Sex Differences in Clustering Unhealthy Lifestyles Among Survivors of COVID-19: Latent Class Analysis.

BACKGROUND: The COVID-19 pandemic has underscored the significance of adopting healthy lifestyles to mitigate the risk of severe outcomes and long-term consequences. OBJECTIVE: This study focuses on assessing the prevalence and clustering of 5 unhealthy lifestyle behaviors among Vietnamese adults after recovering from COVID-19, with a specific emphasis on sex differences. METHODS: The cross-sectional data of 5890 survivors of COVID-19 in Vietnam were analyzed from December 2021 to October 2022. To examine the sex differences in 5 unhealthy lifestyle behaviors (smoking, drinking, unhealthy diet, physical inactivity, and sedentary behavior), the percentages were plotted along with their corresponding 95% CI for each behavior. Latent class analysis was used to identify 2 distinct classes of individuals based on the clustering of these behaviors: the "less unhealthy" group and the "more unhealthy" group. We examined the sociodemographic characteristics associated with each identified class and used logistic regression to investigate the factors related to the "more unhealthy" group. RESULTS: The majority of individuals (male participants: 2432/2447, 99.4% and female participants: 3411/3443, 99.1%) exhibited at least 1 unhealthy behavior, with male participants being more susceptible to multiple unhealthy behaviors. The male-to-female ratio for having a single behavior was 1.003, but it escalated to 25 for individuals displaying all 5 behaviors. Male participants demonstrated a higher prevalence of combining alcohol intake with sedentary behavior (949/2447, 38.8%) or an unhealthy diet (861/2447, 35.2%), whereas female participants tended to exhibit physical inactivity combined with sedentary behavior (1305/3443, 37.9%) or an unhealthy diet (1260/3443, 36.6%). Married male participants had increased odds of falling into the "more unhealthy" group compared to their single counterparts (odds ratio [OR] 1.45, 95% CI 1.14-1.85), while female participants exhibited lower odds (OR 0.65, 95% CI 0.51-0.83). Female participants who are underweight showed a higher likelihood of belonging to the "more unhealthy" group (OR 1.11, 95% CI 0.89-1.39), but this was not observed among male participants (OR 0.6, 95% CI 0.41-0.89). In both sexes, older age, dependent employment, high education, and obesity were associated with higher odds of being in the "more unhealthy" group. CONCLUSIONS: The study identified notable sex differences in unhealthy lifestyle behaviors among survivors of COVID-19. Male survivors are more likely to engage in unhealthy behaviors compared to female survivors. These findings emphasize the importance of tailored public health interventions targeting sex-specific unhealthy behaviors. Specifically, addressing unhealthy habits is crucial for promoting post-COVID-19 health and well-being.

New Estimates of Smoking-Attributable Mortality in the U.S. From 2020 Through 2035.

INTRODUCTION: The often-cited Centers for Disease Control and Prevention (CDC) estimate of 480,000 annual U.S. smoking-attributable deaths (SADs), including 439,000 first-hand smoke deaths, derives from 2005 to 2009 data. Since then, adult smoking prevalence has decreased by 40%, while the population has grown and the smoking population aged. An updated estimate is presented to determine whether the CDC figure remains accurate or has changed substantially. In addition, the likely annual smoking-related mortality toll is projected through 2035. METHODS: A well-established model of smoking prevalence and health effects is employed to estimate annual SADs among individuals exposed to first-hand smoke in the U.S. for two distinct periods: 2005-2009 and 2020-2035. The estimate for 2005-2009 serves as a benchmark to evaluate the reliability of the model's estimate in comparison to CDC's. The projections for 2020-2035 provide up-to-date figures for SADs, predicting how annual SADs are likely to change in the coming years. Data were collected between 2005 and 2020. The analysis was conducted in 2023. RESULTS: This study's estimate of 420,000 first-hand smoke deaths over 2005-2009 is 95.7% of CDC's estimate during the same period. The model projections indicate that SADs among individuals who currently smoke or formerly smoked have increased modestly since 2005-2009. Beginning in 2020, annual SADs will remain relatively stable at approximately 450,000 before starting to decline around 2030. CONCLUSIONS: These findings suggest that the CDC estimate of the annual mortality burden of smoking remains valid. Despite U.S. population growth and the aging of the smoking population, substantial reductions in smoking will finally produce a steady, if gradual, decline in SADs beginning around 2030.

Rapid inactivation and sample preparation for SARS-CoV-2 PCR-based diagnostics using TNA-Cifer Reagent E.

RT-qPCR remains a key diagnostic methodology for COVID-19/SARS-CoV-2. Typically, nasal or saliva swabs from patients are placed in virus transport media (VTM), RNA is extracted at the pathology laboratory, and viral RNA is measured using RT-qPCR. In this study, we describe the use of TNA-Cifer Reagent E in a pre-clinical evaluation study to inactivate SARS-CoV-2 as well as prepare samples for RT-qPCR. Adding 1 part TNA-Cifer Reagent E to 5 parts medium containing SARS-CoV-2 for 10 min at room temperature inactivated the virus and permitted RT-qPCR detection. TNA-Cifer Reagent E was compared with established column-based RNA extraction and purification methodology using a panel of human clinical nasal swab samples (n = 61), with TNA-Cifer Reagent E showing high specificity (100%) and sensitivity (97.37%). Mixtures of SARS-CoV-2 virus and TNA-Cifer Reagent E could be stored for 3 days at room temperature or for 2 weeks at 4°C without the loss of RT-qPCR detection sensitivity. The detection sensitivity was preserved when TNA-Cifer Reagent E was used in conjunction with a range of VTM for saliva samples but only PBS (Gibco) and Amies Orange for nasal samples. Thus, TNA-Cifer Reagent E improves safety by rapidly inactivating the virus during sample processing, potentially providing a safe means for molecular SARS-CoV-2 testing outside traditional laboratory settings. The reagent also eliminates the need for column-based and/or automated viral RNA extraction/purification processes, thereby providing cost savings for equipment and reagents, as well as reducing processing and handling times.

The evolution of age-specific smoking cessation rates in the United States from 2009 to 2017: a Kalman filter based approach.

BACKGROUND: Tracking the US smoking cessation rate over time is of great interest to tobacco control researchers and policymakers since smoking cessation behaviors have a major effect on the public's health. Recent studies have employed dynamic models to estimate the US cessation rate through observed smoking prevalence. However, none of those studies has provided annual estimates of the cessation rate by age group. Hence, the primary objective of this study is to estimate annual smoking cessation rates specific to different age groups in the US from 2009 to 2017. METHODS: We employed a Kalman filter approach to investigate the annual evolution of age-group-specific cessation rates, unknown parameters of a mathematical model of smoking prevalence, during the 2009-2017 period using data from the 2009-2018 National Health Interview Surveys. We focused on cessation rates in the 25-44, 45-64 and 65 + age groups. RESULTS: The findings show that cessation rates followed a consistent u-shaped curve over time with respect to age (i.e., higher among the 25-44 and 65 + age groups, and lower among 45-64-year-olds). Over the course of the study, the cessation rates in the 25-44 and 65 + age groups remained nearly unchanged around 4.5% and 5.6%, respectively. However, the rate in the 45-64 age group exhibited a substantial increase of 70%, from 2.5% to 2009 to 4.2% in 2017. The estimated cessation rates in all three age groups tended to converge to the weighted average cessation rate over time. CONCLUSIONS: The Kalman filter approach offers a real-time estimation of cessation rates that can be helpful for monitoring smoking cessation behavior.

Key Risk Factors Associated With Electronic Nicotine Delivery Systems Use Among Adolescents.

Importance: The prevalence of electronic nicotine delivery systems (ENDS) use among US youths has increased significantly during the past decade. Identifying key factors highly associated with ENDS use is essential in monitoring and preventing this harmful behavior among youths. Objective: To identify the most important risk factors in wave 4.5 (ie, December 2017 to December 2018) of the Population Assessment of Tobacco and Health Study (PATH) data that are associated with ENDS use in wave 5 (ie, December 2018 to November 2019) among adolescents who were tobacco-naive at baseline. Design, Setting, and Participants: This prognostic study examined data from waves 4.5 and 5 of the PATH youth data set using machine learning techniques. The PATH study is a nationally representative longitudinal cohort study of tobacco use and health in the United States among individuals aged 12 years and older. The data analysis was carried out between January and April 2023. Main Outcomes and Measures: Wave 5 current ENDS use status of wave 4.5 adolescents who were tobacco-naive. Results: The analyzed data set comprised 7943 individuals who were tobacco-naive in wave 4.5. Among this group, 332 participants (4.2%) indicated their present use of ENDS in wave 5, 5047 (63.5%) were aged 12 to 14 years, 4066 (51.2%) were male, and 2455 (30.9%) were Hispanic. The most important risk factors of ENDS use in wave 5 among adolescents who were tobacco-naive in wave 4.5 were the likelihood of using ENDS if offered by a best friend (mean SHAP value, 0.184), the number of best friends using e-cigarettes (mean SHAP value, 0.167), household tobacco usage (mean SHAP value, 0.161), curiosity about ENDS use (mean SHAP value, 0.088), future intention to use ENDS (mean SHAP value, 0.068), youth's total average weekly earnings (mean SHAP value, 0.060), and perceptions of tobacco product safety (mean SHAP value, 0.026). Conclusions and Relevance: The findings of this study suggest that family and friends play an important role in ENDS use among adolescents. The top-ranking factors associated with ENDS use in this study are areas for further exploration, given the increasing prevalence of ENDS use among youths in recent years. Additionally, these findings highlight the important role of families and schools in shaping adolescents' tobacco-related knowledge, which can protect them from using ENDS.

The Evolution of Age-Specific Smoking Cessation Rates in the United States From 2009 to 2018.

Objective: Tracking the US smoking cessation rate over time is of great interest to tobacco control researchers and policymakers since smoking cessation behaviors have a major effect on the public's health. A couple of recent studies have employed dynamic models to estimate the US cessation rate through observed smoking prevalence. However, none of those studies has provided recent annual estimates of the cessation rate by age group. Methods: We employed a Kalman filter approach to investigate the annual evolution of age-group-specific cessation rates, unknown parameters of a mathematical model of smoking prevalence, during the 2009-2018 period using data from the National Health Interview Survey. We focused on cessation rates in the 24-44, 45-64 and 65 + age groups. Results: The findings show that cessation rates follow a consistent u-shaped curve over time with respect to age (i.e., higher among the 25-44 and 65 + age groups, and lower among 45-64-year-olds). Over the course of the study, the cessation rates in the 25-44 and 65 + age groups remained nearly unchanged around 4.5% and 5.6%, respectively. However, the rate in the 45-64 age group exhibited a substantial increase of 70%, from 2.5% in 2009 to 4.2% in 2017. The estimated cessation rates in all three age groups tended to converge to the weighted average cessation rate over time. Conclusions: The Kalman filter approach offers a real-time estimation of cessation rates that would be helpful for monitoring smoking cessation behavior, of interest in general but also for tobacco control policymakers.

Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study.

Identifying determinants of smoking cessation is critical for developing optimal cessation treatments and interventions. Machine learning (ML) is becoming more prevalent for smoking cessation success prediction in treatment programs. However, only individuals with an intention to quit smoking cigarettes participate in such programs, which limits the generalizability of the results. This study applies data from the Population Assessment of Tobacco and Health (PATH), a United States longitudinal nationally representative survey, to select primary determinants of smoking cessation and to train ML classification models for predicting smoking cessation among the general population. An analytical sample of 9,281 adult current established smokers from the PATH survey wave 1 was used to develop classification models to predict smoking cessation by wave 2. Random forest and gradient boosting machines were applied for variable selection, and the SHapley Additive explanation method was used to show the effect direction of the top-ranked variables. The final model predicted wave 2 smoking cessation for current established smokers in wave 1 with an accuracy of 72% in the test dataset. The validation results showed that a similar model could predict wave 3 smoking cessation of wave 2 smokers with an accuracy of 70%. Our analysis indicated that more past 30 days e-cigarette use at the time of quitting, fewer past 30 days cigarette use before quitting, ages older than 18 at smoking initiation, fewer years of smoking, poly tobacco past 30-days use before quitting, and higher BMI resulted in higher chances of cigarette cessation for adult smokers in the US.

Are the Relevant Risk Factors Being Adequately Captured in Empirical Studies of Smoking Initiation? A Machine Learning Analysis Based on the Population Assessment of Tobacco and Health Study.

INTRODUCTION: Cigarette smoking continues to pose a threat to public health. Identifying individual risk factors for smoking initiation is essential to further mitigate this epidemic. To the best of our knowledge, no study today has used machine learning (ML) techniques to automatically uncover informative predictors of smoking onset among adults using the Population Assessment of Tobacco and Health (PATH) study. AIMS AND METHODS: In this work, we employed random forest paired with Recursive Feature Elimination to identify relevant PATH variables that predict smoking initiation among adults who have never smoked at baseline between two consecutive PATH waves. We included all potentially informative baseline variables in wave 1 (wave 4) to predict past 30-day smoking status in wave 2 (wave 5). Using the first and most recent pairs of PATH waves was found sufficient to identify the key risk factors of smoking initiation and test their robustness over time. The eXtreme Gradient Boosting method was employed to test the quality of these selected variables. RESULTS: As a result, classification models suggested about 60 informative PATH variables among many candidate variables in each baseline wave. With these selected predictors, the resulting models have a high discriminatory power with the area under the specificity-sensitivity curves of around 80%. We examined the chosen variables and discovered important features. Across the considered waves, two factors, (1) BMI, and (2) dental and oral health status, robustly appeared as important predictors of smoking initiation, besides other well-established predictors. CONCLUSIONS: Our work demonstrates that ML methods are useful to predict smoking initiation with high accuracy, identifying novel smoking initiation predictors, and to enhance our understanding of tobacco use behaviors. IMPLICATIONS: Understanding individual risk factors for smoking initiation is essential to prevent smoking initiation. With this methodology, a set of the most informative predictors of smoking onset in the PATH data were identified. Besides reconfirming well-known risk factors, the findings suggested additional predictors of smoking initiation that have been overlooked in previous work. More studies that focus on the newly discovered factors (BMI and dental and oral health status,) are needed to confirm their predictive power against the onset of smoking as well as determine the underlying mechanisms.

Comparative Efficacy of Mayaro Virus-Like Particle Vaccines Produced in Insect or Mammalian Cells.

Mayaro virus (MAYV) is a mosquito-transmitted alphavirus that causes often debilitating rheumatic disease in tropical Central and South America. There are currently no licensed vaccines or antiviral drugs available for MAYV disease. Here, we generated Mayaro virus-like particles (VLPs) using the scalable baculovirus-insect cell expression system. High-level secretion of MAYV VLPs in the culture fluid of Sf9 insect cells was achieved, and particles with a diameter of 64 to 70 nm were obtained after purification. We characterize a C57BL/6J adult wild-type mouse model of MAYV infection and disease and used this model to compare the immunogenicity of VLPs from insect cells with that of VLPs produced in mammalian cells. Mice received two intramuscular immunizations with 1 µg of nonadjuvanted MAYV VLPs. Potent neutralizing antibody responses were generated against the vaccine strain, BeH407, with comparable activity seen against a contemporary 2018 isolate from Brazil (BR-18), whereas neutralizing activity against chikungunya virus was marginal. Sequencing of BR-18 illustrated that this virus segregates with genotype D isolates, whereas MAYV BeH407 belongs to genotype L. The mammalian cell-derived VLPs induced higher mean neutralizing antibody titers than those produced in insect cells. Both VLP vaccines completely protected adult wild-type mice against viremia, myositis, tendonitis, and joint inflammation after MAYV challenge. IMPORTANCE Mayaro virus (MAYV) is associated with acute rheumatic disease that can be debilitating and can evolve into months of chronic arthralgia. MAYV is believed to have the potential to emerge as a tropical public health threat, especially if it develops the ability to be efficiently transmitted by urban mosquito vectors, such as Aedes aegypti and/or Aedes albopictus. Here, we describe a scalable virus-like particle vaccine against MAYV that induced neutralizing antibodies against a historical and a contemporary isolate of MAYV and protected mice against infection and disease, providing a potential new intervention for MAYV epidemic preparedness.

Warmer ambient air temperatures reduce nasal turbinate and brain infection, but increase lung inflammation in the K18-hACE2 mouse model of COVID-19.

Warmer climatic conditions have been associated with fewer COVID-19 cases. Herein we infected K18-hACE2 mice housed at the standard animal house temperature of ∼22 °C, or at ∼31 °C, which is considered to be thermoneutral for mice. On day 2 post infection, RNA-Seq analyses showed no significant differential gene expression lung in lungs of mice housed at the two temperatures, with almost identical viral loads and type I interferon responses. There was also no significant difference in viral loads in lungs on day 5, but RNA-Seq and histology analyses showed clearly elevated inflammatory signatures and infiltrates. Thermoneutrality thus promoted lung inflammation. On day 2 post infection mice housed at 31 °C showed reduced viral loads in nasal turbinates, consistent with increased mucociliary clearance at the warmer ambient temperature. These mice also had reduced virus levels in the brain, and an ensuing amelioration of weight loss and a delay in mortality. Warmer air temperatures may thus reduce infection of the upper respiratory track and the olfactory epithelium, resulting in reduced brain infection. Potential relevance for anosmia and neurological sequelae in COVID-19 patients is discussed.

Mouse models of COVID-19 recapitulate inflammatory pathways rather than gene expression.

How well mouse models recapitulate the transcriptional profiles seen in humans remains debatable, with both conservation and diversity identified in various settings. Herein we use RNA-Seq data and bioinformatics approaches to analyze the transcriptional responses in SARS-CoV-2 infected lungs, comparing 4 human studies with the widely used K18-hACE2 mouse model, a model where hACE2 is expressed from the mouse ACE2 promoter, and a model that uses a mouse adapted virus and wild-type mice. Overlap of single copy orthologue differentially expressed genes (scoDEGs) between human and mouse studies was generally poor (≈15-35%). Rather than being associated with batch, sample treatment, viral load, lung damage or mouse model, the poor overlaps were primarily due to scoDEG expression differences between species. Importantly, analyses of immune signatures and inflammatory pathways illustrated highly significant concordances between species. As immunity and immunopathology are the focus of most studies, these mouse models can thus be viewed as representative and relevant models of COVID-19.

Evolution of ACE2-independent SARS-CoV-2 infection and mouse adaption after passage in cells expressing human and mouse ACE2.

Human ACE2 Human angiotensin converting enzyme 2 (hACE2) is the key cell attachment and entry receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with the original SARS-CoV-2 isolates unable to use mouse ACE2 (mACE2). Herein we describe the emergence of a SARS-CoV-2 strain capable of ACE2-independent infection and the evolution of mouse-adapted (MA) SARS-CoV-2 by in vitro serial passaging of virus in co-cultures of cell lines expressing hACE2 and mACE2. MA viruses evolved with up to five amino acid changes in the spike protein, all of which have been seen in human isolates. MA viruses replicated to high titers in C57BL/6J mouse lungs and nasal turbinates and caused characteristic lung histopathology. One MA virus also evolved to replicate efficiently in several ACE2-negative cell lines across several species, including clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) ACE2 knockout cells. An E484D substitution is likely involved in ACE2-independent entry and has appeared in only ≈0.003 per cent of human isolates globally, suggesting that it provided no significant selection advantage in humans. ACE2-independent entry reveals a SARS-CoV-2 infection mechanism that has potential implications for disease pathogenesis, evolution, tropism, and perhaps also intervention development.

Synthetic Heparan Sulfate Mimetic Pixatimod (PG545) Potently Inhibits SARS-CoV-2 by Disrupting the Spike-ACE2 Interaction.

Heparan sulfate (HS) is a cell surface polysaccharide recently identified as a coreceptor with the ACE2 protein for the S1 spike protein on SARS-CoV-2 virus, providing a tractable new therapeutic target. Clinically used heparins demonstrate an inhibitory activity but have an anticoagulant activity and are supply-limited, necessitating alternative solutions. Here, we show that synthetic HS mimetic pixatimod (PG545), a cancer drug candidate, binds and destabilizes the SARS-CoV-2 spike protein receptor binding domain and directly inhibits its binding to ACE2, consistent with molecular modeling identification of multiple molecular contacts and overlapping pixatimod and ACE2 binding sites. Assays with multiple clinical isolates of SARS-CoV-2 virus show that pixatimod potently inhibits the infection of monkey Vero E6 cells and physiologically relevant human bronchial epithelial cells at safe therapeutic concentrations. Pixatimod also retained broad potency against variants of concern (VOC) including B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), and B.1.1.529 (Omicron). Furthermore, in a K18-hACE2 mouse model, pixatimod significantly reduced SARS-CoV-2 viral titers in the upper respiratory tract and virus-induced weight loss. This demonstration of potent anti-SARS-CoV-2 activity tolerant to emerging mutations establishes proof-of-concept for targeting the HS-Spike protein-ACE2 axis with synthetic HS mimetics and provides a strong rationale for clinical investigation of pixatimod as a potential multimodal therapeutic for COVID-19.

Monitoring the Increase in the U.S. Smoking Cessation Rate and Its Implication for Future Smoking Prevalence.

INTRODUCTION: We calculate the U.S. adult smoking cessation rate for 2014-2019, compare it to the historical trend, and estimate the implication for future smoking prevalence. METHODS: We repeated an earlier analysis, which examined the cessation rate from 1990 to 2014, extending the period to 2019. Employing National Health Interview Survey (NHIS) and National Survey on Drug Use and Health (NSDUH) data, we estimated the adult cessation rate in 6-year intervals, using weighted nonlinear least squares. We then employed a meta-regression model to test whether the cessation rate has increased beyond expectation. We used cessation rate estimates and smoking initiation rate estimates to project smoking prevalence in 2030 and eventual steady-state prevalence. RESULTS: The annual cessation rate increased 29% using NHIS data (from 4.2% in 2008-2013 to 5.4% in 2014-2019) and 33% with NSDUH data (4.2%-5.6%). The cessation rate increase accounts for 60% of a smoking prevalence decline in the most recent period exceeding the 1990-2013 predicted trend. The remaining 40% owes to declining smoking initiation. With current initiation and cessation rates, smoking prevalence should fall to 8.3% in 2030 and eventually reach a steady state of 3.53%. CONCLUSIONS: The smoking cessation rate continued to increase during 2014-2019. NHIS and NSDUH results are practically identical. The larger share (60%) of the smoking prevalence decrease, beyond expectation, attributable to the increased cessation rate is encouraging since the positive health effects of cessation occur much sooner than those derived from declining initiation. IMPLICATIONS: The smoking cessation rate in the United States continues to increase, accelerating the decline in smoking prevalence. This increase suggests that the Healthy People 2030 goal of 5% adult smoking prevalence, while ambitious, is attainable. Our findings can be used in simulation and statistical models that aim to predict future prevalence and population health effects due to smoking under various scenarios.

The association between smoking behaviors and prices and taxes per cigarette pack in the United States from 2000 through 2019.

OBJECTIVE: The conclusions on how tax and price increases affect smoking behaviors are mixed. This work is devoted to re-evaluating the relationship between cigarette prices and taxes and smoking behaviors. METHODS: Using 2000-2019 Behavioral Risk Factor Surveillance System data, we employed linear mixed-effect models to re-examine the impact of cigarette prices and taxes on smoking prevalence and the proportion of current smokers having tried to quit smoking in the past 12 months. All the analyses were conducted for the general population, then by age group, gender, race/ethnicity, and income level. RESULTS: The results indicate that higher cigarette prices and taxes were associated with a decrease in smoking prevalence and an increased likelihood of quitting smoking. Cigarette tax and price increases produced the most powerful impact on the smoking prevalence of 18- to 24-year-olds. The estimates also show that males tended to be more price-sensitive than females. Raising cigarette prices and taxes was estimated to be more effective in reducing the smoking prevalence among non-Hispanic Blacks and Hispanics when compared to non-Hispanic whites. Cigarette price and tax changes were likely to have a smaller effect on individuals with annual income under $25,000 relative to individuals with higher income levels. CONCLUSIONS: Increases in cigarette prices and taxes are significantly associated with a reduction in smoking prevalence and an increased likelihood of quitting smoking among adults across different demographic and socioeconomic groups. However, as cigarette price and tax changes disproportionately affect low-income individuals, raising cigarette prices and taxes may deepen income disparities.

Predictors of eHealth Literacy and Its Associations with Preventive Behaviors, Fear of COVID-19, Anxiety, and Depression among Undergraduate Nursing Students: A Cross-Sectional Survey.

Background: The infodemic has been co-existing with the COVID-19 pandemic with an influx of misinformation and conspiracy theories. These affect people's psychological health and adherence to preventive measures. eHealth literacy (eHEALS) may help with alleviating the negative effects of the infodemic. As nursing students are future healthcare professionals, having adequate eHEALS skills is critically important in their clinical practice, safety, and health. This study aimed to (1) explore the eHEALS level and its associated factors, and (2) examine the associations of eHEALS with preventive behaviors, fear of COVID-19 (FCV-19S), anxiety, and depression among nursing students. Methods: We surveyed 1851 nursing students from 7 April to 31 May 2020 from eight universities across Vietnam. Data were collected, including demographic characteristics, eHEALS, adherence to preventive behaviors (handwashing, mask-wearing, physical distancing), FCV-19S, anxiety, and depression. Linear and logistic regression analyses were performed appropriately to examine associations. Results: The mean score of eHEALS was 31.4 ± 4.4. The eHEALS score was significantly higher in males (unstandardized regression coefficient, B, 0.94; 95% confidence interval, 95% CI, 0.15 to 1.73; p = 0.019), and students with a better ability to pay for medication (B, 0.79; 95% CI, 0.39 to 1.19; p < 0.001), as compared to their counterparts. Nursing students with a higher eHEALS score had a higher likelihood of adhering to hand-washing (odds ratio, OR, 1.18; 95% CI, 1.15 to 1.22; p < 0.001), mask-wearing (OR, 1.15; 95% CI, 1.12 to 1.19; p < 0.001), keeping a safe physical distance (OR, 1.20; 95% CI, 1.15 to 1.25; p < 0.001), and had a lower anxiety likelihood (OR, 0.95; 95% CI, 0.92 to 0.99; p = 0.011). Conclusions: Nursing students who were men and with better ability to pay for medication had higher eHEALS scores. Those with higher eHEALS scores had better adherence to preventive measures, and better psychological health. The development of strategies to improve eHEALS of nursing students may contribute to COVID-19 containment and improve their psychological health.

Impact of simulated acid rain on the growth of three species Brassica integrifolia, Brassica rapa, Brassica juncea in Hanoi, Vietnam.

Over the past decade, the extent and magnitude of acid rain in Vietnam and other Asian countries have become more apparent. In this study, the effect of simulated acid rain (pH 5.0, 4.0, and 3.0) and control treatment (pH 6.0) are observed for three species Brassica integrifolia, Brassica rapa, and Brassica juncea in Hanoi. The pot experiment was conducted for 42 days and arranged according to a randomized complete block design (RCBD), replicated 3 times with acid rain exposure being supplied every 4 days. The results show that acid rain causes direct damage to leaves. Observations reveal white spots on leaves; leaves getting discolored and gradually turning yellow, curling leaf marginals, and turning dark blue, with the most severe symptoms being necrotic leaves. Parameters of the shoot and root length, leaf area, biomass, and chlorophyll content all decrease as pH drops. However, the accumulation of proline content in leaves tends to increase with greater acidity. In conclusion, Brassica rara has the highest resistance capability to acid rain compared with Brassica integrifolia and Brassica juncea, especially its proline content is the highest at pH 3.0 in three Brassicaceae species.

Widespread discrepancy in Nnt genotypes and genetic backgrounds complicates granzyme A and other knockout mouse studies.

Granzyme A (GZMA) is a serine protease secreted by cytotoxic lymphocytes, with Gzma-/- mouse studies having informed our understanding of GZMA's physiological function. We show herein that Gzma-/- mice have a mixed C57BL/6J and C57BL/6N genetic background and retain the full-length nicotinamide nucleotide transhydrogenase (Nnt) gene, whereas Nnt is truncated in C57BL/6J mice. Chikungunya viral arthritis was substantially ameliorated in Gzma-/- mice; however, the presence of Nnt and the C57BL/6N background, rather than loss of GZMA expression, was responsible for this phenotype. A new CRISPR active site mutant C57BL/6J GzmaS211A mouse provided the first insights into GZMA's bioactivity free of background issues, with circulating proteolytically active GZMA promoting immune-stimulating and pro-inflammatory signatures. Remarkably, k-mer mining of the Sequence Read Archive illustrated that ≈27% of Run Accessions and ≈38% of BioProjects listing C57BL/6J as the mouse strain had Nnt sequencing reads inconsistent with a C57BL/6J genetic background. Nnt and C57BL/6N background issues have clearly complicated our understanding of GZMA and may similarly have influenced studies across a broad range of fields.

Pathophysiological Response to SARS-CoV-2 Infection Detected by Infrared Spectroscopy Enables Rapid and Robust Saliva Screening for COVID-19.

Fourier transform infrared (FTIR) spectroscopy provides a (bio)chemical snapshot of the sample, and was recently used in proof-of-concept cohort studies for COVID-19 saliva screening. However, the biological basis of the proposed technology has not been established. To investigate underlying pathophysiology, we conducted controlled infection experiments on Vero E6 cells in vitro and K18-hACE2 mice in vivo. Potentially infectious culture supernatant or mouse oral lavage samples were treated with ethanol or 75% (v/v) Trizol for attenuated total reflectance (ATR)-FTIR spectroscopy and proteomics, or RT-PCR, respectively. Controlled infection with UV-inactivated SARS-CoV-2 elicited strong biochemical changes in culture supernatant/oral lavage despite a lack of viral replication, determined by RT-PCR or a cell culture infectious dose 50% assay. Nevertheless, SARS-CoV-2 infection induced additional FTIR signals over UV-inactivated SARS-CoV-2 infection in both cell and mouse models, which correspond to aggregated proteins and RNA. Proteomics of mouse oral lavage revealed increased secretion of kallikreins and immune modulatory proteins. Next, we collected saliva from a cohort of human participants (n = 104) and developed a predictive model for COVID-19 using partial least squares discriminant analysis. While high sensitivity of 93.48% was achieved through leave-one-out cross-validation, COVID-19 patients testing negative on follow-up on the day of saliva sampling using RT-PCR was poorly predicted in this model. Importantly, COVID-19 vaccination did not lead to the misclassification of COVID-19 negatives. Finally, meta-analysis revealed that SARS-CoV-2 induced increases in the amide II band in all arms of this study and in recently published cohort studies, indicative of altered ß-sheet structures in secreted proteins. In conclusion, this study reveals a consistent secretory pathophysiological response to SARS-CoV-2, as well as a simple, robust method for COVID-19 saliva screening using ATR-FTIR.