Acceptance, hesitancy and refusal towards COVID-19 vaccination.

Problem: The determinants of COVID-19 vaccine acceptance, hesitancy, and refusal remain poorly understood. We assessed the general population of Pune after visiting tertiary care hospital for their willingness to accept the vaccine and the reason for their hesitancy and refusal. Methodology: A six-month descriptive cross-sectional study with 386 community members over the age of 18 who visited the Tertiary Hospital OPD was conducted. The vaccine acceptance and hesitancy was assessed using a self designed detailed questionnaire with a one-time face-to-face interview. Results: Acceptance for vaccine was observed in 235 (60.8%) participants, while 151 (39.2%) hesitated and refused. Participants with lower education (P < 0.00001), no employment did not readily accept the vaccine (P < 0.00001). Pregnant, breast-feeding women (21.8%) hesitated vaccine because of lack of evidence regarding vaccine safety. Participants (73.6%) were readily accepting (third dose) booster dose of vaccine when available. The reason for vaccine hesitance was the concern about side effects (58.2%) and reason for acceptance was to get immunity against COVID-19 (76.1%). Vaccine information source appears to be an influential aspect, as participants who obtained vaccination information from healthcare providers had no concerns regarding vaccination. Conclusion: The study has found a link between average education level, unemployment and vaccine acceptance and hesitancy. Factors influencing vaccine hesitancy include lack of vaccine information, vaccine side effects, and misinformation spread via social media. Clinical pharmacists can play an important role in boosting up vaccine acceptance by providing appropriate information in community.

Contribution of Airplane Engine Emissions on the Local Air Quality around Stuttgart Airport during and after COVID-19 Lockdown Measures

Air quality investigations at airports have shown that aircrafts cause a significant increase in air pollution at and around the vicinity of the airport, which can cause adverse effects on human health. The objective of this research was to investigate the aircraft-sourced pollutant levels at the Stuttgart airport and in the surrounding areas during and after COVID-19 lockdown measures. Three phases of stationary measurements of ultrafine particles (UFP), particulate matter (PM), black carbon (BC), CO2, O3, NO, and NO2 were made at various points on the east and west sides of the airport in the extension of the airport runway. In first phase of measurement, the airport was closed for construction, and no air traffic took place. In the second phase, the airport was reopened with limited operation due to a lockdown period at the beginning of the COVID-19 pandemic. Finally, in the third phase, measurements were performed during the peak summer holiday travel season to measure the air quality during maximum air traffic, after the end of the first lockdown period. While there were fewer notable changes in the BC concentrations, coarse PM fractions, and gases across the three phases, there were significant increases in the UFP concentrations from aircraft emissions. Throughout the three phases, the peak particle concentration decreased from between 27 and 86 nm in phase 1. to between 27 and 35 nm in phase 2, to finally 11 nm in phase 3 on all days in which the aircraft plumes were measured. During flight arrivals, definite increases in UFP particle number concentration (PNC) were observed, with the majority of the particles being in the 10 nm size class. These results were measured repeatedly on both sides of the airport in the direct prolongation of the runway and even at distances of up to 3 km away in nearby neighbouring communities. While the overall PM and UFP levels are affected by vehicular traffic, the freeway measurements showed particles from aircrafts and vehicles are distinguishable using the parameters PNC and Dp. The BC concentrations were rarely influenced by aircraft activity, while only some NO and NO2 peaks were measured depending on the consistency of the wind.

Repurposing food molecules as a potential BACE1 inhibitor for Alzheimer's disease.

Alzheimer's disease (AD) is a severe neurodegenerative disorder of the brain that manifests as dementia, disorientation, difficulty in speech, and progressive cognitive and behavioral impairment. The emerging therapeutic approach to AD management is the inhibition of ß-site APP cleaving enzyme-1 (BACE1), known to be one of the two aspartyl proteases that cleave ß-amyloid precursor protein (APP). Studies confirmed the association of high BACE1 activity with the proficiency in the formation of ß-amyloid-containing neurotic plaques, the characteristics of AD. Only a few FDA-approved BACE1 inhibitors are available in the market, but their adverse off-target effects limit their usage. In this paper, we have used both ligand-based and target-based approaches for drug design. The QSAR study entails creating a multivariate GA-MLR (Genetic Algorithm-Multilinear Regression) model using 552 molecules with acceptable statistical performance (R 2 = 0.82, Q 2 loo = 0.81). According to the QSAR study, the activity has a strong link with various atoms such as aromatic carbons and ring Sulfur, acceptor atoms, sp2-hybridized oxygen, etc. Following that, a database of 26,467 food compounds was primarily used for QSAR-based virtual screening accompanied by the application of the Lipinski rule of five; the elimination of duplicates, salts, and metal derivatives resulted in a truncated dataset of 8,453 molecules. The molecular descriptor was calculated and a well-validated 6-parametric version of the QSAR model was used to predict the bioactivity of the 8,453 food compounds. Following this, the food compounds whose predicted activity (pKi) was observed above 7.0 M were further docked into the BACE1 receptor which gave rise to the Identification of 4-(3,4-Dihydroxyphenyl)-2-hydroxy-1H-phenalen-1-one (PubChem I.D: 4468; Food I.D: FDB017657) as a hit molecule (Binding Affinity = -8.9 kcal/mol, pKi = 7.97 nM, Ki = 10.715 M). Furthermore, molecular dynamics simulation for 150 ns and molecular mechanics generalized born and surface area (MMGBSA) study aided in identifying structural motifs involved in interactions with the BACE1 enzyme. Molecular docking and QSAR yielded complementary and congruent results. The validated analyses can be used to improve a drug/lead candidate's inhibitory efficacy against the BACE1. Thus, our approach is expected to widen the field of study of repurposing nutraceuticals into neuroprotective as well as anti-cancer and anti-viral therapeutic interventions.

QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CLpro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking, molecular dynamics simulation and MMGBSA calculation approaches.

Congruous coronavirus drug targets and analogous lead molecules must be identified as quickly as possible to produce antiviral therapeutics against human coronavirus (HCoV SARS 3CLpro) infections. In the present communication, we bear recognized a HIT candidate for HCoV SARS 3CLpro inhibition. Four Parametric GA-MLR primarily based QSAR model (R2:0.84, R2adj:0.82, Q2loo: 0.78) was once promoted using a dataset over 37 structurally diverse molecules along QSAR based virtual screening (QSAR-VS), molecular docking (MD) then molecular dynamic simulation (MDS) analysis and MMGBSA calculations. The QSAR-based virtual screening was utilized to find novel lead molecules from an in-house database of 100 molecules. The QSAR-vS successfully offered a hit molecule with an improved PEC50 value from 5.88 to 6.08. The benzene ring, phenyl ring, amide oxygen and nitrogen, and other important pharmacophoric sites are revealed via MD and MDS studies. Ile164, Pro188, Leu190, Thr25, His41, Asn46, Thr47, Ser49, Asn189, Gln191, Thr47, and Asn141 are among the key amino acid residues in the S1 and S2 pocket. A stable complex of a lead molecule with the HCoV SARS 3CLpro was discovered using MDS. MM-GBSA calculations resulted from MD simulation results well supported with the binding energies calculated from the docking results. The results of this study can be exploited to develop a novel antiviral target, such as an HCoV SARS 3CLpro Inhibitor.

Mechanistic and Predictive QSAR Analysis of Diverse Molecules to Capture Salient and Hidden Pharmacophores for Anti-Thrombotic Activity.

Thrombosis is a life-threatening disease with a high mortality rate in many countries. Even though anti-thrombotic drugs are available, their serious side effects compel the search for safer drugs. In search of a safer anti-thrombotic drug, Quantitative Structure-Activity Relationship (QSAR) could be useful to identify crucial pharmacophoric features. The present work is based on a larger data set comprising 1121 diverse compounds to develop a QSAR model having a balance of acceptable predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The developed six parametric model fulfils the recommended values for internal and external validation along with Y-randomization parameters such as R2tr = 0.831, Q2LMO = 0.828, R2ex = 0.783. The present analysis reveals that anti-thrombotic activity is found to be correlated with concealed structural traits such as positively charged ring carbon atoms, specific combination of aromatic Nitrogen and sp2-hybridized carbon atoms, etc. Thus, the model captured reported as well as novel pharmacophoric features. The results of QSAR analysis are further vindicated by reported crystal structures of compounds with factor Xa. The analysis led to the identification of useful novel pharmacophoric features, which could be used for future optimization of lead compounds.

Selection of an Effective Hand Sanitizer to Reduce COVID-19 Effects and Extension of TOPSIS Technique Based on Correlation Coefficient under Neutrosophic Hypersoft Set

Correlation coefficients are used to tackle many issues that include indistinct as well as blurred information excluding is not able to deal with the general fuzziness along with obscurity of the problems that have various information. The correlation coefficient (CC) between two variables plays an important role in statistics. Likewise, the accuracy of relevance assessment depends on the information in a set of discourses. The data collected for numerous statistical studies is full of exceptions. The concept of the neutrosophic hypersoft set (NHSS) is a parameterized family that deals with the subattributes of the parameters and is a proper extension of the neutrosophic soft set to accurately assess the deficiencies, anxiety, and uncertainty in decision-making. Compared with existing research, NHSS can accommodate more uncertainty, which is the most significant technique for describing fuzzy information in the decision-making process. The core objective of follow-up research is to develop the concept and characteristics of CC and the weighted correlation coefficient (WCC) of NHSS. We also introduced some aggregation operators in the considered environment, which can help us establish a prioritization technique for order preference by similarity to the ideal solution (TOPSIS) based on CC and WCC under NHSS. A decision-making strategy is established to solve multicriteria group decision-making (MCGDM) problems utilizing developed methodology. Moreover, the proposed method is utilized for the selection of an effective hand sanitizer during the COVID-19 pandemic to ensure the validity of the proposed approach. The practicality, effectivity, and flexibility of the current approach are proved through comparative analysis with the assistance of some existing studies.

DESPOTIC LEADERSHIP AND JOB SATISFACTION AMONG NURSES: ROLE OF EMOTIONAL EXHAUSTION

[...]the component of nurses' 'job satisfaction' in the healthcare sector, in particular, is problematic globally and acquiring importance not only in the 'developed economies' such as the USA but also in 'under-developed' economies such as Rwanda, Philippines, Ghana, Malaysia, India, and Thailand (Hamid et al., 2014;Mills, 2014;Shipley, 2015;Atefi, Abdullah & Wong, 2016;Shah et al., 2018). According to AMN Healthcare (2017) reveals that 82% of registered nurses reported that leadership is indeed the call of the time in terms of quantity and quality. [...]leadership effect on job satisfaction may vary according to the leadership style and a weaker relationship was also reported by (Voon et al., 2011) directing to a mediating variable between direct effect of leadership and job satisfaction. [...]Tayfur, Bayhan Karapinar, and Metin Camgoz, (2013) reported weak mediation of 'emotional exhaustion' between 'distributive justice' and turnover, proposing for more assessments. [...]a new mediational aspect of emotional exhaustion is assumed in this study between the relationship of despotic leadership and job satisfaction among healthcare nurses in Pakistan. 2.