Effect of coating time on the formation of coating layer and degradation behavior of hydroxyapatite coated ZK60 alloy.

OBJECTIVES: This study aims to investigate the effect of coating time on the formation of hydroxyapatite (HA) coating layer on ZK60 substrate and understand the biodegradation behavior of the coated alloy for biodegradable implant applications. METHODS: Biodegradable ZK60 alloy was coated by HA layer for different times of 0.5, 1, 2, and 4 h by chemical conversion method. After coating, all the coated specimens were used for immersion test in Hanks' solution to understand the effect of coating time on the degradation behavior of the alloy. The degradation rate of the coated alloy was evaluated by Mg2+ ion quantification and pH change during immersion test. The microstructure of the coating layer was examined by scanning electron microscope (SEM) equipped with an energy-dispersive X-ray spectroscopy (EDS) before and after immersion to understand the degradation behavior of the coated alloy. RESULTS: HA coating layers were formed successfully on surface of ZK60 specimens after 0.5, 1, 2, and 4 h with different microstructure. Optimal coating quality was observed at 1 or 2 h, characterized by well-formed and uniform HA layers. However, extending the coating duration to 4 h led to the formation of cracks within the HA layer, accompanied by Mg(OH)2. Specimens coated for 1 and 2 h exhibited the lowest degradation rates, while specimens coated for 0.5 and 4 h showed the highest degradation rates. Furthermore, analysis of degradation products revealed the predominance of calcium phosphates formed on the surface of specimens coated for 1 and 2 h. Conversely, specimens coated for 0.5 and 4 h exhibited Mg(OH)2 as the primary degradation product, suggesting a less effective corrosion barrier under these conditions. CONCLUSION: The HA layer formed after 2 h demonstrated as the most effective coating layer for enhancing the corrosion resistance of the ZK60 alloy for biomedical applications.

Sleep Quality and Poor Sleep-related Factors Among Healthcare Workers During the COVID-19 Pandemic in Vietnam / 예방의학회지

Objectives@#The coronavirus disease 2019 (COVID-19) pandemic has increased the workload of healthcare workers (HCWs), impacting their health. This study aimed to assess sleep quality using the Pittsburgh Sleep Quality Index (PSQI) and identify factors associated with poor sleep among HCWs in Vietnam during the COVID-19 pandemic. @*Methods@#In this cross-sectional study, 1000 frontline HCWs were recruited from various healthcare facilities in Vietnam between October 2021 and November 2021. Data were collected using a 3-part self-administered questionnaire, which covered demographics, sleep quality, and factors related to poor sleep. Poor sleep quality was defined as a total PSQI score of 5 or higher. @*Results@#Participants’ mean age was 33.20±6.81 years (range, 20.0-61.0), and 63.0% were women. The median work experience was 8.54±6.30 years. Approximately 6.3% had chronic comorbidities, such as hypertension and diabetes mellitus. About 59.5% were directly responsible for patient care and treatment, while 7.1% worked in tracing and sampling. A total of 73.8% reported poor sleep quality. Multivariate logistic regression revealed significant associations between poor sleep quality and the presence of chronic comorbidities (odds ratio [OR], 2.34; 95% confidence interval [CI], 1.17 to 5.24), being a frontline HCW directly involved in patient care and treatment (OR, 1.59; 95% CI, 1.16 to 2.16), increased working hours (OR, 1.84; 95% CI,1.37 to 2.48), and a higher frequency of encountering critically ill and dying patients (OR, 1.42; 95% CI, 1.03 to 1.95). @*Conclusions@#The high prevalence of poor sleep among HCWs in Vietnam during the COVID-19 pandemic was similar to that in other countries. Working conditions should be adjusted to improve sleep quality among this population.

Tobacco and electronic cigarette smoking among in-school adolescents in Vietnam between 2013 and 2019: prevalence and associated factors.

BACKGROUND: Smoking among adolescents in schools is a major global public health concern. There is limited evidence regarding prevalence and associated factors in Vietnam. OBJECTIVE: To compare the prevalence of smoking and associated factors among in-school adolescents aged 13-17 years in Vietnam between 2013 and 2019. METHODS: Data were collected from two rounds of the national representative Vietnam Global School-based Student Health Survey (GSHS) conducted in 2013 (n = 3,331) and 2019 (n = 7,690). Logistic regression was used to identify the factors associated with tobacco and electronic cigarette smoking among in-school adolescents. RESULTS: There was a significant reduction in the prevalence of current smoking (water pipes and cigarettes) from 5.4% (95% CI: 4.0-7.2) in 2013 to 2.8% (95% CI: 2.2-3.6) in 2019. In 2019, 2.6% of the in-school adolescents reported having used electronic cigarette products 30 days prior to the survey. Factors associated with a significantly higher likelihood of current smoking status included gender, loneliness, suicidal ideation, sexual activity, truancy, and alcohol consumption. Similar patterns were observed for e-cigarettes. CONCLUSION: Smoking among in-school adolescents in Vietnam decreased between 2013 and 2019. Follow-up studies are needed to further investigate causal factors so that future policies and communication programmes can be more effectively targeted to reduce smoking in adolescents.

Antioxidant activity and α-glucosidase inhibitability of Distichochlamys citrea M.F. Newman rhizome fractionated extracts: in vitro and in silico screenings.

Distichochlamys citrea M.F. Newman (commonly known as "Black Ginger") is an endemic plant to Vietnam and has been extensively exploited by folk medication for treatments of infection-related diseases and diabetes. In this work, its rhizomes were subjected to fractionated extraction, phytochemical examination, evaluation of antioxidant effect by DDPH free radical neutralization, and inhibitory activity toward α-glucosidase. The compositional components were subjected to in silico screening, including density functional theory calculation, molecular docking simulation, physicochemical analysis, and pharmacokinetic regression. In the trials, EtOAc fraction is found as the bioactive part of most effectiveness, regarding both antioxidant effect (IC50 = 90.27 µg mL-1) and α-glucosidase inhibitory activity (IC50 = 115.75 µg mL-1). Chemical determination reveals there are 13 components of its composition. DFT-based calculations find no abnormal constraints in their structures. Docking-based simulation provides order of inhibitory effectiveness: 3-P53341 > 12-P53341 > 7-P53341 > 4-P53341 > 11-P53341 > 10-P53341. QSARIS-based investigations implicate their biocompatibility. ADMET-based regressions indicate that all candidates are generally safe for medicinal applications. The findings would contribute to the basis for further studies on the chemical compositions of Distichochlamys citrea and their biological activities. Supplementary Information: The online version contains supplementary material available at 10.1007/s11696-022-02273-2.

An AI-based Prediction Model for Drug-drug Interactions in Osteoporosis and Paget's Diseases from SMILES.

The skeleton is one of the most important organs in the human body in assisting our motion and activities; however, bone density attenuates gradually as we age. Among common bone diseases are osteoporosis and Paget's, two of the most frequently found diseases in the elderly. Nowadays, a combination of multiple drugs is the optimal therapy to decelerate osteoporosis and Paget's pathologic process, which comes with various underlying adverse effects due to drug-drug interactions (DDIs). Artificial intelligence (AI) has the potential to evaluate the interaction, pharmacodynamics, and possible side effects between drugs. In this research, we created an AI-based machine-learning model to predict the outcomes of interactions between drugs used for osteoporosis and Paget's treatment, which helps mitigate the cost and time to implement the best combination of medications in clinical practice. In this study, a DDI dataset was collected from the DrugBank database within the osteoporosis and Paget diseases. We then extracted a variety of chemical features from the simplified molecular-input line-entry system (SMILES) of defined drug pairs that interact with each other. Finally, machine-learning algorithms were implemented to learn the extracted features. Our stack ensemble model from Random Forest and XGBoost reached an average accuracy of 74 % in predicting DDIs. It was superior to individual models as well as previous methods in terms of most measurement metrics. This study showed the potential of AI models in predicting DDIs of Osteoporosis-Paget's disease in particular, and other diseases in general.

Clustering Lifestyle Risk Behaviors among Vietnamese Adolescents and Roles of School: A Bayesian Multilevel Analysis of Global School-Based Student Health Survey 2019.

BACKGROUND: Adolescence is a vulnerable period for many lifestyle risk behaviors. In this study, we aimed to 1) examine a clustering pattern of lifestyle risk behaviors; 2) investigate roles of the school health promotion programs on this pattern among adolescents in Vietnam. METHODS: We analyzed data of 7,541 adolescents aged 13-17 years from the 2019 nationally representative Global School-based Student Health Survey, conducted in 20 provinces and cities in Vietnam. We applied the latent class analysis to identify groups of clustering and used Bayesian 2-level logistic regressions to evaluate the correlation of school health promotion programs on these clusters. We reassessed the school effect size by incorporating different informative priors to the Bayesian models. FINDINGS: The most frequent lifestyle risk behavior among Vietnamese adolescents was physical inactivity, followed by unhealthy diet, and sedentary behavior. Most of students had a cluster of at least two risk factors and nearly a half with at least three risk factors. Latent class analysis detected 23% males and 18% females being at higher risk of lifestyle behaviors. Consistent through different priors, high quality of health promotion programs associated with lower the odds of lifestyle risk behaviors (highest quality schools vs. lowest quality schools; males: Odds ratio (OR) = 0·67, 95% Highest Density Interval (HDI): 0·46 - 0·93; females: OR = 0·69, 95% HDI: 0·47 - 0·98). INTERPRETATION: Our findings demonstrated the clustering of specific lifestyle risk behaviors among Vietnamese in-school adolescents. School-based interventions separated for males and females might reduce multiple health risk behaviors in adolescence. FUNDING: The 2019 Global School-based Student Health Survey was conducted with financial support from the World Health Organization. The authors received no funding for the data analysis, data interpretation, manuscript writing, authorship, and/or publication of this article.

Machine Learning Model for Identifying Antioxidant Proteins Using Features Calculated from Primary Sequences.

Antioxidant proteins are involved importantly in many aspects of cellular life activities. They protect the cell and DNA from oxidative substances (such as peroxide, nitric oxide, oxygen-free radicals, etc.) which are known as reactive oxygen species (ROS). Free radical generation and antioxidant defenses are opposing factors in the human body and the balance between them is necessary to maintain a healthy body. An unhealthy routine or the degeneration of age can break the balance, leading to more ROS than antioxidants, causing damage to health. In general, the antioxidant mechanism is the combination of antioxidant molecules and ROS in a one-electron reaction. Creating computational models to promptly identify antioxidant candidates is essential in supporting antioxidant detection experiments in the laboratory. In this study, we proposed a machine learning-based model for this prediction purpose from a benchmark set of sequencing data. The experiments were conducted by using 10-fold cross-validation on the training process and validated by three different independent datasets. Different machine learning and deep learning algorithms have been evaluated on an optimal set of sequence features. Among them, Random Forest has been identified as the best model to identify antioxidant proteins with the highest performance. Our optimal model achieved high accuracy of 84.6%, as well as a balance in sensitivity (81.5%) and specificity (85.1%) for antioxidant protein identification on the training dataset. The performance results from different independent datasets also showed the significance in our model compared to previously published works on antioxidant protein identification.

Tuberculous Arthritis of the Knee with Rice Body Formation: A Report of a Rare Case.

In this report, we present the case of a 53-year-old man with rice body formation in the right knee caused by tuberculous arthritis (TB arthritis). The patient visited our hospital in January 2018 with a seven-month history of swelling and pain in the right knee. He had no previous history of tuberculosis, and the results of the routine laboratory tests were within normal limits; he also tested negative for rheumatoid factor. Magnetic resonance (MR) imaging revealed multiple rice bodies in the right knee, measuring 5-8 mm. He underwent an arthroscopic operation in the right knee in January 2018 and received antituberculosis polytherapy for 6 months. He was followed-up for more than 01 year. The patient regained good function of the operated knee with no evidence of recurrence during the last follow-up in February 2019. Conclusion. The biggest challenge in diagnosing tuberculosis arthritis is the consideration of its possibility in the differential diagnosis, not only in endemic countries where tuberculosis is frequent. A high level of suspicion for TB should be maintained for every infection of the knee joint, particularly in the case of intra-articular rice bodies.