The National University Toribio Rodríguez de Mendoza (UNTRM) is a state-owned university in Chachapoyas, Peru. The UNTRM was founded on September 18, 2000.
BACKGROUND:Peru recorded one of the world's highest COVID-19 mortality rates, with nearly 4.5 million reported cases and 220,000 deaths by March 2024. Understanding the emergence and spread of SARS-CoV-2 variants in this context is key to informing effective public health responses. This study describes the genomic diversity, transmission dynamics, and geographic spread of SARS-CoV-2 in Peru from 2020 to 2024. METHODS:We analyzed nearly 50,000 high-quality public SARS-CoV-2 genome sequences collected nationwide between March 2020 and March 2024. Phylogeographic and mutational analyses were performed to identify variant lineages, trace their origins, and map viral movements within and beyond Peru. RESULTS:We show that Peru's epidemic waves were shaped by the emergence of locally evolved variants, including Lambda (C.37), Gamma (P.1.12), and Omicron (XBB.2.6 and DJ.1) sub-lineages. The city of Lima acted as the primary hub for inter-regional spread, accounting for 47.3% of inferred viral movements to other departments, notably Ancash, Cusco, and Piura. Peru was the source of various lineages that spread internationally, primarily to Chile, the USA, and Europe. Mutational analysis highlighted critical mutations in the spike protein, including L452Q and F490S in Lambda, associated with immune evasion and increased transmissibility. CONCLUSIONS:This work demonstrates the capacity of genomic surveillance in Peru to detect and track emerging SARS-CoV-2 variants, providing insights into regional and global transmission dynamics in a high-transmission, middle-income country setting. Sustained, cost-effective genomic monitoring, combined with strengthened bioinformatics and laboratory capacity, is essential for pandemic preparedness in resource-limited settings.
Background Dengue remains a major public health challenge in endemic settings. Evidence on the psychosocial predictors of dengue vaccination intention remains limited in Peru. This study aimed to identify predictors of dengue vaccination intention among adults in selected endemic areas of Peru using partial least squares structural equation modeling (PLS-SEM). Methods A cross-sectional study was conducted among 573 adults from selected provinces in Amazonas and Ancash, Peru, between June 2024 and December 2025. Data was collected using an adapted and validated multidimensional questionnaire. The model was estimated using SmartPLS 4.1.1.8 with 5,000 bootstrap resamples. Perceived benefit and attitude toward the dengue vaccine were integrated into a second-order construct termed favorable evaluation of the vaccine (FEV) to address their lack of discriminant validity. Results FEV was the strongest predictor of vaccination intention (β = 0.849, p < 0.001; f 2 = 1.749). Trust in key actors significantly predicted FEV (β = 0.557, p < 0.001) and perceived risk (β = 0.351, p < 0.001). Perceived risk did not significantly predict vaccination intention directly (β = −0.035, p = 0.237) but showed a significant positive indirect effect through FEV (β = 0.173, p < 0.001). Religiosity significantly predicted FEV and perceived risk, although its effect sizes were small. The model explained 69.2% of the variance in vaccination intention (R 2 = 0.692). Conclusions Favorable evaluation of the vaccine emerged as the strongest predictor of dengue vaccination intention in the structural model. Trust in key actors was an important predictor of favorable vaccine evaluation and perceived risk, whereas perceived risk predicted vaccination intention indirectly through FEV rather than directly. These findings may inform vaccination communication strategies in dengue-endemic settings.
Modern energy networks constitute complex dynamic systems characterized by operational uncertainty and nonlinear behavior. The objective of this research was to develop a physical-computational framework to analyze the stability and adaptive capacity of energy networks subjected to stochastic perturbations. A nonlinear dynamic model based on ordinary differential equations was employed, integrating Monte Carlo simulation, Latin Hypercube sampling, an Energy Antifragility Index (EAI), sensitivity analysis using Sobol indices, and bifurcation analysis. The results revealed fragile, resilient, and antifragile behaviors, with resilient scenarios predominating. Coupling intensity and perturbation magnitude were the parameters with the greatest influence on the system. Likewise, a critical threshold associated with the emergence of multiple equilibrium states and dynamic transitions was identified. It is concluded that the integration of nonlinear dynamics and probabilistic simulation makes it possible to understan
Quita la maquetación en latex y deja el texto original: Algal blooms in tropical reservoirs can affect water quality and resource management, but continuous monitoring is often limited by the low frequency of \textit{in situ} sampling. This study assessed algal bloom potential through a reproducible workflow integrating remote sensing and meteorology. Satellite time series of chlorophyll-\textit{a} and a complementary indicator of floating cyanobacteria were analyzed together with precipitation and daily temperatures aggregated to the same temporal interval. Processing included clipping the area of interest, quality and coverage control, calculation of spatiotemporal statistics, recurrence maps, monthly synthesis, nonparametric lagged correlations, and an operational bloom-potential classification based on robust exceedances. The results showed intra-annual variability of chlorophyll-\textit{a} with areas of spatial recurrence, weak meteorological associations, and a minimal floating-cyanobacteria signal, supporting the use of this approach for regional monitoring and sampling prioritization.
Cervical lymphadenitis is a significant infectious disease in guinea pig (Cavia porcellus) production, although the microbiota associated with affected lymph nodes remains poorly characterized. This study compared the microbiota of cervical lymph nodes from healthy guinea pigs and those with lymphadenitis using 16S rRNA gene sequencing, bioinformatics analysis, and molecular validation by PCR. The results showed that healthy lymph nodes harbor diverse bacterial communities, while infected lymph nodes exhibit a marked reduction in microbial diversity and a dominance of Streptococcus equi subsp. zooepidemicus. Furthermore, a subset of samples revealed an alternative etiology characterized by the dominance of the genus Caviibacter, suggesting etiological heterogeneity of the disease in this study. Taken together, these findings suggest that cervical lymphadenitis in guinea pigs is associated with dysbiosis of the lymph node microbiome and support the use of metagenomic approaches for etiological characterization.
