Modulation of Host Immune Response Is an Alternative Strategy to Combat SARS-CoV-2 Pathogenesis

A biomathematical model of SARS-CoV-2 in Syrian hamsters

When infected with SARS-CoV-2, Syrian hamsters (Mesocricetus auratus) develop moderate disease severity presenting key features of human COVID-19. We here develop a biomathematical model of the disease course by translating known biological mechanisms of virus-host interactions and immune responses into ordinary differential equations. We explicitly describe the dynamics of virus population, affected alveolar epithelial cells, and involved relevant immune cells comprising for example CD4+ T cells, CD8+ T cells, macrophages, natural killer cells and B cells. We also describe the humoral response dynamics of neutralising antibodies and major regulatory cytokines including CCL8 and CXCL10. The model is developed and parametrized based on experimental data collected at days 2, 3, 5, and 14 post infection. Pulmonary cell composition and their transcriptional profiles were obtained by lung single-cell RNA (scRNA) sequencing analysis. Parametrization of the model resulted in a good agreement of model and data. The model can be used to predict, for example, the time course of the virus population, immune cell dynamics, antibody production and regeneration of alveolar cells for different therapy scenarios or after multiple-infection events. We aim to translate this model to the human situation in the future.

Advancing CRISPR-Based Solutions for COVID-19 Diagnosis and Therapeutics

Since the onset of the COVID-19 pandemic, a variety of diagnostic approaches, including RT-qPCR, RAPID, and LFA, have been adopted, with RT-qPCR emerging as the gold standard. However, a significant challenge in COVID-19 diagnostics is the wide range of symptoms presented by patients, necessitating early and accurate diagnosis for effective management. Although RT-qPCR is a precise molecular technique, it is not immune to false-negative results. In contrast, CRISPR-based detection methods for SARS-CoV-2 offer several advantages: they are cost-effective, time-efficient, highly sensitive, and specific, and they do not require sophisticated instruments. These methods also show promise for scalability, enabling diagnostic tests. CRISPR technology can be customized to target any genomic region of interest, making it a versatile tool with applications beyond diagnostics, including therapeutic development. The CRISPR/Cas systems provide precise gene targeting with immense potential for creating next-generation diagnostics and therapeutics. One of the key advantages of CRISPR/Cas-based therapeutics is the ability to perform multiplexing, where different sgRNAs or crRNAs can target multiple sites within the same gene, reducing the likelihood of viral escape mutants. Among the various CRISPR systems, CRISPR/Cas13 and CARVER (Cas13-assisted restriction of viral expression and readout) are particularly promising. These systems can target a broad range of single-stranded RNA viruses, making them suitable for the diagnosis and treatment of various viral diseases, including SARS-CoV-2. However, the efficacy and safety of CRISPR-based therapeutics must be thoroughly evaluated in pre-clinical and clinical settings. While CRISPR biotechnologies have not yet been fully harnessed to control the current COVID-19 pandemic, there is an optimism that the limitations of the CRISPR/Cas system can be overcome soon. This review discusses how CRISPR-based strategies can revolutionize disease diagnosis and therapeutic development, better preparing us for future viral threats.

A SARS-CoV-2 Mpro fluorescent sensor for exploring pharmacodynamic substances from traditional Chinese medicine

The main protease of SARS-CoV-2 (SARS-CoV-2 Mpro) plays a critical role in the replication and life cycle of the virus. Currently, how to screen SARS-CoV-2 Mpro inhibitors from complex traditional Chinese medicine (TCM) is the bottleneck for exploring the pharmacodynamic substances of TCM against SARS-CoV-2. In this study, a simple, cost-effective, rapid, and selective fluorescent sensor (TPE-S-TLG sensor) was designed with an AIE (aggregation-induced emission) probe (TPE-Ph-In) and the SARS-CoV-2 Mpro substrate (S-TLG). The TPE-S-TLG sensor was characterized using UV-Vis absorption spectroscopy, fluorescence spectroscopy, dynamic light scattering (DLS), transmission electron microscopy (TEM), zeta potential, and Fourier transform infrared (FTIR) spectroscopy techniques. The limit of detection of this method to detect SARS-CoV-2 Mpro was measured to be 5 ng mL-1. Furthermore, the TPE-S-TLG sensor was also successfully applied to screen Mpro inhibitors from Xuebijing injection using the separation and collection of the HPLC-fully automatic partial fraction collector (HPLC-FC). Six active compounds, including protocatechualdehyde, chlorogenic acid, hydroxysafflower yellow A, caffeic acid, isoquercetin, and pentagalloylglucose, were identified using UHPLC-Q-TOF/MS that could achieve 90% of the Mpro inhibition rate for the Xuebijing injection. Accordingly, the strategy can be broadly applied in the detection of disease-related proteases as well as screening active substances from TCM.

[Application of HS221GI in treatment of influenza and ARVI in adults: a new approach - managing virus-induced inflammation. Results of a double-blind, randomized, placebo-controlled, multicenter clinical trial]

AIM

Evaluation of the efficacy and safety of the drug Aterixen® (XC221GI, 1-[2-(1-methylimidazole-4-yl)-ethyl]perhydroazine-2,6-dione), in the treatment of uncomplicated forms of influenza and other ARVI in adults.

MATERIALS AND METHODS

The phase III clinical trial enrolled 260 people aged 18-65 years with mild and moderate forms of influenza or other ARVI. Patients were randomly assigned to two groups: in group 1 (n=130), patients were prescribed the drug Aterixen® in tablets of 100 mg 2 times a day for 5 days; in group 2 (n=130) - a placebo corresponding to the drug, in the same regimen. The primary endpoint of the efficacy assessment was the time in hours from the first administration of the drug to clinical improvement. The main efficacy analysis was performed in a population of patients with PCR-confirmed influenza or ARVI who completed the study according to the protocol (per protocol infected). Additionally, efficacy was evaluated in ITT and PP populations, including patients with both identified and undetected pathogen. The population for safety analysis included all patients, without exception, who were exposed to at least one exposure to the study drug or placebo.

RESULTS

A statistically significant superiority of the drug Aterixen® over placebo in primary endpoint was revealed in both the main and additional analysis in all studied populations: clinical improvement in the group of the studied drug occurred 24 hours faster compared with the placebo group. The evaluation of the effectiveness of secondary endpoints confirmed the superiority of the drug Aterixen® over placebo in terms of relief of catarrhal symptoms and symptoms of intoxication. A favorable safety profile of the drug has been demonstrated.

CONCLUSION

The drug has demonstrated a favorable safety profile for use in outpatient practice. Aterixen® is an effective and safe treatment for influenza and other ARVI in adults.

COVID-19 and primary wound healing: A new insights and advance

With the outbreak and pandemic of coronavirus disease-2019 (COVID-19), a huge number of people died of it. Apart from lung injuries, multiple organs have been confirmed to be impaired. In COVID-19 time, primary wound healing processes always prolong, however, its possible underlying mechanisms are still unclear. Therefore, to overcome this clinical problem, clarifying its underlying mechanisms clearly is necessary and urgently needed. In this review, we summarized that COVID-19 can prolong primary wound healing by inducing excessive inflammation and oxidative stress, disturbing immune system and haematological system, as well as influencing the functions and viability of epidermal stem cells (ESCs). Otherwise, we summarized that the strict control measures of blocking up COVID-19 pandemic can also have side effects on primary wound healing process.

Modulation of the Host Response as a Therapeutic Strategy in Severe Lung Infections

Respiratory pathogens such as influenza and SARS-CoV-2 can cause severe lung infections leading to acute respiratory distress syndrome (ARDS). The pathophysiology of ARDS includes an excessive host immune response, lung epithelial and endothelial cell death and loss of the epithelial and endothelial barrier integrity, culminating in pulmonary oedema and respiratory failure. Traditional approaches for the treatment of respiratory infections include drugs that exert direct anti-pathogen effects (e.g., antivirals). However, such agents are typically ineffective or insufficient after the development of ARDS. Modulation of the host response has emerged as a promising alternative therapeutic approach to mitigate damage to the host for the treatment of respiratory infections; in principle, this strategy should also be less susceptible to the development of pathogen resistance. In this review, we discuss different host-targeting strategies against pathogen-induced ARDS. Developing therapeutics that enhance the host response is a pathogen-agnostic approach that will help prepare for the next pandemic.

Stem cell therapy: a novel approach against emerging and re-emerging viral infections with special reference to SARS-CoV-2

The past several decades have witnessed the emergence and re-emergence of many infectious viral agents, flaviviruses, influenza, filoviruses, alphaviruses, and coronaviruses since the advent of human deficiency virus (HIV). Some of them even become serious threats to public health and have raised major global health concerns. Several different medicinal compounds such as anti-viral, anti-malarial, and anti-inflammatory agents, are under investigation for the treatment of these viral diseases. These therapies are effective improving recovery rates and overall survival of patients but are unable to heal lung damage caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Therefore, there is a critical need to identify effective treatments to combat this unmet clinical need. Due to its antioxidant and immunomodulatory properties, stem cell therapy is considered a novel approach to regenerate damaged lungs and reduce inflammation. Stem cell therapy uses a heterogeneous subset of regenerative cells that can be harvested from various adult tissue types and is gaining popularity due to its prodigious regenerative potential as well as immunomodulatory and anti-inflammatory properties. These cells retain expression of cluster of differentiation markers (CD markers), interferon-stimulated gene (ISG), reduce expression of pro-inflammatory cytokines and, show a rapid proliferation rate, which makes them an attractive tool for cellular therapies and to treat various inflammatory and viral-induced injuries. By examining various clinical studies, this review demonstrates positive considerations for the implications of stem cell therapy and presents a necessary approach for treating virally induced infections in patients.

SARS-CoV-2 vaccines: A double-edged sword throughout rapid evolution of COVID-19

After more than 2 years of the coronavirus disease 2019 pandemic caused by severe acute respiratory syndrome coronavirus 2, several questions have remained unanswered that affected our daily lives. Although substantial vaccine development could resist this challenge, emerging new variants in different countries could be considered as potent concerns regarding the adverse effects of reinfection or postvaccination. Precisely, these concerns address some significant and probable outcomes in vaccinated or reinfected models, followed by some virus challenges, such as antibody-dependent enhancement and cytokine storm. Therefore, the importance of evaluating the effectiveness of neutralizing antibodies (nAbs) elicited by vaccination and the rise of new variants must be addressed.

COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images

COVID-19 (SARS-CoV-2), which causes acute respiratory syndrome, is a contagious and deadly disease that has devastating effects on society and human life. COVID-19 can cause serious complications, especially in patients with pre-existing chronic health problems such as diabetes, hypertension, lung cancer, weakened immune systems, and the elderly. The most critical step in the fight against COVID-19 is the rapid diagnosis of infected patients. Computed Tomography (CT), chest X-ray (CXR), and RT-PCR diagnostic kits are frequently used to diagnose the disease. However, due to difficulties such as the inadequacy of RT-PCR test kits and false negative (FN) results in the early stages of the disease, the time-consuming examination of medical images obtained from CT and CXR imaging techniques by specialists/doctors, and the increasing workload on specialists, it is challenging to detect COVID-19. Therefore, researchers have suggested searching for new methods in COVID- 19 detection. In analysis studies with CT and CXR radiography images, it was determined that COVID-19-infected patients experienced abnormalities related to COVID-19. The anomalies observed here are the primary motivation for artificial intelligence researchers to develop COVID-19 detection applications with deep convolutional neural networks. Here, convolutional neural network-based deep learning algorithms from artificial intelligence technologies with high discrimination capabilities can be considered as an alternative approach in the disease detection process. This study proposes a deep convolutional neural network, COVID-DSNet, to diagnose typical pneumonia (bacterial, viral) and COVID-19 diseases from CT, CXR, hybrid CT + CXR images. In the multi-classification study with the CT dataset, 97.60 % accuracy and 97.60 % sensitivity values were obtained from the COVID-DSNet model, and 100 %, 96.30 %, and 96.58 % sensitivity values were obtained in the detection of typical, common pneumonia and COVID-19, respectively. The proposed model is an economical, practical deep learning network that data scientists can benefit from and develop. Although it is not a definitive solution in disease diagnosis, it may help experts as it produces successful results in detecting pneumonia and COVID-19.

Antiviral innate immunity is diminished in the upper respiratory tract of severe COVID-19 patients

Understanding early innate immune responses to coronavirus disease 2019 (COVID-19) is crucial to developing targeted therapies to mitigate disease severity. Severe acute respiratory syndrome coronavirus (SARS-CoV)-2 infection elicits interferon expression leading to transcription of IFN-stimulated genes (ISGs) to control viral replication and spread. SARS-CoV-2 infection also elicits NF-κB signaling which regulates inflammatory cytokine expression contributing to viral control and likely disease severity. Few studies have simultaneously characterized these two components of innate immunity to COVID-19. We designed a study to characterize the expression of interferon alpha-2 (IFNA2) and interferon beta-1 (IFNB1), both type-1 interferons (IFN-1), interferon-gamma (IFNG), a type-2 interferon (IFN-2), ISGs, and NF-κB response genes in the upper respiratory tract (URT) of patients with mild (outpatient) versus severe (hospitalized) COVID-19. Further, we characterized the weekly dynamics of these responses in the upper and lower respiratory tracts (LRTs) and blood of severe patients to evaluate for compartmental differences. We observed significantly increased ISG and NF-κB responses in the URT of mild compared with severe patients early during illness. This pattern was associated with increased IFNA2 and IFNG expression in the URT of mild patients, a trend toward increased IFNB1-expression and significantly increased STING/IRF3/cGAS expression in the URT of severe patients. Our by-week across-compartment analysis in severe patients revealed significantly higher ISG responses in the blood compared with the URT and LRT of these patients during the first week of illness, despite significantly lower expression of IFNA2, IFNB1, and IFNG in blood. NF-κB responses, however, were significantly elevated in the LRT compared with the URT and blood of severe patients during peak illness (week 2). Our data support that severe COVID-19 is associated with impaired interferon signaling in the URT during early illness and robust pro-inflammatory responses in the LRT during peak illness.

Microalgae as an Efficient Vehicle for the Production and Targeted Delivery of Therapeutic Glycoproteins against SARS-CoV-2 Variants

Severe acute respiratory syndrome–Coronavirus 2 (SARS-CoV-2) can infect various human organs, including the respiratory, circulatory, nervous, and gastrointestinal ones. The virus is internalized into human cells by binding to the human angiotensin-converting enzyme 2 (ACE2) receptor through its spike protein (S-glycoprotein). As S-glycoprotein is required for the attachment and entry into the human target cells, it is the primary mediator of SARS-CoV-2 infectivity. Currently, this glycoprotein has received considerable attention as a key component for the development of antiviral vaccines or biologics against SARS-CoV-2. Moreover, since the ACE2 receptor constitutes the main entry route for the SARS-CoV-2 virus, its soluble form could be considered as a promising approach for the treatment of coronavirus disease 2019 infection (COVID-19). Both S-glycoprotein and ACE2 are highly glycosylated molecules containing 22 and 7 consensus N-glycosylation sites, respectively. The N-glycan structures attached to these specific sites are required for the folding, conformation, recycling, and biological activity of both glycoproteins. Thus far, recombinant S-glycoprotein and ACE2 have been produced primarily in mammalian cells, which is an expensive process. Therefore, benefiting from a cheaper cell-based biofactory would be a good value added to the development of cost-effective recombinant vaccines and biopharmaceuticals directed against COVID-19. To this end, efficient protein synthesis machinery and the ability to properly impose post-translational modifications make microalgae an eco-friendly platform for the production of pharmaceutical glycoproteins. Notably, several microalgae (e.g., Chlamydomonas reinhardtii, Dunaliella bardawil, and Chlorella species) are already approved by the U.S. Food and Drug Administration (FDA) as safe human food. Because microalgal cells contain a rigid cell wall that could act as a natural encapsulation to protect the recombinant proteins from the aggressive environment of the stomach, this feature could be used for the rapid production and edible targeted delivery of S-glycoprotein and soluble ACE2 for the treatment/inhibition of SARS-CoV-2. Herein, we have reviewed the pathogenesis mechanism of SARS-CoV-2 and then highlighted the potential of microalgae for the treatment/inhibition of COVID-19 infection.

COVID-19 inflammation and implications in drug delivery

Growing evidence indicates that hyperinflammatory syndrome and cytokine storm observed in COVID-19 severe cases are narrowly associated with the disease's poor prognosis. Therefore, targeting the inflammatory pathways seems to be a rational therapeutic strategy against COVID-19. Many anti-inflammatory agents have been proposed; however, most of them suffer from poor bioavailability, instability, short half-life, and undesirable biodistribution resulting in off-target effects. From a pharmaceutical standpoint, the implication of COVID-19 inflammation can be exploited as a therapeutic target and/or a targeting strategy against the pandemic. First, the drug delivery systems can be harnessed to improve the properties of anti-inflammatory agents and deliver them safely and efficiently to their therapeutic targets. Second, the drug carriers can be tailored to develop smart delivery systems able to respond to the microenvironmental stimuli to release the anti-COVID-19 therapeutics in a selective and specific manner. More interestingly, some biosystems can simultaneously repress the hyperinflammation due to their inherent anti-inflammatory potency and endow their drug cargo with a selective delivery to the injured sites.

Black-White Risk Differentials in Pediatric COVID-19 Hospitalization and Intensive Care Unit Admissions in the USA

Purpose

The COVID-19 morbidity with SARS-CoV-2 as a causative pathogenic microbe remains a pandemic with children experiencing less mortality but with severe manifestations. The current study aimed to assess SARS-CoV-2 cumulative incidence, COVID-19 hospitalization, and ICU admission with respect to racial differentials.

Materials and Methods

A cross-sectional nonexperimental epidemiologic design was used to examine pediatric COVID-19 data from CDC during 2020. The variables assessed were ICU admissions, hospitalization, sex, race, and region. The Chi-Square (X²) statistic was used to examine the independence of the variables by race, while the binomial regression model was used to predict racial risk differentials in hospitalization and ICU admissions.

Results

The pediatric COVID-19 data observed the cumulative incidence of hospitalization to be 96,376, while ICU admission was 12,448. Racial differences were observed in hospitalization, ICU admissions, sex, and region. With respect to COVID-19 hospitalization, Black/African American (AA) children were two times as likely to be hospitalized compared to their White counterparts, prevalence risk ratio (pRR) = 2.20, 99% confidence interval (CI = 2.12–2.28). Similarly, Asians were 45% more likely to be hospitalized relative to their White counterparts, pRR = 1.45, 99% CI = 1.32–1.60. Regarding ICU admission, there was a disproportionate racial burden, implying excess ICU admission among Black/AA children relative to their White counterparts, pRR = 5.18, 99% CI = 4.44–6.04. Likewise, Asian children were 3 times as likely to be admitted to the ICU compared to their White counterparts, pRR = 3.36, 99% CI = 2.37–4.77. Additionally, American Indians/Alaska Natives were 2 times as likely to be admitted to ICU, pRR = 2.54, 99% CI = 0.82–7.85.

Conclusion

Racial disparities were observed in COVID-19 hospitalization and ICU admission among the US children, with Black/AA children being disproportionately affected, implying health equity transformation.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.