Social support and perceived uncertainties during COVID-19: Consequences for employees' wellbeing.

The COVID-19 crisis has drastically affected organizations worldwide, thereby influencing the employees' psychological wellbeing. Since it is a new pandemic, research is sparse in the domain of employees' psychological wellbeing in relation to the phenomenon. Drawing on social support and job demand-resource perspectives, this research adds to the factors affecting employees' wellbeing due to the coronavirus outbreak. Specifically, this study is an investigation of co-workers' instrumental support in predicting employees' emotional exhaustion via employees' perceived uncertainties experienced due to the COVID-19 pandemic. Further, we tested for the contextual specificity of family support on uncertainties and its link with employees' emotional exhaustion. With data drawn from two universities (n = 275), the findings reveal a negative association between co-worker task support and an employee's emotional exhaustion, and an employee's perceived uncertainties mediate this relationship. Moreover, the moderating analysis exhibits that family support mitigates the negative effect of uncertainty perception on emotional exhaustion. Our study reveals that coworker and family support are extremely important during the COVID-19 pandemic. These findings are equally valuable for organizations and society to mitigate the detrimental effects of the COVID-19 pandemic on employees' wellbeing.

Pharmacometrics: A New Era of Pharmacotherapy and Drug Development in Low- and Middle-Income Countries.

Pharmacotherapy, in many cases, is practiced at a suboptimal level of performance in low- and middle-income countries (LMICs) although stupendous amounts of data are available regularly. The process of drug development is time-consuming, costly, and is also associated with loads of hurdles related to the safety concerns of the compounds. This review was conducted with the objective to emphasize the role of pharmacometrics in pharmacotherapy and the drug development process in LMICs for rational drug therapy. Pharmacometrics is widely applied for the rational clinical pharmacokinetic (PK) practice through the population pharmacokinetic (popPK) modeling and physiologically based pharmacokinetic (PBPK) modeling approach. The scope of pharmacometrics practice is getting wider day by day with the untiring efforts of pharmacometricians. The basis for pharmacometrics analysis is the computer-based modeling and simulation of pharmacokinetics/pharmacodynamics (PK/PD) data supplemented by characterization of important aspects of drug safety and efficacy. Pharmacometrics can be considered an invaluable tool not only for new drug development with maximum safety and efficacy but also for dose optimization in clinical settings. Due to the convenience of using sparse and routine patient data, a significant advantage exists in this regard for LMICs which would otherwise lag behind in clinical trials.

Is it safe and effective to administer COVID-19 vaccines during pregnancy? A systematic review and meta-analysis.

OBJECTIVE: To evaluate the safety and efficacy of COVID-19 vaccines in pregnant women performing an updated meta-analysis. METHODS: We searched PubMed, Cochrane Central, and SCOPUS from inception to March 2022. Outcomes of interest were incidence of adverse maternal, fetal and neonatal consequences pertaining to safety of the vaccines. Secondarily, we analyzed the number of SARS-CoV-2 infections, hospitalization for COVID-19, and admission to the I.C.U. for COVID-19 assessing the effectiveness of vaccines. Results were pooled using a random effects model. RESULTS: Ten observational studies (n=326,499) analyzing pregnant women were included. Our results suggest that COVID-19 vaccination prevents infection (OR: 0.56, 95% CI: 0.47, 0.67; P = <0.00001) and related hospitalizations (OR: 0.50, 95% CI: 0.31, 0.82; P = 0.006) effectively. It was also observed that vaccination does not change adverse outcomes in pregnancy, namely preeclampsia or eclampsia, stroke (four weeks of delivery), meconium-stained amniotic fluid, spontaneous vaginal delivery, operative vaginal delivery, cesarean delivery, postpartum hemorrhage, and blood transfusions. Furthermore, the vaccine was observed to be protective against neonatal COVID-19 I.C.U. admissions (OR: 0.85; 95% CI: 0.81, 0.90; P = <0.00001). CONCLUSION: Our pooled analysis suggests that the COVID-19 vaccination in pregnant women prevents infection effectively and has no adverse outcomes. Future large-scale trials in a randomized fashion are needed to confirm our results.

Safety, effectiveness and hesitancy of COVID-19 vaccination in children: A cross-sectional study in Pakistan.

Background: The elevated risk of serious complications like myocarditis and pericarditis after COVID-19 vaccination, especially in adolescent has been reported in some instances that need to be tested in regional populations and different ethnicity groups. The purpose of the study was to evaluate the side effects, hesitancy, and effectiveness outcomes following COVID-19 vaccination among children in Pakistan. Methods: The study was planned using a cross-sectional design and data from Children and Adolescents (CA) was collected through a convenient sampling method using a validated questionnaire between February to July 2022. A total of 1,108 CA between the age of 12-18 years who received one or two doses of vaccine were selected and data were collected through direct interviews with respondents. Results: The results showed that among 99.8% of respondents who received the Pfizer COVID-19 vaccine, 72.3% of respondents were partially vaccinated (with one dose) while 27.7% were fully vaccinated (with two doses). COVID vaccination regime had a favorable safety profile in children as compared to adults. Vaccine hesitancy in children was reported to be 52.4% and the most common reasons for hesitance were the assumption that the vaccine is not safe (23.7%), the vaccine is not required (19.6%) and the vaccine is not effective (10.4%). The reported side effects were mainly mild (88.5%) followed by moderate (10.6%) and only 0.8% were of severe intensity. Post-vaccination local side effects of mild intensity were common with an onset of an average of 24 h (68%) and a duration of 2-3 days (60.6%). The reported side effects were significantly associated with gender (p = 0.00) while age had no significant effect on the occurrence of side effects. Overall, the vaccine was well tolerated by children and adolescents and was effective in preventing the reoccurrence of COVID-19 infection in 99.9% of participants. Conclusion: COVID-19 vaccine by Pfizer approved by the FDA for use in CA 12-18 years of age was well tolerated with a good safety profile and no serious adverse drug reactions were reported. The vaccine side effects were mild (88.5%) and lasted for an average of 2-3 days only (60.4%). The vaccine was effective in safeguarding Children against COVID-19 infection.

Qualitative exploration of perspectives of the pharmacists working in public-sector hospitals during COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic, a serious global health threat, has excruciating social and economic implications given its transmissibility, lack of therapy, and severity. In such a situation, pharmacists as frontline healthcare professionals hold a significant position to tackle. This study was designed to explore the perception and preparedness of pharmacists working in public sector hospitals amid such a pandemic in Pakistan. METHODS: A total of 11 pharmacists were interviewed for this qualitative study design through a semi-structured interview guide. The interviews were recorded and transcribed verbatim. RESULTS: The thematic content analysis yielded six major themes; understanding of COVID-19, perceptions towards COVID-19, preventive aspects, management aspects, changes to lifestyle, and psychological aspects. Though efficient preparedness and approach to fighting against such pandemics were reported, pharmacists were found susceptible to infection and psychological stress. They also expressed lockdown as an effective measure to prevent the disease from spreading but still were concerned about its economic and social impact. CONCLUSIONS: Adequate planning and facilities from the national level should be made available for strengthening the hospital pharmacy service that helps improve the overall healthcare system of low- and middle-income countries like Pakistan. The provision of a protective facility, incentives, and occupational health surveillance packages are deemed necessary to boost the self-esteem and morale of hospital pharmacists that safeguard the early and effective management of such disasters.

COVID-19, travel restrictions and environmental consequences.

The component of human life that has been most significantly altered by the COVID-19 epidemic is travel. Due to the upheaval produced by the pandemic breakout, countries are becoming increasingly avaricious and are scrambling to stockpile vaccines. The world has been locked down to reduce/control the pandemic outbreak, driving countries to shut their doors to other people from countries. The recent pandemic has had a short-term, positive effect on the environment, but travel restrictions have caused problems for the common person and are expected to deteriorate more soon, necessitating longer quarantines, vaccination requirements, vaccine passports, and immunization certificates required by countries for safe travel. Thus, this study has three objectives. First, we investigate the impact of COVID-19 on travel and the environment, as well as the role that tourists play in the transmission of the virus. Second, we examine how countries are handling COVID-19 vaccines. Finally, we pinpoint differences in vaccination coverage.

Do Laboratory Biomarkers Predict Survival In Severe Covid-19? A Cross-Sectional Study.

Background: This study aims to compare lymphocyte count, C-reactive protein (CRP), ferritin, Lactate Dehydrogenase (LDH) and D-dimer among survivors and non-survivors of severe COVID-19. Methods: This retrospective cross-sectional analytical study included 69 patients for whom a record of the biomarkers and survival status was available. Baseline and peak values were selected for serum CRP, ferritin, LDH and D-Dimer. Baseline and trough lymphocyte counts were selected. Data were analyzed using SPSS version 21. Mean and standard deviation were used to compare the biomarkers with paired t-test. p-value <0.05 was taken as significant. Results: The mean age of the study population was 55.5±9.1 years and 50 (72.5%) were male. Among survivors, the increase in CRP level was not significant (from 15.80±9.8 mg/dl to 17.87±8.4 mg/dl, p=0.45) while it was significant in non-survivors (from 16.68±10.90 mg/dl to 20.77±12.69 mg/dl, p=0.04). There was no significant rise in LDH levels in survivors (from 829.59±499 U/L to 1018.6±468 U/L, p=0.20) while it increased significantly in non-survivors (from 816.2±443.08 U/L to 1056.61±480.54 U/L, p=0.003). The decrease in lymphocyte count and increase in D-Dimers in both the groups was significant (p=0.001). There was no significant elevation in ferritin in both the groups (p>0.05). Conclusion: In severe COVID-19 patients, serum CRP and LDH can be used for risk stratification and predicting survival. Lymphopenia, increase in serum ferritin and D-dimers may not predict survival.

Low-Power Negative-Differential-Resistance Device for Sensing the Selective Protein via Supporter Molecule Engineering.

Van der Waals (vdW) heterostructures composed of atomically thin two-dimensional (2D) materials have more potential than conventional metal-oxide semiconductors because of their tunable bandgaps, and sensitivities. The remarkable features of these amazing vdW heterostructures are leading to multi-functional logic devices, atomically thin photodetectors, and negative differential resistance (NDR) Esaki diodes. Here, an atomically thin vdW stacking composed of p-type black arsenic (b-As) and n-type tin disulfide (n-SnS2 ) to build a type-III (broken gap) heterojunction is introduced, leading to a negative differential resistance device. Charge transport through the NDR device is investigated under electrostatic gating to achieve a high peak-to-valley current ratio (PVCR), which improved from 2.8 to 4.6 when the temperature is lowered from 300 to 100 K. At various applied-biasing voltages, all conceivable tunneling mechanisms that regulate charge transport are elucidated. Furthermore, the real-time response of the NDR device is investigated at various streptavidin concentrations down to 1 pm, operating at a low biasing voltage. Such applications of NDR devices may lead to the development of cutting-edge electrical devices operating at low power that may be employed as biosensors to detect a variety of target DNA (e.g., ct-DNA) and protein (e.g., the spike protein associated with COVID-19).

Impact of COVID­19 infection in patients with neurodegenerative diseases with particular focus on Alzheimer's and Parkinson's disease.

Neurodegenerative disorders (NDD) are chronic neurological diseases characterized by loss and/or damage to neurons along with the myelin sheath, and patients are at higher risk of severe infection with the SARS­CoV­2. A comprehensive literature search was performed using relevant terms and inclusion­exclusion criteria. Recent articles, subjects older than 50 years, and articles written in the English language were included, whereas letters to the editor and articles related to pregnant women were excluded from the review study. COVID­19 appears to damage angiotensin­II receptors which cause natural killer cells to lose the ability to clear virus­infected cells, owing to worse outcomes in patients with NDD. COVID­19 can worsen the symptoms of Alzheimer's disease. In addition, COVID­19 worsens drug­responsive motor symptoms in Parkinson's disease (PD) and other symptoms like fatigue and urinary complaints. Vitamin D is essential in decreasing pro­inflammatory and increasing anti­inflammatory cytokines in ongoing COVID­19 infections and reducing angiotensin receptors and, hence, decreasing COVID­19 infection severity. Telemedicine shows promise for patients with NDD but is yet to overcome legal issues and personal barriers. COVID­19 has a significant effect on neurodegenerative conditions, which appears partly to the nature of the NDD and the neuro­invasive capabilities of the SARS­CoV­2. The protective role of vitamin D in patients with NDD further supports this hypothesis. Modifications in current health care, like the telemedicine platform, are required to address the increased risk of serious infection in this population. Further studies will be required to clarify conflicting reports in many fields.

A Multiscale Geospatial Dataset and an Interactive Visualization Dashboard for Computational Epidemiology and Open Scientific Research.

The coronavirus disease (COVID-19) continued to strike as a highly infectious and fast-spreading disease in 2020 and 2021. As the research community actively responded to this pandemic, we saw the release of many COVID-19-related datasets and visualization dashboards. However, existing resources are insufficient to support multiscale and multifaceted modeling or simulation, which is suggested to be important by the computational epidemiology literature. This work presents a curated multiscale geospatial dataset with an interactive visualization dashboard under the context of COVID-19. This open dataset will allow researchers to conduct numerous projects or analyses relating to COVID-19 or simply geospatial-related scientific studies. The interactive visualization platform enables users to visualize the spread of the disease at different scales (e.g., country level to individual neighborhoods), and allows users to interact with the policies enforced at these scales (e.g., the closure of borders and lockdowns) to observe their impacts on the epidemiology.

Dynamic Link Prediction for Discovery of New Impactful COVID-19 Research Approaches.

In fighting the COVID-19 pandemic, the main challenges include the lack of prior research and the urgency to find effective solutions. It is essential to accurately and rapidly summarize the relevant research work and explore potential solutions for diagnosis, treatment and prevention of COVID-19. It is a daunting task to summarize the numerous existing research works and to assess their effectiveness. This paper explores the discovery of new COVID-19 research approaches based on dynamic link prediction, which analyze the dynamic topological network of keywords to predict possible connections of research concepts. A dynamic link prediction method based on multi-granularity feature fusion is proposed. Firstly, a multi-granularity temporal feature fusion method is adopted to extract the temporal evolution of different order subgraphs. Secondly, a hierarchical feature weighting method is proposed to emphasize actively evolving nodes. Thirdly, a semantic repetition sampling mechanism is designed to avoid the negative effect of semantically equivalent medical entities on the real structure of the graph, and to capture the real topological structure features. Experiments are performed on the COVID-19 Open Research Dataset to assess the performance of the model. The results show that the proposed model performs significantly better than existing state-of-the-art models, thereby confirming the effectiveness of the proposed method for the discovery of new COVID-19 research approaches.

Safety, effectiveness and hesitancy of COVID-19 vaccination in children: A cross-sectional study in Pakistan

Background The elevated risk of serious complications like myocarditis and pericarditis after COVID-19 vaccination, especially in adolescent has been reported in some instances that need to be tested in regional populations and different ethnicity groups. The purpose of the study was to evaluate the side effects, hesitancy, and effectiveness outcomes following COVID-19 vaccination among children in Pakistan. Methods The study was planned using a cross-sectional design and data from Children and Adolescents (CA) was collected through a convenient sampling method using a validated questionnaire between February to July 2022. A total of 1,108 CA between the age of 12–18 years who received one or two doses of vaccine were selected and data were collected through direct interviews with respondents. Results The results showed that among 99.8% of respondents who received the Pfizer COVID-19 vaccine, 72.3% of respondents were partially vaccinated (with one dose) while 27.7% were fully vaccinated (with two doses). COVID vaccination regime had a favorable safety profile in children as compared to adults. Vaccine hesitancy in children was reported to be 52.4% and the most common reasons for hesitance were the assumption that the vaccine is not safe (23.7%), the vaccine is not required (19.6%) and the vaccine is not effective (10.4%). The reported side effects were mainly mild (88.5%) followed by moderate (10.6%) and only 0.8% were of severe intensity. Post-vaccination local side effects of mild intensity were common with an onset of an average of 24 h (68%) and a duration of 2–3 days (60.6%). The reported side effects were significantly associated with gender (p = 0.00) while age had no significant effect on the occurrence of side effects. Overall, the vaccine was well tolerated by children and adolescents and was effective in preventing the reoccurrence of COVID-19 infection in 99.9% of participants. Conclusion COVID-19 vaccine by Pfizer approved by the FDA for use in CA 12–18 years of age was well tolerated with a good safety profile and no serious adverse drug reactions were reported. The vaccine side effects were mild (88.5%) and lasted for an average of 2–3 days only (60.4%). The vaccine was effective in safeguarding Children against COVID-19 infection.

Densely attention mechanism based network for COVID-19 detection in chest X-rays.

Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%.

Death anxiety among street-level bureaucrats: how does it affect their work drive and performance?

Drawing on the depletion model of self-regulation, this study investigates how death anxiety depletes the energy of SLBs, resulting in negative consequences for their work drive and public service performance. The study also examines the importance of trait mindfulness as a boundary condition for determining whether the psychological and work-related outcomes of death anxiety are more or less severe for some SLBs. Using time-lagged and supervisor-matched data from 417 respondents, our findings provide new insights into SLBs' psychological response and job performance in the face of a life-threatening global pandemic.

Antifungal Drugs Shortage in India amidst Looming Increase in Invasive Fungal Infections among COVID-19 Patients: An Impending Crisis.

The widespread prevalence of fungal infections in the second wave of COVID-19 pandemic could be owed to ubiquitous and injudicious use of steroids and immunosuppressive nature of the virus. However, these fungal infections also meant increased use of antifungal drugs, hence endangering their supply. Amphotericin B is the first line drug for mucormycosis which was declared as an epidemic in India during the second wave. With the increasing demand of the drug, came challenges to manufacture and supply large quantities of the drug and exploitation by creating a black market and spread of false information and imprudent usage. It is of utmost importance to be prepared with adequate supply all over the nation and implementing safety regulations in manufacturing and supply of large quantities of drugs during the demanding times and make them accessible at a reasonable rate.

MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images.

Efficient diagnosis of COVID-19 plays an important role in preventing the spread of the disease. There are three major modalities to diagnose COVID-19 which include polymerase chain reaction tests, computed tomography scans, and chest X-rays (CXRs). Among these, diagnosis using CXRs is the most economical approach; however, it requires extensive human expertise to diagnose COVID-19 in CXRs, which may deprive it of cost-effectiveness. The computer-aided diagnosis with deep learning has the potential to perform accurate detection of COVID-19 in CXRs without human intervention while preserving its cost-effectiveness. Many efforts have been made to develop a highly accurate and robust solution. However, due to the limited amount of labeled data, existing solutions are evaluated on a small set of test dataset. In this work, we proposed a solution to this problem by using a multi-task semi-supervised learning (MTSSL) framework that utilized auxiliary tasks for which adequate data is publicly available. Specifically, we utilized Pneumonia, Lung Opacity, and Pleural Effusion as additional tasks using the ChesXpert dataset. We illustrated that the primary task of COVID-19 detection, for which only limited labeled data is available, can be improved by using this additional data. We further employed an adversarial autoencoder (AAE), which has a strong capability to learn powerful and discriminative features, within our MTSSL framework to maximize the benefit of multi-task learning. In addition, the supervised classification networks in combination with the unsupervised AAE empower semi-supervised learning, which includes a discriminative part in the unsupervised AAE training pipeline. The generalization of our framework is improved due to this semi-supervised learning and thus it leads to enhancement in COVID-19 detection performance. The proposed model is rigorously evaluated on the largest publicly available COVID-19 dataset and experimental results show that the proposed model attained state-of-the-art performance.

Evaluation of clinical laboratory parameters in COVID-19 positive cases admitted in Islamabad

Objective: To assess the variation of laboratory parameters in COVID-19 positive patients with different genders and age groups and to clarify the consequences of COVID-19 infection on different patients. Study Design: Prospective study. Setting: IHITC (Isolation Hospital & Infectious Treatment Center), Islamabad. Period: 20th May, 2021 to 25th July, 2021. Material & Methods: Study was conducted With 222 participants among them 119 were COVID positive serve as Case and 103 were COVID negative considered as control. Blood samples were drawn from all participants of study to measure biochemical and hematological laboratory parameters with demographic characteristics. Mean ± standard deviation (SD) of different lab parameters analyzed by using IBM SPSS Statistics 20. Results: Total 222 participants were analyzed having 115 (50.7%) male and 107 (49.3%) female having mean age 60±13.8. No significant variation has been seen in ALP, total bilirubin, creatinine and uric acid having mean values with in normal range. In 119 positive patients, ALT (p=0.001) (t=2.031), urea (p=0.001) (t=7.590), Ferritin (p=0.001) (t=7.13), CRP (p=0.001) (t=9.90) and D-dimer (p=0.001) (t=5.962) were elevated and good predictor of poor prognosis of disease. Pathological impacts of COVID-19 were also represented by hematological parameters including WBC count (p=0.001) (t=7.126), Neutrophil to Lymphocyte Ratio (p=0.001) (t=9.042) and Lymphocyte count (p=0.001) (t=-12.707). Conclusion: According to this research, males and old age population is more susceptible to SARS-2. Our study suggests that laboratory biomarkers including ALT, Urea, Ferritin, CRP, D-dimer and WBC count are significantly associated with poor prognosis in Covid-19 patients. [ FROM AUTHOR]

Organic food and obesity: factors influencing actual purchase of organic food in COVID-19 pandemic with moderating role of organic food availability

PurposeOrganic food consumption decreases the risk of becoming obese or overweight. This study intends to see the influence of customer perceived value, COVID-19 fear, food neophobia, effort and natural content on the intention to purchase organic food (IPOF) that leads to the actual purchase of organic food (APOF). Moreover, organic food availability is a moderator between IPOF and APOF.Design/methodology/approachPLS-SEM is used for hypothesis testing. A purposive sampling technique was followed to gather data from organic food consumers in Lahore, Gujranwala and Islamabad and a total of 479 questionnaires were part of the analysis.FindingsThe outcomes show that customer perceived value, effort and natural content is positively related to IPOF. Despite this, COVID-19 fear and food neophobia are negatively associated with IPOF. IPOF and organic food availability are positively related to APOF. Finally, organic food availability significantly moderated between IPOF and APOF.Practical implicationsThis study outcome reveals that companies of organic food can recognize customer perceived value, COVID-19 fear, food neophobia, effort, natural content and organic food availability in their decision-making if they determine the actual purchase of organic food. This study offers a valuable policy to companies of organic food to enhance customer's behavior in purchasing organic food in Pakistan. Besides, practitioners and academicians can benefit from this study finding.Originality/valueThis initial research integrates customer perceived value, COVID-19 fear, food neophobia, effort, natural content, IPOF and organic food availability to determine APOF in the COVID-19 pandemic. Moreover, consumption value theory is followed to develop the framework.