Covid-19 - Navigating the Uncharted

Next-Generation Sequencing for Confronting Virus Pandemics

Virus pandemics have happened, are happening and will happen again. In recent decades, the rate of zoonotic viral spillover into humans has accelerated, mirroring the expansion of our global footprint and travel network, including the expansion of viral vectors and the destruction of natural spaces, bringing humans closer to wild animals. Once viral cross-species transmission to humans occurs, transmission cannot be stopped by cement walls but by developing barriers based on knowledge that can prevent or reduce the effects of any pandemic. Controlling a local transmission affecting few individuals is more efficient that confronting a community outbreak in which infections cannot be traced. Genetic detection, identification, and characterization of infectious agents using next-generation sequencing (NGS) has been proven to be a powerful tool allowing for the development of fast PCR-based molecular assays, the rapid development of vaccines based on mRNA and DNA, the identification of outbreaks, transmission dynamics and spill-over events, the detection of new variants and treatment of vaccine resistance mutations, the development of direct-acting antiviral drugs, the discovery of relevant minority variants to improve knowledge of the viral life cycle, strengths and weaknesses, the potential for becoming dominant to take appropriate preventive measures, and the discovery of new routes of viral transmission.

Endothelial Dysfunction in SARS-CoV-2 Infection

One of the hallmarks of the SARS-CoV-2 infection has been the inflammatory process that played a role in its pathogenesis, resulting in mortality within susceptible individuals. This uncontrolled inflammatory process leads to severe systemic symptoms via multiple pathways; however, the role of endothelial dysfunction and thrombosis have not been truly explored. This review aims to highlight the pathogenic mechanisms of these inflammatory triggers leading to thrombogenic complications. There are direct and indirect pathogenic pathways of the infection that are examined in detail. We also describe the case of carotid artery thrombosis in a patient following SARS-CoV-2 infection while reviewing the literature on the role of ACE2, the endothelium, and the different mechanisms by which SARS-CoV-2 may manifest both acutely and chronically. We also highlight differences from the other coronaviruses that have made this infection a pandemic with similarities to the influenza virus.

Continuous genomic diversification of long polynucleotide fragments drives the emergence of new SARS-CoV-2 variants of concern

Highly transmissible or immuno-evasive SARS-CoV-2 variants have intermittently emerged, resulting in repeated COVID-19 surges. With over 6 million SARS-CoV-2 genomes sequenced, there is unprecedented data to decipher the evolution of fitter SARS-CoV-2 variants. Much attention has been directed to studying the functional importance of specific mutations in the Spike protein, but there is limited knowledge of genomic signatures shared by dominant variants. Here, we introduce a method to quantify the genome-wide distinctiveness of polynucleotide fragments (3- to 240-mers) that constitute SARS-CoV-2 sequences. Compared to standard phylogenetic metrics and mutational load, the new metric provides improved separation between Variants of Concern (VOCs; Reference = 89, IQR: 65-108; Alpha = 166, IQR: 149-181; Beta 131, IQR: 114-149; Gamma = 164, IQR: 150-178; Delta = 235, IQR: 217-255; and Omicron = 459, IQR: 395-521). Omicron's high genomic distinctiveness may confer an advantage over prior VOCs and the recently emerged and highly mutated B.1.640.2 (IHU) lineage. Evaluation of 883 lineages highlights that genomic distinctiveness has increased over time (R ² = 0.37) and that VOCs score significantly higher than contemporary non-VOC lineages, with Omicron among the most distinctive lineages observed. This study demonstrates the value of characterizing SARS-CoV-2 variants by genome-wide polynucleotide distinctiveness and emphasizes the need to go beyond a narrow set of mutations at known sites on the Spike protein. The consistently higher distinctiveness of each emerging VOC compared to prior VOCs suggests that monitoring of genomic distinctiveness would facilitate rapid assessment of viral fitness.

Biomarkers Associated with Cardiovascular Disease in COVID-19

Coronavirus disease-19 (COVID-19) emerged late December 2019 in the city of Wuhan, China and has since spread rapidly all over the world causing a global pandemic. While the respiratory system is the primary target of disease manifestation, COVID-19 has been shown to also affect several other organs, making it a rather complex, multi-system disease. As such, cardiovascular involvement has been a topic of discussion since the beginning of the COVID-19 pandemic, primarily due to early reports of excessive myocardial injury in these patients. Treating physicians are faced with multiple challenges in the management and early triage of patients with COVID-19, as disease severity is highly variable ranging from an asymptomatic infection to critical cases rapidly deteriorating to intensive care treatment or even fatality. Laboratory biomarkers provide important prognostic information which can guide decision making in the emergency department, especially in patients with atypical presentations. Several cardiac biomarkers, most notably high-sensitive cardiac troponin (hs-cTn) and N-terminal pro-B-type natriuretic peptide (NT-proBNP), have emerged as valuable predictors of prognosis in patients with COVID-19. The purpose of this review was to offer a concise summary on prognostic cardiac biomarkers in COVID-19 and discuss whether routine measurements of these biomarkers are warranted upon hospital admission.

Research on Quantitative Analysis of Multiple Factors Affecting COVID-19 Spread

The Corona Virus Disease 2019 (COVID-19) is spreading all over the world. Quantitative analysis of the effects of various factors on the spread of the epidemic will help people better understand the transmission characteristics of SARS-CoV-2, thus providing a theoretical basis for governments to develop epidemic prevention and control strategies. This article uses public data sets from The Center for Systems Science and Engineering at Johns Hopkins University (JHU CSSE), Air Quality Open Data Platform, China Meteorological Data Network, and WorldPop website to construct experimental data. The epidemic situation is predicted by Dual-link BiGRU Network, and the relationship between epidemic spread and various feature factors is quantitatively analyzed by the Gauss-Newton iteration Method. The study found that population density has the greatest positive correlation to the spread of the epidemic among the selected feature factors, followed by the number of landing flights. The number of newly diagnosed daily will increase by 1.08% for every 1% of the population density, the number of newly diagnosed daily will increase by 0.98% for every 1% of the number of landing flights. The results of this study show that the control of social distance and population movement has a high priority in epidemic prevention and control strategies, and it can play a very important role in controlling the spread of the epidemic.

Data-Driven Prediction for COVID-19 Severity in Hospitalized Patients

Clinicians urgently need reliable and stable tools to predict the severity of COVID-19 infection for hospitalized patients to enhance the utilization of hospital resources and supplies. Published COVID-19 related guidelines are frequently being updated, which impacts its utilization as a stable go-to resource for informing clinical and operational decision-making processes. In addition, many COVID-19 patient-level severity prediction tools that were developed during the early stages of the pandemic failed to perform well in the hospital setting due to many challenges including data availability, model generalization, and clinical validation. This study describes the experience of a large tertiary hospital system network in the Middle East in developing a real-time severity prediction tool that can assist clinicians in matching patients with appropriate levels of needed care for better management of limited health care resources during COVID-19 surges. It also provides a new perspective for predicting patients’ COVID-19 severity levels at the time of hospital admission using comprehensive data collected during the first year of the pandemic in the hospital. Unlike many previous studies for a similar population in the region, this study evaluated 4 machine learning models using a large training data set of 1386 patients collected between March 2020 and April 2021. The study uses comprehensive COVID-19 patient-level clinical data from the hospital electronic medical records (EMR), vital sign monitoring devices, and Polymerase Chain Reaction (PCR) machines. The data were collected, prepared, and leveraged by a panel of clinical and data experts to develop a multi-class data-driven framework to predict severity levels for COVID-19 infections at admission time. Finally, this study provides results from a prospective validation test conducted by clinical experts in the hospital. The proposed prediction framework shows excellent performance in concurrent validation (n=462 patients, March 2020–April 2021) with highest discrimination obtained with the random forest classification model, achieving a macro- and micro-average area under receiver operating characteristics curve (AUC) of 0.83 and 0.87, respectively. The prospective validation conducted by clinical experts (n=185 patients, April–May 2021) showed a promising overall prediction performance with a recall of 78.4–90.0% and a precision of 75.0–97.8% for different severity classes.

The Impact of COVID-19 on the Acute Stroke Care Pathway: Looking Beyond the Short Term

Background

Methods

Results

Conclusion

Lessons From the Impact of the COVID-19 Pandemic at the National Cancer Institute: Cancer Research and Care

The COVID-19 (coronavirus disease 2019) pandemic has worldwide implications on health care, especially in our most vulnerable population: cancer patients. Flexibility and adaptation are needed to continue clinical research and for clinical trial development. At the Intramural Research Program, National Cancer Institute, swift changes have been implemented to protect our patients while maintaining the scientific integrity of our cancer clinical trials. Many lessons have been learned including incorporation of telehealth into clinical trials, partnerships with the oncology community at both academic institutions and community practices, focusing on diversity and inclusion to improve scientific innovation, and strengthened relationships with regulatory agencies and institutional review boards. These changes will enhance the clinical trials we conduct well beyond the pandemic.

Evaluation of the Relationship between Stress and Severity of Covid-19 Symptoms and Sleep Quality in Covid-19 Patients

Introduction and aim:Coronavirus 2019 (Covid-19) is a viral disease that appeared in late 2019 in China. Different factors can cause psychological problems in Covid-19 patients. Stress is one of the most common problems in people with communicable diseases. The aim of the present study was to investigate the relationship between stress and the severity of symptoms and sleep quality in Covid-19 patients. Methods:This was a descriptive-analytical study. The sample size included 300 Covid-19 patients. Demographic information form, Pittsburgh Sleep Quality Index (PSQI) and researcher-made questionnaire were used to collect data. Data analysis was carried out using SPSS ver.16. Results:The majority of participants obtained a stress score between 4 and 6 (moderate). Most of them felt stressed regarding the risk of transmitting the disease to their family members (63%) and more than half (56.3%) had the highest stress level during the first week of the disease because they were frightened by the Covid-19 infection and its prognosis. With regard to sleep, most participants had between six and 10 hours of sleep (53.7%). Conclusion:The results of the present study showed that perceived stress among Covid-19 patients had a direct effect on the severity of respiratory and neurological symptoms and affected their sleep quality.

Increased risk of death from COVID-19 in multiple sclerosis: a pooled analysis of observational studies

OBJECTIVE

To estimate whether the risk of death from COVID-19 in patients with multiple sclerosis (MS) exceeds that of the general population.

METHODS

We conducted a pooled analysis of cohort studies on COVID-19 in patients with MS published until July 31, 2021. We calculated the pooled crude death rate (CDR) and estimated the indirectly-adjusted age-standardized lethality ratio (SLR) to assess the risk of death from COVID-19 in patients with MS as compared to general population.

RESULTS

Out of 520 articles, 18 fulfilled criteria for pooled analysis, with a total of 5634 patients (28.6% males, mean age 41.8 years). Of them, 111 died, yielding a CDR of 1.97% (95% confidence intervals [CIs] 1.61-2.33). The estimated SLR was 1.24 (95% CIs 1.01-1.48) after indirect age-standardization using case-fatality rates obtained from the detailed surveillance data available at the World Health Organization (WHO) website. A leave-one-out sensitivity analysis and the analysis of temporal trends of SLR from March 2020 to July 2021 provided consistent findings.

CONCLUSIONS

Our pooled analysis suggests a 24%-increased risk of death from COVID-19 in patients with MS. These findings must be interpreted with caution, mainly because of the difficulties in COVID-19 case detection (especially in the first pandemic wave) and heterogeneity of the analyzed cohorts. Confirmation in larger population-based studies is warranted.

The Risk of Readmission after Early Postpartum Discharge during the COVID-19 Pandemic

OBJECTIVE

To determine whether early postpartum discharge during the coronavirus disease 2019 (COVID-19) pandemic was associated with a change in the odds of maternal postpartum readmissions.

STUDY DESIGN

This is a retrospective analysis of uncomplicated postpartum low-risk women in seven obstetrical units within a large New York health system. We compared the rate of postpartum readmissions within 6 weeks of delivery between two groups: low-risk women who had early postpartum discharge as part of our protocol during the COVID-19 pandemic (April 1-June 15, 2020) and similar low-risk patients with routine postpartum discharge from the same study centers 1 year prior. Statistical analysis included the use of Wilcoxon's rank-sum and chi-squared tests, Nelson-Aalen cumulative hazard curves, and multivariate logistic regression.

RESULTS

Of the 8,206 patients included, 4,038 (49.2%) were patients who had early postpartum discharge during the COVID-19 pandemic and 4,168 (50.8%) were patients with routine postpartum discharge prior to the COVID-19 pandemic. The rates of postpartum readmissions after vaginal delivery (1.0 vs. 0.9%; adjusted odds ratio [OR]: 0.75, 95% confidence interval [CI]: 0.39-1.45) and cesarean delivery (1.5 vs. 1.9%; adjusted OR: 0.65, 95% CI: 0.29-1.45) were similar between the two groups. Demographic risk factors for postpartum readmission included Medicaid insurance and obesity.

CONCLUSION

Early postpartum discharge during the COVID-19 pandemic was associated with no change in the odds of maternal postpartum readmissions after low-risk vaginal or cesarean deliveries. Early postpartum discharge for low-risk patients to shorten hospital length of stay should be considered in the face of public health crises.

KEY POINTS

· Early postpartum discharge was not associated with an increase in odds of hospital readmissions after vaginal delivery.. · Early postpartum discharge was not associated with an increase in odds of hospital readmissions after cesarean delivery.. · Early postpartum discharge for low-risk patients should be considered during a public health crisis..

A Successful Outcome of Veno-Venous Extracorporeal Membrane Oxygenation in Obese Patients with Respiratory Failure in the Course of COVID-19: A Report of Two Cases

The use of extracorporeal membrane oxygenation (ECMO) in patients with respiratory failure in the course of COVID-19 indicates its limited efficacy and high mortality rates. It seems that one of the conditions for the success of veno-venous ECMO (VV ECMO) in obese patients with COVID-19 is the correct qualification and rapid implementation of this method. We present two cases of obese patients with acute respiratory distress syndrome (ARDS) as a result of SARS-CoV-2 infection with the successful use of ECMO. Two 41-year-old obese patients (Case 1: BMI 31.5 kg/m2 and Case 2: 44.5 kg/m2), with pneumonia and severe respiratory failure in the course of COVID-19, underwent ECMO therapy. The Extracorporeal Life Support Organization (ELSO) guidelines were used to qualify the patients. Due to the persistence of PaO2/FiO2 rate <80 for 6 h, a decision was made to implement VV ECMO. Both patients were discharged from the intensive care unit (Case 1: on day 35; Case 2: on day 22). Rapid implementation of VV ECMO in middle-aged, obese patients with ARDS in the course of COVID-19 showed a positive outcome.

Environmental Health Literacy as Knowing, Feeling, and Believing: Analyzing Linkages between Race, Ethnicity, and Socioeconomic Status and Willingness to Engage in Protective Behaviors against Health Threats

This study investigates the relationships between environmental health literacy, the characteristics of people (race, ethnicity, and socioeconomic status) associated with health disparities, and people's willingness to engage in protective behaviors against environmental health threats. Environmental health literacy is a framework for capturing the continuum between the knowledge of environmental impacts on public health, and the skills and decisions needed to take health-protective actions. We pay particular attention to three dimensions of environmental health literacy: factual knowledge (knowing the facts), knowledge sufficiency (feeling ready to decide what to do), and response efficacy (believing that protective behaviors work). In June 2020, we collected survey data from North Carolina residents on two topics: the viral infection COVID-19 and industrial contaminants called per- and polyfluoroalkyl substances (PFAS). We used their responses to test stepwise regression models with willingness to engage in protective behaviors as a dependent variable and other characteristics as independent variables, including environmental health literacy. For both topics, our results indicated that no disparities emerged according to socioeconomic factors (level of education, household income, or renting one's residence). We observed disparities in willingness according to race, comparing Black to White participants, but not when comparing White to American Indian, Alaska Native, Asian, Native Hawaiian, or Pacific Islander participants nor Hispanic to non-Hispanic participants. The disparities in willingness between Black and White participants persisted until we introduced the variables of environmental health literacy, when the difference between these groups was no longer significant in the final regression models. The findings suggest that focusing on environmental health literacy could bridge a gap in willingness to protect oneself based on factors such as race/ethnicity and socioeconomic status, which have been identified in the environmental health literature as resulting in health disparities.

Characteristics of Acute Appendicitis before and during the COVID-19 Pandemic: Single Center Experience

The aim of the study was to investigate whether the COVID-19 pandemic caused an increased incidence of complicated appendicitis due to the late presentation when compared to the pre-COVID-19 period. Summary Background Data. Acute appendicitis is one of the most common surgical emergencies. During the coronavirus-19 (COVID-19) pandemic, there has been a reported delay in the presentation of some urgencies to the emergency hospital departments. Methods. A total of 427 patients who underwent surgical treatment due to suspected acute appendicitis from June 2019 to November 2020 were retrospectively included in this study. The patients were divided into two groups: the first (pre-COVID-19) group consisted of patients who had surgery before the onset of COVID-19 pandemic (n = 240), while the second (COVID-19) group consisted of those who were operated during the COVID-19 pandemic (n = 187). The primary outcome of the study was to compare the incidence of perforated appendicitis before and during the onset of COVID-19. Results. Overall, 84 patients (19.67%) were diagnosed with perforated appendicitis. We found a weak significance (p=0.085) in the rate of perforated appendicitis between the pre-COVID-19 (17.08%) and the COVID-19 era (22.99%). Conclusions. We did not observe any significant difference in the complications of acute appendicitis before and during the COVID-19 pandemic in a university hospital in Rijeka. An emergent medical care should always be accessible.

DirectDetect SARS-CoV-2 Direct Real-Time RT-PCR Study Using Patient Samples

COVID-19 is an infectious disease that caused a global pandemic affecting people worldwide. As disease detection and vaccine rollout continue to progress, there is still a need for efficient diagnostic tools to satisfy continued testing needs. This preliminary study evaluated a novel SARS-CoV-2 diagnostic test called DirectDetect SARS-CoV-2 Direct Real-time reverse transcriptase polymerase chain reaction (RT-PCR) based on a limited sample size of 24 respiratory samples from 14 SARS-CoV-2-positive patients. The test is advantageous compared to others on the market since it does not require viral transport medium or viral RNA extraction prior to nucleic acid amplification and detection. This capability transforms the hours-long sample preparation time into a minutes-long procedure while also eliminating the need for many costly reagents which may be difficult to obtain during the surge in nucleic acid-based testing during the pandemic. The results show a positive agreement of 94.7, 100, and 94.7% between dry sample swabs, treated samples, and untreated samples tested using the DirectDetect SARS-CoV-2 Direct Real-time RT-PCR compared to tests used in a clinical laboratory, respectively. The findings indicate that DirectDetect can be used for multiple different sample types while reducing the number of reagents and time needed for diagnosis. Although this study shows promising results using the DirectDetect results, further validation of this test using a larger sample set is required to assess the true performance of this test.

Persistent Endothelial Dysfunction in Coronavirus Disease-2019 Survivors Late After Recovery

Background

Coronavirus disease 2019 (COVID-19) can result in an endothelial dysfunction in acute phase. However, information on the late vascular consequences of COVID-19 is limited.

Methods

Brachial artery flow-mediated dilation (FMD) examination were performed, and inflammatory biomarkers were assessed in 86 survivors of COVID-19 for 327 days (IQR 318–337 days) after recovery. Comparisons were made with 28 age-matched and sex-matched healthy controls and 30 risk factor-matched patients.

Results

Brachial artery FMD was significantly lower in the survivors of COVID-19 than in the healthy controls and risk factor-matched controls [median (IQR) 7.7 (5.1–10.7)% for healthy controls, 6.9 (5.5–9.4)% for risk factor-matched controls, and 3.5(2.2–4.6)% for COVID-19, respectively, p < 0.001]. The FMD was lower in 25 patients with elevated tumor necrosis factor (TNF)-α [2.7(1.2–3.9)] than in 61 patients without elevated TNF-α [3.8(2.6–5.3), p = 0.012]. Furthermore, FMD was inversely correlated with serum concentration of TNF-α (r = −0.237, p = 0.007).

Conclusion

Survivors of COVID-19 have a reduced brachial artery FMD, which is inversely correlated with increased serum concentration of TNF-α. Prospective studies on the association of endothelial dysfunction with long-term cardiovascular outcomes, especially the early onset of atherosclerosis, are warranted in survivors of COVID-19.

Perceptions of risk of SARS-CoV-2 transmission in social and educational activities by infectious diseases and general pediatric healthcare providers, a pre-vaccine risk perception cross-sectional survey

BACKGROUND

The perception of the transmission risks of SARS-CoV-2 in social and educational settings by US healthcare providers have not been previously quantified.

METHODS

Respondents completed an online survey between September and October 2020 to estimate the risk of SARS-CoV-2 transmission on a scale of 0-10 for different social and educational activities prior to the availability of the SARS-CoV-2 vaccines. Demographic information and experiences during the pandemic were also collected. The risk assessment was emailed to three listservs of healthcare providers, including national listservs of pediatric (PID) and adult infectious diseases (AID) providers, and a listserv of general pediatric practitioners in the St Louis, USA metropolitan area.

RESULTS

Respondents identified the highest risk of SARS-CoV-2 transmission in spending time in a bar, eating at a restaurant, and attending an indoor sporting event. In the school setting, lower risk was identified in elementary and daycare students compared to high school or university-level students. Comparatively, the risk of transmission to students and teachers was lower than the identified high-risk social activities. Factors increasing risk perception in social activities included the absence of children in the respondent's household and female gender. For the school setting, AID providers perceived greater risk compared to PID providers or pediatric practitioners.

CONCLUSIONS

Respondents identified high risk activities that were associated with a high density of participants in an indoor space where masks are removed for eating and drinking. Differences were apparent in the school setting where pediatric providers perceived lower risks when compared to adult providers.

An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images

The tragic pandemic of COVID-19, due to the Severe Acute Respiratory Syndrome coronavirus-2 or SARS-CoV-2, has shaken the entire world, and has significantly disrupted healthcare systems in many countries. Because of the existing challenges and controversies to testing for COVID-19, improved and cost-effective methods are needed to detect the disease. For this purpose, machine learning (ML) has emerged as a strong forecasting method for detecting COVID-19 from chest X-ray images. In this paper, we used a Deep Learning Method (DLM) to detect COVID-19 using chest X-ray (CXR) images. Radiographic images are readily available and can be used effectively for COVID-19 detection compared to other expensive and time-consuming pathological tests. We used a dataset of 10,040 samples, of which 2143 had COVID-19, 3674 had pneumonia (but not COVID-19), and 4223 were normal (not COVID-19 or pneumonia). Our model had a detection accuracy of 96.43% and a sensitivity of 93.68%. The area under the ROC curve was 99% for COVID-19, 97% for pneumonia (but not COVID-19 positive), and 98% for normal cases. In conclusion, ML approaches may be used for rapid analysis of CXR images and thus enable radiologists to filter potential candidates in a time-effective manner to detect COVID-19.

Moyamoya disease with Sjogren disease and autoimmune thyroiditis presenting with left intracranial hemorrhage after messenger RNA-1273 vaccination: A case report

RATIONALE

The new vaccines are emergently authorized and currently approved for use to protect against the coronavirus disease 2019 (COVID-19) pandemic and serious adverse events are uncommon. Moyamoya disease (MMD) with autoimmune disease is a rare entity and usually presents with intracranial hemorrhage in adults.

PATIENT CONCERNS

We reported a 40-year-old female patient with Sjogren disease and autoimmune thyroiditis, who had received the second dose of Moderna (mRNA-1273) vaccination. Three days later, she presented with left intraventricular and intracerebral hemorrhage as a complication.

DIAGNOSIS

After a series of diagnostic workups, left intracranial hemorrhage was associated with MMD.

INTERVENTIONS

Emergent external ventricular drainage and subsequent stereotactic evacuation of hematoma with insertion of intracranial pressure monitoring were performed.

OUTCOMES

Under the care of the neurocritical care team, her physical condition improved gradually. The neurological sequelae was noted by defects of cognitive function, apraxia, agnosia, and impaired executive function. She was discharged after eight weeks with a follow-up in the vascular neurology clinic planning for performing revascularization.

LESSONS

To the best of our knowledge, no similar case has been reported before, and this is the first case of MMD complicated with intracerebral and intraventricular hemorrhage after mRNA-1273 vaccination. It is noticeable to assess the vaccine safety surveillance and raise the alertness about moyamoya in patients with autoimmune diseases during the COVID-19 pandemic. Further studies for risk evaluation of COVID-19 vaccines in patients with autoimmune diseases might be required in the future.

Characterization of the First SARS-CoV-2 Isolates from Aotearoa New Zealand as Part of a Rapid Response to the COVID-19 Pandemic

SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has wreaked havoc across the globe for the last two years. More than 300 million cases and over 5 million deaths later, we continue battling the first real pandemic of the 21st century. SARS-CoV-2 spread quickly, reaching most countries within the first half of 2020, and New Zealand was not an exception. Here, we describe the first isolation and characterization of SARS-CoV-2 variants during the initial virus outbreak in New Zealand. Patient-derived nasopharyngeal samples were used to inoculate Vero cells and, three to four days later, a cytopathic effect was observed in seven viral cultures. Viral growth kinetics was characterized using Vero and VeroE6/TMPRSS2 cells. The identity of the viruses was verified by RT-qPCR, Western blot, indirect immunofluorescence assays, and electron microscopy. Whole-genome sequences were analyzed using two different yet complementary deep sequencing platforms (MiSeq/Illumina and Ion PGM™/Ion Torrent™), classifying the viruses as SARS-CoV-2 B.55, B.31, B.1, or B.1.369 based on the Pango Lineage nomenclature. All seven SARS-CoV-2 isolates were susceptible to remdesivir (EC₅₀ values from 0.83 to 2.42 µM) and β-D-N⁴-hydroxycytidine (molnupiravir, EC₅₀ values from 0.96 to 1.15 µM) but not to favipiravir (>10 µM). Interestingly, four SARS-CoV-2 isolates, carrying the D614G substitution originally associated with increased transmissibility, were more susceptible (2.4-fold) to a commercial monoclonal antibody targeting the spike glycoprotein than the wild-type viruses. Altogether, this seminal work allowed for early access to SARS-CoV-2 isolates in New Zealand, paving the way for numerous clinical and scientific research projects in the country, including the development and validation of diagnostic assays, antiviral strategies, and a national COVID-19 vaccine development program.

A narrative review on the implementation of liquid biopsy as a diagnostic tool in thoracic tumors during the COVID-19 pandemic

Objective

In this review, we evaluate the role of liquid biopsy in managing lung cancer patients during the still ongoing coronavirus disease 2019 (COVID-19) healthcare emergency.

Background

The novel influenza coronavirus (severe acute respiratory syndrome coronavirus or SARS-CoV-2) has upended several aspects of our lives, including medical activities. In this setting, many routine cancer diagnostic and therapeutic procedures have been suspended, leading to delays in diagnosis, treatments, and, ultimately, increases in cancer mortality rates. Equally drastic has been the impact of COVID-19 on clinical trials, many of which have been stalled or have never begun. This has left many patients who were hoping to receive innovative treatments in a limbo. Although, as of today, the introduction of drastic security measures has been crucially important to contain the pandemic, one cannot ignore the need to continue providing chronically ill patients all the health care they need, in terms of detection, prevention, and treatment. In these unprecedented times, liquid biopsy, more than ever before, may play a relevant role in the adequate management of these frail patients.

Methods

we performed a deep analysis of the recent international literature published in English on PUBMED in the last six months focused on the impact of SARS-CoV-2 on the management of lung cancer patients, focusing the attention on the role of liquid biopsy.

Conclusions

COVID-19 pandemic has significantly modified our lives and overall medical practice. In these unprecedented times, liquid biopsy may represent a valid and less time-consuming diagnostic approach than conventional tissue and cytological specimens.

Understanding the Spread of COVID-19 Based on Economic and Socio-Political Factors

Since the beginning of the COVID-19 outbreak, one of the main focuses worldwide has been to collect data on cases and deaths in the widest collaborative innovation to better understand the epidemic spread and keep its evolution under control. Under an “Open Innovation in Science” approach, researchers can contribute to producing new scientific knowledge on the features and, thus, the evolution of the epidemic, by which policymakers can have the tools to adopt optimal containment measures in order to maximize the economic growth and minimize the social impact. Little focus was given to identifying economic and socio-political factors that could drive the spreading of the coronavirus. In its initial stages, the spread of the virus seemed to follow specific paths without a clear explanation. The study aimed to analyze the relationships between mathematical factors obtained from the curves characterizing the COVID-19 spread during its first wave and economic and socio-political factors of the considered countries with an exploratory approach based on data available from different sources. The intent was to identify the elements affecting the contagion and, thus, the COVID-19 cases. Twenty factors for specific countries were selected. In MATLAB environment, a homemade software was used to obtain the mathematical factors, and statistical software was used to identify the potential correlations existing between the mathematical parameters and the economic and socio-political factors. Interesting relationships were found with economic factors such as economic growth forecast and health spending as a percentage of Gross Domestic Product (GDP), just to cite a few, with the spread of the epidemic. This study shows that a better understanding of the link between the epidemic and the economic and socio-political factors of human society can be the key to forecast more accurately the evolution of the pandemic, and even to predict the progress of future insurgencies. Vulnerabilities and weaknesses of our societies could also have the opportunity to be addressed with the ultimate goal of improving the economic, social, and environmental sustainability of human society.

Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks

This article presents a systematic overview of artificial intelligence (AI) and computer vision strategies for diagnosing the coronavirus disease of 2019 (COVID-19) using computerized tomography (CT) medical images. We analyzed the previous review works and found that all of them ignored classifying and categorizing COVID-19 literature based on computer vision tasks, such as classification, segmentation, and detection. Most of the COVID-19 CT diagnosis methods comprehensively use segmentation and classification tasks. Moreover, most of the review articles are diverse and cover CT as well as X-ray images. Therefore, we focused on the COVID-19 diagnostic methods based on CT images. Well-known search engines and databases such as Google, Google Scholar, Kaggle, Baidu, IEEE Xplore, Web of Science, PubMed, ScienceDirect, and Scopus were utilized to collect relevant studies. After deep analysis, we collected 114 studies and reported highly enriched information for each selected research. According to our analysis, AI and computer vision have substantial potential for rapid COVID-19 diagnosis as they could significantly assist in automating the diagnosis process. Accurate and efficient models will have real-time clinical implications, though further research is still required. Categorization of literature based on computer vision tasks could be helpful for future research; therefore, this review article will provide a good foundation for conducting such research.

A one year follow of patients with multiple sclerosis during COVID-19 pandemic: A cross-sectional study in Qom province, Iran

Background

In the current COVID-19 pandemic, Multiple Sclerosis (MS) patients represent a population of particular interest as they might be at higher risk of COVID-19 infection and it's complications. The present study aimed to investigate a one year follow up of patients with MS during the COVID-19 pandemic, in Qom province, Iran.

Methods

This study was performed at the MS Clinic of Beheshti Hospital from June 1, 2020 to November 1, 2021. 202 patients with a diagnosis of MS and negative self-reported history of COVID-19 at the beginning of the pandemic, were enrolled. First, the demographic characteristics of patients were collected. Second, the patients underwent serological testing for anti-SARS-CoV-2 IgG antibodies. Then, a year later, they were revalauted and asked about the occurrence of clinical relapse leading to hospitalization, disease progression, DMT profile, COVID-19 vaccination, and history of COVID-19 infection. We considered six weeks after COVID-19 regarding relapse occurrence. Eventually, statistical analysis was carried out by using SPSS 26.0

Results

Of 202 patients, 26 patients (12.87%) had initially a positive index antibody result. During the follow-up periods, 25 patients (12.37%) were infected with COVID-19 which was mainly mild (74.8%), and significantly lower than general population. 118 patients (58.41%) were vaccinated for COVID-19 which reduced the risk of COVID-19 development (P<001). Except a case of myelitis associated with vaccination, no serious adverse event was reported. Additionally, only one patient developed MS relapse following COVID-19 infection. Except clinical relapse (P = 0.001), other demographic and MS characteristics, and DMT type were not associated with COVID-19. In terms of MS course, 12 patients (5.94%) discontinued their DMTs regardless of the DMT adverse events or treatment failure. 41 patients (20.3%) experienced a clinical relapse, of whom 12 were escalated to a second line DMT. Further, 27 patients (13.4%) noted a history of worsening disability which mainly occurred after COIVD-19 infection.

Conclusion

The present study showed a significant lower incidence of COVID-19 infection in MS patients. Except for clinical relapse, other demographic and MS characteristics, and DMT type were not associated with COVID-19 infection. In addition, COVID-19 vaccination reduced the risk of COVID-19 development, and the prognosis was favorable in the majority of MS patients.

Molecular Landscape of Lung Epithelium Contributes to High Severity and Comorbidities for COVID-19 and Lung Cancer

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The heterogeneous and complex nature of cancer is extensively revealed at molecular, genetic, and tissue microenvironment levels. Currently, co-occurrence of coronavirus disease 2019 (COVID-19) to lung cancer patients and severity of infections by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been understood at preclinical and clinical levels. However, molecular and cellular insights are not discussed in those papers that support the increased COVID-19 severity and comorbidities in several cancer types, including lung cancer patients. Therefore, this perspective highlights the basis of high severity and comorbidities among lung cancer patients infected by COVID-19 with an emphasis on translational aspects.

Trends in Violent Penetrating Injuries During the First Year of the COVID-19 Pandemic

IMPORTANCE

Public health measures instituted to reduce the spread of COVID-19 led to severe disruptions to the structure of daily life, and the resultant social and financial impact may have contributed to an increase in violence.

OBJECTIVE

To examine the trends in violent penetrating injuries during the first COVID-19 pandemic year compared with previous years.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cross-sectional study was performed to compare the prevalence of violent penetrating injuries during the first COVID-19 pandemic year, March 2020 to February 2021, with the previous 5 years, March 2015 to February 2020. This study was performed among all patients with a violent penetrating injury presenting at Boston Medical Center, an urban, level I trauma center that is the largest safety-net hospital and busiest trauma center in New England. Data were analyzed from January 4 to November 29, 2021.

MAIN OUTCOMES AND MEASURES

The primary outcomes were the incidence and timing of emergency department presentation for violent penetrating injuries during the first year of the COVID-19 pandemic compared with the previous 5 years. Patient demographics and injury characteristics were also assessed.

RESULTS

A total of 2383 patients (median [IQR] age, 29.5 [23.4-39.3] years; 2032 [85.4%] men and 351 [14.6%] women) presenting for a violent penetrating injury were evaluated, including 1567 Black patients (65.7%), 448 Hispanic patients (18.8%), and 210 White patients (8.8%). There was an increase in injuries during the first pandemic year compared with the previous 5 years, with an increase in shootings (mean [SD], 0.61 [0.89] injuries per day vs 0.46 [0.76] injuries per day; P = .002) but not stabbings (mean [SD], 0.60 [0.79] injuries per day vs 0.60 [0.82] injuries per day; P = .78). This surge in firearm violence began while Massachusetts was still under a stay-at-home advisory and before large-scale racial justice protests began. Patients presenting with violent penetrating injuries in the pandemic surge months (April-October 2020) compared with the same period in previous years were disproportionately male (153 patients [93.3%] vs 510 patients [87.6%]; P = .04), unemployed (70 patients [57.4%] vs 221 patients [46.6%]; P = .03), and Hispanic (40 patients [26.0%] vs 99 patients [17.9%]; P = .009), with a concurrent decrease in White patients (0 patients vs 26 patients [4.7%]), and were more likely to have no previous history of violent penetrating injury (146 patients [89.0%] vs 471 patients [80.9%]; P = .02).

CONCLUSIONS AND RELEVANCE

These findings suggest that unprecedented measures implemented to mitigate the spread of COVID-19 were associated with an increase in gun violence. As the pandemic abates, efforts at community violence prevention and intervention must be redoubled to defend communities against the epidemic of violence.

Hyper-inflammatory responses in COVID-19 and anti-inflammatory therapeutic approaches

The coronavirus disease 2019 (COVID-19) is an ongoing global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Patients with severe COVID-19 exhibit hyper-inflammatory responses characterized by excessive activation of myeloid cells, including monocytes, macrophages, and neutrophils, and a plethora of pro-inflammatory cytokines and chemokines. Accumulating evidence also indicates that hyper-inflammation is a driving factor for severe progression of the disease, which has prompted the development of anti-inflammatory therapies for the treatment of patients with COVID-19. Corticosteroids, IL-6R inhibitors, and JAK inhibitors have demonstrated promising results in treating patients with severe disease. In addition, diverse forms of exosomes that exert anti-inflammatory functions have been tested experimentally for the treatment of COVID-19. Here, we briefly describe the immunological mechanisms of the hyper-inflammatory responses in patients with severe COVID-19. We also summarize current anti-inflammatory therapies for the treatment of severe COVID-19 and novel exosome-based therapeutics that are in experimental stages.

Deep learning via LSTM models for COVID-19 infection forecasting in India

The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes modelling attempts difficult and unreliable. Hence, we need to re-look at the situation with reliable data sources and innovative forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences. In this paper, we apply recurrent neural networks such as long short term memory (LSTM), bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) COVID-19 infection forecasting. We select Indian states with COVID-19 hotpots and capture the first (2020) and second (2021) wave of infections and provide two months ahead forecast. Our model predicts that the likelihood of another wave of infections in October and November 2021 is low; however, the authorities need to be vigilant given emerging variants of the virus. The accuracy of the predictions motivate the application of the method in other countries and regions. Nevertheless, the challenges in modelling remain due to the reliability of data and difficulties in capturing factors such as population density, logistics, and social aspects such as culture and lifestyle.

Emergence of SARS-CoV-2 Variants in the World: How Could This Happen?

The COVID-19 pandemic has had a significant global impact, with more than 280,000,000 people infected and 5,400,000 deaths. The use of personal protective equipment and the anti-SARS-CoV-2 vaccination campaigns have reduced infection and death rates worldwide. However, a recent increase in infection rates has been observed associated with the appearance of SARS-CoV-2 variants, including the more recently described lineage B.1.617.2 (Delta variant) and lineage B.1.1.529/BA.1 (Omicron variant). These new variants put the effectiveness of international vaccination at risk, with the appearance of new outbreaks of COVID-19 throughout the world. This emergence of new variants has been due to multiple predisposing factors, including molecular characteristics of the virus, geographic and environmental conditions, and the impact of social determinants of health that favor the genetic diversification of SARS-CoV-2. We present a literature review on the most recent information available on the emergence of new variants of SARS-CoV-2 in the world. We analyzed the biological, geographical, and sociocultural factors that favor the development of these variants. Finally, we evaluate the surveillance strategies for the early detection of new variants and prevent their distribution outside these regions.

Infection dynamics of COVID-19 virus under lockdown and reopening

Motivated by COVID-19, we develop and analyze a simple stochastic model for the spread of disease in human population. We track how the number of infected and critically ill people develops over time in order to estimate the demand that is imposed on the hospital system. To keep this demand under control, we consider a class of simple policies for slowing down and reopening society and we compare their efficiency in mitigating the spread of the virus from several different points of view. We find that in order to avoid overwhelming of the hospital system, a policy must impose a harsh lockdown or it must react swiftly (or both). While reacting swiftly is universally beneficial, being harsh pays off only when the country is patient about reopening and when the neighboring countries coordinate their mitigation efforts. Our work highlights the importance of acting decisively when closing down and the importance of patience and coordination between neighboring countries when reopening.

Endothelial cells and blood vessels are major targets for COVID-19-induced tissue injury and spreading to various organs

The coronavirus disease 2019 (COVID-19) infected so far over 250 million people and caused the death of over 5 million worldwide. Aging, diabetes, and cardiovascular diseases, conditions with preexisting impaired endothelial functions predispose to COVID-19. While respiratory epithelium is the main route of virus entry, the endothelial cells (ECs) lining pulmonary blood vessels are also an integral part of lung injury in COVID-19 patients. COVID-19 not only affects the lungs and respiratory system but also gastrointestinal (GI) tract, liver, pancreas, kidneys, heart, brain, and skin. Blood vessels are likely conduits for the virus dissemination to these distant organs. Importantly, ECs are also critical for vascular regeneration during injury/lesions healing and restoration of vascular network. The World Journal of Gastroenterology has published in last two years over 67 outstanding papers on COVID-19 infection with a focus on the GI tract, liver, pancreas, etc., however, the role of the endothelial and vascular components as major targets for COVID-19-induced tissue injury, spreading to various organs, and injury healing have not been sufficiently emphasized. In the present article, we focus on these subjects and on current treatments including the most recent oral drugs molnupiravir and paxlovid that show a dramatic, significant efficacy in controlling severe COVID-19 infection.

SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting

Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the epidemiological SIR model. For each region, SIMLR tracks the changes in the policies implemented at the government level, which it uses to estimate the time-varying parameters of an SIR model for forecasting the number of new infections one to four weeks in advance. It also forecasts the probability of changes in those government policies at each of these future times, which is essential for the longer-range forecasts. We applied SIMLR to data from in Canada and the United States, and show that its mean average percentage error is as good as state-of-the-art forecasting models, with the added advantage of being an interpretable model. We expect that this approach will be useful not only for forecasting COVID-19 infections, but also in predicting the evolution of other infectious diseases.

C-reactive protein and neutrophil-lymphocyte ratio as predictors of mortality in coronavirus disease 2019

OBJECTIVE

This study investigates whether C-reactive protein, platelet-lymphocyte ratio, and neutrophil-lymphocyte ratio could be useful to predict mortality in COVID-19.

METHODS

Data of 635 patients with COVID-19 followed up in Sinop Ataturk State Hospital from February to May 2020 were evaluated retrospectively. Diagnosis of COVID-19 was made according to the interim guidance of the World Health Organization. Patients were grouped into two groups based on mortality as survived and non-survived patients. Age, gender, neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, and C-reactive protein of the groups were investigated and compared.

RESULTS

The mean age of the participants was 55.8±22.3 years. Among the patients, 584 survived and 51 patients died. Age was significantly different between the groups, 54.2±22.3 in the survived group and 75.6±11.1 in the dead group (p=0.000). In addition, neutrophil, C-reactive protein, and neutrophil-lymphocyte ratio values were significantly higher in the dead group (p=0.000). platelet-lymphocyte ratio was slightly higher in the dead group, but this difference was not significant (p=0.42). The area under the curve values for age, lymphocyte, platelet, C-reactive protein, and neutrophil-lymphocyte ratio are 0.797, 0.424, 0.485, 0.778, and 0.729, respectively.

CONCLUSIONS

Our results showed that neutrophil-lymphocyte ratio and C-reactive protein are significantly higher in patients leading to death and could be effective biomarkers in predicting COVID-19 fatality. Furthermore, C-reactive protein could be used as an independent biomarker to predict death in patients with COVID-19, regardless of gender and age (p=0.000).

Kuala Lumpur train collision during the COVID-19 pandemic

BACKGROUND

Two city trains collided in an underground tunnel on 24 May 2021 at the height of COVID-19 pandemic near the Petronas Towers, Kuala Lumpur, Malaysia, immediately after the evening rush hours. We aim to evaluate the management of this mass casualty incident highlighting the lessons learned to be used in preparedness for similar incidents that may occur in other major cities worldwide.

METHODS

Information regarding incident site and hospital management response were analysed. Data on demography, triaging, injuries and hospital management of patients were collected according to a designed protocol. Challenges, difficulties and their solutions were reported.

RESULTS

The train's emergency response team (ERT) has shut down train movements towards the incident site. Red zone (in the tunnel), yellow zone (the station platform) and green zone (outside the station entrance) were established. The fire and rescue team arrived and assisted the ERT in the red zone. Incident command system was established at the site. Medical base station was established at the yellow zone. Two hundred and fourteen passengers were in the trains. Sixty-four of them were injured. They had a median (range) ISS of 2 (1-43), and all were sent to Hospital Kuala Lumpur (HKL). Six (9.4%) patients were clinically triaged as red (critical), 19 (29.7%) as yellow (semi-critical) and 39 (60.9%) as green (non-critical). HKL's disaster plan was activated. All patients underwent temperature and epidemiology link assessment. Seven (10.9%) patients were admitted to the hospital (3 to the ICU, 3 to the ward and 1 to a private hospital as requested by the patient), while the rest 56 (87.5%) were discharged home. Six (9.4%) needed surgery. The COVID-19 tests were conducted on seven patients (10.9%) and were negative. There were no deaths.

CONCLUSIONS

The mass casualty incident was handled properly because of a clear standard operating procedure, smooth coordination between multi-agencies and the hospitals, presence of a 'binary' system for 'COVID-risk' and 'non-COVID-risk' areas, and the modifications of the existing disaster plan. Preparedness for MCIs is essential during pandemics.

Industrial construction safety policies and practices with cost impacts in a COVID-19 pandemic environment: A Louisiana DOW case study

There are always significant challenges in improving the safety culture by changing and adding additional safety protocols. The unknown impacts of COVID-19 and how it quickly spreads led the industry to institute essential safety protocols. This paper addresses two problem statements. The first problem statement is: what are the additional safety protocols for process safety, construction & maintenance, and personal protective equipment requirements? The second problem statement is: what are the cost and schedule impacts of industrial construction projects resulting from implementing safety protocols and process safety during construction with the added PPE?

While complying with added safety protocols, the industrial construction industry cannot forget that it has a distinct reputation for high incident rates and less than desirable safety performance. In 2017, the construction industry suffered 971 fatalities. This alarming number is compared to 1123 total fatalities in 2017 for the Gulf Coast States. The objective is to share the rationale and practices of social distancing, required additional PPE, and personal hygiene practices to reduce spreading and outbreaks during a pandemic within an industrial construction environment. Before any construction work, the process safety teams must clear, isolate, and tag out process lines, equipment, and instruments to be repaired or replaced. The information presented demonstrates the significant cost and schedule impacts that industrial construction companies will encounter during a pandemic like COVID-19.

This paper aims to improve safety processes, cost & schedule impacts, and prescribe additional personal protective equipment in industrial construction during a pandemic such as COVID-19. The COVID-19 pandemic spread globally in a very short period. The reactions in mitigating the spread were suggestive, with little to no data on safety protective equipment and practices. The contribution this paper addresses are how to employ efficient safety practices and policies during a pandemic in an industrial construction environment.

A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and tainted with plausible breaches of privacy. This paper presents a privacy-preserving adaptive social distance estimation and crowd monitoring solution for camera surveillance systems. We develop a novel person localization strategy through pose estimation, build a privacy-preserving adaptive smoothing and tracking model to mitigate occlusions and noisy/missing measurements, compute inter-personal distances in the real-world coordinates, detect social distance infractions, and identify overcrowded regions in a scene. Performance evaluation is carried out by testing the system's ability in person detection, localization, density estimation, anomaly recognition, and high-risk areas identification. We compare the proposed system to the latest techniques and examine the performance gain delivered by the localization and smoothing/tracking algorithms. Experimental results indicate a considerable improvement, across different metrics, when utilizing the developed system. In addition, they show its potential and functionality for applications other than social distancing.

Multi-criteria text mining model for COVID-19 testing reasons and symptoms and temporal predictive model for COVID-19 test results in rural communities

This study is conducted to build a multi-criteria text mining model for COVID-19 testing reasons and symptoms. The model is integrated with a temporal predictive classification model for COVID-19 test results in rural underserved areas. A dataset of 6895 testing appointments and 14 features is used in this study. The text mining model classifies the notes related to the testing reasons and reported symptoms into one or more categories using look-up wordlists and a multi-criteria mapping process. The model converts an unstructured feature to a categorical feature that is used in building the temporal predictive classification model for COVID-19 test results and conducting some population analytics. The classification model is a temporal model (ordered and indexed by testing date) that uses machine learning classifiers to predict test results that are either positive or negative. Two types of classifiers and performance measures that include balanced and regular methods are used: (1) balanced random forest and (2) balanced bagged decision tree. The balanced or weighted methods are used to address and account for the biased and imbalanced dataset and to ensure correct detection of patients with COVID-19 (minority class). The model is tested in two stages using validation and testing sets to ensure robustness and reliability. The balanced classifiers outperformed regular classifiers using the balanced performance measures (balanced accuracy and G-score), which means the balanced classifiers are better at detecting patients with positive COVID-19 results. The balanced random forest achieved the best average balanced accuracy (86.1%) and G-score (86.1%) using the validation set. The balanced bagged decision tree achieved the best average balanced accuracy (83.0%) and G-score (82.8%) using the testing set. Also, it was found that the patient history, age, testing reasons, and time are the key features to classify the testing results.

Increased complications of COVID-19 in people with cardiovascular disease: Role of the renin-angiotensin-aldosterone system (RAAS) dysregulation

The rapid spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19), has had a dramatic negative impact on public health and economies worldwide. Recent studies on COVID-19 complications and mortality rates suggest that there is a higher prevalence in cardiovascular diseases (CVD) patients. Past investigations on the associations between pre-existing CVDs and susceptibility to coronavirus infections including SARS-CoV and the Middle East Respiratory Syndrome coronavirus (MERS-CoV), have demonstrated similar results. However, the underlying mechanisms are poorly understood. This has impeded adequate risk stratification and treatment strategies for CVD patients with SARS-CoV-2 infections. Generally, dysregulation of the expression of angiotensin-converting enzyme (ACE) and the counter regulator, angiotensin-converting enzyme 2 (ACE2) is a hallmark of cardiovascular risk and CVD. ACE2 is the main host receptor for SARS-CoV-2. Although further studies are required, dysfunction of ACE2 after virus binding and dysregulation of the renin-angiotensin-aldosterone system (RAAS) signaling may worsen the outcomes of people affected by COVID-19 and with preexisting CVD. Here, we review the current knowledge and outline the gaps related to the relationship between CVD and COVID-19 with a focus on the RAAS. Improved understanding of the mechanisms regulating viral entry and the role of RAAS may direct future research with the potential to improve the prevention and management of COVID-19.

Investigating the impact of vaccination and non-pharmaceutical measures in curbing COVID-19 spread: A South Africa perspective

The year 2020 brought about a pandemic that caught most of the world population by surprise and wreaked unimaginable havoc before any form of effective reaction could be put in place. COVID-19 is proving to be an epidemic that keeps on having an upsurge whenever it looks like it is being curbed. This pandemic has led to continuous strategizing on approaches to quelling the surge. The recent and welcome introduction of vaccines has led to renewed optimism for the population at large. The introduction of vaccines has led to the need to investigate the effect of vaccination among other control measures in the fight against COVID-19. In this study, we develop a mathematical model that captures the dynamics of the disease taking into consideration some measures that are easier to implement majorly within the African context. We consider quarantine and vaccination as control measures and investigate the efficacy of these measures in curbing the reproduction rate of the disease. We analyze the local stability of the disease-free equilibrium point. We also perform sensitivity analysis of the effective reproduction number to determine which parameters significantly lowers the effective reproduction number. The results obtained suggest that quarantine and a vaccine with at least 75% efficacy and reducing transmission probability through sanitation and wearing of protective gears can significantly reduce the number of secondary infections.

Caspases and therapeutic potential of caspase inhibitors in moderate-severe SARS-CoV-2 infection and long COVID

BACKGROUND

COVID-19 can present with lymphopenia and extraordinary complex multiorgan pathologies that can trigger long-term sequela.

AIMS

Given that inflammasome products, like caspase-1, play a role in the pathophysiology of a number of co-morbid conditions, we investigated caspases across the spectrum of COVID-19 disease.

MATERIALS & METHODS

We assessed transcriptional states of multiple caspases and using flow cytometry, the expression of active caspase-1 in blood cells from COVID-19 patients in acute and convalescent stages of disease. Non-COVID-19 subject presenting with various comorbid conditions served as controls.

RESULTS

Single-cell RNA-seq data of immune cells from COVID-19 patients showed a distinct caspase expression pattern in T cells, neutrophils, dendritic cells, and eosinophils compared with controls. Caspase-1 was upregulated in CD4+ T-cells from hospitalized COVID-19 patients compared with unexposed controls. Post-COVID-19 patients with lingering symptoms (long-haulers) also showed upregulated caspase-1activity in CD4+ T-cells that ex vivo was attenuated with a select pan-caspase inhibitor. We observed elevated caspase-3/7levels in red blood cells from COVID-19 patients compared with controls that was reduced following caspase inhibition.

DISCUSSION

Our preliminary results suggest an exuberant caspase response in COVID-19 that may facilitate immune-related pathological processes leading to severe outcomes. Further clinical correlations of caspase expression in different stages of COVID-19 will be needed.

CONCLUSION

Pan-caspase inhibition could emerge as a therapeutic strategy to ameliorate or prevent severe COVID-19.

Natural vaccines accumulated in face masks during COVID-19: Underappreciated role of facial masking

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a causal agent behind coronavirus disease 2019 (COVID-19). Despite promising developments in therapeutic and preventive avenues, the importance of facial masking is a key factor for the protective measures among exposed human populations. Preclinical and clinical data on the importance of facial masking concerning asymptomatic over symptomatic COVID-19 cases is limited. The recent introduction of the concept of SARS-CoV-2 associated molecular particle patterns (SAMPPs) as a natural vaccine has opened new avenues for the comprehensive development of immunity. To take this further, the scope of natural vaccines accumulated in facemasks during facial masking needs to be highlighted that may directly or indirectly contribute to building adaptive immunity among human populations. This paper attempts to discuss the underappreciated contributions of facial masking in the management of COVID-19 at the global level.

Influenza-Like Illness Among Personnel Responding to U.S. Quarantine of Cruise Ship Passengers Exposed to SARS-CoV-2

OBJECTIVES

Before community transmission of COVID-19 was recognized in the United States, cruise ship passengers with high risk for exposure to SARS-CoV-2 were repatriated and quarantined. We describe cases of influenza-like illness (ILI) among responders.

METHODS

We reviewed situation reports and responder illness reports to characterize ill responders, including illness onset date, symptoms, fever, diagnostic tests, potential breaches in PPE use, and return to work status.

RESULTS

Among 339 responders, nine (3%) reported ILI. No breaches in PPE were reported. Three responders with ILI were tested for both SARS-CoV-2 infection and influenza A; none tested positive for SARS-CoV-2 infection and two tested positive for influenza A.

CONCLUSIONS

Despite an outbreak of ILI among responders, none were diagnosed with COVID-19, suggesting preventive measures in place might have been sufficient to prevent responders from SARS-CoV-2 exposure.

Computational screening of natural compounds from Salvia plebeia R. Br. for inhibition of SARS-CoV-2 main protease

The novel Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) has emerged to be the reason behind the COVID-19 pandemic. It was discovered in Wuhan, China and then began spreading around the world, impacting the health of millions. Efforts for treatment have been hampered as there are no antiviral drugs that are effective against this virus. In the present study, we have explored the phytochemical constituents of Salvia plebeia R. Br., in terms of its binding affinity by targeting COVID-19 main protease (M^pro) using computational analysis. Molecular docking analysis was performed using PyRx software. The ADMET and drug-likeness properties of the top 10 compounds showing binding affinity greater than or equal to - 8.0 kcal/mol were analysed using pkCSM and DruLiTo, respectively. Based on the docking studies, it was confirmed that Rutin and Plebeiosides B were the most potent inhibitors of the main protease of SARS-CoV-2 with the best binding affinities of - 9.1 kcal/mol and - 8.9 kcal/mol, respectively. Further, the two compounds were analysed by studying their biological activity using the PASS webserver. Molecular dynamics simulation analysis was performed for the selected protein-ligand complexes to confirm their stability at 300 ns. MM-PBSA provided the basis for analyzing the affinity of the phytochemicals towards M^pro by calculating the binding energy, and secondary structure analysis indicated the stability of protease structure when it is bound to Rutin and Plebeiosides B. Altogether, the study identifies Rutin and Plebeiosides B to be potent M^pro inhibitors of SARS-CoV-2.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s42535-021-00304-z.

Effect of coronavirus disease-2019 infection on left atrial functions

Objective:

Left atrial (LA) dysfunction is a crucial risk factor for cardiovascular events, and various pathologies may affect LA function. Coronavirus disease-2019 (COVID-19) is an ongoing global pandemic causing morbidity and mortality. In the present study, we aimed to evaluate LA functions in patients who recovered from COVID-19.

Methods:

Sixty consecutive patients recovered from COVID-19 and 60 healthy individuals as a control group were included in the study. Blood samples and echocardiography measurements were obtained from each subject. The two groups were compared in terms of demographic and echocardiographic characteristics.

Results:

In the COVİD-19 group, LA maximum volume (LAV_max) (P = 0.040), LA pre-A volume (LAVpre-A) (P = 0.014), and LA active emptying fraction (P = 0.027) were higher, while LA passive emptying fraction (P = 0.035) was lower. In addition, left ventricular ejection fraction (P = 0.006) and isovolumetric relaxation time (P = 0.008) were decreased in this group. Although LA volume index was higher in the COVID-19 group, it does not reach statistical significance.

Conclusion:

LA functions may be impaired in patients recovered from COVID-19 infection.

Slovak parents' mental health and socioeconomic changes during the COVID-19 pandemic

The changes in people's mental health have become one of the hot topics during the COVID-19 pandemic. Parents have been said to be among the most vulnerable groups in terms of the imposed anti-pandemic measures. The present paper analyzes the trends in mental health indicators in a sample of Slovak parents (N = 363) who participated in four waves of data collection over a year and a half of the COVID-19 pandemic. The mental health indicators were represented by general levels of depression and anxiety as well as COVID-related stress and anxiety. While there were only minor changes in depression and anxiety, the dynamic in COVID-related stress and especially anxiety was more noteworthy. Besides some exceptions, the results hold even after controlling for the socioeconomic situation. The gender differences in the mental health trends were found to be negligible. Overall, we observed no substantial deterioration in the mental health indicators across the four waves of the study.