Inflammatory and vascular biomarkers in post-COVID-19 syndrome: A systematic review and meta-analysis of over 20 biomarkers.

Severe acute respiratory syndrome coronavirus 2 may inflict a post-viral condition known as post-COVID-19 syndrome (PCS) or long-COVID. Studies measuring levels of inflammatory and vascular biomarkers in blood, serum, or plasma of COVID-19 survivors with PCS versus non-PCS controls have produced mixed findings. Our review sought to meta-analyse those studies. A systematic literature search was performed across five databases until 25 June 2022, with an updated search on 1 November 2022. Data analyses were performed with Review Manager and R Studio statistical software. Twenty-four biomarkers from 23 studies were meta-analysed. Higher levels of C-reactive protein (Standardized mean difference (SMD) = 0.20; 95% CI: 0.02-0.39), D-dimer (SMD = 0.27; 95% CI: 0.09-0.46), lactate dehydrogenase (SMD = 0.30; 95% CI: 0.05-0.54), and leukocytes (SMD = 0.34; 95% CI: 0.02-0.66) were found in COVID-19 survivors with PCS than in those without PCS. After sensitivity analyses, lymphocytes (SMD = 0.30; 95% CI: 0.12-0.48) and interleukin-6 (SMD = 0.30; 95% CI: 0.12-0.49) were also significantly higher in PCS than non-PCS cases. No significant differences were noted in the remaining biomarkers investigated (e.g., ferritin, platelets, troponin, and fibrinogen). Subgroup analyses suggested the biomarker changes were mainly driven by PCS cases diagnosed via manifestation of organ abnormalities rather than symptomatic persistence, as well as PCS cases with duration of <6 than ≥6 months. In conclusion, our review pinpointed certain inflammatory and vascular biomarkers associated with PCS, which may shed light on potential new approaches to understanding, diagnosing, and treating PCS.

Exploration of potent antiviral phytomedicines from Lauraceae family plants against SARS-CoV-2 RNA-dependent RNA polymerase.

RNA-dependent RNA polymerase, also known as RdRp, is a possible therapeutic target that could be used to suppress the proliferation of RNA viruses such as SARS-CoV-2. This protein has two major functional sites (a) catalytic and (b) substrate entry, which regulate the natural substrate entry and its corresponding interaction with the protein. In this study, a computational drug design pipeline was applied to investigate potential inhibitors against SARS-CoV-2 RdRp from Lauraceae plants, and five top hits were selected based on the docked score (< -7 kcal/mol). The docking study suggested that the Glochidioboside had a minimum binding score of -7.8 kcal/mol. This compound showed total five hydrogen bonds while two of them were with catalytic residues Asp618 and Asp760. However, another compound, Sitogluside showed a binding score of -7.3 kcal/mol with four hydrogen bonds targeting three functional residues (Arg555, Ser759, and Asp760). Later, 100 ns explicit solvent molecular dynamics (MD) simulation was performed to evaluate the stability of the protein-ligand docked system. These compounds translocated their positions from the catalytic site to the substrate entry site, as observed in the MD simulation trajectory. However, translocation did not affect the binding strength of these compounds, and they retained the strong binding affinity (ΔG < -11.5 kcal/mol), estimated using the MM/GBSA method. In general, the findings of this study indicated the potential therapeutic compounds that may be used targeting SARS-CoV-2 RdRp. However, these compounds still need to be validated by experimentation in order to determine their inhibitory function.Communicated by Ramaswamy H. Sarma.

Unleashing the power of artificial intelligence for diagnosing and treating infectious diseases: A comprehensive review.

Infectious diseases present a global challenge, requiring accurate diagnosis, effective treatments, and preventive measures. Artificial intelligence (AI) has emerged as a promising tool for analysing complex molecular data and improving the diagnosis, treatment, and prevention of infectious diseases. Computer-aided detection (CAD) using convolutional neural networks (CNN) has gained prominence for diagnosing tuberculosis (TB) and other infectious diseases such as COVID-19, HIV, and viral pneumonia. The review discusses the challenges and limitations associated with AI in this field and explores various machine-learning models and AI-based approaches. Artificial neural networks (ANN), recurrent neural networks (RNN), support vector machines (SVM), multilayer neural networks (MLNN), CNN, long short-term memory (LSTM), and random forests (RF) are among the models discussed. The review emphasizes the potential of AI to enhance the accuracy and efficiency of diagnosis, treatment, and prevention of infectious diseases, highlighting the need for further research and development in this area.

An updated review on pathogenic coronaviruses (CoVs) amid the emergence of SARS-CoV-2 variants: A look into the repercussions and possible solutions.

SARS-CoV-2, responsible for COVID-19, shares 79% and 50% of its identity with SARS-CoV-1 and MERS-CoV, respectively. It uses the same main cell attachment and entry receptor as SARS-CoV-1, which is the ACE-2 receptor. However, key residues in the receptor-binding domain of its S-protein seem to give it a stronger affinity for the receptor and a better ability to hide from the host immune system. Like SARS-CoV-1 and MERS-CoV, cytokine storms in critically ill COVID-19 patients cause ARDS, neurological pathology, multiorgan failure, and increased death. Though many issues remain, the global research effort and lessons from SARS-CoV-1 and MERS-CoV are hopeful. The emergence of novel SARS-CoV-2 variants and subvariants raised serious concerns among the scientific community amid the emergence of other viral diseases like monkeypox and Marburg virus, which are major concerns for healthcare settings worldwide. Hence, an updated review on the comparative analysis of various coronaviruses (CoVs) has been developed, which highlights the evolution of CoVs and their repercussions.

The State-of-the-Art of Gene Editing and its Application to Viral Infections and Diseases Including COVID-19.

Gene therapy delivers a promising hope to cure many diseases and defects. The discovery of gene-editing technology fueled the world with valuable tools that have been employed in various domains of science, medicine, and biotechnology. Multiple means of gene editing have been established, including CRISPR/Cas, ZFNs, and TALENs. These strategies are believed to help understand the biological mechanisms of disease progression. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been designated the causative virus for coronavirus disease 2019 (COVID-19) that emerged at the end of 2019. This viral infection is a highly pathogenic and transmissible disease that caused a public health pandemic. As gene editing tools have shown great success in multiple scientific and medical areas, they could eventually contribute to discovering novel therapeutic and diagnostic strategies to battle the COVID-19 pandemic disease. This review aims to briefly highlight the history and some of the recent advancements of gene editing technologies. After that, we will describe various biological features of the CRISPR-Cas9 system and its diverse implications in treating different infectious diseases, both viral and non-viral. Finally, we will present current and future advancements in combating COVID-19 with a potential contribution of the CRISPR system as an antiviral modality in this battle.