An insight into the antioxidant activities and the exploration of record high quantities of quercetin and luteolin in the plant parts of Myxopyrum smilacifolium (wall.) Blume.

This study attempts to reveal antioxidants in the plant parts of Myxopyrum smilacifolium (Wall.) Blume using antioxidant assays and LC-MS/MS analysis. Methanol is the most effective solvent for collecting antioxidants. The roots-derived methanol extract demonstrates the greatest antioxidant activity, corresponding to the extremely low IC50 values of 16.39 µg/mL and 19.80 µg/mL for DPPH and ABTS radicals, respectively. The high phenolic and flavonoid contents are the primary reason for outstanding total antioxidant capacity (TAC; i.e. 247.73 ± 1.62 mg GA/g or 163.93 ± 0.83 mg AS/g) of the root extract. LC-MS/MS quantification of five phenolic compounds reveals exceptionally high amounts of quercetin and luteolin in the root extract, ranging from 238.86 ± 5.74 to 310.99 ± 1.44 µg/g and from 201.49 ± 7.84 to 234.10 ± 2.54 µg/g, respectively, in the root-derived methanol extract. The achievement highlights M. smilacifolium as a promising source of natural antioxidants for large-scale medical applications.

BM-BronchoLC - A rich bronchoscopy dataset for anatomical landmarks and lung cancer lesion recognition.

Flexible bronchoscopy has revolutionized respiratory disease diagnosis. It offers direct visualization and detection of airway abnormalities, including lung cancer lesions. Accurate identification of airway lesions during flexible bronchoscopy plays an important role in the lung cancer diagnosis. The application of artificial intelligence (AI) aims to support physicians in recognizing anatomical landmarks and lung cancer lesions within bronchoscopic imagery. This work described the development of BM-BronchoLC, a rich bronchoscopy dataset encompassing 106 lung cancer and 102 non-lung cancer patients. The dataset incorporates detailed localization and categorical annotations for both anatomical landmarks and lesions, meticulously conducted by senior doctors at Bach Mai Hospital, Vietnam. To assess the dataset's quality, we evaluate two prevalent AI backbone models, namely UNet++ and ESFPNet, on the image segmentation and classification tasks with single-task and multi-task learning paradigms. We present BM-BronchoLC as a reference dataset in developing AI models to assist diagnostic accuracy for anatomical landmarks and lung cancer lesions in bronchoscopy data.

Continuity of primary care for type 2 diabetes and hypertension and its association with health outcomes and disease control: insights from Central Vietnam.

BACKGROUND: Vietnam is undergoing a rapid epidemiological transition with a considerable burden of non-communicable diseases (NCDs), especially hypertension and diabetes (T2DM). Continuity of care (COC) is widely acknowledged as a benchmark for an efficient health system. This study aimed to determine the COC level for hypertension and T2DM within and across care levels and to investigate its associations with health outcomes and disease control. METHODS: A cross-sectional study was conducted on 602 people with T2DM and/or hypertension managed in primary care settings. We utilized both the Nijmegen continuity of care questionnaire (NCQ) and the Bice - Boxerman continuity of care index (COCI) to comprehensively measure three domains of COC: interpersonal, informational, and management continuity. ANOVA, paired-sample t-test, and bivariate and multivariable logistic regression analysis were performed to examine the predictors of COC. RESULTS: Mean values of COC indices were: NCQ: 3.59 and COCI: 0.77. The proportion of people with low NCQ levels was 68.8%, and that with low COCI levels was 47.3%. Primary care offered higher informational continuity than specialists (p < 0.01); management continuity was higher within the primary care team than between primary and specialist care (p < 0.001). Gender, living areas, hospital admission and emergency department encounters, frequency of health visits, disease duration, blood pressure and blood glucose levels, and disease control were demonstrated to be statistically associated with higher levels of COC. CONCLUSIONS: Continuity of primary care is not sufficiently achieved for hypertension and diabetes mellitus in Vietnam. Strengthening robust primary care services, improving the collaboration between healthcare providers through multidisciplinary team-based care and integrated care approach, and promoting patient education programs and shared decision-making interventions are priorities to improve COC for chronic care.

Hexagonal-borocarbonitride (h-BCN) based heterostructure photocatalyst for energy and environmental applications: A review.

Formulation of heterojunction with remarkable high efficiency by utilizing solar light is promising to synchronously overcome energy and environmental crises. In this concern, hexagonal-borocarbonitride (h-BCN) based Z-schemes have proved potential candidates due to their spatially separated oxidation and reduction sites, robust light-harvesting ability, high charge pair migration and separation, and strong redox ability. H-BCN has emerged as a hotspot in the research field as a metal-free photocatalyst with a tunable bandgap range of 0-5.5 eV. The BCN photocatalyst displayed synergistic benefits of both graphene and boron nitride. Herein, the review demonstrates the current state-of-the-art in the Z-scheme photocatalytic application with a special emphasis on the predominant features of their photoactivity. Initially, fundamental aspects and various synthesis techniques are discussed, including thermal polymerization, template-assisted, and template-free methods. Afterward, the reaction mechanism of direct Z-scheme photocatalysts and indirect Z-scheme (all-solid-state) are highlighted. Moreover, the emerging Step-scheme (S-scheme) systems are briefly deliberated to understand the charge transfer pathway mechanism with an induced internal electric field. This review critically aims to comprehensively summarize the photo-redox applications of various h-BCN-based heterojunction photocatalysts including CO2 photoreduction, H2 evolution, and pollutants degradation. Finally, some challenges and future direction of h-BCN-based Z-scheme photocatalyst in environmental remediation are also proposed.

Investigating Effects of COVID-19 Pandemic on the Mental Health of Nursing Students During Remote Learning: An Experience from Dong A University, Vietnam.

Introduction: There was a radically changed in nursing education during the nationwide lockdown due to the COVID-19 outbreaks. The transition to remote learning stressed nursing students in many countries, particularly in Vietnam. However, there is still lacking a novel study to describe the mental characteristics of nursing students in detail. Objectives: To assess the mental health of nursing students, including stress, anxiety, and depression, and to identify the related factors to their mental health during the online study period because of the COVID-19 pandemic. Methods: A cross-sectional survey was conducted on 540 nursing students at Dong A university using a socio-demographic questionnaire, the Depression, Anxiety, and Stress Scale - 21 Items (DASS-21). Data were analyzed by descriptive statistics and tests, including Mann-Whitney, Kruskal-Wallis, and Spearman's correlation to identify the related factors. Results: In total 540 participants, nursing students reported stress (N = 120, 22.2%), anxiety (n = 195, 36.1%), and depression symptoms (n = 135, 23.1%). There was a significant relationship between age, work status, married status, number of children, stress, anxiety, and depression (P < 0.01). In addition, our study showed a negative correlation between frequency of physical activity, perceived health and stress (r = -0.117; p < 0.01, r = -0.127, p < 0.01), anxiety (r = -0.133; p < 0.01, r = -0.112, p < 0.01), depression (r = -0.134; p < 0.01, r = -0.135, p < 0.01). A significant relationship was observed between e-learning space and Internet status with mental health (p < 0.05). Especially, there was no association between average online learning time, academic workload, stress, anxiety, and depression (p > 0.05). However, the authors found a positive association between perceived level of stress related to evaluative activities and stress, anxiety, depression (r = 0.120, p < 0.01; r = 0.089, p < 0.05; r = 0.088, p < 0.05). Conclusion: Nursing students suffered stress, anxiety, and depression during online learning due to the COVID-19 pandemic in the presence of some related factors. Therefore, this study may increase more attention of universities, families, and governments to reduce the stress of nursing students during distance education.

Developing a new approach for design support of subsurface constructed wetland using machine learning algorithms.

Knowing the effluent quality of treatment systems in advance to enable the design of treatment systems that comply with environmental standards is a realistic strategy. This study aims to develop machine learning - based predictive models for designing the subsurface constructed wetlands (SCW). Data from the SCW literature during the period of 2009-2020 included 618 sets and 10 features. Five algorithms namely, Random forest, Classification and Regression trees, Support vector machines, K-nearest neighbors, and Cubist were compared to determine an optimal algorithm. All nine input features including the influent concentrations, C:N ratio, hydraulic loading rate, height, aeration, flow type, feeding, and filter type were confirmed as relevant features for the predictive algorithms. The comparative result revealed that Cubist is the best algorithm with the lowest RMSE (7.77 and 21.77 mg.L-1 for NH4-N and COD, respectively) corresponding to 84% of the variance in the effluents explained. The coefficient of determination of the Cubist algorithm obtained for NH4-N and COD prediction from the test data were 0.92 and 0.93, respectively. Five case studies of the application of SCW design were also exercised and verified by the prediction model. Finally, a fully developed Cubist algorithm-based design tool for SCW was proposed.

Potential application of machine learning for exploring adsorption mechanisms of pharmaceuticals onto biochars.

The increasing accumulation of pharmaceuticals in aquatic ecosystems could impair freshwater quality and threaten human health. Despite the adsorption of pharmaceuticals on biochars is one of the most cost-effective and eco-friendly removal methods, the wide variation of experimental designs and research aims among previous studies pose significant challenge in selecting biochar for optimal removal. In this work, literature data of 1033 sets with 21 variables collected from 267 papers over ten years (2010-2020) covering 19 pharmaceuticals onto 88 biochars were assessed by different machine learning (ML) algorithms i.e., Linear regression model (LM), Feed-forward neural networks (NNET), Deep neutral networks (DNN), Cubist, K-nearest neighbor (KNN), and Random forest (RF), to predict equilibrium adsorption capacity (Qe) and explore adsorption mechanisms. LM showed the best performance on ranking importance of input variables. Except for initial concentration of pharmaceuticals, Qe was strongly governed by biochars' properties including specific surface area (BET), pore volume (PV), and pore structure (PS) rather than pharmaceuticals' properties and experimental conditions. The most accurate model for estimating Qe was achieved by Cubist, followed by KNN, RF, KNN, NNET and LM. The generalization ability was observed by the tuned Cubist with 26 rules for the prediction of the unseen data. This study not only provides an insightful evidence for data-based adsorption mechanisms of pharmaceuticals on biochars, but also offers a potential method to accurately predict the biochar adsorption performance without conducting any experiments, which will be of high interests in practice in terms of water/wastewater treatment using biochars.

Status of water use and potential of rainwater harvesting for replacing centralized supply system in remote mountainous areas: a case study.

The failure of the centralized water supply system forced XY community to become more dependent on uncertain and unstable water sources. The results of surveying 50 households showed that 89.18% of total households depended on water collected from rivers, which contributed 58.3% of the total water volume used for the domestic demands. The average water volume consumed was 19.5 liters/person/day (l/p/d), and 86.5% of households used more than one source; 13.5% of households collected water only from rivers, and 45.94% of families had rainwater harvesting (RWH) for their activities (domestic water demand); however, RWH only provided 9.9% of total water consumption. In this study, basic methods were applied to calculate the storage tanks necessary to balance the water deficit created by drought months. Three levels of water demand (14, 20, and 30 l/p/d) can be the best choices for RWH; for a higher demand (40 and 60 l/p/d), small roof area (30-40 m2), and many people (six to seven) per family, RWH might be impractical because of unsuitable rainfall or excessively large storage tanks.

Etiology of vaginal hypersecretion syndrome in patients at Consulting Department of the Institute of Dermatology and Venereology from January to Septeber 2002

A study was carried out on 987 women with etiology of vaginal hypersecretion syndrome at Consulting Department of the Institute of Dermatology and Venerology from January to September 2002. The patients’ age was 18-25 and having sex previously. Results of the study: the rate of 24.4% infected with Candida fungi; 2.6% with gonococci, 2% with Trichomonas, 19.1% with Chlamydia trachomatis and 7.2% with anaerobic bacteria