COVID-19 Infected Persons Research Articles

Role of exosomes in false-positive COVID-19 PCR tests: Non-specificity of SARS-CoV-2-RNA in vivo detection explains artificial post-pandemic peaks

Background: The COVID-19 pandemic priorities have focused on prevention by detection and response. National governments’ prevention response decisions are based upon detection statistics from PCR (polymerase chain reaction) tests that are used to define numbers of (i) COVID-19 infected persons, (ii) COVID-19 hospitalisations, and (iii) COVID-19 deaths. These statistics assume a priori that PCR tests are nigh 100% true detectors of COVID-19 infections. Here we will provide an alternative interpretation, along with the compelling evidence, that false positives have distorted to some degree the statistics of the primary outbreaks, and account for almost the whole of the 2nd and subsequent apparent COVID-19 outbreak peaks in various countries. Methods: We extract from the published literature on PCR-test outcomes graphical data that reveals the evidence for a very large percentage of false positive results. We review the role of exosomes in the immune response to all respiratory viral infections and its effect on PCR tests. We hypothesise that exosomes, triggered by all viral respiratory infections, are largely responsible for positive outcomes from PCR tests for COVID-19. We test our alternative interpretation for consistency with the empirical epidemiological trends as published by the World Health Organization (WHO). The Scientific Method is used to direct our research efforts. Findings: We find that PCR testing data for the second and following waves of the COVID-19 pandemic indicate that these waves are mainly artefacts of false-positive results. We find that this interpretation provides a more consistent explanation of the known epidemiology of COVID-19 than the hitherto consensus notion of extremely contagious and rapidly mutating viruses. Interpretation: The RNA (ribonucleic acid) code detected in PCR tests, previously attributed to SARS-CoV-2, belongs instead to a respiratory-virus-induced immune system response by human cells that liberate exosomes, and that vitiate PCR test results. PCR tests have zero specificity in vivo due to the exosome RNA. PCR tests exhibit excellent specificity in vitro on pure samples of other respiratory viruses. The low success rate of vaccines in preventing COVID-19 is explained by the inexact identification of the SARS-CoV-2 RNA.

Sociodemographic and clinical predictors for COVID-19 preventive measures compliance among pregnant women in Saudi Arabia: a multicenter study

ObjectiveTo assess the levels of adherence among pregnant women to the basic COVID-19 preventive measures, and to analyze the effect of risk perception and sociodemographic and clinical factors on adherence.MethodA multicenter, cross-sectional study was conducted at the obstetrics clinics of 50 primary care centers selected using a multistage sampling method. An online-administered, structured questionnaire was used to collect self-reported levels of adherence to four basic preventive measures against COVID-19, along with perceived COVID-19 severity, infectiousness, and harmfulness to the baby, besides sociodemographic and clinical data including obstetrical and other medical history.ResultsA total of 2460 pregnant women were included with a mean (SD) age of 30.21 (6.11) years. Levels of self-reported compliance were highest for hand hygiene (95.7%), followed by social distancing (92.3%), masking (90.0%), and avoidance of contact with a COVID-19 infected person (70.3%). Perceived COVID-19 severity and infectiousness, and harmfulness to the baby were observed in 89.2%, 70.7%, and 85.0% of the participants, respectively, and were variably associated with compliance to preventive measures. Analysis of sociodemographic factors highlighted the significance of education and economic status in determining adherence to preventive measures, which represents a potential inequity in the risk of COVID-19 infection.ConclusionThis study highlights the importance of patients’ education to enable functional perception of COVID-19 that promotes self-efficacy, besides investigating the specific social determinants of health to tackle inequalities in terms of prevention efficiency and the subsequent health outcomes.

The independent prediction study of the effect of heart-type fatty acid binding protein on the severity and long-term cardiac function in Covid-19 patients.

The study was conducted to assess the predictive ability of the heart-type fatty acid binding protein (HFABP) on the severity and long-term cardiac function of Covid-19 infected persons. In the case of negative HsTn-T, we determined whether HFABP was related to the severity of Covid-19 or it was the long-term impact of cardiac function. Chi-square test and t-test were used to evaluate whether HFABP level was an independent predictor of myocardial injury and whether it was related to the severity of Covid-19 and the long-term impact of cardiac function. Among the 20 patients in each of the two groups (mild and severe), 27.5% of all had elevated HFABP. Two were HFABP positive in the mild group, and nine were HFABP positive in the severe group, with a significant difference between the two groups (P=0.013). The mean serum level of HFABP in the mild group was 3.96 ±1.80, compared with 6.70±3.77 in the severe group, with a significant difference between the two groups (P=0.003). In addition, after two years of follow-up, there was a statistically significant difference in the changes of cardiac function between the HFABP-positive group and the HFABP-negative group (P=0.037). These data indicate that among HsTn-T-negative Covid-19 patients, HFABP is a more sensitive and independent predictor of myocardial damage, and it is useful for distinguishing mild and severe Covid-19. The level of HFABP has a significant effect on the long-term changes of heart function in Covid-19 patients.

The independent prediction study of the effect of heart-type fatty acid binding protein on the severity and long-term cardiac function in Covid-19 patients

The study was conducted to assess the predictive ability of the heart-type fatty acid binding protein (HFABP) on the severity and long-term cardiac function of Covid-19 infected persons. In the case of negative HsTn-T, we determined whether HFABP was related to the severity of Covid-19 or it was the long-term impact of cardiac function. Chi-square test and t-test were used to evaluate whether HFABP level was an independent predictor of myocardial injury and whether it was related to the severity of Covid-19 and the long-term impact of cardiac function. Among the 20 patients in each of the two groups (mild and severe), 27.5% of all had elevated HFABP. Two were HFABP positive in the mild group, and nine were HFABP positive in the severe group, with a significant difference between the two groups (P=0.013). ---Continue

Severity of Symptoms and Mortality in Diabetic Patients with COVID- 19 Infection. Review

Background: COVID-19 pneumonia is an illness that was spreading rapidly around the world and causes many deaths. Diabetes, is considered as a risk factor which adds severity and mortality to COVID-19 infected persons. There are many studies aiming to explain the
 exact association between diabetes and COVID-19. This review aims to link between diabetes and COVID-19 risk factors, discuss the management of patients with diabetes and COVID-19 and provide perception into the COVID-19 disease complications relevant to diabetes .In this review, many scientific articles and reports about COVID- 19 and Type 2 DM were collected from different databases (e.g., PubMed, Google Scholar, and Web of Science) using keywords such as SARS-CoV2, COVID-19, TYPE 2 DM, Mortality, and CO-morbidities .The result from this study found that diabetic patients has poor prognosis, severe symptoms, worsen outcomes, and the mortality rate is higher among COVID-19 patients who have diabetes mellitus. Many complications associated with diabetes like peripheral neuropathy and vascular insufficiency can make patients more susceptible to infections. One of the sever complication of Covid-19 infection is, the cytokine storm, that result in excessive release of inflammatory cytokines (TNF-a, IL-6, IL-10), and this may be aggravated by the inflammatory process together with the hyper-coagulable condition in diabetic patients. In conclusion: Hyperglycemia is established as risk factor for increased morbidity and mortality in covid 19 infection.

Modeling the positive testing rate of COVID-19 in South Africa using a semi-parametric smoother for binomial data.

Identification and isolation of COVID-19 infected persons plays a significant role in the control of COVID-19 pandemic. A country's COVID-19 positive testing rate is useful in understanding and monitoring the disease transmission and spread for the planning of intervention policy. Using publicly available data collected between March 5th, 2020 and May 31st, 2021, we proposed to estimate both the positive testing rate and its daily rate of change in South Africa with a flexible semi-parametric smoothing model for discrete data. There was a gradual increase in the positive testing rate up to a first peak rate in July, 2020, then a decrease before another peak around mid-December 2020 to mid-January 2021. The proposed semi-parametric smoothing model provides a data driven estimates for both the positive testing rate and its change. We provide an online R dashboard that can be used to estimate the positive rate in any country of interest based on publicly available data. We believe this is a useful tool for both researchers and policymakers for planning intervention and understanding the COVID-19 spread.

Conv-CapsNet: capsule based network for COVID-19 detection through X-Ray scans.

Coronavirus, a virus that spread worldwide rapidly and was eventually declared a pandemic. The rapid spread made it essential to detect Coronavirus infected people to control the further spread. Recent studies show that radiological images such as X-Rays and CT scans provide essential information in detecting infection using deep learning models. This paper proposes a shallow architecture based on Capsule Networks with convolutional layers to detect COVID-19 infected persons. The proposed method combines the ability of the capsule network to understand spatial information with convolutional layers for efficient feature extraction. Due to the model's shallow architecture, it has 23M parameters to train and requires fewer training samples. The proposed system is fast and robust and correctly classifies the X-Ray images into three classes, i.e. COVID-19, No Findings, and Viral Pneumonia. Experimental results on the X-Ray dataset show that our model performs well despite having fewer samples for the training and achieved an average accuracy of 96.47% for multi-class and 97.69% for binary classification on 5-fold cross-validation. The proposed model would be useful to researchers and medical professionals for assistance and prognosis for COVID-19 infected patients.

A study of Real-Time Non-invasive Detection Techniques to Identify COVID-19 contagious person

The whole world has witnessed the global pandemic situation caused and hampered very badly due to COVID-19. We had seen the adverse effect globally, in terms of health, economy, social lifestyle. So, it’s an urgent need to find a rapid detection technique/test to avoid the spread of the virus. The most effective and world-wide accepted detection method of COVID-19 is the RT-PCR. But due to its slow detection time and False-negative rates, researchers and scientists are trying different detection methods such as use of GC-MS, E-nose, Electrochemical method, use of nanomaterial-based sensor arrays. But all these have limitations in terms of real time sensing, detection time, sample preparation, etc. In order to overcome said drawbacks and to get real-time analysis, we are proposing a concept for COVID-19 detection based on the reported literature. As per recent advancement researchers have evident the presence of VOCs in COVID-19 infected person’s breath by GC-MS method. A real time system is very much necessary to detect the VOCs in the Exhaled breath of the COVID-19 infected person to minimize the burden of healthcare system. In this article we will discuss and propose the probable detection techniques for real time sensing of the VOCs presence in the Exhaled breath of the COVID-19 infected person.

SARS-CoV-2 detecting rapid metasurface-based sensor.

We have proposed a novel approach to detect COVID-19 by detecting the ethyl butanoate which high volume ratio is present in the exhaled breath of a COVID-19 infected person. We have employed a refractive index sensor (RIS) with the help of a metasurface-based slotted T-shape perfect absorber that can detect ethyl butanoate present in exhaled breath of COVID-19 infected person with high sensitivity and in-process SARS-CoV-2. The optimized structure of the sensor is obtained by varying several structure parameters including structure length and thickness, slotted T-shape resonator length, width, and thickness. Sensor's performance is evaluated based on numerous factors comprising of sensitivity, Q factor, detection limit, a figure of merit (FOM), detection accuracy, and other performance defining parameters. The proposed slotted T-shape RIS achieved the largest sensitivity of 2500 nm/RIU, Q factor of 131.06, a FOM of 131.58 RIU-1, detection limit of 0.0224 RIU.

A Granular Computing-Driving Hesitant Fuzzy Linguistic Method for Supporting Large-Scale Group Decision Making

Considering the conditions that: 1) same linguistic term means different things for different people; 2) flexible semantics cannot be represented by original linguistic term; and 3) some semantics given by decision makers are possible to be changed during the consistency improving process, we bring some flexibility and personality into hesitant fuzzy linguistic preference matrix structures by allowing the linguistic preference matrices to be granular rather than numeric, providing a new characterization of linguistic preference matrices. Inspired by the thought of granular computing, this article proposes a new hesitant fuzzy linguistic method to deal with issues when a lot of decision makers provide hesitant and uncertain preference information in the decision-making process. First, we design a multiplicative consistency index and calculate its thresholds corresponding to different dimensions of preference matrix by the Monte Carlo experiment. Then, we construct a hesitant fuzzy linguistic model with granularity level, so as to recharacterize original assessment information and improve the consistency of preference matrices as far as possible. Considering the features of some large-scale group decision-making situation, where the decision makers have little opportunity to take part in multiple consensus reaching processes, hesitant fuzzy linguistic fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> -means clustering algorithm is developed to integrate the assessment information given by decision makers. Finally, the final decision-making results are derived. An illustrative example of assessing psychological situation of some COVID-19 infected persons clarifies the reasonability of the proposed method. Finally, we complete some comparative studies and simulation experiments to demonstrate the method’s validity and advantages.

Mathematical Analysis of SIR Model for COVID-19 Transmission

Due to the recent threatening pandemic COVID-19, the research area of this disease is increasing. This paper tries to establish COVID-19 infection transmission by Susceptible-Infectious-Recovered (SIR) compartmental model for epidemic prediction and prevention. The model is built based on the secondary data of the infected persons and discharged patients. It is considered as a valuable tool in public health sector, as it can provide suggestions about the fatality of pandemic to take necessary actions for preventing the infections. COVID-19 is spreading worldwide extremely, and at present it becomes both local and global concern. This model can show the fatality of COVID-19 with time and can predict whether the disease will further spread or abolish completely. This study stresses on vaccination to reduce the infection of the disease. It can provide how many people are needed to be vaccinated to create herd immunity against COVID-19. Overtime the immunity due to vaccination may decrease and after a fixed period the immunity of COVID-19 due to vaccination may extinct completely. The article attempts to give a mathematical presentation to aware the immunity loss individuals with other susceptible. It also tries to alert the people about the re-infection of the previous COVID-19 infected persons. The aim of this study is to minimize both global economic losses and deaths due to COVID-19.

A transfer learning based deep learning model to diagnose covid-19 CT scan images.

To save the life of human beings during the pandemic conditions we need an effective automated method to deal with this situation. In pandemic conditions when the available resources becomes insufficient to handle the patient’s load, then we needed some fast and reliable method which analyse the patient medical data with high efficiency and accuracy within time limitations. In this manuscript, an effective and efficient method is proposed for exact diagnosis of the patient whether it is coronavirus disease-2019 (covid-19) positive or negative with the help of deep learning. To find the correct diagnosis with high accuracy we use pre-processed segmented images for the analysis with deep learning. In the first step the X-ray image or computed tomography (CT) of a covid-19 infected person is analysed with various schemes of image segmentation like simple thresholding at 0.3, simple thresholding at 0.6, multiple thresholding (between 26–230) and Otsu’s algorithm. On comparative analysis of all these methods, it is found that the Otsu’s algorithm is a simple and optimum scheme to improve the segmented outcome of binary image for the diagnosis point of view. Otsu’s segmentation scheme gives more precise values in comparison to other methods on the scale of various image quality parameters like accuracy, sensitivity, f-measure, precision, and specificity. For image classification here we use Resnet-50, MobileNet and VGG-16 models of deep learning which gives accuracy 70.24%, 72.95% and 83.18% respectively with non-segmented CT scan images and 75.08%, 80.12% and 99.28% respectively with Otsu’s segmented CT scan images. On a comparative study we find that the VGG-16 models with CT scan image segmented with Otsu’s segmentation gives very high accuracy of 99.28%. On the basis of the diagnosis of the patient firstly we go for an arterial blood gas (ABG) analysis and then on the behalf of this diagnosis and ABG report, the severity level of the patient can be decided and according to this severity level, proper treatment protocols can be followed immediately to save the patient's life. Compared with the existing works, our deep learning based novel method reduces the complexity, takes much less time and has a greater accuracy for exact diagnosis of coronavirus disease-2019 (covid-19).

Mobile sensors based platform of Human Physical Activities Recognition for COVID-19 spread minimization

The development of smartphones technologies has determined the abundant and prevalent computation. An activity recognition system using mobile sensors enables continuous monitoring of human behavior and assisted living. This paper proposes the mobile sensors-based Epidemic Watch System (EWS) leveraging the AI models to recognize a new set of activities for effective social distance monitoring, probability of infection estimation, and COVID-19 spread prevention. The research focuses on user activities recognition and behavior concerning risks and effectiveness in the COVID-19 pandemic. The proposed EWS consists of a smartphone application for COVID-19 related activities sensors data collection, features extraction, classifying the activities, and providing alerts for spread presentation. We collect the novel dataset of COVID-19 associated activities such as hand washing, hand sanitizing, nose–eyes touching, and handshaking using the proposed EWS smartphone application. We evaluate several classifiers such as random forests, decision trees, support vector machine, and Long Short-Term Memory for the collected dataset and attain the highest overall classification accuracy of 97.33%. We provide the Contact Tracing of the COVID-19 infected person using GPS sensor data. The EWS activities monitoring, identification, and classification system examine the infection risk of another person from COVID-19 infected person. It determines some everyday activities between COVID-19 infected person and normal person, such as sitting together, standing together, or walking together to minimize the spread of pandemic diseases.

Assessment of Knowledge and Acceptance of Covid-19 Vaccinations among Healthcare Workers in Kano State, Nigeria

Coronavirus is an ongoing global viral disease firstly identified in Wuhan, China in December 2019, hence its name Covid-19. The World Health Organization (WHO) declared Covid-19 as a pandemic with Public Health Emergency of International Concern (PHEIC). People get infected when they inhale small airborne particles or droplets exhaled by a Covid-19 infected person. Some of the recommended preventive measures include social or physical distancing, covering the mouth when sneezing or coughing, wearing of face masks and regular hand wash. A multi-stage sampling method was used to select the study locations, hospitals, healthcare departments and survey groups. A quantitative method - using structured questionnaires was used. Statistical analysis was conducted using Statistical Package for Social Science (SPSS) version 26. Out of the 1004 participants that were surveyed, 864 responses were retrieved. The bulk of the respondents (59%) were male. Knowledge of Covid–19 was poor, 24.3%, while acceptance of Covid-19 vaccination was high (74.2%). Important predictors of Covid-19 vaccination are the number of years in service, marital status, designated work, and type of medical facility. Knowledge of Covid-19 is low, but acceptance of its vaccination was high among the study population. This could be due to fear of contacting the disease early plus the associated high mortality among the study population. There is a need for an enlightenment campaign to increase knowledge and further improve acceptance of the Covid-19 vaccination.

An Overview of the Artificial Neural Network-Based Applications and Impact Assessment in COVID-19

The article is regarding infectious disease, which has been affecting people all around the world since December 2019. Write-up gives a brief introduction of the Coronavirus from its emergence to the final solution of its exit. It gives us a glimpse of the biological structure of the virus, precautions to be taken in care to reduce the spread of the disease. The description shows us the consequences of the virus-like the mortality rate and the life expectancy rate. The article also presents a few vaccines development projects as a whole to fight COVID-19. It also describes the efficacy of vaccination and the post-vaccination symptoms. In the end, the write-up presents real-time neural network-based applications to fight COVID-19 and a few machine learning and deep learning techniques to diagnose a COVID-19 infected person. The final part of the article concludes with an artificial neural network and statistical neural network-based approaches to identify the rate of infection risk in a country.

Risk perception and preventive behaviours of COVID-19 among university students, Gondar, Ethiopia: a cross-sectional study

ObjectiveThis study aimed to assess COVID-19 preventive behaviours and their associated factors among university students.MethodsAn institution-based cross-sectional study was employed among 405 university students and the study participants were chosen...

Measurement of risk and stressors of post-traumatic stress disorder during the first lockdown period of COVID-19 among Industrial workers, Hyderabad, India

Introduction: Post-traumatic stress disorder usually occurs after a traumatic event and this COVID-19 pandemic has affected mental health to a great extent. While there is extensive research being conducted about this aspect among the general population, the most neglected are the Industrial workers during the first lockdown period. The aim of the study was to assess the risk of post-traumatic stress disorder symptoms using the impact of event scale-revised (IES-R), their main stressors and coping measurements among the industrial workers of Hyderabad. Methods: A telephonic interview schedule was adopted to fill the online questionnaire among Industrial workers of age group 20-55 years during the lockdown period. Hundred and seventy-four participants were included in our study and data was collected using standardized tools IES-R and brief resilient coping scale. Various stressors and also their coping strategies were elucidated. Results: At the risk of post-traumatic stress disorder symptoms were found among 20.6% and at risk of suppressed immune system functioning which is presumed to have its effect for the next 10 years was 13.8%. The main stressors of COVID-19 with increased IES-R score were not staying with family members, influence of social media, Any acquaintance with COVID-19 infected person effect on daily life and white-collar group. Coping measurements with a brief resilient coping scale found 17.4% as low resilient copers. Conclusion: This identification of high-risk post-traumatic stress disorder symptoms needs to be addressed earlier to bring up preventive strategies such as counseling services in the occupational health care centers of our industries.

Evaluating e-health literacy, knowledge, attitude and practice regarding COVID-19 prevention and Self-Protection among Iranian students: a cross-sectional online survey

BackgroundThe coronavirus 2019 (COVID-19) pandemic requires integrated intervention by both the governments and individuals. University students have a great role in distributing reliable information about disease prevention behaviors. The aim of this study was to identify the Knowledge, Attitude and Practice of COVID-19 Prevention and Self-Protection behaviors in students.MethodsThis cross-sectional online survey was conducted on Iranian university students. All students filled a questionnaire consisting of demographic characteristics, e-Health Literacy Scale (EHEALS) questionnaire and a researcher-made knowledge, attitude and practice (KAP) questionnaire. Data was analyzed using the SPSS software.ResultsA total of 925 students (69.9% female) participated in this study. The median age of the students was 23 years old. Majority of students (641, 69.3%) were non-medical students. The median and interquartile range (IQR) for knowledge, attitude, practice and P-EHEALS scores in Medical students were 52.00 (27.00), 4.00 (1.00), 28.00 (8.00), and 26.00 (9.00), respectively which were significantly higher than non-medical students, 28.00 (15.00), 3.00 (2.00), 20.00 (8.00), and 26.00 (9.00), respectively (p < 0.001). Practice score was a significant related to knowledge (p < 0.001), attitude (p < 0.001), having a COVID-19 infected family member (p < 0.001), older age (p < 0.001), medical field of education (p = 0.001), higher EHEALS score (p = 0.018), and female gender (p = 0.013). Knowledge, attitude and having a COVID-19 infected person in family were the strongest predictors of preventive practices.ConclusionsKAP and E-Health literacy of university students, especially non-medical students, should be considered in order to improve COVID-19 preventive behaviors in the society.

Vitamin D significance in Combating; Progression and Austerity of COVID-19

Humankind on our planet is under high pressure at present, facing an alarming pandemic in the form of coronavirus infection 2019 (Covid-19), which is known to be stimulated by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). Covid-19 can lead to acute respiratory distress syndrome (ARDS). Due to the minimal treatments or research, it is essential to consider therapeutic strategies with immunomodulatory effects. According to past studies, supplementing with vitamin D has been shown to minimize or even prohibit respiratory infections in some situations, according to some studies. Recent research has also demonstrated an association concerning vitamin D imbalance and adverse COVID-19 outcomes. It is a debatable situation because vitamin D deficiency is an important health issue worldwide. There may be a benefit in supplementing the low vitamin D levels in COVID-19 infected persons to assist in preventing the disease from more complicating matters. Covid-19 may benefit from vitamin D's immunomodulatory properties, and a comprehensive examination of the effects of vitamin D on Covid-19 is outlined in this brief review. Keywords: Covid-19, Acute respiratory disease syndrome (ARDS), Vitamin D, Cardiovascular disorder (CVD), Dipeptidyl peptidase-4 receptors (DPP-4/CD26)

Neighborhood Rough Neural Network Approach for COVID-19 Image Classification.

The rapid spread of the new Coronavirus, COVID-19, causes serious symptoms in humans and can lead to fatality. A COVID-19 infected person can experience a dry cough, muscle pain, headache, fever, sore throat, and mild to moderate respiratory illness, according to a clinical report. A chest X-ray (also known as radiography) or a chest CT scan are more effective imaging techniques for diagnosing lung cancer. Computed Tomography (CT) scan images allow for fast and precise COVID-19 screening. In this paper, a novel hybridized approach based on the Neighborhood Rough Set Classification method (NRSC) and Backpropagation Neural Network (BPN) is proposed to classify COVID and NON-COVID images. The proposed novel classification algorithm is compared with other existing benchmark approaches such as Neighborhood Rough Set, Backpropagation Neural Network, Decision Tree, Random Forest Classifier, Naive Bayes Classifier, K- Nearest Neighbor, and Support Vector Machine. Various classification accuracy measures are used to assess the efficacy of the classification algorithms.