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1.
Ieee/acm Transactions on Computational Biology & Bioinformatics. PP ; 14:14, 2022.
Article in English | MEDLINE | ID: covidwho-2029248

ABSTRACT

Machine learning (ML) models, such as SVM, for tasks like classification and clustering of sequences, require a definition of distance/similarity between pairs of sequences. Several methods have been proposed to compute the similarity between sequences, such as the exact approach that counts the number of matches between k-mers (sub-sequences of length k) and an approximate approach that estimates pairwise similarity scores. Although exact methods yield better classification performance, they pose high computational costs, limiting their applicability to a small number of sequences. The approximate algorithms are proven to be more scalable and perform comparably to (sometimes better than) the exact methods - they are designed in a "general" way to deal with different types of sequences (e.g., music, protein, etc.). Although general applicability is a desired property of an algorithm, it is not the case in all scenarios. For example, in the current COVID-19 (coronavirus) pandemic, there is a need for an approach that can deal specifically with the coronavirus. To this end, we propose a series of ways to improve the performance of the approximate kernel (using minimizers and information gain) in order to enhance its predictive performance pm coronavirus sequences. More specifically, we improve the quality of the approximate kernel using domain knowledge (computed using information gain) and efficient preprocessing (using minimizers computation) to classify coronavirus spike protein sequences corresponding to different variants (e.g., Alpha, Beta, Gamma). We report results using different classification and clustering algorithms and evaluate their performance using multiple evaluation metrics. Using two datasets, we show that our proposed method helps improve the kernel's performance compared to the baseline and state-of-the-art approaches in the healthcare domain.

2.
Frontiers in Immunology ; 13, 2022.
Article in English | Web of Science | ID: covidwho-2022707

ABSTRACT

The COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has created an urgent global situation. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability and disease comorbidities. The pandemic continues to spread worldwide, despite intense efforts to develop multiple vaccines and therapeutic options against COVID-19. However, the precise role of SARS-CoV-2 in the pathophysiology of the nasopharyngeal tract (NT) is still unfathomable. This study utilized machine learning approaches to analyze 22 RNA-seq data from COVID-19 patients (n = 8), recovered individuals (n = 7), and healthy individuals (n = 7) to find disease-related differentially expressed genes (DEGs). We compared dysregulated DEGs to detect critical pathways and gene ontology (GO) connected to COVID-19 comorbidities. We found 1960 and 153 DEG signatures in COVID-19 patients and recovered individuals compared to healthy controls. In COVID-19 patients, the DEG-miRNA, and DEG-transcription factors (TFs) interactions network analysis revealed that E2F1, MAX, EGR1, YY1, and SRF were the highly expressed TFs, whereas hsa-miR-19b, hsa-miR-495, hsa-miR-340, hsa-miR-101, and hsa-miR-19a were the overexpressed miRNAs. Three chemical agents (Valproic Acid, Alfatoxin B1, and Cyclosporine) were abundant in COVID-19 patients and recovered individuals. Mental retardation, mental deficit, intellectual disability, muscle hypotonia, micrognathism, and cleft palate were the significant diseases associated with COVID-19 by sharing DEGs. Finally, the detected DEGs mediated by TFs and miRNA expression indicated that SARS-CoV-2 infection might contribute to various comorbidities. Our results provide the common DEGs between COVID-19 patients and recovered humans, which suggests some crucial insights into the complex interplay between COVID-19 progression and the recovery stage, and offer some suggestions on therapeutic target identification in COVID-19 caused by the SARS-CoV-2.

3.
Advances in Medical Education and Practice ; 13:913-926, 2022.
Article in English | Web of Science | ID: covidwho-2022199

ABSTRACT

Purpose: This study aimed to assess the burnout among faculty members of King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Jeddah, during the COVID-19 pandemic and investigate their adaptations to online teaching.Patients and Methods: The study utilized a survey research design, and a validated questionnaire was e-mailed to faculty members. The Maslach Burnout Inventory - Educators Survey was used to assess burnout in three domains (emotional exhaustion, depersona-lization, and personal accomplishment), in addition to their adaptations to online teaching.Results: A total of 112 faculty members completed the survey with a response rate of 25%. Females comprised 50.9% of the sample. Burnout assessment among faculty showed moderate emotional exhaustion and personal accomplishment. In contrast, the level of depersonalization was low. When assessing the impact of the shift to online education during the pandemic, 87.5% of the respondents reported increased confidence in online teaching and learning effectiveness.Conclusion: Faculty members at KSAU-HS reported moderate emotional exhaustion. Fortunately, this had a moderate impact on students' intellectual development and well-being. Most of the faculty feedback supported online teaching during the pandemic.

4.
PLoS ONE [Electronic Resource] ; 17(8):e0272905, 2022.
Article in English | MEDLINE | ID: covidwho-2021898

ABSTRACT

BACKGROUND: Facebook addiction (FA) has been suggested as a potential behavioral addiction. There is a severe lack of research evidence regarding the Facebook addiction behavior among university students during the ongoing COVID-19 pandemic. The aim of this study was to determine factors associated with Facebook addiction among Bangladeshi university students. METHODS: A cross-sectional online survey was conducted among 2,161 Bangladeshi university students during the COVID-19 pandemic from June 2021 to September 2021. A well fitted regression model in R programming language was used for this study. RESULTS: Female respondents and those whose family monthly income was <25,000 BDT were more addicted to Facebook than other respondents. Respondents who lost a family member or a relative to COVID-19, engaged in physical activities (exercise) during the pandemic, used Facebook for work purposes or used Facebook to relieve daily stress were more addicted to Facebook. CONCLUSION: Overuse of social media is problematic as it can trigger several mental health symptoms, especially among students. Adequate and effective interventions are required to educate students about the dangers of Facebook addiction and to provide an alternative, healthy options.

5.
Vision ; 2022.
Article in English | Scopus | ID: covidwho-2020910

ABSTRACT

This study aims to offer insightful knowledge on organizational members’ real-life experience of working in a ‘new normal’ environment and explores changes in organizational HR practices and the future of work culture during this pandemic. Applying the qualitative methodology through implementing an in-depth interview technique, this study revealed subjective insights on pandemic impacts within diverse organizations and their coping strategies, that is, remote work practices and technological adaptations. The study found out that HR functionalities powered by different online tools and remote work or flexible roster duties are ensuring employee betterment and organizational productivity at the same time. Pandemic countermeasure oriented or transformed HR practices like online training and e-recruitment are keeping the workforce steady in this distressing time, but the ‘new normal’ lifestyle and evolved work environment, practices are putting much stress on and changing the dimension of work policies like employee well-being, compensation, leave, and so on, through isolation, quarantine and strict health guideline type issues. © 2022 Management Development Institute.

6.
BioMed Research International ; 2022:9932483, 2022.
Article in English | MEDLINE | ID: covidwho-2020563

ABSTRACT

The aim of this study is to predict the COVID-19 infection fifth wave in South Africa using the Gaussian mixture model for the available data of the early four waves for March 18, 2020-April 13, 2022. The quantification data is considered, and the time unit is used in days. We give the modeling of COVID-19 in South Africa and predict the future fifth wave in the country. Initially, we use the Gaussian mixture model to characterize the coronavirus infection to fit the early reported cases of four waves and then to predict the future wave. Actual data and the statistical analysis using the Gaussian mixture model are performed which give close agreement with each other, and one can able to predict the future wave. After that, we fit and predict the fifth wave in the country and it is predicted to be started in the last week of May 2022 and end in the last week of September 2022. It is predicted that the peak may occur on the third week of July 2022 with a high number of 19383 cases. The prediction of the fifth wave can be useful for the health authorities in order to prepare themselves for medical setup and other necessary measures. Further, we use the result obtained from the Gaussian mixture model in the new model formulated in terms of differential equations. The differential equations model is simulated for various values of the model parameters in order to determine the disease's possible eliminations.

7.
Development in Practice ; : 1-12, 2022.
Article in English | Web of Science | ID: covidwho-2017269

ABSTRACT

In democratic South Africa, many Black African women are still subjugated by being employed as domestic workers. Increasing evidence emerged amid the COVID-19 pandemic revealing unmistakable signs of modern-day slavery among South African Black domestic workers. This paper proposes a clinical model which examines how gender, class, and race intersections affect the ways in which specifically identified change agents offer new, transforming interventions via clinical intervention. Adopting a clinical approach augments identification of a specific social problem from a scientifically systematic applied approach built on applied theory. We report on the conditions facing vulnerable Black African women using a bricolage research approach. The resulting model explicitly identifies systemic inequalities and indicates how to reduce exploitation and protect workers. The bricolage approach aided the secondary qualitative analysis of complex bonded-labour intersections. The problem of Black African women living as bonded domestic labour is augmented by the girl children's primary socialisation, Western patriarchal re-socialisation which sustains apartheid, and race, class, occupational, and gender inequalities.

8.
J Biomol Struct Dyn ; : 1-17, 2022.
Article in English | PubMed | ID: covidwho-2017223

ABSTRACT

Salmonella infections are continuously growing. Causative serovars have gained enhanced drug resistance and virulence. Current vaccines have fallen short of providing sufficient protection. mRNA vaccines have come up with huge success against SARS-CoV-2;Pfizer-BioNTech and Moderna vaccines have resulted in >90% efficacy with efficient translocation, expression, and presentation of antigen to the host immune system. Herein, based on the same approach a mRNA vaccine construct has been designed and analyzed against Salmonella by joining regions of genes of outer membrane proteins C and F of S. Typhi through a flexible linker. Construct was flanked by regulatory regions that have previously shown better expression and translocation of encoded protein. GC content of the construct was improved to attain structural and thermodynamic stability and smooth translation. Sites of strong binding miRNAs were removed through codon optimization. Protein encoded by this construct is structurally plausible, highly antigenic, non-allergen to humans, and does not cross-react to the human proteome. It is enriched in potent, highly antigenic, and conserved linear and conformational epitopes. Most conserved conformational epitopes of core protein lie on extended beta hairpins exposed to the cellular exterior. Stability and thermodynamic attributes of the final construct were found highly comparable to the Pfizer-BioNTech vaccine construct. Both contain a stable stem-loop structure downstream of the start codon and do not offer destabilizing secondary structures upstream of the start codon. Given structural and thermodynamic stability, effective immune response, and epitope composition the construct is expected to provide broad-spectrum protection against clinically important Salmonella serovars.Communicated by Ramaswamy H. Sarma.

9.
Nonlinear Dyn ; : 1-20, 2022.
Article in English | Web of Science | ID: covidwho-2014315

ABSTRACT

The coronavirus disease 2019 (COVID-19) is a recent outbreak of respiratory infections that have affected millions of humans all around the world. Initially, the major intervention strategies used to combat the infection were the basic public health measure, nevertheless, vaccination is an effective strategy and has been used to control the incidence of many infectious diseases. Currently, few safe and effective vaccines have been approved to control the inadvertent transmission of COVID-19. In this paper, the modeling approach is adopted to investigate the impact of currently available anti-COVID vaccines on the dynamics of COVID-19. A new fractional-order epidemic model by incorporating the vaccination class is presented. The fractional derivative is considered in the well-known Caputo sense. Initially, the proposed vaccine model for the dynamics of COVID-19 is developed via integer-order differential equations and then the Caputo-type derivative is applied to extend the model to a fractional case. By applying the least square method, the model is fitted to the reported cases in Pakistan and some of the parameters involved in the models are estimated from the actual data. The threshold quantity ( R 0 ) is computed by the Next-generation method. A detailed analysis of the fractional model, such as positivity of model solution, equilibrium points, and stabilities on both disease-free and endemic states are discussed comprehensively. An efficient iterative method is utilized for the numerical solution of the proposed model and the model is then simulated in the light of vaccination. The impact of important influential parameters on the pandemic dynamics is shown graphically. Moreover, the impact of different intervention scenarios on the disease incidence is depicted and it is found that the reduction in the effective contact rate (up to 30%) and enhancement in vaccination rate (up to 50%) to the current baseline values significantly reduced the disease new infected cases.

11.
International Journal of Environmental Science and Technology ; : 1-12, 2022.
Article in English | PMC | ID: covidwho-2007297

ABSTRACT

The study examines the role of technology transfer in preventing communicable diseases, including COVID-19, in a heterogeneous panel of selected 65 countries. The study employed robust least square regression and innovation accounting matrixes to get robust inferences. The results found that overall technological innovation, including innovative capability, absorptive capacity, and healthcare competency, helps reduce infectious diseases, including the COVID-19 pandemic. Patent applications, scientific and technical journal articles, trade openness, hospital beds, and physicians are the main factors supporting the reduction of infectious diseases, including the COVID-19 pandemic. Due to inadequate research and development, healthcare infrastructure expenditures have caused many communicable diseases. The increasing number of mobile phone subscribers and healthcare expenditures cannot minimize the coronavirus pandemic globally. The impulse response function shows an increasing number of patent applications, mobile penetration, and hospital beds that will likely decrease infectious diseases, including COVID-19. In contrast, insufficient resource spending would likely increase death rates from contagious diseases over a time horizon. It is high time to digitalize healthcare policies to control coronavirus worldwide.

12.
Indian Journal of Critical Care Medicine ; 26:S96-S97, 2022.
Article in English | EMBASE | ID: covidwho-2006384

ABSTRACT

Aim and background: Although the evidence for rapid response team (RRT) effectiveness remains uncertain, RRT are implemented across many hospitals in the world. We aimed to determine the impact of RRT on outcomes in our hospital. Materials and methods: Our hospital is a 30-bedded non-COVID-19 tertiary care teaching hospital. We collected prospective observational data after implementation of the RRT (February 1, 2021, to September 30, 2021, RRT Period) for a period of 8 months and compared it with retrospective cohort data for 8 months before implementation (February 1, 2020, to September 30, 2020, control period). We conduct a 12th hourly team round consists of a Critical care physician, Anesthesiologist, Duty RMO, Duty Medical officer, and Nurse Supervisor. All the ward patients in the hospital were charted with a Modified early warning score (MEWS) and RRT enrollment will be done if the score is >5 or a single variable score of 3. If the final MEWS ≥ 7 will be transferred immediately to the ICU. The outcomes monitored were hospital mortality and morbidity. Results: During the Control period (February 2020 to September 2020), we analyzed 5522 hospital admissions and 18951 patient days of which 77 patients were transferred to ICU, and mean age of these patients is 55.17 years. Male patients were 53, average length of stay post ICU transfer 4.27 days, of transferred patients medical are 66 and surgical are 11. Death of ICU transferred patients is 14. Number of code blue and death in the ward during this period is 22 and 21, respectively. During RRT period, we analyzed 6956 hospital admissions and 24072 patient days of which 83 patients were transferred to ICU, and mean age of these patients is patients is 58.12, male patients were 55, average length of stay post ICU transfer 3.6 days of which medical are 53 and surgical are 30. Death in ICU transferred patients is 8. Number of code blue and death in the ward during this period is 25 and 43 respectively. Of 43 ward deaths 18 contribute for DNR. Most common reason for transfer to ICU is respiratory failure, Oncology patients were predominant in both groups. The RRT was activated 83 times (11.9 calls per 1,000 patients and 3.44 calls per 1,000 patient-days). The Code blue rate for Control vs RRT were 1.16 and 1.03 per 1,000 patient days, respectively. The hospital mortality for control vs RRT were 1.84 and 1.78 per 1,000 patient days, respectively. The length of stay for control vs RRT were 0.22 and 0.14 per 1,000 patient days, respectively. The ICU mortality of transferred patients for Control vs RRT were 0.73 and 0.33 per 1,000 patient days, respectively. We found a decrease in the trend in code blue rate and hospital mortality in the ward, length of stay, and mortality in ICU transferred patients in the RRT period compared with the control period. Conclusion: We observed a trend towards decline in mortality and morbidity after implementation of RRT, and continuing for a longer duration may give us robust data.

13.
15.
Pediatrics ; 149, 2022.
Article in English | EMBASE | ID: covidwho-2003210

ABSTRACT

Background: The Zio® (Zio) XT Patch (iRhythm Technologies, Inc, San Francisco, California) is a 14-day continuous ambulatory ECG monitor. During the Covid-19 Pandemic, Zios were shipped directly to patients for self-application. The purpose of this study was to compare the quality of in-clinic (IC) to mail-home (MH) in our pediatric population. Methods: A single-center, IRB-approved study (1/1/18 - 6/1/21) of patients < 21 years of age with Zios were studied for wear and artifact time filtered out based on iRhythm's inherent algorithm. A control group of patients were age-matched from Zios placed IC and compared to MH Zios throughout the pandemic. Results: A total of 284 Zios were analyzed for total wear time and artifact filtered out. Of these, 149 were IC, and 135 were MH. Average percent of artifact of IC vs MH was 7.1% and 8.3% (p = 0.58). The average age of patients with Zios placed in clinic was older (12.84 years) than those placed at home (11.12 years, p<0.02). There was no significant difference in artifact when adjusted for age. Age was inversely associated with percent artifact with a 0.34% reduction in percent artifact for every additional year of age (p < 0.049). Location was not associated with percent artifact after controlling for age. By two proportion Z-test, there was no statistically significant difference between IC and MH Zios wasted (p = 0.66) or repeated (p = 0.96). Conclusion: In conclusion, IC and MH Zios did not demonstrate any significant difference in artifact time filtered out. This highlights the potential for home application during current pandemic and future telemedicine utilization.

16.
Journal of General Internal Medicine ; 37:S525, 2022.
Article in English | EMBASE | ID: covidwho-1995850

ABSTRACT

CASE: Patient is a 63 y/o F with PMH of relapsed AML on treatment with Gilteritinib, Meniere's Disease, asthma, GERD, PRA positive, CKD Stage 3. She was on cycle 1 day + 20 of Gilteritinib when she presented with a neutropenic fever of 101.9. She reported congestion and headache. She was pan cultured and started on empiric Cefepime. Her blood cultures, COVID test and CXR were all negative for sources of infection. Eventually, Cefepime was stopped, and she was transited to PO Cefdinir and Cipro but redeveloped fevers and a maculopapular rash. Repeat pan-cultures were negative. Antibiotics were broadened to Merrem, Linezolid and Cresemba and her fevers improved. However, the rash continued to worsen. There was concern that nodular rash was secondary to infection or possible drug reaction from her antibiotics. Her rash showed no improvement with Benadryl or withholding drugs. She underwent skin punch biopsy before discharge. Biopsy showed florid superficial inflammation with benign ulcer that was highly suggestive of Sweet Syndrome given history of AML. IMPACT/DISCUSSION: Sweet syndrome (SS), or acute febrile neutrophilic dermatosis is a rare inflammatory condition characterized by painful cutaneous nodules and neutrophilic infiltrate in the dermis, in the absence of vasculitis. This syndrome is associated with malignancies with AML and MDS being the most reported. Malignancy associated Sweet Syndrome accounts for 15-20% of cases of SS. The atypical production of both pro-inflammatory cytokines (IL - 6, TNF - alpha) and signaling molecules demonstrated in AML is suspected to affect neutrophil function leasing to dermal clumping of the mature neutrophils. In our patient the fever presented prior to the rash with sudden onset of nodular as it has been commonly reported in literature review. Glucocorticoids, either topical or systemic, together with antibiotics and wound care, represent the mainstays of SS therapy. The rash heals without scarring if no ulcerations are present. The signs and symptoms of Sweet syndrome can mimic infection and be treated inaccurately, thus, it is important to make a correct diagnosis. Our patient's tissue cultures were negative for microorganisms. She was started on glucocorticoid with good response in regards to her rash but did have some scars and hyperpigmentation. Unfortunately due to her aggressive AML and complications patient elected to go to Hospice. CONCLUSION: When SS is established, the physician should keep a high index of suspicion to search underlying malignancies. Sweet Syndrome generally responds promptly to treatment with glucocorticoid.

18.
4th International Conference on Innovative Computing (ICIC) ; : 360-+, 2021.
Article in English | Web of Science | ID: covidwho-1985467

ABSTRACT

Facemask detection is a need of time as we are suffering in a pandemic situation of COVID-19, and facemask is considered the best preventive measure to stop the rapid spread. The vast majority of the world population is still unvaccinated, especially young and kids. Moreover, despite the vaccination, people are still getting Covid positive, and the majority are due to the Delta variant. So, we still need to have strict SOP implementation. The best way is to have some autonomous system to monitor SOP compliance and alert the authority to take countermeasures. Many people wear the mask, but the mask is usually on the chin and does not serve the purpose because the facemask must cover the mouth and nose to stop the spread. This study has proposed the improved version of the YOLOv4 model for the robust detection of face masks and checks whether the mask is worn in the recommended way. 2D convolutions of Yolov4 are replaced with the spatially separable convolutional in YOLOv4 to reduce the parameters so that the model can work in real-time. We have achieved an accuracy of 86.61% in terms of proper mask-wearing. Unlike other proposed approaches, our model is not only detecting the mask but also determines that whether the mask is worn in the recommended manner.

19.
4th International Conference on Innovative Computing (ICIC) ; : 541-+, 2021.
Article in English | Web of Science | ID: covidwho-1985465

ABSTRACT

The catastrophic outbreak of SARS-CoV-2 or COVID-19 has taken the world to uncharted waters. Detecting such an outbreak at its early stages is crucial to minimize its spread but is very difficult as well. The pandemic situation is not yet under control as the virus tends to evolve and develop mutations. This further complicates the development of machine learning or AI models that can automatically detect the disease in the general public. However, researchers worldwide have been putting their incredible efforts into devising mechanisms that help analyze and control the pandemic situation. Many prediction models have been developed to predict COVID-19 infection risk that helps in mitigating the burden on the healthcare system. These models help the medical staff, especially when healthcare resources are limited. As a contribution to society's well-being, this research work deploys a machine learning prediction model that predicts COVID-19 patients with COVID-19 symptoms. Key pieces of information from RT-PCR test data results by the Israeli ministry of health publicly available have been distilled, preprocessed, and then used to train our prediction model. The model is trained on eight features, out of which five are the primary clinical symptoms of this fatal virus: cough, sore throat, fever, headache, breath shortness;and the other three features are gender, test indication, and age. Machine learning models can be considered for COVID-19 testing, especially when resources are limited. We have achieved highly accurate results in COVID-19 prediction with our prediction model. The model is best suited in urgent situations where there is a limitation of testing resources.

20.
4th International Conference on Innovative Computing (ICIC) ; : 120-128, 2021.
Article in English | Web of Science | ID: covidwho-1985464

ABSTRACT

The COVID-19 virus spread around the globe very rapidly during early 2020. Identification of the evolution pattern, and genome scale mutations in SARS-CoV-2 is essential to study the dynamics of this disease. The genomic sequences of thousands of SARS-CoV-2 infected patients from different countries are publicly available for sequence based in-depth analysis. In this study, the DNA sequences of SARS-CoV-2 from the COVID-19 infected patients (having or lacking a travel history) from Pakistan and India, the two highest populous neighboring countries in South Asia, have been analyzed by using computational tools of phylogenetics. These analyses revealed that the SARS-CoV-2 strain in Pakistani traveler COVID-19 patients is closely related to Iranian strains, the strain in non-traveler patients is related to the strain of Wuhan, China. Likewise, in India, the SARS-CoV-2 strains in travelers and non-travelers are closely related to Italy, Germany, and Mexico. The selected approach has also been utilized to find out the identical genomic regions and similar strains around the world. Collectively, our study suggested distinct strains and routes of viral transmission in Pakistan and India. These differences may infer partially the reason for the decline phase in viral propagation in Pakistan two months after the peak COVID-19 load, and rapid viral propagation in India making it the second worst-hit country in the world after the USA.

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