Blood filtering system for COVID-19 management: novel modality of the cytokine storm therapeutics.

The newly emerged coronavirus (SARS-CoV-2) is virulent, contagious, and has rapidly gained many mutations, which makes it highly infectious and swiftly transmissible around the world. SARS-CoV-2 infects people of all ages and targets all body organs and their cellular compartments, starting from the respiratory system, where it shows many deleterious effects, to other tissues and organs. Systemic infection can lead to severe cases that require intensive intervention. Multiple approaches were elaborated, approved, and successfully used in the intervention of the SARS-CoV-2 infection. These approaches range from the utilization of single and/or mixed medications to specialized supportive devices. For critically ill COVID-19 patients with acute respiratory distress syndrome, both extracorporeal membrane oxygenation (ECMO) and hemadsorption are utilized in combination or individually to support and release the etiological factors responsible for the "cytokine storm" underlying this condition. The current report discusses hemadsorption devices that can be used as part of supportive treatment for the COVID-19-associated cytokine storm.

Study epidemiology and determinants of COVID-19 infection among the population in Jeddah city, Saudi Arabia

Background: Coronavirus disease 2019 (COVID-19) is a global pandemic caused by a highly infectious respiratory virus (SARS-CoV-2). With all these consequences that threaten the health of the individual and society due to this infection, it has become necessary to have a vaccination that limits the spread of this virus. The objective of this study was to study the epidemiological aspects of COVID-19 pandemic in Jeddah, Saudi Arabia. Method: It was a cross sectional study of 405 subjects, who gave their responses through online Google form, as face to face interview of the subjects during outpatient clinic visits was not possible because of the pandemic of COVID-19 infection. Data was analyzed using SPSS software version 23. The level of significance was 0.05%. Results: The prevalence of Covid-19 infection was 23.7%. The vaccination coverage by the Covid-19 vaccine was 90.4%. Those who got the infection before getting the vaccine were 76%;on the other hand those who got the infection after getting one dose were 17.7% of the cases, while 6.3% got the infection after getting two doses of the vaccine. Those with autoimmune disorders, kidney failure and obesity were reluctant to get the vaccine (p <0.05). Married subjects, those with average income, and those who are employed were significantly more infected by COVID-19 (p<0.05). Patients with asthma, with autoimmune disorders, and those who did not take the COVID-19 vaccine, were significantly more vulnerable to COVID - 19 infection (p<0.05). Post vaccination clinical manifestations were mainly feeling tired (90.2%), pain and swelling at the site of injection (76.9%), fever (65.9%) and headache (61.8%%). Clinical manifestations associated with COVID-19 infection included fever (76.0%), headache (72.9%), loss of taste and smell (68.8%), body aches (67.7%), and sore throat (57.3%). Conclusions: Prevalence of COVID-19 infection was 23.7%. About one third of the population did not accept the vaccine. Subjects with asthma, autoimmune disorders, and those who were unvaccinated were more likely to catch the infection. Increasing the knowledge of the people about the clinical aspects, and health impact of the COVID-19 virus on the community are important intervention tools to increase the acceptance rate of COVID-19 vaccination among the population.

Sales trends of psychotropic drugs in the COVID-19 pandemic: A national database study in Brazil.

Background: The social restrictions among coronavirus disease 2019 (COVID-19) pandemic have posed a thoughtful risk to mental health and have implications in the use of drugs, including antidepressants, anxiolytics and other psychotropics. Objective: This study analyzed the sales data of the psychotropics prescribed in Brazil, in order to verify the change in consumption trends of these drugs during the COVID-19 pandemic. Methods: This interrupted time-series analyzed psychotropic sales data, between January 2014 and July 2021, using the National System of Controlled Products Management from The Brazilian Health Regulatory Agency. The monthly mean DDDs per 1,000 inhabitants per day of psychotropic drugs was evaluated by analysis of variance (ANOVA) followed by Dunnett Multiple Comparisons Test. The changes in monthly trends in the use of the psychotropic studied were evaluated by Joinpoint regression. Results: During the period studied, clonazepam, alprazolam, zolpidem and escitalopram were the most sold psychotropic drugs in Brazil. According to Joinpoint regression, an upward trend was observed in sales during the pandemic of pregabalin, escitalopram, lithium, desvenlafaxine, citalopram, buproprion and amitriptyline. An increase in psychotropic consumption was noted throughout the pandemic period, with the maximum consumption (2.61 DDDs) occurring in April 2021, with a downward trend in consumption that accompanied the drop in the number of deaths. Conclusions: The increase in sales, mainly of antidepressants during the COVID-19 pandemic, draws attention to issues related to the mental health of the Brazilian population and on the need for greater monitoring in the dispensing of these drugs.

Examining the immune signatures of SARS-CoV-2 infection in pregnancy and the impact on neurodevelopment: Protocol of the SIGNATURE longitudinal study.

The COVID-19 pandemic represents a valuable opportunity to carry out cohort studies that allow us to advance our knowledge on pathophysiological mechanisms of neuropsychiatric diseases. One of these opportunities is the study of the relationships between inflammation, brain development and an increased risk of suffering neuropsychiatric disorders. Based on the hypothesis that neuroinflammation during early stages of life is associated with neurodevelopmental disorders and confers a greater risk of developing neuropsychiatric disorders, we propose a cohort study of SARS-CoV-2-infected pregnant women and their newborns. The main objective of SIGNATURE project is to explore how the presence of prenatal SARS-CoV-2 infection and other non-infectious stressors generates an abnormal inflammatory activity in the newborn. The cohort of women during the COVID-19 pandemic will be psychological and biological monitored during their pregnancy, delivery, childbirth and postpartum. The biological information of the umbilical cord (foetus blood) and peripheral blood from the mother will be obtained after childbirth. These samples and the clinical characterisation of the cohort of mothers and newborns, are tremendously valuable at this time. This is a protocol report and no analyses have been conducted yet, being currently at, our study is in the recruitment process step. At the time of this publication, we have identified 1,060 SARS-CoV-2 infected mothers and all have already given birth. From the total of identified mothers, we have recruited 537 SARS-COV-2 infected women and all of them have completed the mental health assessment during pregnancy. We have collected biological samples from 119 mothers and babies. Additionally, we have recruited 390 non-infected pregnant women.

Mathematical model based on fractional trace operator for COVID-19 image enhancement.

The medical image enhancement is major class in the image processing which aims for improving the medical diagnosis results. The improving of the quality of the captured medical images is considered as a challenging task in medical image. In this study, a trace operator in fractional calculus linked with the derivative of fractional Rényi entropy is proposed to enhance the low contrast COVID-19 images. The pixel probability values of the input image are obtained first in the proposed image enhancement model. Then the covariance matrix between the input image and the probability of a pixel intensity of the input image to be calculated. Finally, the image enhancement is performed by using the convolution of covariance matrix result with the input image. The proposed enhanced image algorithm is tested against three medical image datasets with different qualities. The experimental results show that the proposed medical image enhancement algorithm achieves the good image quality assessments using both the BRISQUE, and PIQE quality measures. Moreover, the experimental results indicated that the final enhancement of medical images using the proposed algorithm has outperformed other methods. Overall, the proposed algorithm has significantly improved the image which can be useful for medical diagnosis process.

Ischemic stroke revealing COVID-19 infection: Case report.

INTRODUCTION: SARS-COV-2 viral infection primarily targets the respiratory system with a clinical picture that varies from simple symptoms to respiratory distress syndrome requiring hospitalization in the intensive care unit; SARS-CoV-2 also has neuro-invasive capabilities and could spread from the respiratory system to the central nervous system. Recently, some patients with COVID-19 have been shown to have neurological symptoms such as headache, anosmia, dysgeusia, dizziness, impaired consciousness, and ischemic stroke. CASE PRESENTATION: we describe a case of ischemic stroke as the main presentation of COVID-19 in a 68 years old man with no previous history, without any associated respiratory signs; clinical examination revealed left hemiparesis with dysarthria and left facial paralysis, NIHSS score was at 11, the brain CT scan performed 1h30min later, completed by a brain MRI that came back in favor of right frontal, temporal and parietal ischemic stroke. The decision of thrombolysis was indicated in urgency, and the patient benefited from thrombolysis, which proceeded without incidents; the outcome was favorable with regression of symptoms.Ischemic stroke has been widely described among the thromboembolic complications of COVID-19, but only a few papers have reported it as a primary manifestation of COVID-19. CONCLUSION: SARS-COV 2 infection can spread from the respiratory system to the central nervous system, resulting in an inflammatory response and excessive secretion of inflammatory markers, leading to ischemic stroke.

The Need for Revitalization of Islamic Social Finance Instruments in the COVID-19 Period in Nigeria: The Role of Digitalization

The COVID-19 pandemic has seriously damaged the socio-economic development of Nigerians. Meanwhile, the country is in a debt crisis, which is largely attributed to the fall in the price of crude oil revenue that is used to finance its budget. This makes it difficult to provide sustainable support towards poverty alleviation in the country during the pandemic. Thus, the objective of this study is to explore various technological applications to apply in the revitalization of Islamic social finance instruments in Nigeria. Through a review of literature, the study provides various fintech applications that are essential for reviving the role of Islamic social finance instruments, particularly zakat and waqf institutions for sustainable poverty alleviation in the country. Besides, the benefits of supporting Islamic social finance instruments with technological applications were also provided for the efficient and effective administration, collections and distributions of Islamic social funds. Hence, the study recommends the adaption of the best practices found in Malaysia, Indonesia, Bahrain, the U.A.E, etc. Besides, five specific recommendations have been proffered for the successful digitalization of Islamic social finance instruments in Nigeria. These recommendations can feasibly be implemented as it currently has a Ministry of Communications and Digital Economy, which is saddled with the responsibility of transforming the country into a fully digital economy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Assessment of the Effect of the COVID-19 Pandemic on the Lifestyle of the Population in Saudi Arabia: A Cross-Sectional Online Survey Study.

Background The coronavirus (COVID-19) pandemic has created an unprecedented problem in people's lives around the world. Lockdown measures altered the routine lifestyle aspects of people including diet, exercise, sleep, stress, smoking, job status, recreation, and application of modern technologies. Understanding the lifestyle profile of individuals could help in designing effective interventions to minimize the risk factors of COVID-19-related health problems. Objectives The aim of this research is to investigate the lifestyle changes among adults living in Saudi Arabia (SA) during the COVID-19 pandemic.  Methods A cross-sectional survey study was done to investigate the lifestyle changes during the COVID-19 pandemic in Saudi Arabia from August to September 2020. A pre-designed questionnaire was used for data collection and distributed online through social media. The questionnaire included items about sleep patterns, dietary habits, physical activity, employment status, recreation activities, use of social media, and screen time before and during the pandemic. Results A total of 338 adults with a median age of 40 years participated in the study. During the pandemic, employment, smoking decreased significantly (53.3 vs. 55.6%, p<0.001 & 15.7% vs. 18.3%, p=0.049) with significant increases in daily intake of fruits or vegetables (47% to 60.2%, p<0.001), caffeinated beverages (seven or more times caffeine, 3% vs. 0.9%, p<0.001), and water (more than eight water cups daily, 18.4 vs. 11.9, p<0.001). Overweight group increased significantly from 28.5% to 32% (p=0.009). Sleeping more than nine hours increased significantly from 8.3% to 21.8% (p<0.001) with increasing sleeping aids from 11.6% to 15.7% (p<0.001). Both the screen and the social media times increased significantly for six or more hours daily (14.8% vs. 35.3% and 9.5% vs. 28.2% respectively, p=<0.001 for both). There were significant decreases in socialization (91.4% vs. 37.8%) and fast food (71.98% vs. 47.04%), and increases in physically inactivity (19% vs. 5.3%) and stress (90.8% vs. 85.2%) (p<0.001 for all). Most participants perceived stress during the pandemic (307, 90.8%). Conclusion There were remarkable behavioral changes in all aspects of the lifestyle of the participants living in SA during the COVID-19 pandemic with some positive effects on smoking and dietary habits. However, negative changes included unemployment, physical inactivity, sleep disturbances, social isolation, and excessive weight gain. There is a need to study the possible consequences of such changes on the future population health in SA.

Fighting Social Isolation With Nostalgia: Nostalgia as a Resource for Feeling Connected and Appreciated and Instilling Optimism and Vitality During the COVID-19 Pandemic.

In two experiments, we tested the influence of bringing to mind a memory of a special moment versus an ordinary moment on nostalgia and whether this elicited nostalgia was related directly to gratitude and the satisfaction of need for relatedness and indirectly to optimism and vitality. Participants from Mexico were first asked to state how the pandemic of COVID-19 has affected their lives. After, participants were randomly assigned to one of two conditions: Memory of special moment versus memory of ordinary recent moment (study 1) or memory of special moment versus or memory of ordinary moment from the same life period as the special moment (study 2). After, participants completed a battery of questionnaires assessing nostalgia, gratitude and optimism (study 1) or nostalgia, satisfaction of need for relatedness, and vitality (study 2). Results from study 1 showed a positive influence of bringing to mind a special moment on nostalgia. Nostalgia was positively related to gratitude, which was then related positively to optimism. Similarly, results from study 2 showed a positive influence of bringing to mind a special moment on nostalgia. Nostalgia was positively related to satisfaction of need for relatedness, which then had a positive relationship with vitality. In both studies, the indirect sequential effect of bringing to mind a special moment on optimism and vitality was significant.

Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a novel deep neural network.

BACKGROUND: The nextwave of COVID-19 pandemic is anticipated to be worse than the initial one and will strain the healthcare systems even more during the winter months. Our aim was to develop a novel machine learning-based model to predict mortality using the deep learning Neo-V framework. We hypothesized this novel machine learning approach could be applied to COVID-19 patients to predict mortality successfully with high accuracy. METHODS: We collected clinical and laboratory data prospectively on all adult patients (≥18 years of age) that were admitted in the inpatient setting at Aga Khan University Hospital between February 2020 and September 2020 with a clinical diagnosis of COVID-19 infection. Only patients with a RT-PCR (reverse polymerase chain reaction) proven COVID-19 infection and complete medical records were included in this study. A Novel 3-phase machine learning framework was developed to predict mortality in the inpatients setting. Phase 1 included variable selection that was done using univariate and multivariate Cox-regression analysis; all variables that failed the regression analysis were excluded from the machine learning phase of the study. Phase 2 involved new-variables creation and selection. Phase 3 and final phase applied deep neural networks and other traditional machine learning models like Decision Tree Model, k-nearest neighbor models, etc. The accuracy of these models were evaluated using test-set accuracy, sensitivity, specificity, positive predictive values, negative predictive values and area under the receiver-operating curves. RESULTS: After application of inclusion and exclusion criteria (n=)1214 patients were selected from a total of 1228 admitted patients. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being septic shock (hazard ratio [HR], 4.30; 95% confidence interval [CI], 2.91-6.37), supportive treatment (HR, 3.51; 95% CI, 2.01-6.14), abnormal international normalized ratio (INR) (HR, 3.24; 95% CI, 2.28-4.63), admission to the intensive care unit (ICU) (HR, 3.24; 95% CI, 2.22-4.74), treatment with invasive ventilation (HR, 3.21; 95% CI, 2.15-4.79) and laboratory lymphocytic derangement (HR, 2.79; 95% CI, 1.6-4.86). Machine learning results showed our deep neural network (DNN) (Neo-V) model outperformed all conventional machine learning models with test set accuracy of 99.53%, sensitivity of 89.87%, and specificity of 95.63%; positive predictive value, 50.00%; negative predictive value, 91.05%; and area under the receiver-operator curve of 88.5. CONCLUSION: Our novel Deep-Neo-V model outperformed all other machine learning models. The model is easy to implement, user friendly and with high accuracy.

Children with COVID-19 behaving milder may challenge the public policies: a systematic review and meta-analysis.

BACKGROUND: The emerging virus is rampaging globally. A growing number of pediatric infected cases have been reported. Great efforts are needed to cut down the transmission. METHODS: A single-arm meta-analysis was conducted. We searched PubMed, Google Scholar, Web of Science, and several Chinese databases for studies presenting characteristics of children confirmed with Coronavirus Disease 2019 (COVID-19) from December 12, 2019 to May 10, 2020. Quality Appraisal of Case Series Studies Checklist was used to assess quality and publication bias was analyzed by Egger's test. Random-effect model was used to calculate the pooled incidence rate (IR) or mean difference (MD) with 95% confidence intervals (CI), or a fixed model instead when I2 < 50%. We conducted subgroup analysis according to geographic region. Additionally, we searched United Nations Educational Scientific and Cultural Organization to see how different countries act to the education disruption in COVID-19. RESULTS: 29 studies with 4300 pediatric patients were included. The mean age was 7.04 (95% CI: 5.06-9.08) years old. 18.9% of children were asymptomatic (95% CI: 0.121-0.266), 37.4% (95% CI: 0.280-0.474) had no radiographic abnormalities. Besides, a proportion of 0.1% patients were admitted to intensive care units (0, 95% CI: 0.000-0.013) and four deaths were reported (0, 95% CI: 0.000-0.000). Up to 159 countries have implemented nationwide school closures, affecting over 70% of the world's students. CONCLUSION: Children were also susceptible to SARS-CoV-2, while critical cases or deaths were rare. Characterized by mild presentation, the dilemma that children may become a potential spreader in the pandemic, while strict managements like prolonged school closures, may undermine their well-beings. Thus, the public policies are facing challenge.