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Shigella sonnei is a human-adapted pathogen that is emerging globally as the dominant agent of bacterial dysentery. To investigate local establishment, we sequenced the genomes of 263 Vietnamese S. sonnei isolated over 15 y. Our data show that S. sonnei was introduced into Vietnam in the 1980s and has undergone localized clonal expansion, punctuated by genomic fixation events through periodic selective sweeps. We uncover geographical spread, spatially restricted frontier populations, and convergent evolution through local gene pool sampling. This work provides a unique, high-resolution insight into the microevolution of a pioneering human pathogen during its establishment in a new host population.

The highest burden of disease from hepatitis C virus (HCV) is found in Southeast Asia, but our understanding of the epidemiology of infection in many heavily burdened countries is still limited. In particular, there is relatively little data on acute HCV infection, the outcome of which can be influenced by both viral and host genetics which differ within the region. We studied HCV genotype and IL28B gene polymorphism in a cohort of acute HCV-infected patients in Southern Vietnam alongside two other cohorts of chronic HCV-infected patients to better understand the epidemiology of HCV infection locally and inform the development of programs for therapy with the increasing availability of directly acting antiviral therapy (DAAs). We analysed plasma samples from patients with acute and chronic HCV infection, including chronic HCV mono-infection and chronic Human Immunodeficiency Virus (HIV)-HCV coinfection, who enrolled in four epidemiological or clinical research studies. HCV infection was confirmed with RNA testing. The 5' UTR, core and NSB5 regions of HCV RNA positive samples were sequenced, and the genotype and subtype of the viral strains were determined. Host DNA from all HCV positive patients and age- and sex-matched non-HCV-infected control individuals were analysed for IL28B single nucleotide polymorphism (SNP) (rs12979860 and rs8099917). Geolocation of the patients were mapped using QGIS. 355 HCV antibody positive patients were analysed; 54.6% (194/355) and 46.4% (161/355) were acute and chronic infections, respectively. 50.4% (81/161) and 49.6.4% (80/161) of chronic infections had HCV mono-infection and HIV-HCV coinfection, respectively. 88.7% (315/355) and 10.1% (36/355) of the patients were from southern and central regions of Vietnam, respectively. 92.4% (328/355) of patients were HCV RNA positive, including 86.1% (167/194) acute and 100% (161/161) chronic infections. Genotype could be determined in 98.4% (322/328) patients. Genotypes 1 (56.5%; 182/322) and 6 (33.9%; 109/322) predominated. Genotype 1 including genotype 1a was significantly higher in HIV-HCV coinfected patients compared to acute HCV patients [43.8% (35/80) versus 20.5% (33/167)], (p = <0.001), while genotype 6 was significantly higher in chronic HCV mono-infected patients [(44.4% (36/81) versus 20.0% (16/80)] (p = < 0.004) compared to HIV-HCV coinfected patients. The prevalence of IL28B SNP (rs12979860) homozygous CC was 86.46% (83/96) in control individuals and was significantly higher in acutely-infected compared to chronically-infected patients [93.2 (82/88) versus 76.1% (35/46)] (p = < 0.005). HCV genotype 6 is highly prevalent in Vietnam and the high prevalence in treatment naïve chronic HCV patients may results from poor spontaneous clearance of acute HCV infection with genotype 6.

Background Sepsis is a global threat but insufficiently studied in Southeast Asia. The objective was to evaluate management, outcomes, adherence to sepsis bundles, and mortality prediction of maximum Sequential Organ Failure Assessment (SOFA) scores in patients with community-acquired sepsis in Southeast Asia. Methods We prospectively recruited hospitalized adults within 24 h of admission with community-acquired infection at nine public hospitals in Indonesia ( n  = 3), Thailand ( n  = 3), and Vietnam ( n  = 3). In patients with organ dysfunction (total SOFA score ≥ 2), we analyzed sepsis management and outcomes and evaluated mortality prediction of the SOFA scores. Organ failure was defined as the maximum SOFA score ≥ 3 for an individual organ system. Results From December 2013 to December 2015, 454 adult patients presenting with community-acquired sepsis due to diverse etiologies were enrolled. Compliance with sepsis bundles within 24 h of admission was low: broad-spectrum antibiotics in 76% (344/454), ≥ 1500 mL fluid in 50% of patients with hypotension or lactate ≥ 4 mmol/L (115/231), and adrenergic agents in 71% of patients with hypotension (135/191). Three hundred and fifty-five patients (78%) were managed outside of ICUs. Ninety-nine patients (22%) died. Total SOFA score on admission of those who subsequently died was significantly higher than that of those who survived (6.7 vs. 4.6, p  < 0.001). The number of organ failures showed a significant correlation with 28-day mortality, which ranged from 7% in patients without any organ failure to 47% in those with failure of at least four organs ( p  < 0.001). The area under the receiver operating characteristic curve of the total SOFA score for discrimination of mortality was 0.68 (95% CI 0.62–0.74). Conclusions Community-acquired sepsis in Southeast Asia due to a variety of pathogens is usually managed outside the ICU and with poor compliance to sepsis bundles. In this population, calculation of SOFA scores is feasible and SOFA scores are associated with mortality. Trial registration ClinicalTrials.gov, NCT02157259 . Registered 5 June 2014, retrospectively registered.

The Coronaviridae family of viruses encompasses a group of pathogens with a zoonotic potential as observed from previous outbreaks of the severe acute respiratory syndrome coronavirus and Middle East respiratory syndrome coronavirus. Accordingly, it seems important to identify and document the coronaviruses in animal reservoirs, many of which are uncharacterized and potentially missed by more standard diagnostic assays. A combination of sensitive deep sequencing technology and computational algorithms is essential for virus surveillance, especially for characterizing novel- or distantly related virus strains. Here, we explore the use of profile Hidden Markov Model-defined Pfam protein domains (Pfam domains) encoded by new sequences as a Coronaviridae sequence classification tool. The encoded domains are used first in a triage to identify potential Coronaviridae sequences and then processed using a Random Forest method to classify the sequences to the Coronaviridae genus level. The application of this algorithm on Coronaviridae genomes assembled from agnostic deep sequencing data from surveillance of bats and rats in Dong Thap province (Vietnam) identified thirty-four Alphacoronavirus and eleven Betacoronavirus genomes. This collection of bat and rat coronaviruses genomes provided essential information on the local diversity of coronaviruses and substantially expanded the number of coronavirus full genomes available from bat and rats and may facilitate further molecular studies on this group of viruses.

In the present paper, we investigate the performance of the simultaneous wireless information and power transfer (SWIPT) based cooperative cognitive radio networks (CCRNs). In particular, the outage probability is derived in the closed-form expressions under the opportunistic partial relay selection. Different from the conventional CRNs in which the transmit power of the secondary transmitters count merely on the aggregate interference measured on the primary networks, the transmit power of the SWIPT-enabled transmitters is also constrained by the harvested energy. As a result, the mathematical framework involves more correlated random variables and, thus, is of higher complexity. Monte Carlo simulations are given to corroborate the accuracy of the mathematical analysis and to shed light on the behavior of the OP with respect to several important parameters, e.g., the transmit power and the number of relays. Our findings illustrate that increasing the transmit power and/or the number of relays is beneficial for the outage probability.

This study aims to investigate the association between service innovation (supportive service innovation and interactive service innovation) and the repurchase intention of Generation Z customers in Vietnam. Specifically, it examines the mediation roles of value co-creation and customer satisfaction in the connection between service innovation and consumers' repurchase intention. The conceptual model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the SmartPLS 4 software based on data from 1393 samples validity of Generation Z customers in Vietnam with diverse demographic characteristics in terms of gender, educational level and place. Simultaneously, the study discovered the existence of differences in repurchase intention among customers with different demographic characteristics. The results of this study indicate the service innovation and interactive service innovations influence each other and affect Generation Z customers' repurchase intention. Specifically, the mediation analysis results confirm that value co-creation acts as a full mediator in the relationship between service innovation and repurchase intention. The findings provide theoretical and practical contributions to enhance Generation Z customers' repurchase intention through service innovation, value co-creation, and satisfaction.

Land Surface Temperature (LST) is considered a critical variable for assessing heat stress in urban environments. Understanding LST and its spatial variation is essential to comprehending the interactions between human activity and urban areas. This study investigates the impact of geospatially derived factors—namely built-up density, Normalized Difference Built-up Index (NDBI), road density, Normalized Difference Vegetation Index (NDVI), Bare Soil Index, distance to water bodies, elevation, slope, and aspect— on LST in Hue City, Vietnam, a region with limited prior documentation on this subject. Landsat 8 imagery data, collected in early 2024 during an exceptional heatwave, is utilized for this purpose. Advanced machine learning techniques, including deep neural networks, random forests, and XGBoost, are employed to model the relationship between LST and these explanatory variables. To deepen the understanding of the factors contributing to LST, the study uses the state-of-the-art Shapley Additive Explanations (SHAP) method. Experimental results show that the machine learning approach can accurately estimate the spatial variation of LST. The coefficient of determinations (R 2 ) achieved by deep neural networks, random forests, and XGBoost are 0.83, 0.83, and 0.85, respectively. Sensitivity analysis based on SHAP reveals that built-up density, road density, and the Bare Soil Index are the most crucial variables that positively affect the LST. The factors of distance to water and slope negatively influence the LST. The established data-driven approach, coupled with SHAP, provides a valuable tool for understanding the spatial distribution of LST as well as mapping hot spots that experienced the highest level of urban heat stress. This tool also supports the analysis of mitigation measures for regulating temperature and reducing the impacts of the urban heat island effect.

Background/purpose. Students' social relationships have a significant impact on their academic performance. This study aimed to examine the predictive impact of teacher-student, student-student relationships, and parental involvement on student academic performance. Materials/methods. This study was conducted with 309 students, using the academic performance, relationships with teachers, perceptions of class relationships, and parental involvement scales as the data collection tools, employing multiple regression analysis. Results. Results showed that good relationships with teachers, peers, and parental involvement positively predicted students' academic performance. Among them, relationships with peers had the most significant influence (β=0.268, p<0.01). Results also showed that having intimate friends in class, good relationships with parents, and participating in extracurricular activities have positive impacts on students' academic performance. Conclusion. The findings suggested recommendations for educational policies.

The synthesis of Ru nanoparticles-decorated porous carbon nanofibers (PCNF) for the negative electrode of high-performance Li-O2 batteries was achieved using electrospinning and solvothermal reaction. The process entailed utilizing electrospinning to combine polyacrylonitrile (PAN) with cross-linked poly(methyl methacrylate) (PMMA) colloids. During the carbonization of electrospun PAN-PMMA nanofibers, the PMMA template decomposes and opening-macropores are created inside the CNF, which may provide more sites for Li-O 2 reaction. Additionally, Ru nanoparticles are decorated along the MCNF (RuNP-PCNF) as catalysts for Li-O 2 reactions. Afterwards, the RuNP-PCNF mats are directly used in Li-O 2 battery as free-standing cathode. The battery performance of the RuNP-PCNF such as discharge capacity, cyclability and O 2 efficiency are examined. The lithium batteries with the RuNP-PCNF electrode exhibits much higher complete capacity of 9400 mAh/g compared to the set utilizing the bare CNF electrode (4079 mAh/g) at a current density of 0.2 mA/g. Furthermore, the cell with RuNP-PCNF cathode can maintain for 340 cycles discharge/recharge with a cutoff of 1 Ah/g at 0.5 A/g. These findings indicate a practical and effective method to enhance the potential use of PCNF as cathode for Li-O 2 battery. Developing new materials for batteries is also an integral mission for the Sustainable Development Goals (SDG), as it contributes to ensuring access to sustainable energy.