Review of the Clinical Characteristics of Coronavirus Disease 2019 (COVID-19)

In vitro evaluation of nebulized eucalyptol nano-emulsion formulation as a potential COVID-19 treatment

Coronavirus is a type of acute atypical respiratory disease representing the leading cause of death worldwide. Eucalyptol (EUC) known also as 1,8-cineole is a potential inhibitor candidate for COVID-19 (main protease-M^pro) with effective antiviral properties but undergoes physico-chemical instability and poor water solubility. Nano-emulsion (NE) is a promising drug delivery system to improve the stability and efficacy of drugs. This work focuses on studying the anti- COVID-19 activity of EUC by developing nebulized eucalyptol nano-emulsion (EUC-NE) as a potentially effective treatment for COVID-19. The EUC -NE formulation was prepared using Tween 80 as a surfactant. In vitro evaluation of the EUC-NE formulation displayed an entrapment efficiency of 77.49 %, a droplet size of 122.37 nm, and an EUC % release of 84.7 %. The aerodynamic characterization and cytotoxicity of EUC-NE formulation were assessed, and results showed high lung deposition and low inhibitory concentration. The antiviral mechanism of the EUC-NE formulation was performed, and it was found that it exerts its action by virucidal, viral replication, and viral adsorption. Our results confirmed the antiviral activity of the EUC-NE formulation against COVID-19 and the efficacy of nano-emulsion as a delivery system, which can improve the cytotoxicity and inhibitory activity of EUC.

COVID-19 Outbreak Related to PM10, PM2.5, Air Temperature and Relative Humidity in Ahvaz, Iran

In this study, we assessed several points related to the incidence of COVID-19 between March 2020 and March 2021 in the Petroleum Hospital of Ahvaz (Iran) by analyzing COVID-19 data from patients referred to the hospital. We found that 57.5% of infected referrals were male, 61.7% of deaths by COVID-19 occurred in subjects over 65 years of age, and only 2.4% of deaths occurred in younger subjects (< 30 years old). Analysis showed that mean PM₁₀ and PM_2.5 concentrations were correlated to the incidence of COVID-19 (r = 0.547, P < 0.05, and r = 0.609, P < 0.05, respectively) and positive chest CT scans (r = 0.597, P < 0.05, and r = 0.541, P < 0.05 respectively). We observed that a high daily air temperature (30–51 °C) and a high relative humidity (60–97%) led to a significant reduction in the daily incidence of COVID-19. The highest number of positive chest CT scans were obtained in June 2020 and March 2021 for daily air temperature ranging from 38 °C and 49 °C and 11 °C and 15 °C, respectively. A negative correlation was detected between COVID-19 cases and air temperature (r =  − 0.320, P < 0.05) and relative humidity (r =  − 0.384, P < 0.05). In Ahvaz, a daily air temperature of 10–28 °C and relative humidity of 19–40% are suitable for the spread of coronavirus. The highest correlation with the number of COVID-19 cases was found at lag3 (r = 0.42) and at lag0 with a positive chest CT scan (r = 0.56). For air temperature and relative humidity, the highest correlations were found at day 0 (lag0). During lockdown (22 March to 21 April 2020), a reduction was observed for PM₁₀ (29.6%), PM_2.5 (36.9%) and the Air Quality Index (33.3%) when compared to the previous month. During the pandemic period (2020–2021), the annual mean concentrations of PM₁₀ (27.3%) and PM_2.5 (17.8%) were reduced compared to the 2015–2019 period.

Human-Immune-System (HIS) humanized mouse model (DRAGA: HLA-A2.HLA-DR4.Rag1KO.IL-2RγcKO.NOD) for COVID-19

We report a Human Immune System (HIS)-humanized mouse model ("DRAGA": HLA-A2.HLA-DR4.Rag1KO.IL-2 RγcKO.NOD) for COVID-19 research. DRAGA mice express transgenically HLA-class I and class-II molecules in the mouse thymus to promote human T cell development and human B cell Ig-class switching. When infused with human hematopoietic stem cells from cord blood reconstitute a functional human immune system, as well as human epi/endothelial cells in lung and upper respiratory airways expressing the human ACE2 receptor for SARS-CoV-2. The DRAGA mice were able to sustain SARS-CoV-2 infection for at least 25 days. Infected mice showed replicating virus in the lungs, deteriorating clinical condition, and human-like lung immunopathology including human lymphocyte infiltrates, microthrombi and pulmonary sequelae. Among the intra-alveolar and peri-bronchiolar lymphocyte infiltrates, human lung-resident (CD103+) CD8+ and CD4+ T cells were sequestered in epithelial (CD326+) lung niches and secreted granzyme B and perforin, suggesting anti-viral cytotoxic activity. Infected mice also mounted human IgG antibody responses to SARS-CoV-2 viral proteins. Hence, HIS-DRAGA mice showed unique advantages as a surrogate in vivo human model for studying SARS-CoV-2 immunopathological mechanisms and testing the safety and efficacy of candidate vaccines and therapeutics.

The congested International Match Calendar in football: views of 1055 professional male players

Background

The International Match Calendar congestion affects players recovery. The views of a worldwide cohort of professional football players is shared in this communication.

Methods

A cross-sectional observational study recruited players through Fédération Internationale des Associations de Footballeurs Professionnel’s national members. An electronic survey was shared in English, French, Italian and Spanish with 1055 players consenting and completing it anonymously in November 2021.

Results

A total of 42% of respondents believe back-to-back matches should be limited to three. Most respondents (69%) felt off or in season breaks are infringed by clubs or national teams and 83% believe regulations should protect them. A total of 55% of players believed they sustained one or more injuries due to the overload and it has affected 52% of respondents’ mental state.

Conclusion

The congested International Match Calendar poses a risk to professional footballers physical and mental health. Poor recovery between matches may affect player availability and performance. Players should be represented by active players when International Match Calendar scheduling decisions are made. Administrators should seek medical guidance regarding the effects of overload on performance prior to making decisions. This study allows the opportunity for a larger national team player sample to be studied.

Characterization of Planktochlorella nurekis Extracts and Virucidal Activity against a Coronavirus Model, the Murine Coronavirus 3

Certain members of the Coronaviridae family have emerged as zoonotic agents and have recently caused severe respiratory diseases in humans and animals, such as SARS, MERS, and, more recently, COVID-19. Antivirals (drugs and antiseptics) capable of controlling viruses at the site of infection are scarce. Microalgae from the Chlorellaceae family are sources of bioactive compounds with antioxidant, antiviral, and antitumor activity. In the present study, we aimed to evaluate various extracts from Planktochlorella nurekis in vitro against murine coronavirus-3 (MHV-3), which is an essential human coronavirus surrogate for laboratory assays. Methanol, hexane, and dichloromethane extracts of P. nurekis were tested in cells infected with MHV-3, and characterized by UV-vis spectrophotometry, nuclear magnetic resonance (NMR) spectroscopy, ultraperformance liquid chromatography-mass spectrometry (UPLC-MS), and the application of chemometrics through principal component analysis (PCA). All the extracts were highly efficient against MHV-3 (more than a 6 Log unit reduction), regardless of the solvent used or the concentration of the extract, but the dichloromethane extract was the most effective. Chemical characterization by spectrophotometry and NMR, with the aid of statistical analysis, showed that polyphenols, carbohydrates, and isoprene derivatives, such as terpenes and carotenoids have a more significant impact on the virucidal potential. Compounds identified by UPLC-MS were mainly lipids and only found in the dichloromethane extract. These results open new biotechnological possibilities to explore the biomass of P. nurekis; it is a natural extract and shows low cytotoxicity and an excellent antiviral effect, with low production costs, highlighting a promising potential for development and implementation of therapies against coronaviruses, such as SARS-CoV-2.

Risk Stratification of COVID-19 Using Routine Laboratory Tests: A Machine Learning Approach

The COVID-19 pandemic placed significant stress on an already overburdened health system. The diagnosis was based on detection of a positive RT-PCR test, which may be delayed when there is peak demand for testing. Rapid risk stratification of high-risk patients allows for the prioritization of resources for patient care. The study aims were to classify patients as severe or not severe based on outcomes using machine learning on routine laboratory tests. Data were extracted for all individuals who had at least one SARS-CoV-2 PCR test conducted via the NHLS between the periods of 1 March 2020 to 7 July 2020. Exclusion criteria: those 18 years, and those with indeterminate PCR tests. Results for 15437 patients (3301 positive and 12,136 negative) were used to fit six machine learning models, namely the logistic regression (LR) (the base model), decision trees (DT), random forest (RF), extreme gradient boosting (XGB), convolutional neural network (CNN) and self-normalising neural network (SNN). Model development was carried out by splitting the data into training and testing set of a ratio 70:30, together with a 10-fold cross-validation re-sampling technique. For risk stratification, admission to high care or ICU was the outcome for severe disease. Performance of the models varied: sensitivity was best for RF at 75% and accuracy of 75% for CNN. The area under the curve ranged from 57% for CNN to 75% for RF. RF and SNN were the best-performing models. Machine Learning (ML) can be incorporated into the laboratory information system and offers promise for early identification and risk stratification of COVID-19 patients, particularly in areas of resource-poor settings.

Pulmonary sequelae of SARS-CoV-2 infection and factors associated with persistent abnormal lung function at six months after infection: Prospective cohort study

BACKGROUND

Information on the long-term pulmonary sequelae following SARS-CoV-2 infection is limited.

METHODS

Prospective cohort study of hospitalized and non-hospitalized adult patients age >18 with documented SARS-CoV-2 infection by RT-PCR three months prior to enrolment between June and December 2020. Participants underwent full pulmonary function test (PFT), cardiopulmonary exercise testing at 3 months and 6 months. Primary outcome was mean differences of forced vital capacity (FVC), diffuse capacity of lung for carbon monoxide (DLCO), and oxygen consumption (VO2) at 6 vs. 3 months. Secondary outcomes were respiratory outcomes classified into 5 clinical groups-no lung disease, resolved lung disease, persistent lung disease, PFT abnormalities attributable to pre-existing lung disease or other factors, and mild PFT abnormalities of uncertain clinical significance.

RESULTS

Fifty-one, 30 hospitalized and 21 non-hospitalized, participants were included. Median age was 51 years; 20 (39.2%) were female. Mean (±SD) percent predicted values of FVC, DLCO and VO2 at 3 vs 6-month-visits were 96.2 ± 15.6 vs. 97.6 ± 15.5, 73.74 ±18 vs. 78.5 ± 15.5, and 75.5 ± 18.9 vs. 76.1 ± 21.5, respectively. Nineteen (37%) patients had physiologic and/or radiographic evidence of lung disease at 3 months with eight (15.7%) continuing to have persistent disease at 6 months. History of diabetes, hypertension, ICU admission and elevated D-Dimer levels were associated with persistent lung disease at 6 months.

INTERPRETATION

Persistent lung disease at 6 months post SARS-CoV-2 infection exists. Changes of lung function between 3- and 6-months are not significant. A longer follow-up is required to determine long-term prognosis.

Modeling the effects of vaccination, nucleic acid testing, and face mask wearing interventions against COVID-19 in large sports events

The transmission of SARS-CoV-2 leads to devastating COVID-19 infections around the world, which has affected both human health and the development of industries dependent on social gatherings. Sports events are one of the subgroups facing great challenges. The uncertainty of COVID-19 transmission in large-scale sports events is a great barrier to decision-making with regard to reopening auditoriums. Policymakers and health experts are trying to figure out better policies to balance audience experiences and COVID-19 infection control. In this study, we employed the generalized SEIR model in conjunction with the Wells-Riley model to estimate the effects of vaccination, nucleic acid testing, and face mask wearing on audience infection control during the 2021 Chinese Football Association Super League from 20 April to 5 August. The generalized SEIR modeling showed that if the general population were vaccinated by inactive vaccines at an efficiency of 0.78, the total number of infectious people during this time period would decrease from 43,455 to 6,417. We assumed that the general population had the same odds ratio of entering the sports stadiums and becoming the audience. Their infection probabilities in the stadium were further estimated by the Wells-Riley model. The results showed that if all of the 30,000 seats in the stadium were filled by the audience, 371 audience members would have become infected during the 116 football games in the 2021 season. The independent use of vaccination and nucleic acid testing would have decreased this number to 79 and 118, respectively. The combined use of nucleic acid testing and vaccination or face mask wearing would have decreased this number to 14 and 34, respectively. The combined use of all three strategies could have further decreased this number to 0. According to the modeling results, policymakers can consider the combined use of vaccination, nucleic acid testing, and face mask wearing to protect audiences from infection when holding sports events, which could create a balance between audience experiences and COVID-19 infection control.

Biomarker Changes in Pediatric Patients With COVID-19: A Retrospective Study from a Single Center Database

BACKGROUND/AIM

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for generating a global pandemic with deadly consequences and life changes worldwide. With the appearance of the new variants of the virus, clinical manifestations have been reported in the pediatric population, some with severe evolution. The aim of this study was to identify the laboratory parameters necessary to establish an effective therapy.

PATIENTS AND METHODS

In the period from August 2020 to September 2021, 234 pediatric patients met the inclusion criteria and were selected for the study. After confirming the COVID-19 diagnosis, laboratory parameters were analyzed and compared to the severity of the illness.

RESULTS

Thrombocytopenia (p<0.001), leukocytosis (p<0.001), and lymphopenia (p<0.001) correlated with the severity of the disease. Also, D-dimer values were closely monitored due to the high association of this parameter with an unsatisfactory prognosis and a severe form of the disease.

CONCLUSION

The D-dimer values and complete blood count are useful parameters in COVID-19 evaluation in children.

Porcine deltacoronavirus E protein induces interleukin-8 production via NF-κB and AP-1 activation

Infection induces the production of proinflammatory cytokines and chemokines such as interleukin-8 (IL-8) and interleukin-6 (IL-6). Although they facilitate local antiviral immunity, their excessive release leads to life-threatening cytokine release syndrome, exemplified by the severe cases of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In the present study, we found that interleukin-8 (IL-8) was upregulated by PDCoV infection. We then demonstrated that PDCoV E protein induced IL-8 production and that the TM domain and the C-terminal domain of the E protein were important for IL-8 production. Subsequently, we showed here that deleting the AP-1 and NF-κB binding motif in porcine IL-8 promoter abrogated its activation, suggesting that IL-8 expression was dependent on AP-1 and NF-κB. Furthermore, PDCoV E induced IL-8 production, which was also dependent on the NF-κB pathway through activating nuclear factor p65 phosphorylation and NF-κB inhibitor alpha (IκBα) protein phosphorylation, as well as inducing the nuclear translocation of p65, eventually resulting in the promotion of IL-8 production. PDCoV E also activated c-fos and c-jun, both of which are members of the AP-1 family. These findings provide new insights into the molecular mechanisms of PDCoV-induced IL-8 production and help us further understand the pathogenesis of PDCoV infection.

Long-term cardiovascular outcomes in COVID-19 survivors among non-vaccinated population: A retrospective cohort study from the TriNetX US collaborative networks

BACKGROUND

The long-term cardiovascular outcomes in COVID-19 survivors remain largely unclear. The aim of this study was to investigate the long-term cardiovascular outcomes in COVID-19 survivors.

METHODS

This study used the data from the US Collaborative Network in TriNetX. From a cohort of more than 42 million records between 1 January 2019 and 31 March 2022, a total of 4,131,717 participants who underwent SARS-CoV-2 testing were recruited. Study population then divided into two groups based on COVID-19 test results. To avoid reverse causality, the follow-up initiated 30 days after the test, and continued until 12 months. Hazard ratios (HRs) and 95% Confidence intervals (CIs) of the incidental cardiovascular outcomes were calculated between propensity score-matched patients with versus without SARS-CoV-2 infection. Subgroup analyses on sex, and age group were also conducted. Sensitivity analyses were performed using different network, or stratified by hospitalization to explore the difference of geography and severity of COVID-19 infection.

FINDINGS

The COVID-19 survivors were associated with increased risks of cerebrovascular diseases, such as stroke (HR [95% CI] = 1.618 [1.545-1.694]), arrhythmia related disorders, such as atrial fibrillation (HR [95% CI] = 2.407 [2.296-2.523]), inflammatory heart disease, such as myocarditis (HR [95% CI] =4.406 [2.890-6.716]), ischemic heart disease(IHD), like ischemic cardiomyopathy (HR [95% CI] = 2.811 [2.477-3.190]), other cardiac disorders, such as heart failure (HR [95% CI] =2.296 [2.200-2.396]) and thromboembolic disorders (e.g. pulmonary embolism: HR [95% CI] =2.648 [2.443-2.870]). The risks of two composite endpoints, major adverse cardiovascular event (HR [95% CI] = 1.871 [1.816-1.927]) and any cardiovascular outcome (HR [95% CI] = 1.552 [1.526-1.578]), were also higher in the COVID-19 survivors than in the controls. Moreover, the survival probability of the COVID-19 survivors dramatically decreased in all the cardiovascular outcomes. The risks of cardiovascular outcomes were evident in both male and female COVID-19 survivors. Furthermore, the risk of mortality was higher in the elderly COVID-19 survivors (age ≥ 65 years) than in the young ones. Sensitivity analyses presented roughly similar results globally. Furthermore, the impact of COVID-19 on cardio-related outcomes appeared to be more pronounced in inpatients than in outpatients.

INTERPRETATION

The 12-month risk of incidental cardiovascular diseases is substantially higher in the COVID-19 survivors than the non-COVID-19 controls. Clinicians and patients with a history of COVID-19 should pay attention to their cardiovascular health in long term.

FUNDING

The Fundamental Research Funds for the Central public welfare research institutes and Young Elite Scientists Sponsorship Program by CACM.

SARS-CoV-2 Morphometry Analysis and Prediction of Real Virus Levels Based on Full Recurrent Neural Network Using TEM Images

The SARS-CoV-2 virus is responsible for the rapid global spread of the COVID-19 disease. As a result, it is critical to understand and collect primary data on the virus, infection epidemiology, and treatment. Despite the speed with which the virus was detected, studies of its cell biology and architecture at the ultrastructural level are still in their infancy. Therefore, we investigated and analyzed the viral morphometry of SARS-CoV-2 to extract important key points of the virus's characteristics. Then, we proposed a prediction model to identify the real virus levels based on the optimization of a full recurrent neural network (RNN) using transmission electron microscopy (TEM) images. Consequently, identification of virus levels depends on the size of the morphometry of the area (width, height, circularity, roundness, aspect ratio, and solidity). The results of our model were an error score of training network performance 3.216 × 10^-11 at 639 epoch, regression of -1.6 × 10^-9, momentum gain (Mu) 1 × 10^-9, and gradient value of 9.6852 × 10^-8, which represent a network with a high ability to predict virus levels. The fully automated system enables virologists to take a high-accuracy approach to virus diagnosis, prevention of mutations, and life cycle and improvement of diagnostic reagents and drugs, adding a point of view to the advancement of medical virology.

Cardiovascular Complications of COVID-19 among Pregnant Women and Their Fetuses: A Systematic Review

Background: COVID-19 is a viral infectious disease leading to a spectrum of clinical complications, especially cardiovascular. Evidence shows that this infection can potentially accompany a worse outcome in pregnant women. Cardiovascular complications in mothers and their fetuses are reported by previous studies. Objective: In this systematic review, we aim to investigate the cardiovascular complications of COVID-19 during pregnancy in the mothers and fetus, according to the published literature. Method: We systematically searched the online databases of PubMed, Scopus, Web of Science, and Google Scholar, using relevant keywords up to April 2022. We included all observational studies reporting cardiovascular complications among COVID-19-affected pregnant women and their fetuses. Results: We included 74 studies containing 47582 pregnant COVID-19 cases. Pre-eclampsia, hypertensive disorders, cardiomyopathy, heart failure, myocardial infarction, thrombosis formation, alterations in maternal–fetal Doppler patterns, and maternal and fetal arrhythmia were reported as cardiovascular complications. The highest incidences of pre-eclampsia/eclampsia among COVID-19 pregnant cases, reported by studies, were 69% and 62%, and the lowest were 0.5% and 3%. The highest and lowest incidences of fetal bradycardia were 20% and 3%, and regarding fetal tachycardia, 5.4% and 1%, respectively. Conclusion: SARS-CoV-2 infection during pregnancy can potentially be associated with cardiovascular complications in the mother, particularly pre-eclampsia and heart failure. Moreover, SARS-CoV-2 infection during pregnancy can potentially cause cardiovascular complications in the fetus, particularly arrhythmia.

Multidistrict Host-Pathogen Interaction during COVID-19 and the Development Post-Infection Chronic Inflammation

Due to the presence of the ACE2 receptor in different tissues (nasopharynx, lung, nervous tissue, intestine, liver), the COVID-19 disease involves several organs in our bodies. SARS-CoV-2 is able to infect different cell types, spreading to different districts. In the host, an uncontrolled and altered immunological response is triggered, leading to cytokine storm, lymphopenia, and cellular exhaustion. Hence, respiratory distress syndrome (ARDS) and systemic multi-organ dysfunction syndrome (MODS) are established. This scenario is also reflected in the composition of the microbiota, the balance of which is regulated by the interaction with the immune system. A change in microbial diversity has been demonstrated in COVID-19 patients compared with healthy donors, with an increase in potentially pathogenic microbial genera. In addition to other symptoms, particularly neurological, the occurrence of dysbiosis persists after the SARS-CoV-2 infection, characterizing the post-acute COVID syndrome. This review will describe and contextualize the role of the immune system in unbalance and dysbiosis during SARS-CoV-2 infection, from the acute phase to the post-COVID-19 phase. Considering the tight relationship between the immune system and the gut-brain axis, the analysis of new, multidistrict parameters should be aimed at understanding and addressing chronic multisystem dysfunction related to COVID-19.

Traditional and Complementary Medicines Methods Used by Patients Diagnosed with COVID-19

AIM

This study aimed to determine traditional and complementary medicines (T&amp;CM) methods used by patients before and after being diagnosed with COVID-19.

METHODS

This descriptive cross-sectional study was carried out between 27 March 2021 and 2 July 2021, in the community health services unit of Düzce Province, Central District, in the western Black Sea region. 547 people participated in the study. Two forms prepared by the researchers, the "Participant Identification Form" and the "Assessment Form for the Use of Traditional and Complementary Medicine Methods," were used to collect the data.

RESULTS

Before the diagnosis of COVID-19, patients reported that they consumed the highest rate of medicinal plants (56.1%) and fruits (40.4%) and prayed (40.4%) to prevent disease. After the diagnosis of COVID-19, they stated that they consumed the highest percentage of medicinal plants (68.4%) and fruit (53.0%) and prayed (48.4%) to reduce disease symptoms. It was seen that herbal products/herbal mixtures (medicinal plants [p < 0.001], fruits [p < 0.001], and fruit vinegars [p < 0.001]), and T&amp;CM methods such as supplements like C, E, D, and other vitamins (p < 0.001), prayer (p < 0.001), massage (p < 0.001), and apitherapy (p < 0.001) were found to be statistically significant after COVID-19 diagnosis compared to before.

CONCLUSION

In this study, it was determined that the patients often used T&amp;CM methods such as herbal products (especially medicinal plants and fruits) and prayer before and after COVID-19 diagnosis, and the use of these T&amp;CM methods increased significantly after diagnosis.

Chinese herbal prescriptions for COVID-19 management: Special reference to Taiwan Chingguan Yihau (NRICM101)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a strain of coronavirus that causes COVID-19 (coronavirus disease 2019), the respiratory illness responsible for the ongoing COVID-19 pandemic. As at June 2022, increasing numbers of newly diagnosed COVID-19-associated pneumonia cases worldwide have attracted close attention from the international community. The present review analyzes and summarizes the treatment of COVID-19 with traditional Chinese medicine (TCM). A systematic analysis of the efficacies and benefits of TCM for the treatment of COVID-19 was performed, and the mechanisms underlying such treatment are summarized. This analysis of the literature highlights the potential of TCM to prevent and treat COVID-19 via antiviral, anti-inflammatory and immunomodulatory activities, with evidence showing that many TCM components act upon multiple targets and pathways. Famous TCM formulas include Qing-Fei-Pai-Du-Tang (QFPDT), Lianhuaqingwen Capsule (LHC), Taiwan Chingguan Yihau (NRICM101), and Jing Si herbal drink (JSHD). In particular, the botanical preparation NRICM101 was developed in 2020 for use in viral respiratory tract infections and is recommended for treating non-severe and mild COVID-19 infections. NRICM101 has been adopted for use in Taiwan for the clinical treatment of COVID-19. The common components and active ingredients of 10 TCM preparations have been analyzed for the most promising substances. This review aims to provide reliable evidence demonstrating the therapeutic efficacy of TCM substances in support of their further development against novel coronavirus infectious diseases in Taiwan.

Estimation of virus-mediated cell fusion rate of SARS-CoV-2

Several viruses have the ability to form large multinucleated cells known as syncytia. Many properties of syncytia and the role they play in the evolution of a viral infection are not well understood. One basic question that has not yet been answered is how quickly syncytia form. We use a novel mathematical model of cell-cell fusion assays and apply it to experimental data from SARS-CoV-2 fusion assays to provide the first estimates of virus-mediated cell fusion rate. We find that for SARS-CoV2, the fusion rate is in the range of 6 × 10-4-12×10-4/h. We also use our model to compare fusion rates when the protease TMPRSS2 is overexpressed (2-4 times larger fusion rate), when the protease furin is removed (one third the original fusion rate), and when the spike protein is altered (1/10th the original fusion rate). The use of mathematical models allows us to provide additional quantitative information about syncytia formation.

Molecular and Physiological Study in Patients with Coronavirus in Thi-Qar Province Iraq

The current study aimed to study the effect of Covid-19 disease on some physiological parameters for assessing the physiological effect of Covid-19. The current study included 100 patients, 50 males, and 50 females, whose ages ranged from 10 - 73 years infected with Covid-19 molecularly diagnosed at AL-Imam Hussein Teaching Hospital in Thi-Qar Province from April to August 2021 and 50 people as a control group. The results illustrated a significant decrease in the level of hemoglobin, lymphocyte, and platelet in the patient's group, whereas a significant increase in the count of total white blood cells (WBC) was recorded in patients compared with the control group. The results showed that the hemoglobin level, WBC, and platelet increased significantly in males compared to females, while the lymphocytes increased significantly in female patients. The results of age groups showed non-significant differences in the hematological parameters. The current results illustrated a significant increase in the level of blood urea, serum creatinine, and random blood sugar in the patient's group. According to the gender of patients, the blood urea increased significantly in the female group, while the blood sugar increased significantly in the male group. On the other hand, the serum creatinine had no significant difference. According to age groups, a significant increase in blood urea was recorded in patients over 70 years, but no differences in both creatinine and random blood sugar level were noted. The current study recorded that the concentration of CRP, ferritin, and D. Dimer was significantly higher in patients; according to gender, the level of CRP and ferritin increased non-significantly compared with a male group of patients, while D. Dimer increased significantly in the female group. According to age group, only CRP increased significantly in the first age group compared with other age groups. The sequencing analysis was performed for 10 isolated Covid-19, and the result indicates that only one isolated sample has a sequencing identity of 99,5% with Covid-19 in Iran.

Clinical-epidemiologic variation in patients treated in the first and second wave of COVID-19 in Lambayeque, Peru: A cluster analysis

OBJECTIVES

To identify differences in the clinical and epidemiologic characteristics of patients during the first and second waves of the COVID-19 pandemic at the EsSalud Lambayeque health care network, Peru.

METHODS

An analytical cross-sectional study of 53,912 patients enrolled during the first and second waves of COVID-19 was conducted. Cluster analysis based on clustering large applications (CLARA) was applied to clinical-epidemiologic data presented at the time of care. The two pandemic waves were compared using clinical-epidemiologic data from epidemiologic surveillance.

RESULTS

Cluster analysis identified four COVID-19 groups with a characteristic pattern. Cluster 1 included the largest number of participants in both waves, and the participants were predominantly female. Cluster 2 included patients with gastrointestinal, respiratory, and systemic symptoms. Cluster 3 was the "severe" cluster, characterized by older adults and patients with dyspnea or comorbidities (cardiovascular, diabetes, obesity). Cluster 4 included asymptomatic, pregnant, and less severe patients. We found differences in all clinical-epidemiologic characteristics according to the cluster to which they belonged.

CONCLUSION

Using cluster analysis, we identified characteristic patterns in each group. Respiratory, gastrointestinal, dyspnea, anosmia, and ageusia symptoms were higher in the second COVID-19 wave than the first COVID-19 wave.

When to test for COVID-19 using real-time reverse transcriptase polymerase chain reaction: a systematic review

OBJECTIVES

The aim of this study was to evaluate the time in days between symptom onset and first positive real-time reverse transcriptase polymerase chain reaction (RT-PCR) result for COVID-19.

METHODS

This systematic review was conducted in the MEDLINE (PubMed), Embase, and Scopus databases using the following descriptors: "COVID-19", "SARS-CoV-2", "coronavirus", "RT-PCR", "real time PCR", and "diagnosis".

RESULTS

The included studies were conducted in 31 different countries and reported on a total of 6831 patients. The median age of the participants was 49.95 years. The three most common symptoms were fever, cough, and dyspnea, which affected 4012 (58.68%), 3192 (46.69%), and 2009 patients (29.38%), respectively. Among the 90 included studies, 13 were prospective cohorts, 15 were retrospective cohorts, 36 were case reports, 20 were case series, and six were cross-sectional studies. The overall mean time between symptom onset and positive test result was 6.72 days. Fourteen articles were analyzed separately for the temporal profile of RT-PCR test results; the best performance was on days 22-24, when 98% of test results were positive.

CONCLUSION

These findings corroborate the RT-PCR COVID-19 testing practices of some health units. In addition, the most frequently described symptoms of these patients can be considered the initial symptoms of infection and used in decision-making about RT-PCR testing.

Growth differentiation factor 15 as an emerging novel biomarker in SARS-CoV-2 infection

BACKGROUND

Growth differentiation factor (GDF)-15 is a member of a transforming growth factor-β cytokine superfamily that regulates metabolism and is released in response to inflammation, hypoxia and tissue injury. It has evolved as one of the most potent cytokines for predicting the severity of infections and inflammatory conditions, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

AIM

To investigate the utility of GDF-15 in predicting the severity of SARS-CoV-2 infection.

METHODS

PubMed, Reference Citation Analysis, CNKI, and Goggle Scholar were explored by using related MeSH keywords and data such as the first author's name, study duration, type and place of study, sample size and subgroups of participants if any, serum/plasma GDF- 15 level in pg/mL, area under the curve and cut-off value in receiver operating characteristic analysis, method of measurement of GDF-15, and the main conclusion were extracted.

RESULTS

In all studies, the baseline GDF-15 level was elevated in SARS-CoV-2-infected patients, and it was significantly associated with severity, hypoxemia, viral load, and worse clinical consequences. In addition, GDF-15 levels were correlated with C-reactive protein, D-dimer, ferritin and procalcitonin, and it had superior discriminatory ability to detect severity and in-hospital mortality of SARS-CoV-2 infection. Hence, GDF-15 might be used to predict the severity and prognosis of hospitalized patients with SARS-CoV-2.

CONCLUSION

Serial estimation of GDF-15 levels in hospitalized patients with SARS-CoV-2 infection appeared to have useful prognostic value and GDF-15 can be considered a clinically prominent sepsis biomarker for SARS-CoV-2 infection.

The Impact of Medical and Health Fiscal Expenditures on Pharmaceutical Industry Stock Index in China

This paper investigates the relationship between fiscal expenditure on health care (FE) and the pharmaceutical industry stock index (SP) by using full-sample and sub-sample rolling-window bootstrap causality tests. It can be observed that there is both a positive and negative relationship between FE and SP. FE will promote the rise of the pharmaceutical stock market, which proves the Keynesian theory, while the result that FE negatively affects SP supports the classical theory. In turn, SP positively impacts FE, which indicates that the development of the pharmaceutical industry and the increase in medical and health expenditures can promote each other. In addition, the negative influence of SP on FE suggests that the impact of the pharmaceutical index on fiscal expenditure needs to be judged in conjunction with other events and market conditions. In complex economic conditions, investors can rationally consider the industry situation of the pharmaceutical market and benefit by optimising their investment portfolios. The government can regulate and guide the pharmaceutical industry by adjusting the fiscal expenditure on health care, thereby promoting the sustainable and stable development of the financial market.

Comparison between emerging adults and adults in terms of contamination fear, post-COVID-19 PTSD and psychiatric comorbidity

The present study compared Chinese emerging adults and adults regarding the association between contamination fear, posttraumatic stress disorder post-COVID-19 and psychiatric comorbidity after controlling for demographic and trauma exposure variables. 1089 Chinese civilians (M = 382; F = 707) with a mean age of 26 years (M = 26.36, SD = 8.58) were recruited from different provinces in China via an online survey posted on mainstream Chinese social networking platforms. They completed a demographic page with questions on trauma exposure, the Vancouver Obsessional Compulsive Inventory, the Posttraumatic Stress Disorder Checklist for DSM-5 and the General Health Questionnaire-28. Results showed that 12.7%, 68.7% and 18.6% met criteria for full, partial and no PTSD, respectively. Emerging adults reported significantly lower levels of symptoms of re-experiencing, avoidance, somatic problems, anxiety and fear of contamination than adults. In both emerging adults and adults, contamination fear was correlated with PTSD and psychiatric comorbidity. High educational attainment was significantly correlated with psychiatric comorbidity in emerging adults, but with PTSD in adults. Length of quarantine was correlated with psychiatric comorbidity only in adults. In conclusion, both emerging adults and adults developed varying levels of contamination fear, posttraumatic stress and general psychological symptoms following the outbreak of COVID-19. Emerging adults were more resilient than adults in coping with distress.

A novel service robot assignment approach for COVID-19 infected patients: a case of medical data driven decision making

Coronavirus Disease-19 (COVID-19) is a major concern for the entire world in the current era. Coronavirus is a very dangerous infectious virus that spreads rapidly from person to person. It spreads in exponential manner on a global scale. It affects the doctors, nurse and other COVID-19 warriors those who are actively involved for the treatment of COVID-19 infected (CI) patients. So, it is very much essential to focus on automation and artificial intelligence (AI) in different hospitals for the treatment of such infected patients and all should be very much careful to break the chain of spreading this novel virus. In this paper, a novel patient service robots (PSRs) assignment framework and a priority based (PB) method using fuzzy rule based (FRB) approach is proposed for the assignment of PSRs for CI patients in hospitals in order to provide safety to the COVID-19 warriors as well as to the CI infected patients. This novel approach is mainly focused on lowering the active involvement of COVID-19 warriors for the treatment of high asymptotic COVID-19 infected (HACI) patients for handling this tough situation. In this work, we have focused on HACI and low asymptotic COVID-19 infected (LACI) patients. Higher priority is given to HACI patients as compared to LACI patients to handle this critical situation in order to increase the survival probability of these patients. The proposed method deals with situations that practically arise during the assignment of PSRs for the treatment of such patients. The simulation of the work is carried out using MATLAB R2015b.

SARS-CoV-2 infection: Pathogenesis, Immune Responses, Diagnosis

COVID-19 has emerged as the most alarming infection of the present time instigated by the virus SARS-CoV-2. In spite of advanced research technologies, the exact pathophysiology and treatment of the condition still need to be explored. However, SARS-CoV-2 has several structural and functional similarities that resemble SARS-CoV and MERS-CoV which may be beneficial in exploring the possible treatment and diagnostic strategies for SARS-CoV-2. This review discusses the pathogen phenotype, genotype, replication, pathophysiology, elicited immune response and emerging variants of SARS-CoV-2 and their similarities with other similar viruses. SARS-CoV-2 infection is detected by a number of diagnostics techniques, their advantages and limitations are also discussed in detail. The review also focuses on nanotechnology-based easy and fast detection of SARS-CoV-2 infection. Various pathways which might play a vital role during SARS-CoV-2 infection have been elaborately discussed since immune response plays a major role during viral infections.

Decomposition of socioeconomic inequality in COVID-19 mortality in Iran: A retrospective cohort study

This study aimed to investigate the relationship between socioeconomic status and COVID-19 mortality in Iran. We performed a retrospective cohort study on data from the hospitalised COVID-19 patients in Qazvin. We collected data on education, self-reported socioeconomic status, and location of residence as a proxy for socioeconomic status (SES). We applied the Blinder-Oaxaca decomposition approach to assess the role of socioeconomic inequality in COVID-19 mortality and determine the main contributors to the observed inequality. Overall, 941 patients (48.96%) had low SES, while only 24.87% (n = 478) were classified in the high SES category. The mortality rate was significantly higher in the low SES group, and we spotted a 17.13% gap in COVID-19 mortality between the high and low SES patients (p < 0.001). Age was the main contributor to the observed inequality, responsible for 6.91% of the gap (p < 0.001). Having co-morbidities (1.53%) and longer length of stay (LOS) in hospitals (0.95%) in the low SES group were other main determinants of the inequality in COVID-19 mortality (p < 0.05). In the unexplained part of our model, the effect of increased age (10.61%) and a positive RT-PCR test result (3.43%) were more substantial in the low SES group compared to the high SES patients (p < 0.05). The low SES people had an increased risk of getting COVID-19, and the disease has been more severe and fatal among them. Increased age, co-morbidities, and LOS were identified as the main drivers of this inequality.

Acute cerebellitis, transverse myelitis and polyradiculoneuritis related to post-COVID-19 infection

CONTEXT

Guillain-Barré syndrome (GBS), acute cerebellitis and transverse myelitis are rare complications of COVID-19 infection separately. The combination of these three, however, has not yet been reported.

FINDINGS

We present an atypical case (42-year-old man) that developed acute ascending flaccid paraparesis, ataxia and urinary retention two weeks after COVID-19 infection. Neurological examination revealed distal and proximal weakness (4/5) on lower extremities, decreased tendon reflexes, sixth cranial nerve palsy and dysmetria without sensory disturbance. His cranial MRI showed cerebellitis whereas the spinal MRI showed transverse myelitis at the T11/12 level. Albuminocytologic dissociation was present in the cerebrospinal fluid. The nerve conduction study was concordant with early findings of GBS. He recovered well after corticosteroid treatment without needing any immunotherapy. On day seven of hospitalization, the modified Rankin Scale score was 0.

CONCLUSION

COVID-19 infection may present with a combination of neurological manifestations such as cerebellitis, transverse myelitis and GBS. This patient presented significant functional recovery after treatment with corticosteroid without immunotherapy.

COVID-19-Induced Seizures: A Meta-Analysis of Case Series and Retrospective Cohorts

The adverse events and complications of coronavirus disease 2019 (COVID-19) continue to challenge the medical profession despite the worldwide vaccination against the severe acute respiratory coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19. Other than typical respiratory manifestations, COVID-19 also presents a wide range of neurological manifestations. This article underlines the pooled incidence of COVID-19-induced seizures in patients with epilepsy and without epilepsy. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols, we conducted a bibliographical search, and an initial search revealed 1,375 articles. In total, 21 articles were included in the final analysis by following the inclusion criteria. A total of 11,526 patients from 21 published articles that met the predetermined search criteria were included. The median age of the patients was 61.9 years, of whom 51.5% were males. A total of 255 patients presented with seizures as the first manifestation of COVID-19 with a prevalence of 2.2% (95% confidence interval = 0.05-0.24, p < 0.01) (I² = 97%), of which 71 patients had previously been diagnosed with epilepsy. Among patients with epilepsy, 49 patients had seizures as an initial presentation of SARA-CoV-2 with an incidence of 72% (0.54-0.85, p = 0.1) (I² = 34). Although the incidence of COVID-19-induced seizures is not high compared to other neurological manifestations, seizure incidence in epileptic patients with COVID-19 is remarkably high. New-onset seizures in any patient should be considered a presentation of COVID-19 in the absence of other causative factors.

CNGOD-An improved convolution neural network with grasshopper optimization for detection of COVID-19

The world is facing the pandemic situation due to a beta corona virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The disease caused by this virus known as Corona Virus Disease 2019 (COVID-19) has affected the entire world. The current diagnosis methods are laboratory based and require specialized testing kits for performing the test. Therefore, to overcome the limitations of testing kits a diagnosis method from chest X-ray images is proposed in this paper. Chest X-ray images can be easily obtained by X-ray machines that are readily available at medical centres. The radiological examinations augmented with chest X-ray images is an effective way of disease diagnosis. The automated analysis of the chest X-ray images requires a highly efficient method for identifying COVID-19 from these images. Thus, a novel deep convolution neural network (CNN) optimized using Grasshopper Optimization Algorithm (GOA) is proposed. The deep learning model comprises depth wise separable convolutions that independently look at cross channel and spatial correlations. The optimization of deep learning models is a complex task due the multiple layers and their non-linearities. In image classification problems optimizers like Adam, SGD etc. get stuck in local minima. Thus, in this paper a metaheuristic optimization algorithm is used to optimize the network. Grasshoper Optimization Algorithm (GOA) is a metaheuristic algorithm that mimics the behaviour of grasshoppers for food search. This algorithm is a fast converging and is capable of exploration and exploitation of large search spaces. Maximum Probability Based Cross Entropy Loss (MPCE) loss function is used as it minimizes the back propogation error of cross entropy and improves the training. The experimental results show that the proposed method gives high classification accuracy. The interpretation of results is augmented with class activation maps. Grad-CAM visualization algorithm is used for class activation maps.

Real-World Evidence for COVID-19 Delta Variant's Effects on the Digestive System and Protection of Inactivated Vaccines from a Medical Center in Yangzhou, China: A Retrospective Observational Study

Background

Coronavirus disease 2019 (COVID-19) is rapidly disseminated worldwide, and it continues to threaten global public health. Recently, the Delta variant has emerged as the most dreaded variant worldwide. COVID-19 predominantly affects the respiratory tract, and studies have reported the transient effects of COVID-19 on digestive system function. However, the relationship between the severity of the Delta variant and digestive system function remains to be investigated. Additionally, data on the ability of the inactive Chinese vaccines (Sinovac or Sinopharm) to protect against the Delta variant or COVID-19-induced gastrointestinal symptoms in the real world are insufficient. Thus, the present retrospective observational study first attempted to use the total gastrointestinal symptom rating scale scores (GSRS) to quantify the possible changes in digestive system functions following the Delta variant infection in the early stage. In addition, the study discusses the potential of inactivated vaccines in preventing severe or critical symptoms or Delta variant-induced digestive system dysfunction.

Methods

To evaluate the difference between mild illness group, moderate illness group, and severe or critical illness group, analysis of variance (ANOVA) was employed to compare the three groups' total gastrointestinal symptom rating scale scores (GSRS). A chi-squared test was used to compare the differences in the ratio of the abnormal biochemical measurements among the three groups first. Then, the percentage of the vaccinated population was compared among the three groups. Additionally, the ratio of the abnormal serum markers between the vaccinated and nonvaccinated cohorts was compared. A P value < 0.05 was considered statistically significant.

Results

Significant differences were observed in the abnormal ratio of alanine aminotransferase (ALT), total bilirubin (TBIL), direct bilirubin (DBIL), lactate dehydrogenase (LDH), and Interleukin 6 (IL-6) ratio among the three groups (P < 0.05). Additionally, no significant difference was observed in the abnormal serum markers ratio between day 14 and day 21 after treatment (P > 0.05). A significant difference was observed in the total GSRS scores among the three groups and the ratio of the vaccinated population among the three groups (P < 0.05). A significant difference was observed in the ratio of the abnormal serum ALT and AST levels between the vaccinated and nonvaccinated cohorts (P < 0.05).

Conclusions

In summary, serum AST, DBIL, LDH, and IL-6 levels are potential markers for distinguishing severe or critical patients in the early stage of the Delta variant infection. Additionally, changes in the levels of these serum makers are transient, and the levels can return to normal after treatment. Furthermore, severe gastrointestinal discomfort was significantly more prevalent in patients with severe or critical diseases and should thus be considered in patients diagnosed with Delta variant infection. Finally, inactivated vaccines may prevent severe or critical symptoms and Delta variant-induced liver dysfunction. Vaccination programs must be promoted to protect public health.

The Evaluation of Diagnostic Values of Clinical Symptoms for COVID-19 Hospitalized Patients in Northern Iran: The Syndromic Surveillance System Data

Background: A novel coronavirus led to a rapidly spreading outbreak of COVID-19, which caused morbidity and mortality worldwide. Appropriate case definitions can help diagnose COVID-19. Objectives: This study aimed to evaluate the COVID-19 clinical symptoms and their potential patterns using latent class analysis (LCA) for identifying confirmed COVID-19 cases among hospitalized patients in northern Iran according to the syndromic surveillance system data. Methods: This cross-sectional study was conducted on patients with COVID-19 admitted to hospitals in Mazandaran Province, Iran. Respiratory specimens were collected by nasopharyngeal swabs from the patients and tested for COVID-¬19 using reverse transcription polymerase chain reaction (RT-PCR). Latent class analysis was used to identify patterns of the symptoms. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) of each symptom pattern were compared and plotted. Also, multiple logistic regression was used to determine the odds ratio for each symptom pattern for predicting COVID-19 infection by adjusting for gender and age groups. Results: Among 13,724 hospitalized patients tested for COVID-19 and included in the analyses, 4,836 (35, 2%) had RT-PCR confirmed COVID-19. The symptoms of fever, chills, cough, shortness of breath, fatigue, myalgia, sore throat, diarrhea, nausea or vomiting, headache, and arthralgia were significantly more common in patients positive for COVID-19 than in other patients and were used in LCA. Latent class analysis suggested six classes (patterns) of clinical symptoms. The AUC of symptom patterns was poor, being 0.43 for class 5, comprising patients without any symptoms, and 0.53 for class 3, comprising patients with fever, chills, and cough. Also, multiple logistic regression showed that class 1, comprising patients with fever, chills, cough, shortness of breath, sore throat, and arthralgia, had an odds ratio of 2.87 (1.39, 3.43) relative to class 5 (patients without any symptoms) for positive COVID-19. Conclusions: This study showed that the clinical symptoms might help diagnose COVID-19. However, the defined clinical symptoms suggested in the surveillance system of COVID-19 in Iran during this time were not appropriate for identifying COVID-19 cases.

IoT-Based COVID-19 Diagnosing and Monitoring Systems: A Survey

To date, the novel Coronavirus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning (ML) algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent IoT-based COVID-19 diagnosing, and monitoring systems have been proposed to tackle the pandemic. In this article we have analyzed a wide range of IoTs technologies which can be used in diagnosing and monitoring the infected individuals and hotspot areas. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.

Musculoskeletal symptoms in patients with long COVID: A cross-sectional study on Iranian patients

Background and objectives

Latest studies have revealed that an increasing number of Corona Virus Disease of 2019 (COVID-19) patients may continue to feel symptoms after the acute phase. This study aimed to evaluate the prevalence of musculoskeletal symptoms after the acute phase of COVID-19 and its associated factors.

Methods

We designed a cross-sectional study from January 2021 to April 2021. An online questionnaire was designed and sent to patients who had recovered from COVID-19. The questionnaire contained questions on participants' demographic characteristics, COVID-19 course at its acute phase, and musculoskeletal symptoms after recovering from COVID-19. Musculoskeletal symptoms associations with patients' characteristic and COVID-19 course was evaluated.

Result

239 patients, including 72 (30.1%) males and 167 (69.9%) females with a mean age of 37.96 years (SD = 11.19), were included in the study. 98.74% of our patients had experienced at least one musculoskeletal symptom after recovering from COVID-19, and the most common symptom was fatigue, as 91.2% of participants experienced this symptom, followed by myalgia, headache, and low back pain. High BMI, hospitalization, and ICU admission were associated with a higher risk of musculoskeletal symptoms.

Conclusion

This study indicated a high prevalence of persistent musculoskeletal symptoms among patients who recovered from COVID-19. Modifiable factors, such as BMI, can be targeted to reduce the prevalence of musculoskeletal symptoms in COVID-19 survivors and reduce its burden.

A novel fusion based convolutional neural network approach for classification of COVID-19 from chest X-ray images

Coronavirus disease is a viral infection caused by a novel coronavirus (CoV) which was first identified in the city of Wuhan, China somewhere in the early December 2019. It affects the human respiratory system by causing respiratory infections with symptoms (mild to severe) like fever, cough, and weakness but can further lead to other serious diseases and has resulted in millions of deaths until now. Therefore, an accurate diagnosis for such types of diseases is highly needful for the current healthcare system. In this paper, a state of the art deep learning method is described. We propose COVDC-Net, a Deep Convolutional Network-based classification method which is capable of identifying SARS-CoV-2 infected amongst healthy and/or pneumonia patients from their chest X-ray images. The proposed method uses two modified pre-trained models (on ImageNet) namely MobileNetV2 and VGG16 without their classifier layers and fuses the two models using the Confidence fusion method to achieve better classification accuracy on the two currently publicly available datasets. It is observed through exhaustive experiments that the proposed method achieved an overall classification accuracy of 96.48% for 3-class (COVID-19, Normal and Pneumonia) classification tasks. For 4-class classification (COVID-19, Normal, Pneumonia Viral, and Pneumonia Bacterial) COVDC-Net method delivered 90.22% accuracy. The experimental results demonstrate that the proposed COVDC-Net method has shown better overall classification accuracy as compared to the existing deep learning methods proposed for the same task in the current COVID-19 pandemic.

Early Detection of COVID-19 outbreaks using Textual Analysis of Electronic Medical Records

Purpose

Our objective was to develop a tool promoting early detection of COVID-19 cases by focusing epidemiological investigations and PCR examinations during a period of limited testing capabilities.

Methods

We developed an algorithm for analyzing medical records recorded by healthcare providers in the Israeli Defense Forces. The algorithm utilized textual analysis to detect patients presenting with suspicious symptoms and was tested among 92 randomly selected units. Detection of a potential cluster of patients in a unit prompted a focused epidemiological investigation aided by data provided by the algorithm.

Results

During a month of follow up, the algorithm has flagged 17 of the units for investigation. The subsequent epidemiological investigations led to the testing of 78 persons and the detection of eight cases in four clusters that were previously gone unnoticed. The resulting positive test rate of 10.25% was five time higher than the IDF average at the time of the study. No cases of COVID-19 in the examined units were missed by the algorithm.

Conclusions

This study depicts the successful development and large scale deployment of a textual analysis based algorithm for early detection of COVID-19 cases, demonstrating the potential of natural language processing of medical text as a tool for promoting public health.

Binding of synthetic carbohydrate receptors to enveloped virus glycans: Insights from molecular dynamics simulations

Can envelope glycans be targeted to stop viral pandemics? Here we address this question by using molecular dynamics simulations to study the binding between 10 synthetic carbohydrate receptors (SCRs) and the 33 N-glycans most commonly found on the surfaces of enveloped viruses, including Zika virus and SARS-CoV-2. Based on association quotients derived from these simulations, we classified the SCRs as weak binders, promiscuous binders, or selective binders. The SCRs almost exclusively associate at the Man₃GlcNAc₂ core, which is common to all N-glycans, but the binding affinity between the SCR⋅glycan pair depends on the noncovalent interactions between the heterocycle rings and the glycan antennae. Systematic variations in the glycan and SCR structures reveal relationships that could guide the design of SCRs to attain affinity and selectivity towards a chosen envelope glycan target. With these results, envelope glycans, which are currently considered "undruggable", could become viable targets for new therapeutic strategies.

Effectiveness of SARS-CoV-2 Vaccines for Short- and Long-Term Immunity: A General Overview for the Pandemic Contrast

BACKGROUND

The recent COVID-19 pandemic produced a significant increase in cases and an emergency state was induced worldwide. The current knowledge about the COVID-19 disease concerning diagnoses, patient tracking, the treatment protocol, and vaccines provides a consistent contribution for the primary prevention of the viral infection and decreasing the severity of the SARS-CoV-2 disease. The aim of the present investigation was to produce a general overview about the current findings for the COVID-19 disease, SARS-CoV-2 interaction mechanisms with the host, therapies and vaccines' immunization findings.

METHODS

A literature overview was produced in order to evaluate the state-of-art in SARS-CoV-2 diagnoses, prognoses, therapies, and prevention.

RESULTS

Concerning to the interaction mechanisms with the host, the virus binds to target with its Spike proteins on its surface and uses it as an anchor. The Spike protein targets the ACE2 cell receptor and enters into the cells by using a special enzyme (TMPRSS2). Once the virion is quietly accommodated, it releases its RNA. Proteins and RNA are used in the Golgi apparatus to produce more viruses that are released. Concerning the therapies, different protocols have been developed in observance of the disease severity and comorbidity with a consistent reduction in the mortality rate. Currently, different vaccines are currently in phase IV but a remarkable difference in efficiency has been detected concerning the more recent SARS-CoV-2 variants.

CONCLUSIONS

Among the many questions in this pandemic state, the one that recurs most is knowing why some people become more seriously ill than others who instead contract the infection as if it was a trivial flu. More studies are necessary to investigate the efficiency of the treatment protocols and vaccines for the more recent detected SARS-CoV-2 variant.

Proton Pump Inhibitors in the COVID-19 Pandemic

The safety of proton pump inhibitors (PPIs) use in coronavirus infection (COVID-19) is not well understood. PPIs are potent suppressors of gastric secretion and become one of the ten most widely used drugs in the world. They are expected to influence virus susceptibility, severity, and outcomes in patients diagnosed with COVID-19. This concern is based on their mechanism of action — suppression of gastric acidity, which is considered the first line of defense against infections. Taken together, the results of most studies and meta-analyses support that PPIs use has been associated with increased risk of COVID-19 and severe outcomes. However, taking into account all potential risk factors for disease severity seems impossible in the real world in the context of COVID-19, so conclusions about causal relationships between PPI use and COVID-19 should be treated with great caution. An additional interesting point about the use of PPIs in the pandemic is that it reduced absorption of certain vitamins. On the other hand, several studies have appeared in the literature regarding the protective therapeutic effects of PPIs. There is growing evidence of an immunomodulatory and antifibrotic role of PPIs that could be used in the treatment of COVID-19. In addition, their ability to alkalize the contents of endosomes and lysosomes serves as an obstacle to the penetration of the virus into host cells. This review analyzes the possible effects of PPIs in patients with COVID-19.

Efficacy of Covexir® (Ferula foetida oleo-gum) treatment in symptomatic improvement of patients with mild to moderate COVID-19: A randomized, double-blind, placebo-controlled trial

The SARS-CoV-2 COVID-19 pandemic has emerged as an unprecedented emergency state in healthcare system and global challenge. In recent decade, the function of exogenous H₂ S in the treatment of respiratory diseases has been investigated using H₂ S-donor agents. Ferula foetida is a medicinal plant that is traditionally used in respiratory diseases including asthma and viral respiratory diseases. The oleo-gum of this plant is a rich source of several organic sulfides including thiophenes, disulfides and polysulfide derivatives, which can act as H₂ S-donor agents. The purpose of this study was to investigate the efficacy of Covexir® (F. foetida oleo-gum) treatment as a rich source of H₂ S-donor compounds in clinical presentations of patients with COVID-19. The efficacy of Covexir® was evaluated in a randomized, double-blind, placebo-controlled trial in outpatients with COVID-19. Covexir® could significantly inhibit cough when compared to the placebo group (p < .01 and p < 001, respectively). Moreover, there was a significant difference (p < 001) between the two groups in dyspnea symptom at follow-up interval of 7 day after receiving Covexir®. Furthermore, on days 3 and 7, statistically significant differences were observed in myalgia, anorexia, anosmia, and sense of taste severity between two groups. Our findings revealed that Covexir® was very safe in the treatment of COVID-19 patients with mild to moderate symptoms and it can be recommended to improve clinical presentations of patients with COVID-19 such as cough, shortness of breath, myalgia, anorexia, anosmia, and sense of taste.

Pediatric Myopia Progression During the COVID-19 Pandemic Home Quarantine and the Risk Factors: A Systematic Review and Meta-Analysis

Background

The COVID-19 pandemic has made many countries adopt restrictive measures like home quarantine. Children were required to study at home, which made parents worried about the rapid myopic progression of their children. To compare myopia progression during the COVID-19 pandemic home quarantine with the time before it and risk factors of myopia progression, we conducted this study.

Methods

We searched PubMed, Embase, the Cochrane Library, and Web of Science to find literature from December 2019 to March 2022 related to COVID-19 pandemic home quarantine and children's myopia progression. Outcomes of myopia progression included axial length and spherical equivalent refraction. Factors of digital screen device time and outdoor activity time were analyzed.

Results

Ten studies were included in this meta-analysis. Compared to the same period before the COVID-19 pandemic, spherical equivalent refraction decreased (OR = −0.27; 95% CI = [−0.33, −0.21]; Z = 8.42; P < 0.00001). However, the subgroup analysis showed that there were no significant differences in spherical equivalent refraction between the two groups in higher-grade school-aged children (grades 4 and above, 11 to 18 years old) (OR = 0.01; 95% CI = [−0.05, 0.07]; Z =0.4; P = 0.69). The outcome of axial length showed no significant difference (OR = 0.06; 95% CI = [−0.31, 0.44]; Z = 0.34; P = 0.74). As for risk factors, the forest plots showed that digital screen device time (OR = 4.56; 95% CI = [4.45, 4.66]; Z = 85.57; P < 0.00001) and outdoor activity time (OR = −1.82; 95% CI = [−2.87, −0.76]; Z = 3.37; P = 0.0008) were risk factors of myopia progression.

Conclusion

Compared with the time before the COVID-19 pandemic, myopia progression in children during COVID-19 pandemic home quarantine was accelerated, especially in younger children. Increased digital screen device and decreased outdoor activity times were risk factors. When home quarantine eases, more time on outdoor activities and less time on digital screen devices are needed for children.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/logout.php.

SARS-CoV-2 Nucleocapsid Protein Has DNA-Melting and Strand-Annealing Activities With Different Properties From SARS-CoV-2 Nsp13

Since December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread throughout the world and has had a devastating impact on health and economy. The biochemical characterization of SARS-CoV-2 proteins is important for drug design and development. In this study, we discovered that the SARS-CoV-2 nucleocapsid protein can melt double-stranded DNA (dsDNA) in the 5′-3′ direction, similar to SARS-CoV-2 nonstructural protein 13. However, the unwinding activity of SARS-CoV-2 nucleocapsid protein was found to be more than 22 times weaker than that of SARS-CoV-2 nonstructural protein 13, and the melting process was independent of nucleoside triphosphates and Mg²⁺. Interestingly, at low concentrations, the SARS-CoV-2 nucleocapsid protein exhibited a stronger annealing activity than SARS-CoV-2 nonstructural protein 13; however, at high concentrations, it promoted the melting of dsDNA. These findings have deepened our understanding of the SARS-CoV-2 nucleocapsid protein and will help provide novel insights into antiviral drug development.

Health-Related Quality of Life of Moroccan COVID-19 Survivors: A Case-Control Study

Background: Research on COVID-19 has mostly focused on transmission, mortality and morbidity associated with the virus. However, less attention has been given to its impact on health-related quality of life (HRQoL) of patients with COVID-19. Therefore, this study aimed to determine the demographic and clinical risk factors associated with COVID-19 and evaluate its impact on the HRQoL of COVID-19 survivors. Methods: A case-control study was carried out between September 2021 and March 2022 on 1105 participants. A total of 354 were COVID-19 survivors and 751 were the control group. The HRQoL was assessed using both EQ-5D-5L and SF-6D generic instruments. Results: The average age of all participants was 56.17 ± 15.46. Older age, urban area, tobacco use, presence of chronic diseases especially type 1 diabetes, kidney and cardiovascular diseases were significantly associated with COVID-19. The COVID-19 survivors had significantly lower HRQoL (EQ-VAS = 50.89) compared to the control group (EQ-VAS = 63.36) (p-value < 0.0001). Pain/ discomfort and anxiety/depression were the most negatively affected by COVID-19 (p-value < 0.0001). Conclusions: The findings from this study could help healthcare professionals and policy makers to better understand the HRQoL sequelae among the COVID-19 survivors and contribute to develop tailored interventions.

Demographic Characteristics, Comorbidities, and Length of Stay of COVID-19 Patients Admitted Into Intensive Care Units in Saudi Arabia: A Nationwide Retrospective Study

Background

This study aimed to describe the demographic characteristics and determine the risk factors associated with disease severity and length of hospital and intensive care unit (ICU) stay in a cohort of COVID-19 patients admitted into ICU in Saudi Arabia.

Methods

This was a national, multi-center, retrospective cross-sectional study of all COVID-19 cases admitted into different ICUs in Saudi Arabia between March 2020 and September 202l. Demographic, clinical features, comorbidities, and length of stay (LOS) data were retrieved from the national Health Electronic Surveillance Network (HESN) and Taqassi databases at the Saudi Ministry of Health (MOH) for subsequent analyses. We used multiple linear regression models to determine risk factors associated with critical outcomes (including LOS in ICU) among COVID-19 cases.

Results

A total of 12,436 COVID-19 patients were included in this study, with a mean age of 59.57 ± 18.30 years and 7,679 (62%) were <65 years old. COVID-19 was more common in males (N = 7,686, 61.9%) and Saudi nationals (N = 8,516, 68.5%). The clinical characteristic findings showed that 36.3% of patients required invasive ventilation whilst 65.4% received tracheostomies for ventilation, and 4% were on dialysis. Our analysis revealed that 2,978 (23.9%) patients had one comorbidity, 4,977 (47.4%) had two or more comorbidities, and diabetes (48.2%) was the most prevalent comorbidity, followed by hypertension (44.2%), and chronic cardiovascular disease (10.5%). Thirteen variables emerged as significant predictors of LOS in ICU using multiple linear regression analyses, with invasive ventilation as the strongest predictor of LOS in the ICU (beta = −0.68, p = 0.001) and hospital admission (beta = −0.65, p = 0.001).

Conclusions

COVID-19 continues to affect millions of people around the world, with a mortality rate of about 2–3% of all infected patients. Our analysis revealed that comorbidities such as chronic kidney disease, cardiovascular disease, diabetes, and older age were significant risk factors associated with a poorer prognosis and longer duration of stay in hospitals and ICU.

Genetic Surveillance of SARS-CoV-2 M pro Reveals High Sequence and Structural Conservation Prior to the Introduction of Protease Inhibitor Paxlovid

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to represent a global health emergency as a highly transmissible, airborne virus. An important coronaviral drug target for treatment of COVID-19 is the conserved main protease (M^pro). Nirmatrelvir is a potent M^pro inhibitor and the antiviral component of Paxlovid. The significant viral sequencing effort during the ongoing COVID-19 pandemic represented a unique opportunity to assess potential nirmatrelvir escape mutations from emerging variants of SARS-CoV-2. To establish the baseline mutational landscape of M^pro prior to the introduction of M^pro inhibitors, M^pro sequences and its cleavage junction regions were retrieved from ~4,892,000 high-quality SARS-CoV-2 genomes in the open-access Global Initiative on Sharing Avian Influenza Data (GISAID) database. Any mutations identified from comparison to the reference sequence (Wuhan-Hu-1) were catalogued and analyzed. Mutations at sites key to nirmatrelvir binding and protease functionality (e.g., dimerization sites) were still rare. Structural comparison of M^pro also showed conservation of key nirmatrelvir contact residues across the extended Coronaviridae family (α-, β-, and γ-coronaviruses). Additionally, we showed that over time, the SARS-CoV-2 M^pro enzyme remained under purifying selection and was highly conserved relative to the spike protein. Now, with the emergency use authorization (EUA) of Paxlovid and its expected widespread use across the globe, it is essential to continue large-scale genomic surveillance of SARS-CoV-2 M^pro evolution. This study establishes a robust analysis framework for monitoring emergent mutations in millions of virus isolates, with the goal of identifying potential resistance to present and future SARS-CoV-2 antivirals.

An effective detection of COVID-19 using adaptive dual-stage horse herd bidirectional long short-term memory framework

COVID-19 is a quickly increasing severe viral disease that affects the human beings as well as animals. The increasing amount of infection and death due to COVID-19 needs timely detection. This work presented an innovative deep learning methodology for the prediction of COVID-19 patients with chest x-ray images. Chest x-ray is the most effective imaging technique for predicting the lung associated diseases. An effective approach with adaptive dual-stage horse herd bidirectional LSTM model is presented for the classification of images into normal, lung opacity, viral pneumonia, and COVID-19. Initially, the input images are preprocessed using modified histogram equalization approach. This is utilized to improve the contrast of the images by changing low-resolution images into high-resolution images. Subsequently, an extended dual tree complex wavelet with trigonometric transform is introduced to extract the high-density features to decrease the complexity of features. Moreover, the dimensionality of the features reduced by adaptive beetle antennae search optimization is utilized. This approach enhances the performance of disease classification by reducing the computational complexity. Finally, an adaptive dual-stage horse herd bidirectional LSTM model is utilized for the classification of images into normal, viral pneumonia, lung opacity, and COVID-19. The implementation platform used in the work is PYTHON. The performance of the presented approach is proved by comparing with the existing approaches in accuracy (99.07%), sensitivity (97.6%), F-measure (97.1%), specificity (99.36%), kappa coefficient (97.7%), precision (98.56%), and area under the receiver operating characteristic curve (99%) for COVID-19 chest x-ray database.

COVID-19 triajında CLUE protokolünün etkinliği

Purpose: The purpose of this study was to evaluate the effectiveness of the CLUE protocol in COVID-19 triage. Materials and Methods: Patients who presented to the emergency department due to dyspnea with oxygen saturation below 95 % and were diagnosed with COVID-19 by reverse transcription polymerase chain reaction (RT-PCR) tests were included in this prospective, observational study. Patients included in the study underwent lung ultrasound (LUS) in the light of the CLUE protocol, and were accordingly given LUS scores of between 0 and 36, also within the scope of the protocol. Patients were placed under observation, and clinical outcomes of discharge from the emergency department, admission to the ward, and admission to intensive care or discharge were recorded. ROC analysis was applied in the calculation of threshold values for LUS scores predicting discharge, admission to intensive care, and mortality. Results: Forty-five patients with a mean age of 63 ± 18 years were included in the study. Fifteen patients (33 %) were treated on an outpatient basis and discharged, while 12 (27 %) were admitted to the ward and 18 (40 %) to the intensive care unit. Mortality occurred in 15 (33 %) patients. An LUS score lower than 3 was 97 % sensitive and 80 % specific for discharge, a score greater than 10 was 94 % sensitive and 78 % specific for admission to the intensive care unit, and a score higher than 11 was 93 % sensitive and 87 % specific for mortality. Based on regression analysis, an LUS score higher than 10 emerged as an independent risk factor for intensive care requirement, a score lower than 3 for discharge, and a score over 11 for mortality. Conclusion: The CLUE protocol may be a useful bedside test in COVID-19 triage, and one that does not involve radiation or require laboratory tests.

Searching for Factors Influencing the Severity of the Symptoms of Long COVID

COVID-19 is a highly contagious respiratory disease. Infection with the virus can occur with differing symptom severity, from mild and moderate to severe cases, but the long-term consequences of infection have not been fully identified or studied. Long COVID is defined as occurring in individuals with a history of probable or confirmed SARS-CoV-2 infection, and symptoms persisting for at least two months within three months of onset that cannot be explained by an alternative diagnosis. The purpose of this study was to look for factors that influence the type and severity of Long COVID symptoms. In total, 932 individuals with a history of COVID-19 were qualified for the study using an original questionnaire based on the COVID-19 Yorkshire Rehab Screen (C19-YRS) questionnaire. Older adults were more likely to report problems with mobility (p < 0.001) and in performing daily activities (p = 0.014). Those with a higher BMI showed significantly more symptoms such as dyspnea at rest (p < 0.001) and on exertion (p < 0.001), feelings of chronic fatigue (p = 0.023), problems with mobility (p < 0.001), and in performing daily activities (p = 0.002). The data show that those with Long COVID should receive multidisciplinary help including additional medical and psychological support. Particular attention should be paid to elderly and obese persons, who should be included in rehabilitation programs after COVID-19 in the first place.