Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics.

Healthcare Informatics is a phenomenon being talked about from the early 21st century in the era in which we are living. With evolution of new computing technologies huge amount of data in healthcare is produced opening several research areas. Managing the massiveness of this data is required while extracting knowledge for decision making is the main concern of today. For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to bring innovation in healthcare. Through all these innovations happening it is not wrong to establish that disease prediction with anticipation of its cure is no longer unrealistic. First, Dengue Fever (DF) and then Covid-19 likewise are new outbreak in infectious lethal diseases and diagnosing at all stages is crucial to decrease mortality rate. In case of Diabetes, clinicians and experts are finding challenging the timely diagnosis and analyzing the chances of developing underlying diseases. In this paper, Louvain Mani-Hierarchical Fold Learning healthcare analytics, a hybrid deep learning technique is proposed for medical diagnostics and is tested and validated using real-time dataset of 104 instances of patients with dengue fever made available by Holy Family Hospital, Pakistan and 810 instances found for infectious diseases including prognosis of; Covid-19, SARS, ARDS, Pneumocystis, Streptococcus, Chlamydophila, Klebsiella, Legionella, Lipoid, etc. on GitHub. Louvain Mani-Hierarchical Fold Learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data model derived from 15696 endocrine records of multiple visits of 100 patients identified by a unique ID. Accuracy for induced rules is evaluated by Laplace (Fig. 8) as 0.727, 0.701 and 0.203 for 41, 18 and 24 rules, respectively. Endocrine diagnostic data is made available by Shifa International Hospital, Islamabad, Pakistan. Our results show that in future this algorithm may be tested for diagnostics on healthcare big data.

COVID-19 Vaccine Hesitancy among the Population of Quetta-Associated Beliefs and Barriers

ABSTRACT Objectives: This study aimed to determine the prevalence of hesitancy towards COVID-19 vaccination among the adult population of Quetta and the likelihood predictors of hesitancy towards COVID-19 vaccination. Study Design: Analytical cross-sectional study. Place and Duration of Study: Study was conducted at Quetta, from Nov 2021 to May 2022. Methodology: A total of 396 individuals participated in the study. Along with socio-demographic details, participants responded to the COVID-VAX scale and questions from WHO determinants of vaccine-hesitancy. A relationship was modelled between WHO determinants of vaccine-hesitancy, socio-demographic characteristics and vaccine-hesitancy using logistic regression. Results: Results showed that majority of individuals were vaccine-hesitant (67.2%). The final model obtained significant predictors among contextual influences as demonstrated past bad experiences on vaccination, non-belief in risking their own and family's health for being non-vaccinated and negative influences by an influential person. Among individual influences, knowledge about someone having bad experiences to vaccines, confusion about scheduling of vaccines, lack of trust in the healthcare system and provider, disbelieving in immunization as a social norm and concerns regarding vaccine. Among factors directly related to vaccine/vaccination, difficult vaccination schedule, fear of pain/needles, and non-willingness to pay for vaccination were found as significant predictors of the COVID-19 vaccine-hesitancy (p-value <0.05). Conclusions: More than half of the participants showed hesitancy towards COVID-19 vaccine. Factors contributing to vaccine-hesitancy must be addressed to build confidence regarding COVID-19 vaccines among the people to attain the goal of herd immunization against COVID-19.

Next Generation Infectious Diseases Monitoring Gages via Incremental Federated Learning: Current Trends and Future Possibilities

Infectious diseases are always alarming for the survival of human life and are a key concern in the public health domain. Therefore, early diagnosis of these infectious diseases is a high demand for modern-era healthcare systems. Novel general infectious diseases such as coronavirus are infectious diseases that cause millions of human deaths across the globe in 2020. Therefore, early, robust recognition of general infectious diseases is the desirable requirement of modern intelligent healthcare systems. This systematic study is designed under Kitchenham guidelines and sets different RQs (research questions) for robust recognition of general infectious diseases. From 2018 to 2021, four electronic databases, IEEE, ACM, Springer, and ScienceDirect, are used for the extraction of research work. These extracted studies delivered different schemes for the accurate recognition of general infectious diseases through different machine learning techniques with the inclusion of deep learning and federated learning models. A framework is also introduced to share the process of detection of infectious diseases by using machine learning models. After the filtration process, 21 studies are extracted and mapped to defined RQs. In the future, early diagnosis of infectious diseases will be possible through wearable health monitoring cages. Moreover, these gages will help to reduce the time and death rate by detection of severe diseases at starting stage.

A Rare Case of Splenic Artery Thrombosis Provoked By Medroxyprogesterone Acetate Requiring Splenectomy.

Splenic artery thrombosis is estimated to occur in only 0.016% of hospital admissions. Hormonal contraception is known to have hypercoagulable side effects, but splenic artery thrombosis (SAT) followed by functional autosplenectomy is a very rare side effect. We report a case of a 48-year-old female with persistent SAT provoked by depot medroxyprogesterone acetate (DMPA). She initially presented with severe left lower quadrant abdominal pain, and imaging revealed an extensive thrombus in the splenic artery. She was immediately started on intravenous heparin, and her symptoms improved after a few days, at which point she was discharged on oral apixaban. Three months after discharge, the patient presented with symptoms similar to the initial presentation. Further history revealed that she received an injectable DMPA shot prior to her initial admission. Other possible causes of SAT were ruled out. On imaging, her previous thrombus had increased in size and now filled the entire splenic artery. Therefore, the patient underwent robotic splenectomy with remarkable improvement in her symptoms. This case represents a rare clinical manifestation of a hypercoagulable state induced by DMPA. We review the existing literature to explain the epidemiology, presentation, diagnosis, and treatment of SAT, and incorporate our patient's presentation into the existing literature regarding the effect of contraception in inducing thrombotic events.

Development of the Systems Thinking for Health Actions framework: a literature review and a case study.

BACKGROUND: Systems thinking is an approach that views systems with a holistic lens, focusing on how components of systems are interconnected. Specifically, the application of systems thinking has proven to be beneficial when applied to health systems. Although there is plenty of theory surrounding systems thinking, there is a gap between the theoretical use of systems thinking and its actual application to tackle health challenges. This study aimed to create a framework to expose systems thinking characteristics in the design and implementation of actions to improve health. METHODS: A systematised literature review was conducted and a Taxonomy of Systems Thinking Objectives was adapted to develop the new 'Systems Thinking for Health Actions' (STHA) framework. The applicability of the framework was tested using the COVID-19 response in Pakistan as a case study. RESULTS: The framework identifies six key characteristics of systems thinking: (1) recognising and understanding interconnections and system structure, (2) identifying and understanding feedback, (3) identifying leverage points, (4) understanding dynamic behaviour, (5) using mental models to suggest possible solutions to a problem and (6) creating simulation models to test policies. The STHA framework proved beneficial in identifying systems thinking characteristics in the COVID-19 national health response in Pakistan. CONCLUSION: The proposed framework can provide support for those aiming to applying systems thinking while developing and implementing health actions. We also envision this framework as a retrospective tool that can help assess if systems thinking was applied in health actions.

Next Generation Infectious Diseases Monitoring Gages via Incremental Federated Learning: Current Trends and Future Possibilities.

Infectious diseases are always alarming for the survival of human life and are a key concern in the public health domain. Therefore, early diagnosis of these infectious diseases is a high demand for modern-era healthcare systems. Novel general infectious diseases such as coronavirus are infectious diseases that cause millions of human deaths across the globe in 2020. Therefore, early, robust recognition of general infectious diseases is the desirable requirement of modern intelligent healthcare systems. This systematic study is designed under Kitchenham guidelines and sets different RQs (research questions) for robust recognition of general infectious diseases. From 2018 to 2021, four electronic databases, IEEE, ACM, Springer, and ScienceDirect, are used for the extraction of research work. These extracted studies delivered different schemes for the accurate recognition of general infectious diseases through different machine learning techniques with the inclusion of deep learning and federated learning models. A framework is also introduced to share the process of detection of infectious diseases by using machine learning models. After the filtration process, 21 studies are extracted and mapped to defined RQs. In the future, early diagnosis of infectious diseases will be possible through wearable health monitoring cages. Moreover, these gages will help to reduce the time and death rate by detection of severe diseases at starting stage.

Entrepreneurial Leadership and Employees' Proactive Behaviour: Fortifying Self Determination Theory

The prevailing pandemic (COVID-19) has increased socioeconomic problems and caused psychological distress due to work uncertainty, specifically in emerging economies. Small and medium enterprises (SMEs) in emerging economies have been severely affected. Particularly, work uncertainty is becoming a hindrance towards proactive work behaviour (PWB) that can be improved by an effective entrepreneurial leadership role and proactive personality attribute. Based on fortifying self-determination theory, this research answered the question to what extent proactive personality moderates the relationship between work uncertainty and PWB and strengthens the relationship between entrepreneurial leadership and PWB. To empirically examine the study's underlying theoretical framework, respondents were selected from SMEs working in Pakistan from the high-tech industry. Multisource data were accumulated from 420 workers and their leaders utilizing a two-wave, time-lagged research design. Conclusions revealed that entrepreneurial leadership first reduced individuals' work uncertainty, which in turn, led to enhanced proactive work behaviour of employees. Furthermore, the results revealed that work uncertainty mediates the relationship between entrepreneurial leadership and proactive work behaviour. Moreover, proactive personality moderates the link concerning work uncertainty and proactive work behaviour, such that this association is significant only when proactive personality is low. Additionally, the moderated mediation analysis indicated that less proactive people, compared with their extraordinarily proactive colleagues, trusted entrepreneurial leadership to be more proactive in the workplace. These findings have important implications to induce PWB among employees.

A population pharmacokinetic model of remdesivir and its major metabolites based on published mean values from healthy subjects.

Remdesivir is a direct-acting anti-viral agent. It was originally evaluated against filoviruses. However, during the COVID-19 pandemic, it was investigated due to its anti-viral activities against (SARS-CoV-2) virus. Therefore remdesivir received conditional approval for treatment of patients with severe coronavirus disease. Yet, its pharmacokinetic properties are inadequately understood. This report describes the population pharmacokinetics of remdesivir and its two plasma-detectable metabolites (GS-704277 and GS-441524) in healthy volunteers. The data was extracted from published phase I single escalating and multiple i.v remdesivir dose studies conducted by the manufacturer. The model was developed by standard methods using non-linear mixed effect modeling. Also, a series of simulations were carried out to test suggested clinical doses. The model describes the distribution of remdesivir and each of its metabolites by respective two compartments with sequential metabolism between moieties, and elimination from central compartments. As individual data were not available, only inter-cohort variability could be assessed. The estimated point estimates for central (and peripheral) volumes of distribution for remdesivir, GS-704277, and GS-441524 were 4.89 L (46.5 L), 96.4 L (8.64 L), and 26.2 L (66.2 L), respectively. The estimated elimination clearances of remdesivir, GS704277, and GS-441524 reached 18.1 L/h, 36.9 L/h, and 4.74 L/h, respectively. The developed model described the data well. Simulations of clinically approved doses showed that GS-441524 concentrations in plasma exceeded the reported EC50 values during the complete duration of treatment. Nonetheless, further studies are needed to explore the pharmacokinetics of remdesivir and its relationship to clinical efficacy, and the present model may serve as a useful starting point for additional evaluations.

Spatio-temporal air quality assessment in Tehran, Iran, during the COVID-19 lockdown periods

Highlights: Based on ground-based and satellite-based data, spatio-temporal analyses of air quality in Tehran were carried out during the lockdown periods (February-April) in 2020 and 2021. We evaluated the differences in temporal emissions of six air pollutants (NO2, CO, SO2, O3, PM2.5, and PM10) at various time scales, including diurnal, monthly, and relative changes. The results of ground-based measurements indicated that for all pollutants except O3, the magnitude decreased in 2020 (11-42%) when compared to the baseline period (2015-2021). As a result of eased restrictions and unfavorable meteorological conditions, the reduction in air pollutants was lower in 2021 (5-32%), and PM2.5 and PM10 levels increased (3.75 and 11.22%). Satellite-based concentrations (NO2, CO, SO2, and AOD) varied from -8 to 54% in 2020 and from -41 to 60% in 2021 compared to 2019 as the pre-lockdown year. Concerning AOD, the trend is consistent with dust events during March and April in the region of interest. Air quality improved in 2020 by lockdown restrictions and in 2021 degraded by eased restrictions and unfavorable meteorological conditions. The diurnal patterns of the air pollutants during lockdown periods remained the same as in the pre-lockdown period with a lower magnitude. Dust events affected the concentration of PM2.5 and PM10 in March and April. Columnar remotely sensed air pollutants, could be used for monitoring air quality in Tehran, especially in terms of NO2 and CO. Air quality improved in 2020 by lockdown restrictions and in 2021 degraded by eased restrictions and unfavorable meteorological conditions.The diurnal patterns of the air pollutants during lockdown periods remained the same as in the pre-lockdown period with a lower magnitude.Dust events affected the concentration of PM2.5 and PM10 in March and April.Columnar remotely sensed air pollutants, could be used for monitoring air quality in Tehran, especially in terms of NO2 and CO. [ FROM AUTHOR]

COVID-19 With Stress Cardiomyopathy Mortality and Outcomes Among Patients Hospitalized in the United States: A Propensity Matched Analysis Using the National Inpatient Sample Database.

Takotsubo syndrome (stress cardiomyopathy) has become a well-known complication of COVID-19 infections, with limited large-scale studies evaluating outcomes. We used the National Inpatient Sample (NIS) database to compare COVID-19 patients with and without stress cardiomyopathy. A total of 1,659,040 patients were included in the study: COVID-19 with stress cardiomyopathy (n = 1665, 0.1%) and COVID-19 without stress cardiomyopathy (n = 1657, 375, and 99.9%). The primary outcome was in-hospital mortality, with secondary analysis with propensity matching performed to confirm results from traditional multivariate analysis. COVID-19 patients with stress cardiomyopathy had significantly increased in-hospital mortality compared to COVID-19 patients without stress cardiomyopathy (32.8% vs 14.6%, adjusted OR [aOR]: 2.3 [95% CI, 1.2-4.5], P = 0.01) along with significantly increased mechanical ventilation and vasopressor support, hospitalization charge, acute kidney injury requiring hemodialysis, cardiogenic shock, and cardiac arrest. These results emphasize the need for more research to reduce worse outcomes with COVID-19-related stress cardiomyopathy patients.

CoviBlock: A Secure Blockchain-Based Smart Healthcare Assisting System

The recent COVID-19 pandemic has underlined the significance of digital health record management systems for pandemic mitigation. Existing smart healthcare systems (SHSs) fail to preserve system-level medical record openness and privacy while including mitigating measures such as testing, tracking, and treating (3T). In addition, current centralised compute architectures are susceptible to denial of service assaults because of DDoS or bottleneck difficulties. In addition, these current SHSs are susceptible to leakage of sensitive data, unauthorised data modification, and non-repudiation. In centralised models of the current system, a third party controls the data, and data owners may not have total control over their data. The Coviblock, a novel, decentralised, blockchain-based smart healthcare assistance system, is proposed in this study to support medical record privacy and security in the pandemic mitigation process without sacrificing system usability. The Coviblock ensures system-level openness and trustworthiness in the administration and use of medical records. Edge computing and the InterPlanetary File System (IPFS) are recommended as part of a decentralised distributed storage system (DDSS) to reduce the latency and the cost of data operations on the blockchain (IPFS). Using blockchain ledgers, the DDSS ensures system-level transparency and event traceability in the administration of medical records. A distributed, decentralised resource access control mechanism (DDRAC) is also proposed to guarantee the secrecy and privacy of DDSS data. To confirm the Coviblock's real-time behaviour on an Ethereum test network, a prototype of the technology is constructed and examined. To demonstrate the benefits of the proposed system, we compare it to current cloud-based health cyber-physical systems (H-CPSs) with blockchain. According to the experimental research, the Coviblock maintains the same level of security and privacy as existing H-CPSs while performing considerably better. Lastly, the suggested system greatly reduces latency in operations, such as 32 milliseconds (ms) to produce a new record, 29 ms to update vaccination data, and 27 ms to validate a given certificate through the DDSS.

Hybrid Approach to Identifying Druglikeness Leading Compounds against COVID-19 3CL Protease.

SARS-CoV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdowns, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In this paper, we developed an in silico study-based hybrid framework to repurpose existing therapeutic agents in finding drug-like bioactive molecules that would cure COVID-19. In the first step, a total of 133 drug-likeness bioactive molecules are retrieved from the ChEMBL database against SARS coronavirus 3CL Protease. Based on the standard IC50, the dataset is divided into three classes: active, inactive, and intermediate. Our comparative analysis demonstrated that the proposed Extra Tree Regressor (ETR)-based QSAR model has improved prediction results related to the bioactivity of chemical compounds as compared to Gradient Boosting-, XGBoost-, Support Vector-, Decision Tree-, and Random Forest-based regressor models. ADMET analysis is carried out to identify thirteen bioactive molecules with the ChEMBL IDs 187460, 190743, 222234, 222628, 222735, 222769, 222840, 222893, 225515, 358279, 363535, 365134, and 426898. These molecules are highly suitable drug candidates for SARS-CoV-2 3CL Protease. In the next step, the efficacy of the bioactive molecules is computed in terms of binding affinity using molecular docking, and then six bioactive molecules are shortlisted, with the ChEMBL IDs 187460, 222769, 225515, 358279, 363535, and 365134. These molecules can be suitable drug candidates for SARS-CoV-2. It is anticipated that the pharmacologist and/or drug manufacturer would further investigate these six molecules to find suitable drug candidates for SARS-CoV-2. They can adopt these promising compounds for their downstream drug development stages.

Lockdown Amid COVID-19 Ascendancy over Ambient Particulate Matter Pollution Anomaly.

Air is a diverse mixture of gaseous and suspended solid particles. Several new substances are being added to the air daily, polluting it and causing human health effects. Particulate matter (PM) is the primary health concern among these air toxins. The World Health Organization (WHO) addressed the fact that particulate pollution affects human health more severely than other air pollutants. The spread of air pollution and viruses, two of our millennium's most serious concerns, have been linked closely. Coronavirus disease 2019 (COVID-19) can spread through the air, and PM could act as a host to spread the virus beyond those in close contact. Studies on COVID-19 cover diverse environmental segments and become complicated with time. As PM pollution is related to everyday life, an essential awareness regarding PM-impacted COVID-19 among the masses is required, which can help researchers understand the various features of ambient particulate pollution, particularly in the era of COVID-19. Given this, the present work provides an overview of the recent developments in COVID-19 research linked to ambient particulate studies. This review summarizes the effect of the lockdown on the characteristics of ambient particulate matter pollution, the transmission mechanism of COVID-19, and the combined health repercussions of PM pollution. In addition to a comprehensive evaluation of the implementation of the lockdown, its rationales-based on topographic and socioeconomic dynamics-are also discussed in detail. The current review is expected to encourage and motivate academics to concentrate on improving air quality management and COVID-19 control.

Molecular docking used as an advanced tool to determine novel compounds on emerging infectious diseases: A systematic review.

Emerging infectious diseases (EID) as well as reappearing irresistible infections are expanding worldwide. Utmost of similar cases, it was seen that the EIDs have long been perceived as a predominant conclusion of host-pathogen adaption. Here, one should get to analyze their host-pathogen interlink and their by needs to look ways, as an example, by exploitation process methodology particularly molecular docking and molecular dynamics simulation, have been utilized in recent time as the most outstanding tools. Hence, we have overviewed some of important factors that influences on EIDs especially HIV/AIDs, H1N1 and coronavirus. Moreover, here we specified the importance of molecular docking applications especially molecular dynamics simulations approach to determine novel compounds on the emerging infectious diseases. Additionally, in vivo and in vitro studies approach to determine novel compounds on the emerging infectious diseases that has implemented to evaluate the limiting affinities between small particles as well as macromolecule that can further, used as a target of HIV/AIDs, H1N1, and coronavirus were also discussed. These novel drug molecules approved in vivo and in vitro studies with reaffirm results and hence, it is clear that the computational methods (mainly molecular docking and molecular dynamics) are found to be more effective technique for drug discovery and medical practitioners.

Cytokine storm and neuropathological alterations in patients with neurological manifestations of COVID-19.

The COVID-19 pandemic is caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2), a respiratory pathogen with neuroinvasive potential. Neurological COVID-19 manifestations include loss of smell and taste, headache, dizziness, stroke, and potentially fatal encephalitis. Several studies found elevated proinflammatory cytokines such as TNF-α, IFN-γ, IL-6 IL-8, IL-10 IL-16, IL-17A, and IL-18 in severely and critically ill COVID-19 patients, which may persist even after apparent recovery from infection. Biomarker studies on CSF and plasma and serum from COVID-19 patients have also shown a high level of IL-6, intrathecal IgG, neurofilament light chain (NFL), glial fibrillary acidic protein (GFAP), and tau protein. Emerging evidence on the matter has established the concept of COVID-19 associated neuroinflammation, in the context of COVID-19 associated cytokine storm. While the short-term implications of this condition are extensively documented, its long-term implications are yet to be understood. The association of the aforementioned cytokines with the pathogenesis of neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington disease, and amyotrophic lateral sclerosis, may increase COVID-19 patients' risk to develop neurodegenerative diseases. Analysis of proinflammatory cytokines and CSF biomarkers in patients with COVID-19 can contribute to the early detection of the disease's exacerbation, monitoring the neurological implications of the disease and devising risk scales, and identifying treatment targets.

Perspectives from remote sensing to investigate the COVID-19 pandemic: A future-oriented approach.

As scientific technology and space science progress, remote sensing has emerged as an innovative solution to ease the challenges of the COVID-19 pandemic. To examine the research characteristics and growth trends in using remote sensing for monitoring and managing the COVID-19 research, a bibliometric analysis was conducted on the scientific documents appearing in the Scopus database. A total of 1,509 documents on this study topic were indexed between 2020 and 2022, covering 165 countries, 577 journals, 5239 institutions, and 8,616 authors. The studies related to remote sensing and COVID-19 have a significant increase of 30% with 464 articles. The United States (429 articles, 28.42% of the global output), China (295 articles, 19.54% of the global output), and the United Kingdom (174 articles, 11.53%) appeared as the top three most contributions to the literature related to remote sensing and COVID-19 research. Sustainability, Science of the Total Environment, and International Journal of Environmental Research and Public Health were the three most productive journals in this research field. The utmost predominant themes were COVID-19, remote sensing, spatial analysis, coronavirus, lockdown, and air pollution. The expansion of these topics appears to be associated with cross-sectional research on remote sensing, evidence-based tools, satellite mapping, and geographic information systems (GIS). Global pandemic risks will be monitored and managed much more effectively in the coming years with the use of remote sensing technology.

SARS-COV-2/COVID-19: scenario, epidemiology, adaptive mutations, and environmental factors.

The coronavirus pandemic of 2019 has already exerted an enormous impact. For over a year, the worldwide pandemic has ravaged the whole globe, with approximately 250 million verified human infection cases and a mortality rate surpassing 4 million. While the genetic makeup of the related pathogen (SARS-CoV-2) was identified, many unknown facets remain a mystery, comprising the virus's origin and evolutionary trend. There were many rumors that SARS-CoV-2 was human-borne and its evolution was predicted many years ago, but scientific investigation proved them wrong and concluded that bats might be the origin of SARS-CoV-2 and pangolins act as intermediary species to transmit the virus from bats to humans. Airborne droplets were found to be the leading cause of human-to-human transmission of this virus, but later studies showed that contaminated surfaces and other environmental factors are also involved in its transmission. The evolution of different SARS-CoV-2 variants worsens the condition and has become a challenge to overcome this pandemic. The emergence of COVID-19 is still a mystery, and scientists are unable to explain the exact origin of SARS-CoV-2. This review sheds light on the possible origin of SARS-CoV-2, its transmission, and the key factors that worsen the situation.

Entrepreneurial Leadership and Employee Wellbeing During COVID-19 Crisis: A Dual Mechanism Perspective

This study examined the potential impacts of entrepreneurial leadership on followers' psychological wellbeing and proactive work behavior through sustainable employability and work uncertainty in a sample of 218 employees employed in SMEs of Pakistan. Hierarchical regression results demonstrated that entrepreneurial leadership was positively connected with sustainable employability and negatively linked with work uncertainty. Sequentially, sustainable employability was positively correlated with proactive work behavior and employees' psychological wellbeing, and work uncertainty was negatively associated with proactive work behavior and employees' psychological wellbeing during the COVID-19 crisis. Furthermore, bootstrapping confirmed the mediation effects of work uncertainty and sustainable employability on proactive work behavior and the psychological wellbeing of employees. Sustainable employability did not mediate the relationship between entrepreneurial leadership and psychological wellbeing. Mediators, sustainable employability, and work uncertainty positively linked employees' psychological wellbeing and proactive work behavior. The results highlighted the significant roles of sustainable employability and work uncertainty and interpreted why entrepreneurial leadership may affect employees' positive behaviors.

Food Safety Control Measures to Address Emerging Omicron SARS-CoV-2 Variant of Concern

The recent emergence of the novel Omicron SARS-CoV-2 Variant, first detected in Gauteng Province of South Africa in mid- November 2021, has become a global concern. Several countries witnessed rapid transmission of omicron in the past months. This variant can increase the rate of virus transmission, risk of reinfection and enable omicron to continue its spread in the community. There is no evidence that COVID-19 can spread through food, and people can catch up with this virus through food materials. This virus can not survive and multiple on the surfaces of food packaging. The main routes of this virus transmission and spread may be during close contact and directly from an infected person to a healthy person. Touching a contaminated surface and objects such as doorknobs, table surfaces, everyday use equipment, touching screens and shaking hands and then touching the mouth, nose, or eyes may also be a source of infection.1 Recent research reported that the COVID-19 virus could survive and remain viable on plastics for up to 72 hours, 4 hours on stainless steel and copper and up to 24 hours on cardboard. Since the food sector has complex processing and manufacturing processes with a series of operational steps, starting from food intake, receiving, preparation, production, processing, packaging, storage and delivery to market and customers. At each stage, food handlers are directly involved, there is a more likelihood of food handlers touching the food or food surfaces directly. In this case, food workers are not following proper food safety control and protective actions, e.g., hand washings, cleaning and disinfection, sanitization, and social distancing. If not vaccinated, it can be a possible source of omicron transmission. To prevent the potential transmission of emerging Omicron SARS-COV-2 Variant of Concern, the food sector should ensure compliance with food safety control measures and prerequisites to safe and protect food workers from omicron variants to mitigate the risks of transmission of the virus by adopting good hygienic practices (GHPs), good manufacturing practices (GMPs), appropriate cleaning and disinfection by approved chemicals and sanitizers to endure the production and consistent stream of safe food. Since all the food workers may well be aware of the precaution’s measures, it is still needed to refresh their information. The staff working in food premises should be provided with frequent refresher training, proper personal protective equipment (PPEs), and written instructions on how to contain the transmission of Omicron and COVID-19 variants.2 One of the most critical steps to restrict the spread and transmission of the COVID-19 during food service processes is to wash and sanitize frequently. Frequent hand washing should be obligatory for Food workers directly involved in food production and preparation activities. The staff should avoid touching the food contact surfaces of tables, utensils, small wares, cutting boards and knives and surfaces of food menu, kiosks, dispensers, door handles, freezers and cooler handles, tablets, computers and equipment controller surfaces, buttons, and touchpads. There is a possibility that the virus can transfer from unwashed hands to other cleaned and sanitized surfaces during food production, preparations, cooking, storage, serving to customers and then moving to other food staff and eventually to customers. Frequent disinfection and sanitization with approved sanitizer to reduce the microorganism’s loads to a safe level determined by food safety authorities and public health codes are crucial to mitigating the risk of virus transmission. Social distancing to keep a space of at least 2 meters during operation hours at food premises to limit the close staff contacts inside food preparation and production areas is critically important to limit the spread.2 It is also recommended by CDC and other food safety authorities. The staff Should avoid unnecessary gatherings and crowds during shift changes, breaks and staff training. The food premises should optim ze the number of food workers to occupy maximum capacity at food premises and ensure fewer food workers are available than normal staff to a smaller amount of COVID-19 transmission risks.3,4 The food facilities should also provide appropriate personnel protective equipments (PPEs) for staff while receiving incoming materials and during deliveries, which can enhance the staff protection from the virus. Finally, the food amenities should look for staff health and monitor the visitors, suppliers, and contractors to recognize any sick person, maximize protection, and report to related health authorities if someone's symptoms are identical to emerging COVID-19 omicron variant concern. [ FROM AUTHOR] Copyright of Journal of Pure & Applied Microbiology is the property of Dr. M. N. Khan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. 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