Digital Healthy Diet Literacy and Fear of COVID-19 as Associated with Treatment Adherence and Its Subscales among Hemodialysis Patients: A Multi-Hospital Study.

Treatment adherence (TA) is a critical issue and is under-investigated in hemodialysis patients. A multi-center study was conducted from July 2020 to March 2021 on 972 hemodialysis patients in eight hospitals in Vietnam to explore the factors associated with TA during the COVID-19 pandemic. Data were collected, including socio-demographics, an End-Stage Renal Disease Adherence Questionnaire (ESRD-AQ), 12-item short-form health literacy questionnaire (HLS-SF12), 4-item digital healthy diet literacy scale (DDL), 10-item hemodialysis dietary knowledge scale (HDK), 7-item fear of COVID-19 scale (FCoV-19S), and suspected COVID-19 symptoms (S-COVID19-S). Bivariate and multivariate linear regression models were used to explore the associations. Higher DDL scores were associated with higher TA scores (regression coefficient, B, 1.35; 95% confidence interval, 95%CI, 0.59, 2.12; p = 0.001). Higher FCoV-19S scores were associated with lower TA scores (B, -1.78; 95%CI, -3.33, -0.24; p = 0.023). In addition, patients aged 60-85 (B, 24.85; 95%CI, 6.61, 43.11; p = 0.008) with "very or fairly easy" medication payment ability (B, 27.92; 95%CI, 5.89, 44.95; p = 0.013) had higher TA scores. Patients who underwent hemodialysis for ≥5 years had a lower TA score than those who received <5 years of hemodialysis (B, -52.87; 95%CI, -70.46, -35.28; p < 0.001). These findings suggested that DDL and FCoV-19S, among other factors, should be considered in future interventions to improve TA in hemodialysis patients.

Upgrading nirmatrelvir to inhibit SARS-CoV-2 Mpro via DeepFrag and free energy calculations.

The first oral drug for the treatment of COVID-19, Paxlovid, has been authorized; however, nirmatrelvir, a major component of the drug, is reported to be associated with some side effects. Moreover, the appearance of many novel variants raises concerns about drug resistance, and designing new potent inhibitors to prevent viral replication is thus urgent. In this context, using a hybrid approach combining machine learning (ML) and free energy simulations, 6 compounds obtained by modifying nirmatrelvir were proposed to bind strongly to SARS-CoV-2 Mpro. The structural modification of nirmatrelvir significantly enhances the electrostatic interaction free energy between the protein and ligand and slightly decreases the vdW term. However, the vdW term is the most important factor in controlling the ligand-binding affinity. In addition, the modified nirmatrelvir might be less toxic to the human body than the original inhibitor.

Post-COVID-19 Pulmonary Fibrosis: Facts-Challenges and Futures: A Narrative Review.

Patients with coronavirus disease 2019 (COVID-19) usually suffer from post-acute sequelae of coronavirus disease 2019 (PASC). Pulmonary fibrosis (PF) has the most significant long-term impact on patients' respiratory health, called post-COVID-19 pulmonary fibrosis (PC19-PF). PC19- PF can be caused by acute respiratory distress syndrome (ARDS) or pneumonia due to COVID-19. The risk factors of PC19-PF, such as older age, chronic comorbidities, the use of mechanical ventilation during the acute phase, and female sex, should be considered. Individuals with COVID-19 pneumonia symptoms lasting at least 12 weeks following diagnosis, including cough, dyspnea, exertional dyspnea, and poor saturation, accounted for nearly all disease occurrences. PC19-PF is characterized by persistent fibrotic tomographic sequelae associated with functional impairment throughout follow-up. Thus, clinical examination, radiology, pulmonary function tests, and pathological findings should be done to diagnose PC19-PF patients. PFT indicated persistent limitations in diffusion capacity and restrictive physiology, despite the absence of previous testing and inconsistency in the timeliness of assessments following acute illness. It has been hypothesized that PC19-PF patients may benefit from idiopathic pulmonary fibrosis treatment to prevent continued infection-related disorders, enhance the healing phase, and manage fibroproliferative processes. Immunomodulatory agents might reduce inflammation and the length of mechanical ventilation during the acute phase of COVID-19 infection, and the risk of the PC19-PF stage. Pulmonary rehabilitation, incorporating exercise training, physical education, and behavioral modifications, can improve the physical and psychological conditions of patients with PC19-PF.

Designing Potential Inhibitors of SARS-CoV-2 Mpro Using Deep Learning and Steered Molecular Dynamic Simulations

The COVID-19 pandemic raised an unprecedented race in biotechnology in search for effective therapies and a preventive vaccine. Scientists worldwide have been attempting to stop the viral infection by interfering with the biological function of the SARS-CoV-2 main protease (Mpro), a critical protein required for viral transcription and replication during infection. In this study, we employed an effective approach integrating deep learning model calculations and steered molecular dynamic simulations to generate highly promising inhibitors of SARS-CoV-2 Mpro. First, using deep learning calculations, a natural molecule that was identified as a potential inhibitor of SARS-CoV-2 Mpro was chemically altered to boost its ligand-binding affinity to the Mpro protease. The proposed compounds were then verified using steered molecular dynamic simulations to estimate their binding free energies to SARS-CoV-2 Mpro. The procedure was repeated until the binding free energies of the proposed compounds did not improve further. Overall, one proposed compound was shown to have a high nanomolar affinity, and two others were estimated to possess nanomolar affinities for SARS-CoV-2 Mpro, indicating that they are highly promising inhibitors of the protease. Absorption, distribution, metabolism, and excretion and toxicity analysis show that all three chemicals are drug-like compounds following the MACCS-II Drug Data Report database, orally absorbed, tightly attached to the plasma membrane, and noncarcinogenic in rats. The results obtained potentially support COVID-19 treatment. [ FROM AUTHOR] Copyright of Journal of Computational Biophysics & Chemistry is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Clinical Approaches to Minimize Readmissions of Patients with COPD: A Narrative Review

Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease and also a lead-ing cause of morbidity and mortality worldwide. The frequent readmissions of patients with COPD may reduce lung function, mental health, and quality of life;it also increases the cost of treatment and mortality rate. Some common factors that may increase the readmission frequency of COPD patients include delay of diagnosis, advanced lung function decline, lack of adherence for COPD treatment, ineffective management of comorbidities, acute exacerbation or stable COPD, and infec-tions. However, these factors might be well controlled with appropriate approaches to minimize the readmission of patients with COPD. In this review, we propose a strategy with a seven-step approach to reduce the readmission in COPD patients, including early diagnosis of COPD, optimal treatment for stable COPD, targeted management of comorbidities, adequate therapy for acute ex-acerbations, individualized action plans for COPD patients, effective prevention of bacterial and viral infections, and adaptive program of pulmonary rehabilitation. Thus, implementing this approach may reduce the risk of readmission in patients with COPD.Copyright © 2023 Bentham Science Publishers.

"Maintaining HIV and HCV prevention and care for people who inject drugs despite COVID-19 in Hai Phong, Vietnam".

BACKGROUND: After the emergence of COVID-19, a one-month strict lockdown was imposed in April 2020 in Vietnam, followed by lighter social distancing restrictions over the year. We investigated whether those measures affected people who inject drugs (PWID) in terms of risk behaviors for HIV and HCV and access to prevention and care in the city of Haiphong, a historic hotspot for HIV and drug use. METHODOLOGY: We carried out a 'before-after' study from 2019 to 2020 using respondent-driven sampling method to enroll PWID. They were interviewed on their socioeconomic situation, drug use and sexual behaviors, relations to care services and tested for drugs and methadone in the urine, for HIV, HCV, and HIV plasma viral load when HIV-positive. Changes following the restrictions were assessed by comparing 'before' to 'after' data. RESULTS: 780 PWID were enrolled. Mean age was 44 years; 94% were male. All were actively injecting heroin 'before', versus 56% 'after'. Among those, frequency of consumption decreased from 24 to 17 days per month. No changes were observed in the frequency and practices of methamphetamine smoking. The proportion of PWID on MMT increased from 68.7% to 75.3%, and that of PWID engaging in risky behaviors related to drug injection decreased from 6.0% to 1.5%. No HIV seroconversions were observed; HCV incidence was 2.6/100 person-years (95% CI [0.7-6.7]). 9% of PWID reported a monthly income of less than 130USD 'before' versus 53% 'after'. CONCLUSION: The case of Hai Phong shows that it is possible, during times of COVID-19 pandemic, to maintain access to harm reduction and care and to prevent HIV and HCV transmission among PWID in a resource-limited setting where severe social distancing restrictions are implemented. Further research is needed to assess the consequences of long-term economic difficulties and the impact of actual spread of SARS-Cov2 that has since emerged in Haiphong.

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.

To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turned out to show that there was evidently good agreement between ML and atomistic simulations. Ten substances were proposed to be able to inhibit SARS-CoV-2 Mpro. Seven of them have high-nanomolar affinity and are very potential inhibitors. The strategy has been proven to be reliable and appropriate for fast prediction of SARS-CoV-2 Mpro inhibitors, benefiting for new emerging SARS-CoV-2 variants in the future accordingly.

COVID and Gender: A Narrative Review of the Asia-Pacific Region.

The COVID-19 pandemic has been the largest infectious disease epidemic to affect the human race since the great influenza pandemic of 1918-19 and is close to approaching the number of deaths from the earlier epidemic. A review of available data and the numerous currently available studies on COVID-19 shows that the rate of clinical cases is about 10% greater in females than males in Asia. However, the number of deaths is greater in males than in females. Women are more likely to experience the psychological effects of COVID-19 during and after acute infections. A significant proportion of acute COVID-19 infections continue and their prolonged symptoms have been reported. Further studies are needed, including detailed serology, to measure and monitor the incidence of COVID-19. The pandemic has had a widespread impact on broader societies including shortages of food, lockdowns and isolation. The number of orphans in developing countries has increased. Women have had to bear the major impacts of these community effects. More research is required to develop better vaccines acting against new strains of the virus and to develop systems to distribute vaccines to all people.

Antigen- and scaffold-specific antibody responses to protein nanoparticle immunogens.

Protein nanoparticle scaffolds are increasingly used in next-generation vaccine designs, and several have established records of clinical safety and efficacy. Yet the rules for how immune responses specific to nanoparticle scaffolds affect the immunogenicity of displayed antigens have not been established. Here we define relationships between anti-scaffold and antigen-specific antibody responses elicited by protein nanoparticle immunogens. We report that dampening anti-scaffold responses by physical masking does not enhance antigen-specific antibody responses. In a series of immunogens that all use the same nanoparticle scaffold but display four different antigens, only HIV-1 envelope glycoprotein (Env) is subdominant to the scaffold. However, we also demonstrate that scaffold-specific antibody responses can competitively inhibit antigen-specific responses when the scaffold is provided in excess. Overall, our results suggest that anti-scaffold antibody responses are unlikely to suppress antigen-specific antibody responses for protein nanoparticle immunogens in which the antigen is immunodominant over the scaffold.

Perceived risk and booking intention in the crisis of Covid-19: comparison of tourist hotels and love hotels

The Covid-19 pandemic has presented a range of economic consequences for the tourism industry. Notably, this has been striking mainly because of the rising fear toward public places and travel avoidance. Vietnam has one particular market segment–love hotels–and these hotels have not suffered as much as the overall hotel industry. Applying Structural Equation Modelling (SEM), and robustness test using Woodside's Accurate case outcome modeling (ACOM) of 2019 the study here confirms that: (i) Covid-19 knowledge increases conventional customer's perceived risk;(ii) Perceived risk (before and after Covid-19) have a negative effect on hotel booking intention;(iii) risk perception and booking intention of customers in two hotel types differ significantly, in which there is a preference for love hotels. Despite love hotels' remarkable market value, the academic attention to this special business model, has to date, been little researched. This research contributes evidence of asymmetric perceived physical risk as well as booking intention of the two market segments namely, tourist and love hotels. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

Correction: Rapid prediction of possible inhibitors for SARS-CoV-2 main protease using docking and FPL simulations.

[This corrects the article DOI: 10.1039/D0RA06212J.].

Correction: Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches.

Correction for 'Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches' by Son Tung Ngo et al., Phys. Chem. Chem. Phys., 2022, https://doi.org/10.1039/d2cp04476e.

Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches.

Computational approaches, including physics- and knowledge-based methods, have commonly been used to determine the ligand-binding affinity toward SARS-CoV-2 main protease (Mpro or 3CLpro). Strong binding ligands can thus be suggested as potential inhibitors for blocking the biological activity of the protease. In this context, this paper aims to provide a short review of computational approaches that have recently been applied in the search for inhibitor candidates of Mpro. In particular, molecular docking and molecular dynamics (MD) simulations are usually combined to predict the binding affinity of thousands of compounds. Quantitative structure-activity relationship (QSAR) is the least computationally demanding and therefore can be used for large chemical collections of ligands. However, its accuracy may not be high. Moreover, the quantum mechanics/molecular mechanics (QM/MM) method is most commonly used for covalently binding inhibitors, which also play an important role in inhibiting the activity of SARS-CoV-2. Furthermore, machine learning (ML) models can significantly increase the searching space of ligands with high accuracy for binding affinity prediction. Physical insights into the binding process can then be confirmed via physics-based calculations. Integration of ML models into computational chemistry provides many more benefits and can lead to new therapies sooner.

Fear of COVID-19, healthy eating behaviors, and health-related behavior changes as associated with anxiety and depression among medical students: An online survey.

Background: Medical students' health and wellbeing are highly concerned during the COVID-19 pandemic. This study examined the impacts of fear of COVID-19 (FCoV-19S), healthy eating behavior, and health-related behavior changes on anxiety and depression. Methods: We conducted an online survey at 8 medical universities in Vietnam from 7th April to 31st May 2020. Data of 5,765 medical students were collected regarding demographic characteristics, FCoV-19S, health-related behaviors, healthy eating score (HES), anxiety, and depression. Logistic regression analyses were used to explore associations. Results: A lower likelihood of anxiety and depression were found in students with a higher HES score (OR = 0.98; 95%CI = 0.96, 0.99; p = 0.042; OR = 0.98; 95%CI = 0.96, 0.99; p = 0.021), and in those unchanged or more physical activities during the pandemic (OR = 0.54; 95%CI = 0.44, 0.66; p < 0.001; OR = 0.44; 95%CI = 0.37, 0.52; p < 0.001) as compared to those with none/less physical activity, respectively. A higher likelihood of anxiety and depression were reported in students with a higher FCoV-19S score (OR = 1.09; 95%CI = 1.07, 1.12; p < 0.001; OR = 1.06; 95%CI = 1.04, 1.08; p < 0.001), and those smoked unchanged/more during the pandemic (OR = 6.67; 95%CI = 4.71, 9.43; p < 0.001; OR = 6.77; 95%CI = 4.89, 9.38; p < 0.001) as compared to those stopped/less smoke, respectively. In addition, male students had a lower likelihood of anxiety (OR = 0.79; 95%CI = 0.65, 0.98; p = 0.029) compared to female ones. Conclusions: During the pandemic, FCoV-19S and cigarette smoking had adverse impacts on medical students' psychological health. Conversely, staying physically active and having healthy eating behaviors could potentially prevent medical students from anxiety and depressive symptoms.

Distinct sensitivities to SARS-CoV-2 variants in vaccinated humans and mice.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019 has led to the development of a large number of vaccines, several of which are now approved for use in humans. Understanding vaccine-elicited antibody responses against emerging SARS-CoV-2 variants of concern (VOCs) in real time is key to inform public health policies. Serum neutralizing antibody titers are the current best correlate of protection from SARS-CoV-2 challenge in non-human primates and a key metric to understand immune evasion of VOCs. We report that vaccinated BALB/c mice do not recapitulate faithfully the breadth and potency of neutralizing antibody responses elicited by various vaccine platforms against VOCs, compared with non-human primates or humans, suggesting caution should be exercised when interpreting data obtained with this animal model.

Searching for AChE inhibitors from natural compounds by using machine learning and atomistic simulations.

Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer's disease treatment. In this work, a combined approach involving machine-learning (ML) model and atomistic simulations was established to predict the ligand-binding affinity to AChE of the natural compounds from VIETHERB database. The trained ML model was first utilized to rapidly and accurately screen the natural compound database for potential AChE inhibitors. Atomistic simulations including molecular docking and steered-molecular dynamics simulations were then used to confirm the ML outcome. Good agreement between ML and atomistic simulations was observed. Twenty compounds were suggested to be able to inhibit AChE. Especially, four of them including geranylgeranyl diphosphate, 2-phosphoglyceric acid, and 2-carboxy-d-arabinitol 1-phosphate, and farnesyl diphosphate are highly potent inhibitors with sub-nanomolar affinities.

Assessing potential inhibitors of SARS-CoV-2 main protease from available drugs using free energy perturbation simulations† † Electronic supplementary information (ESI) available. See DOI: 10.1039/d0ra07352k

The main protease (Mpro) of the novel coronavirus SARS-CoV-2, which has caused the COVID-19 pandemic, is responsible for the maturation of its key proteins. Thus, inhibiting SARS-CoV-2 Mpro could prevent SARS-CoV-2 from multiplying. Because new inhibitors require thorough validation, repurposing current drugs could help reduce the validation process. Many recent studies used molecular docking to screen large databases for potential inhibitors of SARS-CoV-2 Mpro. However, molecular docking does not consider molecular dynamics and thus can be prone to error. In this work, we developed a protocol using free energy perturbation (FEP) to assess the potential inhibitors of SARS-CoV-2 Mpro. First, we validated both molecular docking and FEP on a set of 11 inhibitors of SARS-CoV-2 Mpro with experimentally determined inhibitory data. The experimentally deduced binding free energy exhibits significantly stronger correlation with that predicted by FEP (R = 0.94 ± 0.04) than with that predicted by molecular docking (R = 0.82 ± 0.08). This result clearly shows that FEP is the most accurate method available to predict the binding affinity of SARS-CoV-2 Mpro + ligand complexes. We subsequently used FEP to validate the top 33 compounds screened with molecular docking from the ZINC15 database. Thirteen of these compounds were predicted to bind strongly to SARS-CoV-2 Mpro, most of which are currently used as drugs for various diseases in humans. Notably, delamanid, an anti-tuberculosis drug, was predicted to inhibit SARS-CoV-2 Mpro in the nanomolar range. Because both COVID-19 and tuberculosis are lung diseases, delamanid has higher probability to be suitable for treating COVID-19 than other predicted compounds. Analysis of the complexes of SARS-CoV-2 Mpro and the top inhibitors revealed the key residues involved in the binding, including the catalytic dyad His14 and Cys145, which is consistent with the structural studies reported recently. Free Energy Pertubation (FEP) can be used to accurately predict the binding affinity of a ligand to the main protease (Mpro) of the novel coronavirus SARS-CoV-2.

Thermodynamically coupled biosensors for detecting neutralizing antibodies against SARS-CoV-2 variants.

We designed a protein biosensor that uses thermodynamic coupling for sensitive and rapid detection of neutralizing antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants in serum. The biosensor is a switchable, caged luciferase-receptor-binding domain (RBD) construct that detects serum-antibody interference with the binding of virus RBD to angiotensin-converting enzyme 2 (ACE-2) as a proxy for neutralization. Our coupling approach does not require target modification and can better distinguish sample-to-sample differences in analyte binding affinity and abundance than traditional competition-based assays.

Potential inhibitors for SARS-CoV-2 Mpro from marine compounds† † Electronic supplementary information (ESI) available: Marine compounds and docking energy to SARS-CoV-2 Mpro, docked and MD-refined structures of SARS-CoV-2 Mpro + top-lead ligands, pulling force in displacement dependence over FPL simulations, the computational values using FPL simulations of available inhibitors, and ADME profile of potential compounds. See DOI: 10.1039/d1ra03852d

Preventing the biological activity of SARS-CoV-2 main protease using natural compounds is of great interest. In this context, using a combination of AutoDock Vina and fast pulling of ligand simulations, eleven marine fungi compounds were identified that probably play as highly potent inhibitors for preventing viral replication. In particular, four compounds including M15 (3-O-(6-O-α-l-arabinopyranosyl)-β-d-glucopyranosyl-1,4-dimethoxyxanthone), M8 (wailupemycins H), M11 (cottoquinazolines B), and M9 (wailupemycins I) adopted the predicted ligand-binding free energy of −9.87, −9.82, −9.62, and −9.35 kcal mol−1, respectively, whereas the other adopted predicted ligand-binding free energies in the range from −8.54 to −8.94 kcal mol−1. The results were obtained using a combination of Vina and FPL simulations. Notably, although, AutoDock4 adopted higher accurate results in comparison with Vina, Vina is proven to be a more suitable technique for rapidly screening ligand-binding affinity with a large database of compounds since it requires much smaller computing resources. Furthermore, FPL is better than Vina to classify inhibitors upon ROC-AUC analysis. Preventing the biological activity of SARS-CoV-2 main protease using natural compounds is of great interest.

Computational estimation of potential inhibitors from known drugs against the main protease of SARS-CoV-2† † Electronic supplementary information (ESI) available. See DOI: 10.1039/d1ra02529e

The coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide recently, leading to global social and economic disruption. Although the emergently approved vaccine programs against SARS-CoV-2 have been rolled out globally, the number of COVID-19 daily cases and deaths has remained significantly high. Here, we attempt to computationally screen for possible medications for COVID-19 via rapidly estimating the highly potential inhibitors from an FDA-approved drug database against the main protease (Mpro) of SARS-CoV-2. The approach combined molecular docking and fast pulling of ligand (FPL) simulations that were demonstrated to be accurate and suitable for quick prediction of SARS-CoV-2 Mpro inhibitors. The results suggested that twenty-seven compounds were capable of strongly associating with SARS-CoV-2 Mpro. Among them, the seven top leads are daclatasvir, teniposide, etoposide, levoleucovorin, naldemedine, cabozantinib, and irinotecan. The potential application of these drugs in COVID-19 therapy has thus been discussed. Approved drugs predicted to interact with critical residues in the substrate-binding site of SARS-CoV-2 Mpro can be promising inhibitors.