Evaluating Monkeypox Knowledge Among Jordanian Pharmacists and Pharmacy Students: Bridging the Knowledge Gap (preprint)

Introduction The World Health Organization has declared Monkeypox a global public health emergency. The involvement of healthcare providers, such as pharmacists, plays a crucial role in disease control. Evaluating pharmacists' knowledge and their skills in diagnosing and managing monkeypox is crucial for developing effective response plans to control the disease. Methods A cross-sectional survey was conducted to assess monkeypox knowledge among Jordanian pharmacists and pharmacy students, and their ability to diagnose and manage monkeypox cases. An extensive literature review was conducted to develop the first draft of the survey, which was subsequently assessed for face and content validity. The survey comprised three main sections addressing various topics of interest. Data was analyzed using Statistical Package for Social Science. Results A total of 586 participants completed the survey, with a mean age of 24.94 years. The primary information source about monkeypox was published researches. The total knowledge scores (TK-score) ranged from − 5 to 17, with a mean of 6.37 while the symptoms knowledge scores (SK-score) ranged from − 5 to 9, with a mean of 2.71. A statistically significant difference in TK-score was observed between males and females (p-value = 0.025). The majority of the participants were not confident in their ability to diagnose (82.9%), or manage (79.9%) monkeypox cases. Approximately half of the participants believed that having COVID-19 increases the risk of experiencing serious symptoms from monkeypox. According to nearly half of participants, monkeypox is the potential next epidemic after COVID-19. Conclusion Study participants demonstrated higher knowledge concerning monkeypox virus symptoms, in contrast to their understanding of virus transmission modes and prevention. The findings revealed low levels of confidence among participants in diagnosing and managing monkeypox.

Systemic and Local Disease Manifestations Following COVID-19 Vaccination

Introduction/Objective COVID-19 vaccine-related lymphadenopathy, particularly in the ipsilateral axilla, is a relatively well-known side effect of mRNA vaccines with many reports in radiology, but less is known regarding histopathology and additional sites of lymphadenopathy, as well as other localized potential vaccine-related mass manifestations. In addition to a case of minimal change disease, we report two cases here with associated systemic and local pathologic changes related to COVID-19 vaccination. Methods/Case Report In case #1, a 17-year-old male presented with a 2.4 cm left postauricular mass. He had originally noticed the mass six months prior and thought that it had recently been growing. The mass was soft, nonfluctuant, and nontender to palpation. Given the risk of malignancy, a resection was performed. Histology showed an enlarged lymph node composed of mixed inflammatory cell components consistent with lymphoid hyperplasia and no evidence of malignancy. On further chart review, the patient had received his second COVID-19 vaccination just prior to noticing the mass enlarging. A SARS-CoV-2 Anti-Spike IgG assay was as high as 24,396 AU/ml, suggesting that this benign lymphadenopathy was most likely related to his vaccination. For case #2, a 47-year-old male developed a painless right deltoid mass shortly after receiving his vaccination at the same area that subsequently increased in size over seven months to 6.5 cm. Imaging showed a heterogeneous mass within the deltoid muscle concerning for malignancy and a biopsy was performed. Sections showed wavy, bland spindle cells with nuclei staining diffusely positive for beta-catenin, consistent with fibromatosis at his vaccination site. Results (if a Case Study enter NA) NA. Conclusion In summary, these case reports show potential systemic and local reactive effects in response to COVID-19 vaccination.

High-involvement work practices, work engagement and their effects on bank employees' turnover intentions: the moderating role of functional competence

PurposeThis study aims to propose and examine a research model where work engagement mediates the impacts of high-involvement work practices (HIWPs) on bank employees' turnover intentions. Specifically, the paper assesses: (a) the effects of empowerment, information sharing, rewards and training on work engagement and turnover intention, (b) work engagement as a mediator of the effects of these HIWPs on turnover intention (c) and functional competence as a moderator of the effects of these HIWPs on work engagement.Design/methodology/approachAn online survey was employed to gather data from 343 employees working in commercial banks in Bangladesh. The authors applied partial least squares structural equation modeling to assess the aforesaid linkages.FindingsEmpowerment and information sharing increase bank employees' work engagement, while training and rewards reduce their proclivity to leave. Work engagement partly mediates the relationships of empowerment and information sharing to turnover intention. Functional competence moderates the relationship between three HIWPs (empowerment, information sharing and rewards) on work engagement.Originality/valueThe paper examines the association between HIWPs and turnover intention, which has been subjected to little empirical inquiry among bank employees during a crisis (e.g. Covid-19 pandemic). The paper provides new insights into the underlying mechanism linking HIWPs and turnover intention and highlights the moderating effect of functional competence. Additionally, the study offers new knowledge on the impact of the pandemic on bank employees' HIWPs. Finally, this paper used data gathered from bank employees in Bangladesh, which is an underrepresented Asian country in the extant service research.

Antecedents of students' e-learning continuance intention during COVID-19: An empirical study

This study aims at exploring the underlying determinants influencing students' continuance intention to use an e-Learning platform during the COVID-19 pandemic. Based on the technology acceptance model and expectation-confirmation model, the study investigated the role of contextual (i.e., social isolation), psychological (academic year loss and cyberchondria), and student support-related (government and institutional supports) determinants on students' continuance intention to use an e-Learning platform during the pandemic. The study collected data from 440 respondents and analyzed those with Structural Equation Modeling. The findings showed that an e-Learning continuance intention during the pandemic is affected by usefulness, ease of use, attitudes, and intention to use the e-Learning platform;while the behavioral intention is influenced by usefulness, ease of use, attitudes, contextual, psychological, and student support-related determinants;and attitudes are impacted by usefulness and ease of use. Moreover, usefulness is predicted by confirmation of expectation;e-satisfaction is forecasted by usefulness and confirmation of expectation;whereas, cyberchondria is influenced by social isolation;fear of academic year loss is influenced by cyberchondria. Finally, intention to use mediated the impact of usefulness, ease of use, attitudes, contextual, psychological, and student support-related determinants on continuance intention. The study contributes to e-Learning literature incorporating contextual, psychological, and student support-related determinants into the technology acceptance model and expectation-confirmation model, which guide policymakers to understand how all levels of students can be brought into the e-Learning platforms that eventually help to eliminate digital discrimination barrier in the academia during any emergency. The policymakers must be careful in designing eLearning platforms since students' e-learning continuance intention may vary due to unprecedented crises, such as COVID-19.

Does better access to finance help firms deal with the COVID-19 pandemic? Evidence from firm-level survey data

The impact of the firm's pre-pandemic financial condition on the likelihood of a decline in its sales due to the COVID-19 pandemic in 35 developing and emerging countries is estimated. Results show that better access to finance reduces the likelihood of a decline in sales. Access to finance is more effective in arresting sales declines when firms fear that production cuts may lead to the loss of skilled workers and hard-to-replace input suppliers. It is less effective when workers, like women, do not wish to continue working for health or family reasons. Important policy implications are discussed. © 2023 John Wiley & Sons Ltd.

Anosmia in COVID-19 Patients: Can We Predict the Severity of Chest Manifestations?

Introduction Anosmia is one of the common symptoms of COVID-19, the link between severity of chest infection and anosmia was investigated by few studies. Objectives To find an association between anosmia and severity of chest infection. Methods An analysis of patients admitted to isolation hospital of our university with confirmed polymerase chain reaction positive testing for COVID-19, between March 2021 until September 2021. We called all patients who reported anosmia during their time of illness and asked them about anosmia. We examined their chest CT. A statistical analysis was done. Results A total of 140 patients completed the study; 65% were female and 56.4% had complete anosmia. Anosmia was significantly associated with loss of taste. Smell returned in 92.5% of anosmic patients. Duration of smell loss was ∼ 2 weeks in 40.5%. The most common symptoms associated with anosmia were running nose, sore throat, fever, and cough. Loss of smell was significantly associated with mild chest disease. 73.4% of anosmic patients had mild chest infection, 21.5% of them had moderate infection, and 5.1% had severe chest infection. Conclusion The pattern of anosmia in COVID-19 patients has some common similarities in general; the way it starts, the associated symptoms, the time until smell returns and, the most important, the severity of chest infection. As anosmia is significantly associated with mild chest infection. the presence of anosmia could be an independent predictor of good COVID-19 outcome as reflected by a lower disease severity and less frequent ICU admissions.

Human Lung Non-Invasive Anomaly Detection through UWB Microwave Imaging and Diagnosis of COVID-19: A Possible Application

This study shows a prototype for detecting lung effects using microwave imaging. Continuous monitoring of pulmonary fluid levels is one of the most successful approaches for detecting fluid in the lung;early Chest X-rays, computational tomography (CT)-scans, and magnetic resonance imaging (MRI) are the most commonly used instruments for fluid detection. Nonetheless, they lack sensitivity to ionizing radiation and are inaccessible to the general public. This research focuses on the development of a low-cost, portable, and noninvasive device for detecting Covid-19 or lung damage. The simulation of the system involved the antenna design, a 3D model of the human lung, the building of a COMSOL model, and image processing to estimate the lung damage percentage. The simulation consisted of three components. The primary element requires mode switching for four array antennas (transmit and receive). In the paper, microwave tomography was used. Using microwave near-field imaging, the second component of the simulation analyses the lung's bioheat and electromagnetic waves as well as examines the image creation under various conditions;many electromagnetic factors seen at the receiving device are investigated. The final phase of the simulation shows the affected area of the lung phantom and the extent of the damage. © 2023 IEEE.

A Real-Time Portable IoT System for Telework Tracking.

Telework has become a universal working style under the background of COVID-19. With the increased time of working at home, problems, such as lack of physical activities and prolonged sedentary behavior become more prominent. In this situation, a self-managing working pattern regulation may be the most practical way to maintain worker's well-being. To this end, this paper validated the idea of using an Internet of Things (IoT) system (a smartphone and the accompanying smartwatch) to monitor the working status in real-time so as to record the working pattern and nudge the user to have a behavior change. By using the accelerometer and gyroscope enclosed in the smartwatch worn on the right wrist, nine-channel data streams of the two sensors were sent to the paired smartphone for data preprocessing, and action recognition in real time. By considering the cooperativity and orthogonality of the data streams, a shallow convolutional neural network (CNN) model was constructed to recognize the working status from a common working routine. As preliminary research, the results of the CNN model show accurate performance [5-fold cross-validation: 0.97 recall and 0.98 precision; leave-one-out validation: 0.95 recall and 0.94 precision; (support vector machine (SVM): 0.89 recall and 0.90 precision; random forest: 0.95 recall and 0.93 precision)] for the recognition of working status, suggesting the feasibility of this fully online method. Although further validation in a more realistic working scenario should be conducted for this method, this proof-of-concept study clarifies the prospect of a user-friendly online working tracking system. With a tailored working pattern guidance, this method is expected to contribute to the workers' wellness not only during the COVID-19 pandemic but also take effect in the post-COVID-19 era.

A New Approach for Forecasting Crude Oil Prices Based on Stochastic and Deterministic Influences of LMD Using ARIMA and LSTM Models

Crude oil is one of the non-renewable power sources and is the lifeblood of the contemporary industry. Every significant change in the price of crude oil (CO) will have an effect on how the global economy, including COVID-19, develops. This study developed a novel hybrid prediction technique that depends on local mean decomposition, Autoregressive Integrated Moving Average (ARIMA), and Long Short-term Memory (LSTM) models to increase crude oil price prediction accuracy. The original data is decomposed by local mean decomposition (LMD), and the decomposed components are reconstructed into stochastic and deterministic (SD) components by average mutual information to reduce the computation cost and enhance forecasting accuracy, predict each individual reconstructed component by ARIMA, and integrate the residuals with LSTM to capture the nonlinearity in residuals and help to find the final prediction result. The new hybrid model LMD-SD-ARIMA-LSTM has reduced the volatility and solved the issue of the overfitting problem of neural networks. The proposed hybrid technique is validated using publicly accessible data from the West Texas Intermediate (WTI), and forecast accuracy are compared using accuracy measures. The value of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) for ARIMA, LSTM, LMD-ARIMA, LMD-SD-ARIMA, LMD-ARIMA-LSTM, LMD-SD-ARIMA-LSTM, and Naïve are 1.00, 1.539, 5.289, 0.873, 0.359, 0.106, 4.014 and 2.165, 1.832, 9.165, 1.359, 1.139, 1.124 and 3.821 respectively. From these results, it is concluded that the proposed model LMD-SD-ARIMA-LSTM has minimum values for MAE and MAPE which assured the superiority of the proposed model in One-step ahead forecasting. Moreover, forecasting performance is also compared up to five steps ahead. The findings demonstrate that the suggested approach is a helpful tool for predicting CO prices both in the short and long term. Furthermore, the current study reduces labor costs by combing the stationary and non-stationary Product Functions (PFs) into stochastic and deterministic components with improved accuracy. Meanwhile, the traditional econometric model can strengthen the prediction behavior of CO prices after decomposition and reconstruction, and the new hybrid forecasting method has better performance in medium and long-term forecasting of the CO price. Moreover, accurate predictions can provide reasonable advice for relevant departments to make correct decisions. © 2013 IEEE.

Data-Driven Estimation of Effectiveness of COVID-19 Non-pharmaceutical Intervention Policies

Non-pharmaceutical Interventions (NPIs), such as Stay-at-Home, and Face-Mask-Mandate, are essential components of the public health response to contain an outbreak like COVID-19. However, it is very challenging to quantify the individual or joint effectiveness of NPIs and their impact on people from different racial and ethnic groups or communities in general. Therefore, in this paper, we study the following two research questions: 1) How can we quantitatively estimate the effectiveness of different NPI policies pertaining to the COVID-19 pandemic?;and 2) Do these policies have considerably different effects on communities from different races and ethnicity? To answer these questions, we model the impact of an NPI as a joint function of stringency and effectiveness over a duration of time. Consequently, we propose a novel stringency function that can provide an estimate of how strictly an NPI was implemented on a particular day. Next, we applied two popular tree-based discriminative classifiers, considering the change in daily COVID cases and death counts as binary target variables, while using stringency values of different policies as independent features. Finally, we interpreted the learned feature weights as the effectiveness of COVID-19 NPIs. Our experimental results suggest that, at the country level, restaurant closures and stay-at-home policies were most effective in restricting the COVID-19 confirmed cases and death cases respectively;and overall, restaurant closing was most effective in hold-down of COVID-19 cases at individual community levels such as Asian, White, Black, AIAN and, NHPI. Additionally, we also performed a comparative analysis between race-specific effectiveness and country-level effectiveness to see whether different communities were impacted differently. Our findings suggest that the different policies impacted communities (race and ethnicity) differently. © 2022 IEEE.

Did national holidays accelerate COVID-19 diffusion "during the first phase of the” of pandemic in Bangladesh?

Bangladesh registered 20,117,32 confirmed cases of COVID-19 and the death toll crossed the grim milestone of 29,323 across the country as of August 31st, 2022. Despite the enforcement of stringent COVID-19 measures, the country witnessed an accelerated diffusion of coronavirus cases during the national events, inclusive of short festivals, in 2020. The present study aims to examine the association between these national holidays and the COVID-19 trasmission rate in Bangladesh. We employed a mathematical model and calculated the instantaneous reproduction number, Rt, of the 64 districts in Bangladesh to check the dynamics of COVID-19 diffusion. The comprehensive analysis shows a notable escalation of Rt value and thus the enhanced transmission rate in Dhaka and in all industrialized cities during the major events such as, garments reopening and religious holidays in Bangladesh. We further showcase the COVID-19 diffusion explicitly in Dhaka Division at the first phase of the pandemic in Bangladesh. Based on our analysis, a set of measures, including restricted public mobility and the celebration of festivals, alongside improving the public's awareness of the situation, has been recommended to evade the future pandemic risks while running the national festival activities in Bangladesh. © 2023, Intelektual Pustaka Media Utama. All rights reserved.

Deviance and Pearson Residuals-Based Control Charts with Different Link Functions for Monitoring Logistic Regression Profiles: An Application to COVID-19 Data

In statistical process control, the control charts are an effective tool to monitor the process. When the process is examined based on an exponential family distributed response variable along with a single explanatory variable, the generalized linear model (GLM) provides better estimates and GLM-based charts are preferred. This study is designed to propose GLM-based control charts using different link functions (i.e., logit, probit, c-log-log, and cauchit) with the binary response variable. The Pearson residuals (PR)- and deviance residuals (DR)-based control charts for logistic regression are proposed under different link functions. For evaluation purposes, a simulation study is designed to evaluate the performance of the proposed control charts. The results are compared based on the average run length (ARL). Moreover, the proposed charts are implemented on a real application for COVID-19 death monitoring. The Monte Carlo simulation study and real applications show that the performance of the model-based control charts with the c-log-log link function gives a better performance as compared to model-based control charts with other link functions. © 2023 by the authors.

Knowledge and attitude towards COVID-19 among adult people in Bangladesh: evidence from an online survey

The objective of this study was to examine the levels and determinants of knowledge and attitudes toward COVID-19 among adult people in Bangladesh, using data from an online survey, conducted in May 2021. Descriptive and inferential statistical analysis were performed to extract findings from data. The survey covered 993 respondents. A high level (78.4%) of effective knowledge and positive attitude (78.6%) were observed among the Bangladeshi people. More than eighty percent of the respondents could correctly mention at least one important symptom, mode of transmission, and preventive measures of COVID-19. Good knowledge and positive attitudes were found to be significantly higher among males, living in Dhaka city, having a higher level of education, health professionals, and the wealthiest people. Knowledge about COVID-19 has significant association with positive attitude of people towards COVID-19. Despite the high level of knowledge and positive attitudes, there still prevails some misconceptions among a group of people, as one-third of the respondents opined that COVID-19 is a divine punishment and 13% were in favor of keeping it secrete if COVID-19 infection happen to them. The findings underscore the need for more educational program and behavior change communication (BCC) for the subgroups of the population that has been identified with poor knowledge and negative attitudes towards COVID-19. © 2023, Intelektual Pustaka Media Utama. All rights reserved.

Haematological Indices Predictive Role In Post Contrast Induced Acute Kidney Injury

Contrast-induced acute kidney injury (CI-AKI) is a frequent complication in cardiac patients after percutaneous coronary intervention (PCI). Thus, the early prediction of such cases is essential to improve outcomes and prevent complications. Hemogram-derived indices provide a cheap, easy, and non-invasive test. Systemic immune inflammation index (SII) is a novel marker that can be calculated easily from a complete blood count test. It can be an important indicator in determining the balance between systemic inflammation and immune status and can predict CI-AKI better than neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR).Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Understanding e-satisfaction, continuance intention, and e-loyalty toward mobile payment application during COVID-19: an investigation using the electronic technology continuance model

This research examined the determinants of e-satisfaction, continuance intention, and e-loyalty regarding the use of mobile payment applications (MPAs). It developed and validated the electronic technology continuance model (e-TCM) integrating a psychological factor (perceived threats), e-satisfaction, circumstantial factor (perceived anxiety), and the dimensions of quality. Using a questionnaire, data was collected from 455 respondents and analyzed using structural equation modeling. The influences of information quality, service quality, system quality, perceived usefulness, and confirmation on both e-satisfaction and continuance intention are found positive. However, perceived threats and perceived anxiety do not influence e-satisfaction but influence continuance intention. Moreover, e-satisfaction positively impacts continuance intention and e-loyalty;and continuance intention positively impacts e-loyalty. This research evidences the roles of perceived threat, e-satisfaction, perceived anxiety, and the dimensions of quality on customers' e-satisfaction, continuance intention and e-loyalty, using the integrated framework comprising the health belief model, expectation–confirmation model, and information systems success model. The findings of this research can guide MPA services providers, online businesses, industry analysts, suburban consumers, and respective government authorities when MPA usage is concerned during any unprecedented crisis such as the COVID-19 pandemic. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

Impact of COVID-19 pandemic on franchise performance from franchisee perspectives: the role of entrepreneurial orientation, market orientation and franchisor support

The epidemic of COVID-19, the disease triggered by the SARS-CoV-2 virus, has had major economic, political and social effects worldwide, leading to concern about the disease, especially in the franchise service industry. The purpose of this paper is to review literature on three factors: entrepreneurial orientation, market orientation, franchisor support and figure out the relationship of these three factors on franchisee performance. Based on the literature, entrepreneurial orientation, market orientation, franchisor resource has positive and significant impact to franchisee performance. Further, franchisor support holds very important role on moderating relationship between entrepreneurial orientation, market orientation and franchisee performance in this pandemic Covid-19. This paper highlight the concepts to clarify the distinctions between them and suggests the propositions between franchisee entrepreneurial orientation, market orientation, and franchisor supports to franchisee performance. Study on franchisee performance is necessary as franchisees are also employees, customers, stakeholders of the franchisors, contribute to the success of franchise system. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

In Silico Analysis of White Turmeric Cholorogenic Acid Compound as Angiotensin-Converting Enzyme-2 (ACE2) Inhibitor for COVID-19 Drug Candidate

COVID-19 is a disease caused by SARS-CoV2 that had spread in 218 countries. The use of bioactive compound-based antivirals needed to be considered for COVID-19 treatment. One of the potential bioactive ingredients in COVID-19 treatment is chlorogenic acid of white turmeric (Curcuma zedoaria). One of target proteins for chlorogenic acid is Angiotensin Converting Enzyme-2 (ACE2) which is associated with the SARS-CoV2 infection pathway. A strategy that could be used for COVID-19 drugs design was by inhibiting ACE2, a viral receptor, using chlorogenic acid compounds from white turmeric. This study was aimed to predict chlorogenic acid compounds of white turmeric to inhibit ACE2 through reverse docking. Docking was done with Pyrx and visualized with PyMol and Discovery Studio software. Data gained from software and webserver were analyzed descriptively and compared with three control compounds. The results of this study concluded that chlorogenic acid from white turmeric could be recommended as oral COVID-19 drugs candidate. © 2023 American Institute of Physics Inc.. All rights reserved.

Anosmia in COVID-19 Patients: Can We Predict the Severity of Chest Manifestations?

Introduction  Anosmia is one of the common symptoms of COVID-19, the link between severity of chest infection and anosmia was investigated by few studies. Objectives  To find an association between anosmia and severity of chest infection. Methods  An analysis of patients admitted to isolation hospital of our university with confirmed polymerase chain reaction positive testing for COVID-19, between March 2021 until September 2021. We called all patients who reported anosmia during their time of illness and asked them about anosmia. We examined their chest CT. A statistical analysis was done. Results  A total of 140 patients completed the study;65% were female and 56.4% had complete anosmia. Anosmia was significantly associated with loss of taste. Smell returned in 92.5% of anosmic patients. Duration of smell loss was ∼ 2 weeks in 40.5%. The most common symptoms associated with anosmia were running nose, sore throat, fever, and cough. Loss of smell was significantly associated with mild chest disease. 73.4% of anosmic patients had mild chest infection, 21.5% of them had moderate infection, and 5.1% had severe chest infection. Conclusion  The pattern of anosmia in COVID-19 patients has some common similarities in general;the way it starts, the associated symptoms, the time until smell returns and, the most important, the severity of chest infection. As anosmia is significantly associated with mild chest infection. the presence of anosmia could be an independent predictor of good COVID-19 outcome as reflected by a lower disease severity and less frequent ICU admissions.

The Effect of Google Workspace Assisted Problem Based Learning on Students' Critical Thinking Skills of Biology

This study measured the effect of implementing Google Workspace-assisted Problem Based Learning (PBL) on students' critical thinking skills in biology learning. Due to the emerging of Covid-19, the teaching was carried out online. Google Workspace is an application used to facilitate online learning during the pandemic. This research employed a quasi-experiment method with a non-equivalent control group design. The samples were determined by a purposive sampling technique and involved the students of SMA Laboratorium UM Malang, grade XI IPA 3 (experimental class) and XI IPA 4 (control class). The experimental class was taught using Google Workspace-assisted PBL Model, while the control one used the conventional learning model. Short answer questions compiled based on Greenstein's critical thinking skill indicators were applied for measuring students' critical thinking skills. Pretest and posttest were applied, and the increase of students' scores between the two tests are the basis for determining the improvement of students' critical thinking skills. This study found that the average score increase for the experimental class is 90%, and 56% for the control class is 56%. For each indicator of critical thinking skills, the score increase of the experimental class ranged from 19%-35% and 8%-23% of the control one. The statistical procedure (ANCOVA) confirmed the significant difference in critical thinking skills of the two groups. © 2023 American Institute of Physics Inc.. All rights reserved.