Students' intention on post COVID19 continuance of e-learning

Traditional teaching methods have changed as a result of COVID-19's prominence. For many teachers, the lack of traditional face-to-face training was effectively made up for by online learning. Under emergency management, online learning may support students and schools while also generating special opportunities. In reaction to the epidemic, educational institutions from many nations have introduced extensive online course options. Online education during a pandemic is distinct from regular online education. An investigation on emergency management-related educational reform can be done by surveying students in higher education institutions University students were polled to discover more about their intentions to keep learning online despite the outbreak. Using the task-technology fit model, expectation confirmation theory was broadened to examine if the technical support for promoting online learning assisted students in completing course learning assignments while pandemic was going on and led to a persistent intention to take use of E-learning in the nearby future. When creating eLearning platforms, governments must exercise caution because students' intentions to continue their e-learning may change as a result of unanticipated crises like COVID-19. Through the use of online surveys, data were gathered. The research hypotheses were validated with partial least squares method and structural equation modelling on a total of 513 valid replies. The findings showed that continuing intention was substantially explained by the entire research design. After the COVID-19 pandemic, specific recommendations are made regarding how higher education institutions may support online learning strategies. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Antecedents of Patient Satisfaction in Private Clinical Laboratories toward Patient Loyalty with Switching Cost and Location as Moderating Factors (An Empirical Study from Indonesia)

BACKGROUND: Clinical laboratory (CL) services are at the forefront to support health-care services, particularly during the pandemic of COVID-19. The increasing number of private clinical laboratories at present days indicates the increase in patient needs, causing the health-care service provider to face challenges as people have more options. Therefore, fostering patient loyalty (PL) is a crucial success factor for the business growth of clinical laboratories as health-care providers. AIM: The purpose of this study is to analyze antecedents of patient satisfaction (PS) in clinical laboratories towards PL with the switching cost and location as moderating factors. METHODS: This study was done as a quantitative survey, and data were obtained by a cross-sectional approach with partial least squares structural equation modeling for the data analysis method. There are 266 respondents eligible as samples, who undergo the phlebotomy process (PP) in a private laboratory located within a specific area. RESULTS: This study demonstrated that all the nine hypotheses supported with α: 0.05 and p < 0.05, include six independent variables named administrative process, information availability (IA), the environment in the phlebotomy room, PP, waiting time, and result notification that influence PS. PS has been shown to have a direct effect on PL and also mediate the antecedents. Furthermore, SC and LO have demonstrated a significant effect to moderate this relationship. CONCLUSIONS: PS has been confirmed as the main construct to predict PL whereas the AP is the most important independent variable followed by IA. CL management should pay more attention to these antecedents to ensure PS and retain the clinic’s patients. The cost from the patient’s perspective should be taken into account since this helps the CL keep the patient loyal.

Electrolyte Analysis in Blood Serum by Laser-Induced Breakdown Spectroscopy Using a Portable Laser.

The fast and reliable analysis of electrolytes such as K, Na, Ca in human blood serum has become an indispensable tool for diagnosing and preventing diseases. Laser-induced breakdown spectroscopy (LIBS) has been demonstrated as a powerful analytical technique on elements. To apply LIBS to the quantitative analysis of electrolyte elements in real time, a self-developed portable laser was used to measure blood serum samples supported by glass slides and filter paper in this work. The partial least squares regression (PLSR) method was employed for predicting the concentrations of K, Na, Ca from serum LIBS spectra. Great prediction accuracies with excellent linearity were obtained for the serum samples, both on glass slides and filter paper. For blood serum on glass slides, the prediction accuracies for K, Na, Ca were 1.45%, 0.61% and 3.80%. Moreover, for blood serum on filter paper, the corresponding prediction accuracies were 7.47%, 1.56% and 0.52%. The results show that LIBS using a portable laser with the assistance of PLSR can be used for accurate quantitative analysis of elements in blood serum in real time. This work reveals that the handheld LIBS instruments will be an excellent tool for real-time clinical practice.

A Conceptual Study of Psychological Capital Mediates the Relationship Between Positive Emotions and Work Engagement: Pathways to Achieving Sdgs in Academic Organisations

Higher education institutions (HEIs) are fundamental to fostering sustainable advancement in regions where sustainable strategies are implemented. The positive perspective on personal strengths and resources in academic organizations during the Covid-19 pandemic has led to a high interest in positive emotions and psychological capital in the education setting. Positive emotions and psychological capital are the variables viewed as a sense of well-being and positive attitude in academic organizations. Thus, this study supports Sustainable Development Goals (SDG) 3 (good health and well-being), while work engagement as the primary variable promotes SDG 4 (quality education). An engaged educator can ensure adaptivity and enhance performance to provide quality education. Nevertheless, only limited studies examined the interaction between these variables in the academic context. Previous studies focused only on students and job performance outcomes among corporate employees. However, none focused on academics’ well-being for establishing sustainability-related practices. This study, guided by the broaden-and-build theory, aims to examine the association between job-related positive emotions and work engagement and the mediating role of psychological capital. This study adopts a quantitative research technique, while a simple random method will be employed for data collection. The partial least squares structural equation modelling (PLS-SEM) methodology will be utilized for data analysis. The sample selection will be Malaysian HEIs academicians from different disciplines in line with the SDGs. Academicians’ job-related positive emotions are anticipated to relate to better work engagement through positive relationships with psychological capital levels (i.e., efficacy, hope, optimism, and resilience). This study will provide first-hand information on the relationship between job-related positive emotions, psychological capital, and work engagement among Malaysian HEIs academicians.

IMMUNE EXHAUSTION IS ASSOCIATED with PERSISTENT SARS-CoV-2 VIREMIA and SEVERE DISEASE

Background: SARS-CoV-2 viremia is associated with adverse outcomes in COVID-19. The immunologic mediators of this relationship remain under-explored. In this study, we aimed to evaluate the correlation between immune exhaustion markers, SARS-CoV-2 viremia clearance and clinical outcomes. Methods: We included 126 participants with confirmed SARS-CoV-2 infection who were hospitalized at an urban hospital in Boston, Massachusetts, during the first surge of the COVID-19 pandemic in early 2020. Plasma samples from days 0, 3, and 7 of hospitalization were available for analyses. The plasma SARS-CoV-2 viral load was determined by reverse transcription quantitative PCR (RT-qPCR). Proteomics data were generated using the Olink platform and neutralization level was assessed using a pseudovirus neutralization assay. Viremia persistence was defined as >40 copies/ml (detection limit) if the baseline detectable viremia was <1000 copies/ml, or >100 copies/ml (quantification limit) if the baseline viremia was ≥1000 copies/ml at day 7 of admission. Partial least-squares discriminant analysis (PLS-DA) was used to select exhaustion markers that could distinguish viremia persistence and clearance. An exhaustion score was generated based on features selected by PLS-DA and was divided into four quartiles. Differentially expressed proteins between 1st and 4th quartiles were determined by linear model adjusting for baseline characteristics. R (4.1.0) was used for statistics. Results: Viremia persistence was associated with a higher level of baseline viremia, a higher rate of severe diseases and mortality within 28 days of follow-up. Viremia persistence was associated with elevation of certain exhaustion protein markers including TIM3, PDL1, LGALS9, LAG3 and IL2RA. With PLS-DA, we selected TIM3, PDL1, and LGALS9 into the exhaustion score modeling. A higher exhaustion score was associated with higher baseline viremia, persistent viremia, severe disease, and death (Figure). When compared to the lowest exhaustion score (1st quartile), the highest exhaustion score (4th quartile) was associated with elevation in proteins belonging to IL-18 signaling pathway, lung fibrosis, and immune evasion in COVID-19. The immune exhaustion level was not associated with the neutralization level. Conclusion: In participants with COVID-19, soluble exhaustion markers are associated with delayed viremia clearance, immune evasion independent of humoral immunity development, and adverse outcomes.

MAIN BLOOD SERUM METABOLITES RESPONSIBLE FOR THE DISCRIMINATION OF HOSPITALIZED PATIENTS WITH MODERATE SYMPTOMS WITH OR WITHOUT COVID-19

Introduction: The variation in human blood serum metabolites resulting from an infection can assist in understanding mechanisms of pathogen action and body response and improve diagnosis. Aim: To map serum signatures of hospitalized symptomatic patients, positive or negative to SARS-CoV-2. Methods: Patients (n = 64) admitted to Anhembi Field Municipal Hospital, a hospital set up for initial care to patients with moderate symptoms, were analyzed being discriminated in positive (n = 32) or negative patients. Age and gender were matched to ensure homogeneity in the basal metabolic rates. Three Nuclear Magnetic Resonance (NMR) data set were recorded on Bruker AVANCE III 600 MHz spectrometer for serum samples analyzed in MetaboAnalyst 5.0 software platform. Results and discussion: The mean age of groups was 54.92 ±12.41 and 54.30 ±12.15, for positive and negative patients, divided in 16 female and 16 male. The ethnicity was 56.2% vs 46.8% caucasian, 34.3% mixed race in both groups, and 9.3% 12.5% vs black in positive and negative groups, respectively. BMI was 24 ±6.93 vs 33.5 ±7.85 in comparison to positive and negative patients, respectively. In both groups 50% of patients presented alveolar infiltrate. Although the groups were not paired by comorbidities, they were homogeneous ensuring that the metabolic variation is due to COVID-19 as similar percentage of patients with arterial hypertension, diabetes and dyslipidemia. Clinical symptoms were also remarkably similar between the groups in relation to: fever, dry cough, dyspnea and myalgia. The Partial Least Squares - Discriminant Analysis (PLS-DA) performed onto noesy1d data discriminated positively from negative patients. Also, it covered lower variance. Combining NMR techniques, it was possible to depict the main metabolites that distinguished the COVID-19 signatures. Alanine, glucose, cholesterol, and glutamine were increased, and lactate decreased in COVID-19. Conclusion: These results suggest NMR as an excellent tool to differentiate hospitalized patients with moderate symptoms as COVID-19 positive or negative. The Ethics Research Committee of the University of Campinas approved all of the experimental procedures, and all individuals signed the informed consent form.

Application of Near-infrared Spectroscopy and Multivariate Methods for the Estimation of Isopropyl Alcohol Content in Hand Sanitizer Formulation

To address the need of alcohol-based hand sanitizers during COVID-19, U.S. FDA has issued a guidance for the preparation of hand sanitizers that recommends 80% v/v ethanol or 75%v/v isopropyl alcohol (IPA) along with other ingredients. The aim of this study was to develop a new method to estimate IPA content in hand sanitizers by using Near-infrared (NIR) spectroscopy with a multivariate chemo-metric approach. Calibration samples containing 10-90% of IPA were used for model development. NIR data was mathematically pretreated with multiple scattering correction before development of partial least squares (PLSR) and principal component regressions (PCR) model. Both models showed good linearity over the selected range of IPA content with high R2 (>0.993), low root mean squared error (<2.163), minimum difference between standard errors between calibration and validation models (0.0009). The proposed NIR with multivariate methods provide rapid analysis of IPA content in the hand sanitizer.

Determinants of Effective Telework: An Evidence from Telecommunication Sector in Bahrain

This study investigates the effectiveness of working from home during COVID-19 pandemic based on seven characteristics from telecommunication sector in Bahrain, namely, social, job, teleworker, management, teleworking, crisis as well as demographic variables. The data are collected through a questionnaire using a sample of 104 employers working from home. A partial least squares regression that protects against multicollinearity and nonnormality has been employed as a unique technique to build two models. The results of these models have suggested that the teleworking effectiveness and the teleworker, teleworking and crisis characteristics are statistically significant while social, job, management characteristics and demographic variable are not statistically significant. Such results support the importance of teleworker skills and professional quality variation such as autonomy, self-disciplined self-motivation, management skills, likely to work during the most prolific period and to facilitate working in case of sickness as well as crisis. Decision-makers and managers will the most beneficiary of the study and the results. © 2021 IEEE.

POSB422 Machine Learning-Based Virtual Screening, Molecular Docking and Drug-Likeness to Discover New Inhibitors of the Glycoprotein Spike (S1) of SARS-CoV-2

Objectives: Despite great advancements in COVID-19 immunization, the development of therapeutic interventions is urgent to control the ongoing pandemic, especially infected patients. The spike protein (S1) of SARS-Cov-2 virus plays a major role in attachment to the host and further series of events. We aimed to identify natural bioactive compounds (NBC) that act as potential inhibitors of S1 by means of in silico assays. Methods: NBCs with proved biological in vitro activities were obtained from the ZINC database (https://zinc.docking.org) and analyzed through virtual screening and molecular docking to identify those with higher affinity to the S1. Machine learning models of principal component analysis (PCA), artificial neural networks (ANN), discriminant analysis by partial least squares (PLS-DA) and decision tree (DT) were used to validate the Results: Selected NBCs were submitted to drug-likeness analysis using the Lipinsk and Vebber's five rule. The prediction of pharmacokinetic parameters (i.e. absorption, metabolism, distribution, elimination) and toxicity (e.g. hepatotoxicity, cardiotoxicity, carcinogenicity, immunotoxicity) were performed (ADMET). The influence of the NBC’s stereoisomeric, tautomeric and protonation states at physiological pH on the pharmacodynamics, pharmacokinetics and toxicity analyses were also evaluated. Results: A total of 170,906 compounds were analyzed. Of these, only 36 showed greater affinity with the S1 (affinity energy <0.8 kcal/mol). The PCA and PLS-DA models were able to reproduce the results of the virtual screening and docking analyzes with an accuracy of 97.5%. Of these 36 CNBs, only 12 (33.33%) were drug-likeness. The ADMET analysis showed that the natural compound phaselol (7-[[(1R,4aS,6R,8aR)-6-hydroxy-2,5,5,8a-tetramethyl-1,4,4a,6,7,8-hexahydronaphthalen-1-yl]methoxy]chromen-2-one) was the most promising in inhibiting the SARS-COV-2 spike. Conclusions: Machine learning-based research is efficient for retrieving novel approaches to diseases’ treatment. We identified 12 compounds as possible inhibitors of S1;phaselol was the most promising candidate for treating COVID-19. In vitro, preclinical studies and clinical trials are now needed to confirm these findings.

Metabolomics reveals sex-specific metabolic shifts and predicts the duration from positive to negative in non-severe COVID-19 patients during recovery process.

Metabolic profiling in COVID-19 patients has been associated with disease severity, but there is no report on sex-specific metabolic changes in discharged survivors. Herein we used an integrated approach of LC-MS-and GC-MS-based untargeted metabolomics to analyze plasma metabolic characteristics in men and women with non-severe COVID-19 at both acute period and 30 days after discharge. The results demonstrate that metabolic alterations in plasma of COVID-19 patients during the recovery and rehabilitation process were presented in a sex specific manner. Overall, the levels of most metabolites were increased in COVID-19 patients after the cure relative to acute period. The major plasma metabolic changes were identified including fatty acids in men and glycerophosphocholines and carbohydrates in women. In addition, we found that women had shorter length of hospitalization than men and metabolic characteristics may contribute to predict the duration from positive to negative in non-severe COVID-19 patients. Collectively, this study shed light on sex-specific metabolic shifts in non-severe COVID-19 patients during the recovery process, suggesting a sex bias in prognostic and therapeutic evaluations based on metabolic profiling.