerful and expandable flood mapping system, and RF classification performs well in diverse landscape settings. Full article

SARS-CoV-2 undergoes frequent mutations that drive viral evolution and genomic diversity, influencing transmissibility, immune escape, and disease severity. In this study, we performed whole-genome sequencing on SARS-CoV-2 isolates from patients in New York City and identified several globally rare mutations across multiple viral lineages. The isolates analyzed for rare mutations belonged to three lineages: B.1.1.7 (Alpha), B.1.526 (Iota), and B.1.623. We identified 16 rare mutations (global incidence <1000) in non-structural protein genes, including nsp2, nsp3, nsp4, nsp6, nsp8, nsp13, nsp14, ORF7a, and ORF8. Three of these mutations—located in nsp2, nsp13, and ORF8—have been reported in fewer than 100 individuals worldwide. We also detected five rare mutations in structural proteins (S, M, and N), including two—one in M and one in N—previously reported in fewer than 100 cases globally. We present clinical profiles of three patients, each infected with genetically distinct viral isolates from the three lineages studied. Furthermore, we illustrate a local transmission chain inferred from unique mutation patterns identified in the Omicron genome. These findings underscore the importance of whole-genome sequencing for detecting rare mutations, tracking community spread, and identifying emerging variants with clinical and public health significance. Full article

The city of Acapulco was impacted by two near-consecutive hurricanes. On 25 October 2023, Hurricane Otis made landfall, reaching the highest Category 5 storm on the Saffir–Simpson scale, causing extensive coastal destruction due to extreme winds and waves. Nearly one year later (23 September 2024), Hurricane John—a Category 2 storm—caused severe flooding despite its lower intensity, primarily due to its unusual trajectory and prolonged rainfall. Digital shoreline analysis of PlanetScope images (captured one month before and after Hurricane Otis) revealed that the southern coast of Acapulco, specifically Zona Diamante—where the major seafront hotels are located—experienced substantial shoreline erosion (94 ha) and damage. In the northwestern section of the study area, the Coyuca Bar experienced the most dramatic geomorphological change in surface area. This was primarily due to the complete disappearance of the bar on October 26, which resulted in a shoreline retreat of 85 m immediately after the passage of Hurricane Otis. Sentinel-1 Synthetic Aperture Radar (SAR) showed that Hurricane John inundated 2385 ha, four times greater than Hurricane Otis’s flooding (567 ha). The retrofitted QGIS methodology demonstrated high reliability when compared to limited in situ local reports. Given the increased frequency of intense hurricanes, these methods and findings will be relevant in other coastal areas for monitoring and managing local communities affected by severe climate events. Full article

Background: There is growing evidence suggesting that SARS-CoV-2 may contribute to metabolic dysfunction. SARS-CoV-2 infection is associated with systemic inflammation, oxidative stress, and metabolic dysregulation, all of which may impair liver function and promote glucose intolerance. This study investigated the role of SARS-CoV-2, specifically its Main Protease (M^pro), in accelerating insulin resistance and metabolic dysfunction in HepG2 cells in vitro. Methods: HepG2 cells were treated with varying concentrations of M^pro (2.5, 5, 10, 20, 40, 80, and 160 nmol/mL) for 24 h to assess cytotoxicity and glucose uptake. Based on initial findings, subsequent assays focused on higher concentrations (40, 80, and 160 nmol/mL). The effects of M^pro on cell viability, protein kinase B (AKT) expression, matrix metallopeptidase-1 (MMP1), dipeptidyl peptidase 4 (DPP4), interleukin-6 (IL-6) expression, and lipid peroxidation were investigated. Results: Our findings reveal that the SARS-CoV-2 M^pro treatment led to a concentration-dependent reduction in glucose uptake in HepG2 cells. Additionally, the M^pro treatment was associated with reduced insulin-stimulated AKT activation, particularly at higher concentrations. Inflammatory markers such as IL-6 were elevated in the extracellular medium, while DPP4 expression was decreased. However, extracellular soluble DPP4 (sDPP4) levels did not show a significant change. Despite these changes, cell viability remained relatively unaffected, suggesting that the HepG2 cells were able to maintain overall metabolic functions under M^pro exposure. Conclusions: This study demonstrated the concentration-dependent impairment of hepatic glucose metabolism, insulin signaling, and inflammatory pathways in HepG2 cells acutely exposed to the SARS-CoV-2 M^pro. These findings warrant further investigation to explore the long-term metabolic effects of SARS-CoV-2 and its proteases in the liver and to develop potential therapeutic approaches for post-viral metabolic complications. Full article

Background: The surge in COVID-19 cases during the pandemic created a disease burden. An epidemiological study on COVID-19 is required as not much is known about the impact of containment and mitigation on health. We aimed to compare the epidemiological features during the years of the COVID-19 pandemic in the Vidarbha region in Maharashtra, India, to understand the epidemiology changes throughout the pandemic’s progression. Method: All of the cases reported at our testing centers in Nagpur and its periphery during the three years of the pandemic (i.e., from February 2020 to December 2022) were included. Descriptive analyses of variables of interest and statistical measures were compared. Results: There were 537,320 tests recorded during the study period. Of these, 13,035 (13.29%), 42,909 (13.70%), and 19,936 (15.91%) tested positive in 2020, 2021, and 2022, respectively. Hospitalization decreased from 2020 to 2022. An age group shift from 45 to 16–30 years over the pandemic was noticed. Seasonally, positivity peaked in September (27.04%) in 2020, in April (43.4%) in 2021, and in January in 2022 (35.30%). The estimated case fatality ratio was highest in 2021 (36.68%) over the three years in the hospital setting. Conclusion: Understanding the changing epidemiology of SARS-CoV-2 strengthens our perceptive of this disease, which will aid in improving the healthcare system in terms of both controlling and preventing the spread of COVID-19. Full article

tRNA modifications are crucial for efficient protein synthesis, impacting codon recognition, tRNA stability, and translation rates. RNA viruses hijack the host’s translational machinery, including the pool of modified tRNA, to translate their own genomes. However, the mismatch between viral and host codon usage can lead to a limited availability of specific tRNA leading to ribosome stalling, posing a significant challenge for efficient protein translation. While some viruses address this challenge through codon optimization, we show here that SARS-CoV-2 (Coronavirus) and the Zika virus (ZIKV; Flavivirus) adopt a different approach, manipulating the host tRNA epitranscriptome. Analysis of codon bias indices confirmed a substantial divergence between viral and host codon usage, revealing a strong preference in viral genes for codons decoded by tRNAs requiring U34 wobble modification. Monitoring tRNA modification dynamics in infected cells showed that both SARS-CoV2 and ZIKV enhance U34 tRNA modifications during infection. Strikingly, impairing U34 tRNAs profoundly impacted viral replication, underscoring the strict reliance of SARS-CoV-2 and ZIKV on manipulating the host tRNA epitranscriptome to support the efficient translation of their genome. 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

Introduction: Long COVID syndrome is defined as persistent or new symptoms that appear after an acute SARS-CoV-2 infection and last at least three months without explanation. It is estimated that between 10% and 20% of those infected develop long COVID; however, data is not precise in Latin America. Although high immunization rates have reduced acute symptoms and the pandemic’s impact, there is a lack of evidence of its efficacy in preventing long COVID in the region. Methods: This scoping review followed PRISMA-ScR guidelines. Studies on vaccinated adults with long COVID from Central and South America and the Caribbean were included (Mexico was also considered). A comprehensive search across multiple databases was conducted. Data included study design, participant characteristics, vaccine type, and efficacy outcomes. Results are presented narratively and in tables. Results: Out of 3466 initial records, 8 studies met the inclusion criteria after rigorous selection processes. These studies encompassed populations from Brazil, Mexico, Latin America, and Bonaire, with 11,333 participants, 69.3% of whom were female. Vaccination, particularly with three or more doses, substantially reduces the risk and duration of long COVID. Variability was noted in the definitions and outcomes assessed across studies. Conclusions: This scoping review highlights that SARS-CoV-2 vaccination exhibits potential in reducing the burden of long COVID in the Americas. However, discrepancies in vaccine efficacy were observed depending on the study design, the population studied, and the vaccine regimen employed. Further robust, region-specific investigations are warranted to delineate the effects of vaccination on long COVID outcomes. Full article

The Xiong’an New Area, a newly established national-level zone in China, faces the threat of land subsidence and ground fissure due to groundwater overexploitation and geothermal extraction, threatening urban safety. This study integrates time-series InSAR and GNSS monitoring to analyze spatiotemporal deformation patterns from 2017/05 to 2025/03. The key results show: (1) Three subsidence hotspots, namely northern Xiongxian (max. cumulative subsidence: 591 mm; 70 mm/yr), Luzhuang, and Liulizhuang, strongly correlate with geothermal wells and F4/F5 fault zones; (2) GNSS baseline analysis (e.g., XA01-XA02) reveals fissure-induced differential deformation (max. horizontal/vertical rates: 40.04 mm/yr and 19.8 mm/yr); and (3) InSAR–GNSS cross-validation confirms the high consistency of the results (Pearson’s correlation coefficient = 0.86). Subsidence in Xiongxian is driven by geothermal/industrial groundwater use, without any seasonal variations, while Anxin exhibits agricultural pumping-linked seasonal fluctuations. The use of rooftop GNSS stations reduces multipath effects and improves urban monitoring accuracy. The spatiotemporal heterogeneity stems from coupled resource exploitation and tectonic activity. We propose prioritizing rooftop GNSS deployments to enhance east–west deformation monitoring. This framework balances regional and local-scale precision, offering a replicable solution for geological risk assessments in emerging cities. Full article

Since the onset of the COVID-19 pandemic, remarkable progress has been made in the development of antiviral therapies for SARS-CoV-2. Several direct-acting antivirals, such as remdesivir, molnupiravir, and nirmatrelvir/ritonavir, offer clinical benefits. These agents have significantly contributed to reducing the viral loads and duration of the illness, as well as the disease’s severity and mortality. However, despite these advances, important limitations remain. The continued emergence of resistant SARS-CoV-2 variants highlights the urgent need for adaptable and durable therapeutic strategies. Therefore, this review aims to provide an updated overview of the main antiviral strategies that are used and the discovery of new drugs against SARS-CoV-2, as well as the therapeutic limitations that have shaped clinical management in recent years. The major challenges include resistance associated with viral mutations, limited treatment windows, and unequal access to treatment. Moreover, there is an ongoing need to identify novel compounds with broad-spectrum activity, improved pharmacokinetics, and suitable safety profiles. Combination treatment regimens represent a promising strategy to increase the efficacy of treating COVID-19 while minimizing the potential for resistance. Ideally, these interventions should be safe, affordable, and easy to administer, which would ensure broad global access and equitable treatment and enable control of COVID-19 cases and preparedness for future threats. Full article

Recombinant vesicular stomatitis virus (rVSV) is a promising viral vaccine vector for addressing the COVID-19 pandemic. Inducing mucosal immunity via the intranasal route is an ideal strategy for rVSV-based vaccines, but it requires extremely stringent safety standards. In this study, we constructed two rVSV variants with amino acid mutations in their M protein: rVSV-M2 with M33A/M51R mutations and rVSV-M4 with M33A/M51R/V221F/S226R mutations, and developed COVID-19 vaccines based on these attenuated vectors. By comparing viral replication capacity, intranasal immunization, intracranial injection, and blood cell counts, we demonstrated that the M protein mutation variants exhibit significant attenuation effects both in vitro and in vivo. Moreover, preliminary investigations into the mechanisms of virus attenuation revealed that these attenuated viruses can induce a stronger type I interferon response while reducing inflammation compared to the wild-type rVSV. We developed three candidate vaccines against SARS-CoV-2 using the wildtype VSV backbone with either wild-type M (rVSV-JN.1) and two M mutant variants (rVSV-M2-JN.1 and rVSV-M4-JN.1). Our results confirmed that rVSV-M2-JN.1 and rVSV-M4-JN.1 retain strong immunogenicity while enhancing safety in hamsters. In summary, the rVSV variants with M protein mutations represent promising candidate vectors for mucosal vaccines and warrant further investigation. Full article

During the COVID-19 pandemic, some studies suggested that transmission events could originate from schools. This study aimed to evaluate early-warning methods for identifying asymptomatic COVID-19 cases by implementing screening programs in schools. This study was conducted between September 2021 and May 2023, employing a rotation-screening plan for COVID-19 detection on a sample of students aged 14 to 19 years attending secondary schools in the regions of Tuscany, Veneto, Apulia and Friuli-Venezia Giulia. The schools were divided into two groups: experimental and control, with a ratio of 1:2. Two types of molecular salivary tests for SARS-CoV-2 were used to conduct the screening. This study included 16 experimental schools and 32 control schools. Out of 2527 subjects, 11,475 swabs were administrated, with 9177 tests deemed valid for analysis (a 20% loss of tests). Among these, 89 subjects (3.5%) tested positive. In control schools, 1895 subjects (6.5%) tested positive for SARS-CoV-2. This study recorded peaks in infections during the winter and autumn months, consistent with patterns observed in the general population. Beginning in September 2022, a shift occurred, with 2.6% of positive cases reported in the case schools compared to 0.3% in the control schools. Initially, most cases of COVID-19 were detected in the control schools; however, as the pandemic emergency phase concluded, cases were primarily identified through active screening in experimental schools. Although student participation in the active screening campaign was low during the project’s extension phase, this approach was efficacious in the early identification of positive cases. Full article

In late 2019, a new virus, SARS-CoV-2, emerged in Wuhan, China, causing COVID-19 and the subsequent global pandemic. As of 30 April 2023, more than 774 million cases of COVID-19 had been reported worldwide, including over 7.5 million in Mexico. Despite advances in vaccination, epidemic surges of COVID-19 continued to occur globally, highlighting the importance of sharing and disseminating the experiences gained during these first years to better understand the virus’s evolution and respond accordingly. For this reason, the National Council for Science and Technology (CONACYT) organized the meeting “Challenges and Opportunities for Genomic Surveillance of SARS-CoV-2 in Mexico” from 15 to 17 August 2022, to present the efforts and results accumulated over more than two years of the pandemic. In this meeting report, we summarize the key findings of each participant and provide their contact information. Full article