An Integrated Framework for COVID-19 Classification Based on Ensembles of Deep Features and Entropy Coded GLEO Feature Selection

COVID-19 is a challenging worldwide pandemic disease nowadays that spreads from person to person in a very fast manner. It is necessary to develop an automated technique for COVID-19 identification. This work investigates a new framework that predicts COVID-19 based on X-ray images. The suggested methodology contains core phases as preprocessing, feature extraction, selection and categorization. The Guided and 2D Gaussian filters are utilized for image improvement as a preprocessing phase. The outcome is then passed to 2D-superpixel method for region of interest (ROI). The pre-trained models such as Darknet-53 and Densenet-201 are then applied for features extraction from the segmented images. The entropy coded GLEO features selection is based on the extracted and selected features, and ensemble serially to produce a single feature vector. The single vector is finally supplied as an input to the variations of the SVM classifier for the categorization of the normal/abnormal (COVID-19) X-rays images. The presented approach is evaluated with different measures known as accuracy, recall, F1 Score, and precision. The integrated framework for the proposed system achieves the acceptable accuracies on the SVM Classifiers, which authenticate the proposed approach's effectiveness. © World Scientific Publishing Company.

The Contribution of Covid-19 to Ardsrelated Mortality in the United States

INTRODUCTION: Acute respiratory distress syndrome (ARDS) is the major manifestation of severe respiratory failure due to COVID-19 and is present in the majority of COVID-19-related deaths in autopsy studies. Thus, the COVID-19 pandemic is expected to change substantially the epidemiology of ARDS. However, the contribution of COVID-19 to ARDS-related mortality in the United States (US) is unknown. METHOD(S): We used the CDC WONDER Multiple Cause of Death Data set to identify decedents with a diagnosis of ARDS during 2015-2019, and with a diagnosis of COVID-19, ARDS, or both during 2020. ARDS and COVID-19 were identified by ICD-10 codes J80 and J071, respectively. Negative binomial regression was used on the 2015-2019 data to forecast the number of ARDS-related deaths in 2020. We then compared the number of observed vs expected ARDS-related deaths in 2020. In addition, we examined the reporting of a diagnosis of COVID-19 among decedents with ARDS and the proportion of a diagnosis of ARDS among those with COVID-19. The latter analyses were then repeated across the Department of Health and Human Services (HHS) Regions. RESULT(S): In 2020, there were 51,184 ARDS-related deaths, 384,536 COVID-19-related deaths, and 41,606 deaths with both in the US. The predicted number of ARDSrelated deaths for 2020 was 10,851 (95% CI 9,714-12,120). The ratio of the observed vs expected ARDS-related deaths was 4.71 (95% CI 4.62-4.82). A diagnosis of ARDS was reported in 10.8% of all COVID-19 related deaths, ranging from 8.2% (HHS Regions 1 & 7) to 16.1% (HHS Region 2). COVID-19-related deaths have contributed to 81.3% of observed ARDS-related deaths in 2020, varying from 68.8% (HHS Region 10) to 91.5% (HHS Region 2). CONCLUSION(S): The number of ARDS-related deaths in the US increased nearly 5-fold in 2020, due to the contribution of ARDS among COVID-19 decedents. However, ARDS was reported only in about 1 in 10 COVID-19-related deaths, with the frequency of ARDS diagnosis varying nearly 2-fold across HHS Regions. Our findings suggest that the major rise in ARDS-related deaths in the US in 2020 is nevertheless an underestimate of the actual toll of ARDS-related mortality that year, likely reflecting substantial underdocumentation and possibly underrecognition of ARDS among COVID-19 decedents.

In-Hospital Cardiac Arrest in Patients with Covid-19: A Population-Based Study

INTRODUCTION: Recent reports suggest very low to no hospital survival among COVID-19 patients with in-hospital cardiac arrest (IHCA). However, studies to date included generally very small number of IHCA events and were often single-centered. The population-level outcomes of IHCA among COVID-19 patients is unknown. METHOD(S): We used a statewide data set to identify hospitalizations aged >=18 years in acute care hospitals in Texas with a diagnosis of COVID-19 between April 1st and December 31st, 2020. COVID-19 infection was identified using ICD-10 code U071. Cardiopulmonary resuscitation was identified using ICD-10 code 5A12012. Hospitalizations with cardiac arrest as a primary diagnosis and those without a primary diagnosis of COVID-19 were excluded. Mixed-effects multivariable logistic regression modelling was used to identify predictors of hospital survival among those with IHCA. RESULT(S): Among 65,482 hospitalizations with COVID-19, 893 (1.4%) had IHCA. Among those with IHCA, 57.1% were aged >= 65 years, 64.2% male, 70.9% racial/ethnic minority, and 7.1% had shockable rhythm. IHCA occurred in 12.7% [95% CI 11.8-13.6] of terminal hospitalizations. Hospital survival was 7.3% [95%CI 5.6-9.3], ranging from 6.7% [95% CI 4.6-9.3] among those aged >=65 years to 10.7% [95% CI 4.6-21.0] among those aged < 45 years. On adjusted analyses, among examined patient and hospital characteristics, only shockable rhythm (adjusted odds ratio [aOR] 2.63 [95% CI 1.05-6.56]) and management in hospitals with 200-399 beds (aOR 0.14 [95% CI 0.03- 0.58]), but not demographics, comorbidities, or illness severity, were associated with hospital survival. Among hospital survivors, 23.1% were transferred to hospice and 35.4% were discharged home. CONCLUSION(S): Resuscitation of IHCA among COVID-19 patients occurred more selectively compared to the general population. Hospital survival was very low, and less than 3% of those with IHCA were discharged home. Once developing among patients with COVID-19, the short-term survival of IHCA was no longer affected by demographic characteristics, comorbidity burden, or illness severity. Further large studies, using granular data, are needed to better guide clinicians', patients', and surrogates' decision-making and to improve patients' outcomes.

Information and Communications Technology (ICT) as a Teaching and Learning Tool: A Study of Students' Readiness and Satisfaction

Students and academics in higher education institutions (HEIs) were perilously hit by the unparalleled changes due to the COVID-19 pandemic. Within a span of less than a month, teaching and learning activities were shifted online to warrant continuity. This study intends to probe the online learning readiness and satisfaction among university students within the scope of students' prior ICT knowledge and the university's ICT infrastructure. This study employs a quantitative approach with a questionnaire as the research instrument. A sample size of 1,692 Sunway University students in the Ministry of Education (MOE) General Studies subjects were chosen. The data were analysed descriptively, and the results revealed that students are generally ready for online learning, and they are satisfied with the ICT amenities provided. As a result, both students and Sunway University are wellprepared, with the major implication that student preparation and satisfaction, as well as infrastructures, are critical to scaffold the accelerated transition in the use of online learning. ©Authors.

Evaluation of Clinical Manifestations, Laboratory Findings and Outcome of Children with Covid-19

Background: Coronavirus, a common infectious disease in the 21st century, has not been studied enough in children. Therefore, this study aimed to investigate the clinical manifestations, laboratory findings, and outcomes of children with Covid-19 admitted to Shahid Beheshti Hospital in Kashan during 2020-2022.Method: In this retrospective cohort study, the medical records of children with covid-19 referred to Shahid Beheshti hospital in Kashan between February 2020 and March 2022 were reviewed. The information extracted from the patient's medical records included demographic variables, clinical characteristics, laboratory findings, and the outcome of covid-19. The collected data were analyzed through SPSS 16, using descriptive statistics (frequency distribution, mean and standard deviation) and inferential statistics (chi-square test and ANOVA).Result: The findings of 271 children (159 boys;52% of the age group <= 5 years) showed that fever (57.6%), cough (39.9%), nausea-vomiting (31.7%), and diarrhea were the most common clinical symptoms. Also, the majority of patients were in the abnormal range in terms of Monocyte (89.3%), PTT (84.7%), Lymphocyte (83.6%), Neutrophil (80.4%), and LDH (74.5%). Pulmonary involvement was present in 12.5% of children. Finally, four children (1.5%) died. Conclusion: Severity of lung involvement and the outcome of the covid-19 disease (admission to the ICU and death) among children were at a low level, and in fact, it shows the better condition of children than adults in relation to this disease.

Awareness about COVID 19 regarding its Symptoms, Preventive Measures and compliance of Preventive Measures in rural and urban areas

Aim: To find out presence of awareness about COVID 19 in people of rural and urban areas Duration of study: 2 weeks until get maximum participation response Study design: Cross sectional survey Method: Questionnaires’ proforma was created from WHO and CDC website in google doc online form and was circulated among peoples of urban and rural areas through WhatsApp and Email. Responses were collected through google doc form and rearranged in form of tables/graph or pie chart. Results about knowledge of COVID 19 were presented in frequency and percentages. Conclusion: More awareness is needed about those COVID19 presentations, preventive measures and compliance of preventive measures which are being missed or not implemented by public so that spread of COVID 19 can be reduced and prevented

Understanding the experiences of people with posttraumatic stress disorder (PTSD) during the COVID-19 pandemic: A qualitative study.

OBJECTIVE: There has been great concern about the psychological implications of the coronavirus disease 2019 (COVID-19) pandemic on wellbeing and mental health worldwide. Previous pandemics have been associated with an increased risk of posttraumatic stress disorder (PTSD); however, the experience of a pandemic for those with preexisting diagnoses of PTSD has not previously been researched. We aimed to understand the experience of the COVID-19 pandemic for people with a diagnosis of PTSD before the pandemic. METHOD: Ten people, who were under the care of a specialist outpatient clinic for adults with PTSD during the COVID-19 pandemic, took part in semistructured interviews. Thematic analysis was used to analyze the interview transcripts. RESULTS: Themes were identified relating to changes in how a sense of threat was experienced during the pandemic, with both factors increasing and decreasing threat recognized; challenges related to trying to cope with the pandemic; and resources that helped with coping. CONCLUSIONS: Recommendations for clinicians working with people with PTSD during a pandemic are made. These include assessing for changes in the person's sense of threat and changes in triggers; supporting adaptation of prepandemic ways of coping and engagement with personal and professional support networks; and being alert to a possible increase or change in safety-seeking behaviors and addressing in the treatment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Routine blood report and common laboratory parameters in COVID-19: Experience from Bangladesh

Background: The present study aimed to describe the association of hematological parameters and common clinico-epidemiological features wit hdisease severity among COVID-19 patients. Methods: This is a hospital based observational study done in Dhaka Medical College Hospital from 01 July 2020 to 15 September 2020. Findings from hematological tests along with patient clinic-pathological features were recorded from a total of 309 COVID-19 patients. All the data were analyzed by SPSS 23.0 software. Results: Among the studied hematological parameters hemoglobin percentage, total WBC count, lymphocyte percentage, platelet count, CRP, serum ferritin, d-dimer, and ESRwere significantly associated with disease severity (p<0.05). Association was found between disease severity and other biochemical markers, such as AST, ALT, LDH, and serum bilirubin. Conclusion: With limited resources these cheap, yet highly indicative biochemical markers could be used to assess, treat, and prognose COVID-19 patients in Bangladesh.

Defeating the Credit Card Scams through Machine Learning Algorithms

Credit card fraud is a significant problem that is not going to go away. It is a growing problem and surged during the Covid-19 pandemic since more transactions are done without cash in hand now. Credit card frauds are complicated to distinguish as the characteristics of legitimate and fraudulent transactions are very similar. The performance evaluation of various Machine Learning (ML)-based credit card fraud recognition schemes are significantly pretentious due to data processing, including collecting variables and corresponding ML mechanism being used. One possible way to counter this problem is to apply ML algorithms such as Support Vector Machine (SVM), K nearest neighbor (KNN), Naive Bayes, and logistic regression. This research work aims to compare the ML as mentioned earlier models and its impact on credit card scam detection, especially in situations with imbalanced datasets. Moreover, we have proposed state of the art data balancing algorithm to solve data unbalancing problems in such situations. Our experiments show that the logistic regression has an accuracy of 99.91%, and naive bays have an accuracy of 97.65%. K nearest neighbor has an accuracy is 99.92%, support vector machine has an accuracy of 99.95%. The precision and accuracy comparison of our proposed approach shows that our model is state of the art. © 2021 IEEE.

Risk factors for in-hospital mortality among patients hospitalized with COVID-19 and AKI

Background: Coronavirus disease-2019 (COVID-19) has the highest mortality in patients with advanced age and those with pre-existing chronic medical conditions. Limited data, however, is available with regard to COVID-19 mortality in acute kidney injury (AKI). We aimed to identify risk factors associated with mortality in patients hospitalized for COVID-19 with AKI. Methods: This is a retrospective cohort study conducted at Loma Linda University Medical Center (LLUMC) from March 1st, 2020 to January 31st, 2021. Inclusion criteria included patients admitted to LLUMC with diagnosis of COVID-19 and AKI during the admission based on the Risk Injury Failure Loss ESRD (RIFLE) criteria. Univariable and multivariable logistic regression models were utilized to explore risk factors associated with in-hospital mortality. Results: A total of 320 patients (age 66.5 ± 14.4) were included in the analysis, of which 88 (28%) were deceased. Multivariable regression analysis (Figure 1) demonstrated that age greater than 70 had adjusted odds ratio (OR) with 95% confidence interval (CI) for mortality 1.10 (95% CI: 1.01, 1.20, p=0.03). An Ejection Fraction of less than 50% had OR=1.13 (95% CI: 1.03, 1.23, p=0.01), AKI-injury stage had OR=1.25 (95% CI: 1.14, 1.37, p=<0.001), positive D-dimer levels had OR=1.18 (95% CI: 1.07, 1.30, p=<0.001) and diabetes had OR=1.12 (95% CI 1.03, 1.22, p=0.01), all significant risk factors for mortality. In addition, Hispanics had a higher risk of mortality with OR=1.20 (95% CI 1.09, 1.33, p=<0.001) when compared to Caucasians. Conclusions: Diabetes, age greater than 70, Hispanic background, Heart failure with reduced ejection fraction, AKI-injury stage, and positive D-dimer level are identified as risk factors associated with higher mortality amongst patient admitted with COVID-19 and AKI.

Embracing Healthcare Delivery Challenges during a Pandemic. Review from a nodal designated COVID-19 center in Qatar

Hospitals and healthcare systems are instrumental in the formulation and delivery of a coordinated response to disaster management especially epidemics. In healthcare policy and strategy formation, there are only trade-offs, which with uncertainty are akin to gambles. National organizations play a key role in pandemics through the expression of physician motivation. Effective strategies can facilitate physician action through economies of scale that lower the costs for physicians to meet both community and patients' needs. Moreover, no matter how well clinicians are motivated and positioned to act, their collective actions are likely to fall short without complementary systems for populationbased care that require the operational support of an organization. This review of institutional policy implementation and frameworks intends to highlight how a nodal-designated COVID-19 center in Qatar managed to control the menace by altering its procedural sets and work arrangements to augment an integrated, intrinsic response to a briskly emerging, conceivably complex situation. This outcome was achieved under the guidance of a national leadership team, effectively adapted to its specific challenges by building on current medical evidence, management routines, proficiencies, and health system capacity. This ambitious drive started with the cohesion of services and implementation of evidence-based protocols by assigning a physician-led team to research, strategize and organize improved patient flow and information by arranging analytical compliance and preparedness. Through these service approaches and ongoing efforts, HMGH has realized significant outcome improvements, such as increasing capacity building, reducing healthcare waste, and increasing patient satisfaction rates whilst successfully achieving significantly lower COVID-19 mortality both in terms of absolute numbers and as percent population compared to many developed countries in the world. The strategies outlined in this article might not be all-inclusive or fit other healthcare system models, but they generate a veritable interest to pursue and be subjected to further rigorous study.

Diagnosis of COVID-19 infection using three-dimensional semantic segmentation and classification of computed tomography images

Coronavirus 19 (COVID-19) can cause severe pneumonia that may be fatal. Correct diagnosis is essential. Computed tomography (CT) usefully detects symptoms of COVID-19 infection. In this retrospective study, we present an improved framework for detection of COVID-19 infection on CT images;the steps include pre-processing, segmentation, feature extraction/ fusion/selection, and classification. In the pre-processing phase, a Gabor wavelet filter is applied to enhance image intensities. A marker-based, watershed controlled approach with thresholding is used to isolate the lung region. In the segmentation phase,COVID-19 lesions are segmented using an encoder- /decoder-based deep learning model in which deepLabv3 serves as the bottleneck and mobilenetv2 as the classification head. DeepLabv3 is an effective decoder that helps to refine segmentation of lesion boundaries. The model was trained using fine-tuned hyperparameters selected after extensive experimentation. Subsequently, the Gray Level Co-occurrence Matrix (GLCM) features and statistical features including circularity, area, and perimeters were computed for each segmented image. The computed features were serially fused and the best features (those that were optimally discriminatory) selected using a Genetic Algorithm (GA) for classification. The performance of the method was evaluated using two benchmark datasets: The COVID-19 Segmentation and the POF Hospital datasets. The results were better than those of existing methods. © 2021 Tech Science Press. All rights reserved.

51.10 PARENTAL STRESS AND PARENTING IN TIMES OF THE COVID-19 PANDEMIC IN PAKISTAN

Objectives: As COVID-19 spreads around the globe, parents are being presented with new challenges to meet their children’s needs. We investigated parental stress and its impact on their parenting practices alongside focusing on the impact on mothers of hospitalized children, during the COVID-19 outbreak in Lahore, Pakistan. Methods: Following IRB approval, using a web-based questionnaire and telephonic interviews, data were collected in April 2020. Symptoms of depression and anxiety were assessed by the Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) scale. Parents were also asked to report on their parenting practices as well as emotional and behavior changes noticed in their children in the last 1 month. In-depth semi-structured telephonic interviews were also conducted with 24 mothers of COVID-19–positive children admitted at Mayo Hospital Lahore. Results: A total of 355 parents participated, with a mean age of 35.3 years ± 8.2, and 64.3% were mothers. The majority (55%) of the mothers had at least 1 child between the ages of 1 and 5 years, and 9% had children with special needs. The overall prevalence of depressive symptoms and anxiety were 25.6% and 21.6%, respectively. Mothers of hospitalized COVID-19–positive children reported stress, anxiety, irritability, grief, and fear of death and infecting others. The most commonly identified sources of worry were problems experienced during the hospital stay, worry about the admitted child’s physical and emotional health, care provision for children left at home, rumors, and stigma. Parental stress was affecting parenting, with at least 50% of parents reporting more than the usual consequences (shouting at children, taking privileges away, and slapping child) in the past 1 month. However, positive impacts—that is, parents spending more time in activities with their children (93%)—were also observed. Unhealthy eating and sleeping patterns (24.5%), irritability (21.1%), anxiety (16.3%), aggression (14.6%), and sleep difficulties (12.7%) were the most common problems noticed by parents in their children since the COVID-19 outbreak. Conclusions: Significant parental stress observed during the COVID-19 outbreak can adversely impact a child’s physical and mental health outcomes. Provision of effective strategies to support parents to respond to and care for children are urgently needed. PAT, FAM, STRESS

Covid-19 global spread analyzer: An ML-based attempt

The novel Coronavirus 2019 (COVID-19) has caused a pandemic disease over 200 countries, influencing billions of humans. In this consequence, it is very much essential to the identify factors that correlate with the spread of this virus. The detection of coronavirus spread factors open up new challenges to the research community. Artificial Intelligence (AI) driven methods can be useful to predict the parameters, risks and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. In this study, we introduce two datasets, each of which consists of 25 country-level factors and covers 137 countries summarizing different domains. COVID-19STC aims to detect the increase of the total cases, whereas COVID-19STD aimed for total death detection. For each data set, we applied three feature selection algorithms (vis. correlation coefficient, information gain and gain ratio). We also apply feature selection by the Wrapper methods using four classifiers, namely, NaiveBayes, SMO, J48 and Random Forest. The GDP, GDP Per Capital, E-Government Index and Smoking Habit factors found to be the main factors for the total cases detection with accuracy of 73% using the J48 classifier. The GDP and E-Government Index are found to be the main factors for total deaths detection with accuracy of 71% using J48 classifier. © 2020 Rana Husni Al Mahmoud, Eman Omar, Khaled Taha, Mahmoud Al-Sharif and Abdullah Aref.

1.16 THE EFFECT OF QUARANTINE ON THE EMOTIONAL WELL-BEING OF KIDS: A SYSTEMATIC REVIEW

Objectives: COVID-19 has a significant impact on the mental health of children and adolescents including adverse consequences from quarantine or isolation. In this systematic review, we explore the impact of quarantine and isolation on psychological well-being of youth and propose a comprehensive strategy to reduce psychological burden. Methods: Three electronic databases including PubMed, Scopus, and Web of Science were searched for relevant articles by using the following search terms: (stigma OR stigmas OR stigmatization OR stigmatization) AND (psych* OR mental OR anxiety OR depression OR stress OR insomnia OR adjustment) AND (quarantin* OR patient isolation OR isolate* OR lockdown OR lock-down OR cordon) AND (child* OR adolescent OR adolescence OR youth). Two independent reviewers performed title and abstract screening followed by full-text screening by using predetermined eligibility criteria. Data were extracted for study population, country of study, scales used to measure for outcome, summary of results, and limitations. Results: The initial search found 530 unique citations, and 10 studies were included after thorough screening. Among the included studies, the study design was cohort in 4 studies, cross-sectional in 3, and descriptive qualitative in 3. The most common diagnoses were acute stress disorder, adjustment disorder, grief, and PTSD. There was also evidence for restlessness, irritability, anxiety, clinginess, and inattention with increased screen time in children during quarantine. Conclusions: This review helps in improving the understanding of quarantine's effects on children and adolescents, such as mental health issues, stigma, physical health, education, socialization, and parental perception. We also propose interventions for quarantined children through education, information dissemination, behavioral activation, health care system response, school-based strategies, and other coping techniques. ADOL, PRE, WL

iSCAN: An RT-LAMP-coupled CRISPR-Cas12 module for rapid, sensitive detection of SARS-CoV-2.

The COVID-19 pandemic caused by SARS-CoV-2 affects all aspects of human life. Detection platforms that are efficient, rapid, accurate, specific, sensitive, and user friendly are urgently needed to manage and control the spread of SARS-CoV-2. RT-qPCR based methods are the gold standard for SARS-CoV-2 detection. However, these methods require trained personnel, sophisticated infrastructure, and a long turnaround time, thereby limiting their usefulness. Reverse transcription-loop-mediated isothermal amplification (RT-LAMP), a one-step nucleic acid amplification method conducted at a single temperature, has been used for colorimetric virus detection. CRISPR-Cas12 and CRISPR-Cas13 systems, which possess collateral activity against ssDNA and RNA, respectively, have also been harnessed for virus detection. Here, we built an efficient, rapid, specific, sensitive, user-friendly SARS-CoV-2 detection module that combines the robust virus amplification of RT-LAMP with the specific detection ability of SARS-CoV-2 by CRISPR-Cas12. Furthermore, we combined the RT-LAMP-CRISPR-Cas12 module with lateral flow cells to enable highly efficient point-of-care SARS-CoV-2 detection. Our iSCAN SARS-CoV-2 detection module, which exhibits the critical features of a robust molecular diagnostic device, should facilitate the effective management and control of COVID-19.