Secondhand Smoke and Socio-Demographic Associated Factors: Results From the Vietnam Population-Based Provincial Global Adult Tobacco Survey in 2022.

Background: Secondhand smoke (SHS) has detrimental effects on community health, including coronary heart diseases, stroke, lung cancer etc. This manuscript exploited data from the Vietnam Population-based Provincial Global Adult Tobacco Survey (PGATS) in 2022 to update the prevalence of adult exposure to SHS and associated socio-demographic factors. Methods: With the sample size of 71,981 adults aged 15+ throughout 30 provinces and cities in Vietnam, data was collected using the Global Adult Tobacco Survey (GATS) questionnaire. Bivariate analysis and multivariate logistic regression modelling were performed. Results: In the past 30 days, 44.4% (95% CI: 44.0%-44.9%) adults aged 15+ exposed to SHS at home while 23.1% (95% CI: 22.6%-23.6%) exposed to SHS at work. Female exposure to SHS in the past 30 days was higher at homes but lower at indoor workplaces. Participants aged 15-24 were likely to have higher odds of SHS exposure in the past 30 days to other age groups. Those living in the urban areas had 1.15 times higher odds (95% CI: 1.08-1.22) of exposure to SHS than those in the rural areas. Current smokers tended to have 2.2 times higher odds of exposure to SHS at the indoor workplaces compared to non-smokers (95% CI: 2.05-2.37). Conclusions: The prevalence of exposure to SHS at home was still relatively high amongst the adult population. While there was a significant reduction of SHS exposure at indoor workplaces, there was a higher prevalence of women being exposed to SHS at home. The Government of Vietnam should continue to strictly implement the smoke-free environment resolution at indoor workplaces and appropriate communication campaigns to protect people, especially women from SHS exposure at homes.

Potential of hospital wastewater treatment using locally isolated Chlorella sp. LH2 from cocoon wastewater.

Chlorella sp. is able to grow and transform inorganic and organic contaminants in wastewater to create biomass. In the present study, Chlorella sp. LH2 isolated from cocoon wastewater was able to thrive in hospital wastewater, then remove nutrients and eliminate E. coli ATCC 8739. The results indicated that optimal cultivation conditions of Chlorella sp. LH2 in hospital wastewater were pH of 8, light:dark cycle of 16:8 at 30oC. The inhibitory effect of chlorination on algae growth was accompanied with the chlorine concentration. BOD5:COD ratio of 0.77 indicated biodegradability of hospital wastewater. The untreated and treated wastewatee samples were collected to investigated the nutrient removal efficiency after 10 days. Untreated and treated results were192 ± 8.62 mg/l 23.91 ± 2.19 mg/l for BOD5; 245 ± 9.15 mg/l and 47.31 ± 5.71 mg/l for COD. The treated value met the required standards for hospital wastewater treatment. The removal efficiency total nitrogen and total phosphorus were 68.64% and 64.44% after 10 days, respectively. Elimination of E. coli ATCC 8739 after 7 days by Chlorella sp. LH2 was 88.92%. The results of this study suggest the nutrients and pathogens removal potential of Chlorella sp. LH2 in hospital wastewater for further practical applications.

Analytical validation of NeXT Personal®, an ultra-sensitive personalized circulating tumor DNA assay.

We describe the analytical validation of NeXT Personal®, an ultra-sensitive, tumor-informed circulating tumor DNA (ctDNA) assay for detecting residual disease, monitoring therapy response, and detecting recurrence in patients diagnosed with solid tumor cancers. NeXT Personal uses whole genome sequencing of tumor and matched normal samples combined with advanced analytics to accurately identify up to ~1,800 somatic variants specific to the patient's tumor. A personalized panel is created, targeting these variants and then used to sequence cell-free DNA extracted from patient plasma samples for ultra-sensitive detection of ctDNA. The NeXT Personal analytical validation is based on panels designed from tumor and matched normal samples from two cell lines, and from 123 patients across nine cancer types. Analytical measurements demonstrated a detection threshold of 1.67 parts per million (PPM) with a limit of detection at 95% (LOD95) of 3.45 PPM. NeXT Personal showed linearity over a range of 0.8 to 300,000 PPM (Pearson correlation coefficient = 0.9998). Precision varied from a coefficient of variation of 12.8% to 3.6% over a range of 25 to 25,000 PPM. The assay targets 99.9% specificity, with this validation study measuring 100% specificity and in silico methods giving us a confidence interval of 99.92 to 100%. In summary, this study demonstrates NeXT Personal as an ultra-sensitive, highly quantitative and robust ctDNA assay that can be used to detect residual disease, monitor treatment response, and detect recurrence in patients.

Optimising the manufacturing of a ß-Ti alloy produced via direct energy deposition using small dataset machine learning.

Successful additive manufacturing involves the optimisation of numerous process parameters that significantly influence product quality and manufacturing success. One commonly used criteria based on a collection of parameters is the global energy distribution (GED). This parameter encapsulates the energy input onto the surface of a build, and is a function of the laser power, laser scanning speed and laser spot size. This study uses machine learning to develop a model for predicting manufacturing layer height and grain size based on GED constituent process parameters. For both layer height and grain size, an artificial neural network (ANN) reduced error over the data set compared with multi linear regression. Layer height predictions using ANN achieved an R2 of 0.97 and a root mean square error (RMSE) of 0.03 mm, while grain size predictions resulted in an R2 of 0.85 and an RMSE of 9.68 µm. Grain refinement was observed when reducing laser power and increasing laser scanning speed. This observation was successfully replicated in another α + ß Ti alloy. The findings and developed models show why reproducibility is difficult when solely considering GED, as each of the constituent parameters influence these individual responses to varying magnitudes.

Biophysical Characterization and Cryopreservation of Mammalian Cells Using Ionic Liquids.

Ionic liquids (ILs) are a diverse class of solvents which can be selected for task-specific properties, making them attractive alternatives to traditional solvents. To tailor ILs for specific biological applications, it is necessary to understand the structure-property relationships of ILs and their interactions with cells. Here, a selection of carboxylate anion-based ILs were investigated as cryoprotectants, which are compounds added to cells before freezing to mitigate lethal freezing damage. The cytotoxicity, cell permeability, thermal behavior, and cryoprotective efficacy of the ILs were assessed with two model mammalian cell lines. We found that the biophysical interactions, including permeability of the ILs, were influenced by considering the IL pair together, rather than as single species acting independently. All of the ILs tested had high cytotoxicity, but ethylammonium acetate demonstrated good cryoprotective efficacy for both cell types tested. These results demonstrate that despite toxicity, ILs may be suitable for certain biological applications. It also demonstrates that more research is required to understand the contribution of ion pairs to structure-property relationships and that knowing the behavior of a single ionic species will not necessarily predict its behavior as part of an IL.

Effect of ambient air pollution on hospital admission for respiratory diseases in Hanoi children during 2007-2019.

Air pollution poses a threat to children's respiratory health. This study aims to quantify the association between short-term air pollution exposure and respiratory hospital admissions among children in Hanoi, Vietnam, and estimate the population-attributable burden using local data. A case-crossover analysis was conducted based on the individual records where each case is their own control. The health data was obtained from 13 hospitals in Hanoi and air pollution data was collected from four monitoring stations from 2007 to 2019. We used conditional logistic regression to estimate Percentage Change (PC) and 95% Confidence Interval (CI) in odd of hospital admissions per 10 µg/m3 increase in daily average particulate matter (e.g. PM1, PM2.5, PM10), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), 8-h maximum Ozone and per 1000 µg/m3 increase in daily mean of Carbon Monoxide (CO). We also calculated the number and fraction of admissions attributed to air pollution in Hanoi by using the coefficient at lag 0. A 10 µg/m3 increase in the concentration of PM10, PM2.5, PM1, SO2, NO2, O3 8-h maximum and 1000 µg/m3 increase in CO concentration was associated with 0.6%, 1.2%, 1.4%, 0.8%, 1.6%, 0.3%, and 1.7% increase in odd of admission for all respiratory diseases among children under 16 years at lag 0-2. All PM metrics and NO2 are associated with childhood admission for pneumonia and bronchitis. Admissions due to asthma and upper respiratory diseases are related to increments in NO2 and CO. For attributable cases, PM2.5 concentrations in Hanoi exceeding the World Health Organization Air Quality Guidelines accounted for 1619 respiratory hospital admissions in Hanoi children in 2019. Our findings show that air pollution has a detrimental impact on the respiratory health of Hanoi children and there will be important health benefits from improved air quality management planning to reduce air pollution in Vietnam.

Comorbidities of diabetes and hypertension in Vietnam: current burden, trends over time, and correlated factors.

BACKGROUND: Vietnam conducted the national Noncommunicable Disease Risk-Factor Surveillance (STEPs) surveys in the years 2010, 2015, and 2021. This study aims to use STEPs data to assess the burden of comorbidity between diabetes and hypertension, analyze trends over time, and identify factors associated with this comorbidity. METHODS: The study extracted data for the population aged 25-64 years old from three STEPs surveys. Survey weight was used for all estimations of prevalence and 95% CI. Correlated factors with comorbidity were examined by a multivariate logistics model. RESULTS: The prevalence of comorbidity in 2021 was about 3.92% among Vietnamese people aged 25-64. In the last 10 years, this prevalence has increased more than 8 times (from 0.44% to 3.92%). Sub-populations demonstrating the most significant changes included the male population, people living in urban areas, and older people. Significant factors correlated with comorbidity included demographic factors, body mass index (BMI), and clustering of 4 noncommunicable diseases (NCDs) behavioral risk factors (OR = 3.48, p < 0.05). CONCLUSION: The high comorbidity between hypertension and diabetes underscores the imperative for integrated treatment and management approaches in Vietnam. Coordinated care is essential for addressing the complex interplay between these two prevalent conditions.

Rational Atom Substitution to Obtain Efficient, Lead-Free Photocatalytic Perovskites Assisted by Machine Learning and DFT Calculations.

Inorganic lead-free halide perovskites, devoid of toxic or rare elements, have garnered considerable attention as photocatalysts for pollution control, CO2 reduction and hydrogen production. In the extensive perovskite design space, factors like substitution or doping level profoundly impact their performance. To address this complexity, a synergistic combination of machine learning models and theoretical calculations were used to efficiently screen substitution elements that enhanced the photoactivity of substituted Cs2 AgBiBr6 perovskites. Machine learning models determined the importance of d10 orbitals, highlighting how substituent electron configuration affects electronic structure of Cs2 AgBiBr6 . Conspicuously, d10 -configured Zn2+ boosted the photoactivity of Cs2 AgBiBr6 . Experimental verification validated these model results, revealing a 13-fold increase in photocatalytic toluene conversion compared to the unsubstituted counterpart. This enhancement resulted from the small charge carrier effective mass, as well as the creation of shallow trap states, shifting the conduction band minimum, introducing electron-deficient Br, and altering the distance between the B-site cations d band centre and the halide anions p band centre, a parameter tuneable through d10 configuration substituents. This study exemplifies the application of computational modelling in photocatalyst design and elucidating structure-property relationships. It underscores the potential of synergistic integration of calculations, modelling, and experimental analysis across various applications.

Health and Economic Benefits of Air Pollution Reductions in Vietnam During 2020-2021.

Objectives: This paper explores the potential reduction in the number of deaths and the corresponding economic benefits in Vietnam that could have arisen from the decreased in concentrations of particulate matter with a diameter of 2.5 µm or less (PM2.5) and nitrogen dioxide (NO2). Methods: Using Global Exposure Mortality Models, we estimated the potential health and economic benefits on people aged 25 and above across Vietnam's 63 provinces. The counterfactual scenario assumed reducing PM2.5 and NO2 concentrations to levels observed during the two COVID-19 epidemic waves in 2021 with national lockdowns and activity restrictions. Results: In 2019, PM2.5 concentrations ranged from 12.8 to 40.8 µg/m3 while NO2 concentrations ranged between 2.9 and 36.98 µg/m3. The reduced levels of PM2.5 and NO2 resulted in 3,807 (95% CI: 2,845-4,730) and 2,451 (95% CI: 2,845-4,730) avoided deaths of adults aged 25 and above due to non-injury-related causes, respectively. Considering that every prevented death represents potential tangible and intangible cost savings, reduced levels of PM2.5 and NO2 concentrations during COVID-19 restrictions would have resulted in economic benefits of $793.0 million (95% CI: 592.7-985.4) and $510.6 million (95% CI: 381.3-634.9), respectively. Conclusion: The COVID-19 lockdown led to decreased PM2.5 and NO2 concentrations, benefiting health and economy in Vietnam. Our findings highlight the potential advantages of implementing air quality control policies in the country.

Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptors.

Metal-Organic frameworks (MOFs) have been considered for various gas storage and separation applications. Theoretically, there are an infinite number of MOFs that can be created; however, a finite amount of resources are available to evaluate each one. Computational methods can be adapted to expedite the process of evaluation. In the context of CO2 capture, this paper investigates the method of screening MOFs using machine learning trained on molecular simulation data. New descriptors are introduced to aid this process. Using all descriptors, it is shown that machine learning can predict the CO2 adsorption, with an R2 of above 0.9. The introduced Effective Point Charge (EPoCh) descriptors, which assign values to frameworks' partial charges based on the expected CO2 uptake of an equivalent point charge in isolation, are shown to be the second most important group of descriptors, behind the Henry coefficient. Furthermore, the EPoCh descriptors are hundreds of thousands of times faster to obtain compared with the Henry coefficient, and they achieve similar results when identifying top candidates for CO2 capture using pseudo-classification predictions.

LifeSeeker: an interactive concept-based retrieval system for lifelog data.

Lifelogging was introduced as the process of passively capturing personal daily events via wearable devices. It ultimately creates a visual diary encoding every aspect of one's life with the aim of future sharing or recollecting. In this paper, we present LifeSeeker, a lifelog image retrieval system participating in the Lifelog Search Challenge (LSC) for 3 years, since 2019. Our objective is to support users to seek specific life moments using a combination of textual descriptions, spatial relationships, location information, and image similarities. In addition to the LSC challenge results, a further experiment was conducted in order to evaluate the power retrieval of our system on both expert and novice users. This experiment informed us about the effectiveness of the user's interaction with the system when involving non-experts.

Outbreak of Sexually Transmitted Nongroupable Neisseria meningitidis-Associated Urethritis, Vietnam.

We report on an outbreak of nongroupable Neisseria meningitidis-associated urethritis, primarily among men who have sex with men in southern Vietnam. Nearly 50% of N. meningitidis isolates were resistant to ciprofloxacin. This emerging pathogen should be considered in the differential diagnosis and management of urethritis.

Evaluation of sealants to mitigate the release of per- and polyfluoroalkyl substances (PFAS) from AFFF-impacted concrete: Characterization and forecasting.

Per- and polyfluoroalkyl substances (PFAS) within concrete pads impacted by historical firefighting training using aqueous film-forming foam (AFFF) may be potential secondary sources of PFAS due to surficial leaching. This study aimed to (i) characterize the effectiveness of two commercially available sealants (Product A and Product B) in mitigating leaching of five PFAS (e.g., PFOS, PFOA, PFHxS, PFHxA, 6:2 FTS) from concrete surfaces at the laboratory-scale, and (ii) develop a model to forecast cumulative leaching of the same five PFAS over 20 years from sealed and unsealed concrete surfaces. Laboratory trials demonstrated that both sealants reduced the surficial leaching of the five PFAS studied, and Product B demonstrated a comparatively greater reduction in surface leaching than Product A as measured against unsealed controls. The cumulative PFOS leaching from an unsealed concrete surface is estimated by the model to be about 400 mg/m2 over 20 years and reached asymptotic conditions after 15 years. In contrast, the model output suggests asymptotic conditions were not achieved within the modeled time of 20 years after sealing with Product A and 85% of PFOS was predicted to have leached (∼340 mg/m2). Negligible leaching of PFOS after sealing with Product B was observed ( < 5 × 10-9 mg/m2). Results from modeled rainfall scenarios suggest PFAS leachability is reduced from sealed versus unsealed AFFF-impacted concrete surfaces.

Racial disparity in the genomics of precision oncology of prostate cancer.

BACKGROUND: Significant racial disparities in prostate cancer incidence and mortality have been reported between African American Men (AAM), who are at increased risk for prostate cancer, and European American Men (EAM). In most of the studies carried out on prostate cancer, this population is underrepresented. With the advancement of genome-wide association studies, several genetic predictor models of prostate cancer risk have been elaborated, as well as numerous studies that identify both germline and somatic mutations with clinical utility. RECENT FINDINGS: Despite significant advances, the AAM population continues to be underrepresented in genomic studies, which can limit generalizability and potentially widen disparities. Here we outline racial disparities in currently available genomic applications that are used to estimate the risk of individuals developing prostate cancer and to identify personalized oncology treatment strategies. While the incidence and mortality of prostate cancer are different between AAM and EAM, samples from AAM remain to be unrepresented in different studies. CONCLUSION: This disparity impacts the available genomic data on prostate cancer. As a result, the disparity can limit the predictive utility of the genomic applications and may lead to the widening of the existing disparities. More studies with substantially higher recruitment and engagement of African American patients are necessary to overcome this disparity.

Synthesis of Layered Lead-Free Perovskite Nanocrystals with Precise Size and Shape Control and Their Photocatalytic Activity.

Halide perovskites have attracted enormous attention due to their potential applications in optoelectronics and photocatalysis. However, concerns over their instability, toxicity, and unsatisfactory efficiency have necessitated the development of lead-free all-inorganic halide perovskites. A major challenge in designing efficient halide perovskites for practical applications is the lack of effective methods for producing nanocrystals with precise size and shape control. In this work, a layered perovskite, Cs4ZnSb2Cl12 (CZS), is found from calculations to exhibit size- and facet-dependent optoelectronic properties in the nanoscale, and thus, a colloidal method is used to synthesize the CZS nanoparticles with size-tunable morphologies: zero- (nanodots), one- (nanowires and nanorods), two- (nanoplates), and three-dimensional (nanopolyhedra). The growth kinetics of the CZS nanostructures, along with the effects of surface ligands, reaction temperature, and time were investigated. The optoelectronic properties of the nanocrystals varied with size due to quantum confinement effects and with shape due to anisotropy within the crystals and the exposure of specific facets. These properties could be modulated to enhance the visible-light photocatalytic performance for toluene oxidation. In particular, the 9.7 nm CZS nanoplates displayed a toluene to benzaldehyde conversion rate of 1893 µmol g-1 h-1 (95% selectivity), 500 times higher than the bulk synthesized CZS, and comparable with the reported photocatalysts. This study demonstrates the integration of theoretical calculations and synthesis, revealing an approach to the design and fabrication of novel, high-performance colloidal perovskite nanocrystals for optoelectronic and photocatalytic applications.

Quantum Chemistry-Machine Learning Approach for Predicting Properties of Lewis Acid-Lewis Base Adducts.

Synthetic design allowing predictive control of charge transfer and other optoelectronic properties of Lewis acid adducts remains elusive. This challenge must be addressed through complementary methods combining experimental with computational insights from first principles. Ab initio calculations for optoelectronic properties can be computationally expensive and less straightforward than those sufficient for simple ground-state properties, especially for adducts of large conjugated molecules and Lewis acids. In this contribution, we show that machine learning (ML) can accurately predict density functional theory (DFT)-calculated charge transfer and even properties associated with excited states of adducts from readily obtained molecular descriptors. Seven ML models, built from a dataset of over 1000 adducts, show exceptional performance in predicting charge transfer and other optoelectronic properties with a Pearson correlation coefficient of up to 0.99. More importantly, the influence of each molecular descriptor on predicted properties can be quantitatively evaluated from ML models. This contributes to the optimization of a priori design of Lewis adducts for future applications, especially in organic electronics.

Pharmacist-Led Interventions to Reduce Drug-Related Problems in Prescribing for Pediatric Outpatients in a Developing Country: A Randomized Controlled Trial.

OBJECTIVE: To evaluate a pharmacist-led intervention's effectiveness in reducing drug-related problems (DRPs ( related to prescriptions for pediatric outpatients. METHODS: We conducted a randomized controlled trial. We recruited and randomly assigned 31 physicians to control or intervention groups. We collected 775 prescriptions (375 from the control group and 400 from the intervention group) at the start. For 3 weeks, intervention physicians received additional information and meetings with pharmacists in addition to the usual practices of the hospital. We then collected prescriptions at the end of the study. We classified DRPs, based on reliable references (Supplemental Table S1) at baseline and endpoint (a week after the intervention). The primary outcome was the proportion of prescriptions with DRPs, and secondary outcomes were the proportions of prescriptions with specific DRP types. RESULTS: The influence of the intervention on general DRPs and specific DRPs was the study's main finding. The pharmacist-led intervention helped reduce the prescriptions with DRPs proportion in the intervention group to 41.0%, compared with 49.3% in the control group (p < 0.05). The DRPs proportion related to the timing of administration relative to meals, unlike the other DRP types, increased in the control group (from 31.7% to 34.9%) and decreased in the intervention group (from 31.3% to 25.3%), with a significant difference between the 2 groups at endpoint (p < 0.01). Patients aged >2 to ≤6 years (OR, 1.871; 95% CI, 1.340-2.613) and receiving ≥5 drugs (OR, 5.037; 95% CI, 2.472-10.261) were at greater risk of experiencing DRPs related to prescribing. CONCLUSIONS: A pharmacist-led intervention improved DRP occurrence related to physicians' prescribing. Pharmacists could be involved in in-depth research with physicians in the prescribing process to provide tailored interventions.

Exposure to E-Cigarette Advertising and Its Association With E-Cigarette Use Among Youth and Adolescents in Two Largest Cities in Vietnam 2020.

BACKGROUND: E-cigarette products have become more popular due to the marketing campaigns on various sources but caused adverse health impacts to users, especially adolescents and youths. This paper aims to describe the situation of exposure to e-cigarette advertisements of youth and adolescents living in two cities of Hanoi and Ho Chi Minh in 2020 and identify the associated factors of e-cigarette marketing with e-cigarettes use in these groups. METHODOLOGY: This was a cross-sectional study. The study participants were 1211 youth and adolescents aged 15-24 living in Hanoi and Ho Chi Minh City during the time of data collection (from January 2020 to September 2020). Two primary outcome variables included the ever e-cigarettes use and the intention to use e-cigarettes. Multivariate logistic regression models were used to assess the association between the outcome variables and e-cigarette marketing exposure. RESULTS: The proportion of participants who ever use e-cigarettes was 7.4% and a proportion of 4.8% have intention to use e-cigarettes. The most popular source of exposure to e-cigarettes advertisements was social network (Facebook, Twitter, Instagram, Youtube, etc.) and this source had positive association with the odds of e-cigarette smoking among youth and adolescents (OR = 3.38, 95% CI: 1.59-7.14). In addition, referral marketers also contributed to making the participants more likely to smoke e-cigarettes (OR = 2.68, 95% CI: 1.03-6.95). Attractive color and free sample of e-cigarettes were also found to be the motivated factors associated with smoking behaviour among youth and adolescents. CONCLUSIONS: New policies should be considered to oppose the impact of youth-oriented e-cigarette advertisements which include regulating and restricting e-cigarette advertisements on social media, as well as through referral marketers.