Senna: As immunity boosting herb against Covid-19 and several other diseases.

Introduction: A novel coronavirus outbreak in China (SARS-CoV-2) which began in December 2019, was proven major threat to global health. However, several results from clinical practices indicate that herbal medicine plays an important role in the prevention of COVID-19, which brings new hope for its treatment. The objective of this study is to check the effectivity of senna (Senna alexandrina Mill.) as an immunity-boosting herb against Covid-19 and several other diseases. Method: The literature search was carried out using scientific databases comprising of Scopus, Science Direct, PubMed, Cochrane Library, Science Hub and Google Scholar, up to May 2020, using the following keywords: "senna", "senna makki", "Senna alexandrina", "senna nutrition value", "senna medicinal effect", "vitamins in senna", "mineral in senna", "bioactive compounds in senna", "laxiary components in senna", "senna against diseases", "senna enhance immunity", "covid_19″, "covid_19 symptoms". The authors also obtained data from primary and secondary sources as well. Result: The results of different studies showed that senna was composed of a wide range of immunity-enhancing bioactive components like antioxidants, vitamins, minerals and laxatives. These bioactive components are effective against COVID-19 and other diseases. Conclusion: Senna has medicinal and nutritional effects on the human body and has a key role in boosting immunity to prevent COVID-19 symptoms. Important nutritional components of senna include antioxidants, phytochemicals, vitamins and minerals that aids in reducing the risk of various diseases and also enhances the immune system.

Nurses' job burnout and its association with work environment, empowerment and psychological stress during COVID-19 pandemic.

AIM: The aim of this study was to assess the influence of perceived work environment, empowerment and psychological stress on job burnout among nurses working at the time of the COVID-19 pandemic. BACKGROUND: Nurses experienced high levels of job burnout during the pandemic, which impacted their mental health and well-being. Studies investigating the influence of work environment, empowerment and stress on burnout during the time of COVID-19 are limited. DESIGN: The study utilized a cross-sectional design. METHODS: Data were collected from 351 nurses in Oman between January and March 2021. The Maslach Burnout Inventory, the Practice Environment Scale of the Nursing Work Index, the Conditions of Work Effectiveness Questionnaire and the Perceived Stress Scale were used to assess study variables. RESULTS: About two-thirds of the nurses (65.6%) reported high levels of job burnout. Nurse managers' ability, leadership and support; staffing and resources adequacy; and nurses' access to support were significant factors associated with a reduced level of burnout. CONCLUSION: Supporting nurses during the crisis, ensuring adequate staffing levels and providing sufficient resources are critical to lower job burnout. Creating a positive and empowered work environment is vital to enhance nurses' retention during the pandemic.

Risk Assessment and Air Quality Study during Different Phases of COVID-19 Lockdown in an Urban Area of Klang Valley, Malaysia

Globally, the COVID-19 pandemic has had both positive and negative impacts on humans and the environment. In general, a positive impact can be seen on the environment, especially in regard to air quality. This positive impact on air quality around the world is a result of movement control orders (MCO) or lockdowns, which were carried out to reduce the cases of COVID-19 around the world. Nevertheless, data on the effects on air quality both during and post lockdown at local scales are still sparse. Here, we investigate changes in air quality during normal days, the MCOs (MCO 1, 2 and 3) and post MCOs, namely the Conditional Movement Control Order (CMCO) and the Recovery Movement Control Order (RMCO) in the Klang Valley region. In this study, we used the air sensor network AiRBOXSense that measures carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2) and particulate matter (PM2.5 and PM10) at Petaling Jaya South (PJS), Kelana Jaya (KJ) and Kota Damansara (KD). The results showed that the daily average concentrations of CO and NO2 mostly decreased in the order of normal days > MCO (MCO 1, 2 and 3) > CMCO > RMCO. PM10, PM2.5, SO2 and O3 showed a decrease from the MCO to RMCO. PJS showed that air pollutant concentrations decreased from normal days to the lockdown phases. This clearly shows the effects of ‘work from home’ orders at all places in the PJS city. The greatest percentage reductions in air pollutants were observed during the change from normal days to MCO 1 (24% to 64%), while during MCO 1 to MCO 2, the concentrations were slightly increased during the changes of the lockdown phase, except for SO2 and NO2 over PJS. In KJ, most of the air pollutants decreased from MCO 1 to MCO 3 except for CO. However, the percentage reduction and increments of the gas pollutants were not consistent during the different phases of lockdown, and this effect was due to the sensor location—only 20 m from the main highway (vehicle emissions). The patterns of air pollutant concentrations over the KD site were similar to the PJS site;however, the percentage reduction and increases of PM2.5, O3, SO2 and CO were not consistent. We believe that local burning was the main contribution to these unstable patterns during the lockdown period. The cause of these different changes in concentrations may be due to the relaxation phases during the lockdown at each station, where most of the common activities, such as commuting and industrial activities changed in frequency from the MCO, CMCO and RMCO. Wind direction also affected the concentrations, for example, during the CMCO and RMCO, most of the pollutants were blowing in from the Southeast region, which mostly consists of a city center and industrial areas. There was a weak correlation between air pollutants and the temperature and relative humidity at all stations. Health risk assessment analysis showed that non-carcinogenic risk health quotient (HQ) values for the pollutants at all stations were less than 1, suggesting unlikely non-carcinogenic effects, except for SO2 (HQ > 1) in KJ. The air quality information showed that reductions in air pollutants can be achieved if traffic and industry emissions are strictly controlled.

Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging (preprint)

With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of CT scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients data, which is, open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of computed tomography (CT) images. Finally, our results demonstrate a better performance to detect COVID-19 patients.