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Objectives. We investigated the relationships among early childhood caries (ECC), mouth pain, and nutritional status in children aged 1 to 6 years in Southern and Central Vietnam. Methods. A total of 593 parent–child pairs were recruited from 5 kindergartens or preschools in Ho-Chi Minh City and Da Nang. Parents completed surveys about dietary habits, oral health practices, and children’s mouth pain experience; children received anthropometric assessment and dental examinations. Results. There was a high prevalence of dental caries (74.4%), mostly untreated, and mouth pain (47.1%). Moderate correlations were found between parents’ and children’s consumption of soda (ρ = 0.361; P < .001) and salty snacks (ρ = 0.292; P < .001). Severity of ECC was associated with decreased weight- and body mass index-for-age z-scores. Presence of pulp-involved caries was associated with strikingly lower height-for-age (mean difference = 0.66; P = .001), weight-for-age (mean difference = 1.17; P < .001), and body mass index-for-age (mean difference = 1.18; P < .001) z-scores. Mouth pain was associated with lower body mass index-for-age z-scores (mean difference = 0.29; P = .013). Conclusions. ECC might negatively affect children’s nutritional status, which might be mediated by the depth of decay, chronic inflammation, and mouth pain. Family-based and prevention-oriented nutrition and oral health programs are needed and should start during pregnancy and infancy.

Feasibility design of a Th-TRU fuel assembly for a typical pressurized water reactor was conducted using the SRAC code and the JENDL-4.0 library. The content of TRU in the homogeneously mixed Th-TRU fuel cell was surveyed in the design process. The design objectives include target burnup, pin-wise power distribution, pin power peaking factor (PPF) and safety-related reactivity coefficients. The selected assembly consists of the TRU content of 17 wt% with the outer layer of 19 wt% TRU and some fuel pins of 15 wt% TRU in the middle region for achieving the target burnup of 65 GWd/t. The pin-wise power distribution is flattened with the PPF of 1.068, which is approximate that of the reference design. Negative reactivity coefficients of the new assembly are ensured.

is a commensal bacteria species that can cause various illnesses, from mild skin infections to severe diseases, such as bacteremia. The distribution and antimicrobial resistance (AMR) pattern of varies by population, time, geographic location, and hospital wards. In this study, we elucidated the epidemiology and AMR patterns of isolated from a general hospital in Vietnam. This was a cross-sectional study. Data on all infections from 2014 to 2021 were collected from the Microbiology department of Military Hospital 103, Vietnam. Only the first isolation from each kind of specimen from a particular patient was analyzed using the Cochran-Armitage and chi-square tests. A total of 1130 individuals were diagnosed as infection. Among them, 1087 strains were tested for AMR features. Most patients with infection were in the age group of 41-65 years (39.82%). isolates were predominant in the surgery wards, and pus specimens were the most common source of isolates (50.62%). was most resistant to azithromycin (82.28%), erythromycin (82.82%), and clindamycin (82.32%) and least resistant to teicoplanin (0.0%), tigecycline (0.16%), quinupristin-dalfopristin (0.43%), linezolid (0.62%), and vancomycin (2.92%). Methicillin-resistant (MRSA) and multidrug-resistant (MDR) were prevalent, accounting for 73.02% and 60.90% of the total strains respectively, and the strains isolated from the intensive care unit (ICU) had the highest percentage of multidrug resistance (77.78%) among the wards. These findings highlight the urgent need for continuous AMR surveillance and updated treatment guidelines, particularly considering high resistance in MRSA, MDR strains, and ICU isolates. Future research focusing on specific resistant populations and potential intervention strategies is crucial to combat this rising threat.

Mesenchymal stem cell (MSC)-derived exosomes (EXs) have emerged as promising therapeutic agents for wound healing. However, the optimal conditions for manufacturing MSC-derived EXs that maximize their wound-healing potential have yet to be established. Hence, we compared the efficacy of five different MSC culture media, including three different serum-free, a platelet-supplemented, and a fetal bovine serum-supplemented media, in exosome manufacturing for wound healing applications. Although umbilical cord-derived MSCs (UCMSCs) cultured in these media exhibited similar proliferation, morphology, MSC surface marker expression, and stemness, EXs derived from UCMSCs cultured in different culture media displayed varying levels of growth factors and cytokines. Notably, EXs derived from platelet-supplemented media (DM-PLT_EXs) exhibited significantly higher concentrations of keratinocyte growth factor (KGF), vascular endothelial growth factor (VEGF-A), platelet-derived growth factor (PDGF-BB), interleukin 6 (IL-6), interleukin 7 (IL-7), and interleukin 8 (IL-8) than EXs from other media. These differences correlated with the superior capability of DM-PLT_EXs to promote human skin fibroblast proliferation and stimulate angiogenesis of human umbilical vein endothelial cells, making them a more suitable choice for wound healing applications. Our findings emphasize the significance of the culture medium selection in tailoring the therapeutic potential of UCMSC-derived EXs for wound healing.

Developing a compact circularly polarized (CP) antenna with good radiation characteristics for handheld radio frequency identification (RFID) readers is a very challenging task. Many compact CP antennas have been reported in the open literature, but most suffer from critical drawbacks of low gain and/or high back radiation. This paper presents a metasurface (MS) based CP antenna with compact size, high gain, and high front-to-back ratio characteristics. The compact size of the proposed design is achieved by using a 2 × 2 unit-cell MS, while the CP realization is accomplished through a coupling between the MS and a Y-shaped patch as a primary CP source. The final antenna has compact overall dimensions of 0.45λ × 0.45λ × 0.02λ, where λ is the guided wavelength at the center frequency. The operating bandwidth is about 2.0% (2.43–2.48 GHz) and the broadside gain is about 6.3 dBi. Besides, the front-to-back ratio (FBR) defined by the difference gain levels between the forward and backward directions is about 18 dB. Compared with the related compact CP antennas in the literature, the proposed design has the advantages of high gain and high FBR, making it suitable for compact RFID readers.

Cyber security has become increasingly challenging due to the proliferation of the Internet of things (IoT), where a massive number of tiny, smart devices push trillion bytes of data to the Internet. However, these devices possess various security flaws resulting from the lack of defense mechanisms and hardware security support, therefore making them vulnerable to cyber attacks. In addition, IoT gateways provide very limited security features to detect such threats, especially the absence of intrusion detection methods powered by deep learning. Indeed, deep learning models require high computational power that exceeds the capacity of these gateways. In this paper, we introduce Realguard, an DNN-based network intrusion detection system (NIDS) directly operated on local gateways to protect IoT devices within the network. The superiority of our proposal is that it can accurately detect multiple cyber attacks in real time with a small computational footprint. This is achieved by a lightweight feature extraction mechanism and an efficient attack detection model powered by deep neural networks. Our evaluations on practical datasets indicate that Realguard could detect ten types of attacks (e.g., port scan, Botnet, and FTP-Patator) in real time with an average accuracy of 99.57%, whereas the best of our competitors is 98.85%. Furthermore, our proposal effectively operates on resource-constraint gateways (Raspberry PI) at a high packet processing rate reported about 10.600 packets per second.

Dengue fever (DF) represents a significant health burden in Vietnam, which is forecast to worsen under climate change. The development of an early-warning system for DF has been selected as a prioritised health adaptation measure to climate change in Vietnam. This study aimed to develop an accurate DF prediction model in Vietnam using a wide range of meteorological factors as inputs to inform public health responses for outbreak prevention in the context of future climate change. Convolutional neural network (CNN), Transformer, long short-term memory (LSTM), and attention-enhanced LSTM (LSTM-ATT) models were compared with traditional machine learning models on weather-based DF forecasting. Models were developed using lagged DF incidence and meteorological variables (measures of temperature, humidity, rainfall, evaporation, and sunshine hours) as inputs for 20 provinces throughout Vietnam. Data from 1997-2013 were used to train models, which were then evaluated using data from 2014-2016 by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). LSTM-ATT displayed the highest performance, scoring average places of 1.60 for RMSE-based ranking and 1.95 for MAE-based ranking. Notably, it was able to forecast DF incidence better than LSTM in 13 or 14 out of 20 provinces for MAE or RMSE, respectively. Moreover, LSTM-ATT was able to accurately predict DF incidence and outbreak months up to 3 months ahead, though performance dropped slightly compared to short-term forecasts. To the best of our knowledge, this is the first time deep learning methods have been employed for the prediction of both long- and short-term DF incidence and outbreaks in Vietnam using unique, rich meteorological features. This study demonstrates the usefulness of deep learning models for meteorological factor-based DF forecasting. LSTM-ATT should be further explored for mitigation strategies against DF and other climate-sensitive diseases in the coming years.

In this article, we analyse the non-local model: ∂ t u ( t , x ) = J ⋆ u ( t , x ) - u ( t , x ) + f ( x , u ( t , x ) ) for t > 0 , and x ∈ R N , where J is a positive continuous dispersal kernel and f ( x ,  u ) is a heterogeneous KPP type non-linearity describing the growth rate of the population. The ecological niche of the population is assumed to be bounded (i.e. outside a compact set, the environment is assumed to be lethal for the population). For compactly supported dispersal kernels J , we derive an optimal persistence criteria. We prove that a positive stationary solution exists if and only if the generalised principal eigenvalue λ p of the linear problem J ⋆ φ - φ + ∂ s f ( x , 0 ) φ + λ p φ = 0 in R N , is negative. λ p is a spectral quantity that we defined in the spirit of the generalised first eigenvalue of an elliptic operator. In addition, for any continuous non-negative initial data that is bounded or integrable, we establish the long time behaviour of the solution u ( t ,  x ). We also analyse the impact of the size of the support of the dispersal kernel on the persistence criteria. We exhibit situations where the dispersal strategy has “no impact” on the persistence of the species and other ones where the slowest dispersal strategy is not any more an “Ecological Stable Strategy”. We also discuss persistence criteria for fat-tailed kernels.

Inflammation conditions are associated with autism spectrum disorder (ASD) and cerebral palsy (CP), primarily observed in the peripheral immune system. However, the extent of neuro-inflammation and neuro-immune dysregulation remains poorly studied. In this study, we analyzed the composition of cerebrospinal fluid (CSF) to uncover the inflammatory mediators driving the neuro-immune system in ASD and CP patients. Our findings revealed that ASD patients had elevated levels of four inflammatory cytokines (TNF-α, IL-4, IL-21, and BAFF) compared to controls, while CP patients exhibited increased levels of eight inflammatory cytokines (IFN-γ, GM-CSF, TNF-α, IL-2, IL-4, IL-6, IL-17A and IL-12), one anti-inflammatory cytokine (IL-10), and five growth factors (GFs) (NGF-β, EGF, GDF-15, G-CSF and BMP-9) compared to both controls and ASD patients. Additionally, intrathecal infusion of autologous bone marrow mononuclear cells (BMMNCs) led to a slight decrease in TGF-β and GDF-15 levels in the CSF of ASD and CP patients, respectively. Our study provides new insights into the molecular composition of CSF in ASD and CP patients, with the potential to develop more effective diagnosis methods and improved treatment for these diseases. Clinical trial registration CSF samples used in this study are from clinical trials NCT03225651, NCT05307536, NCT02569775, NCT03123562, NCT02574923, NCT05472428 and previous reports [7, 9, 17–19].