Genetic diversity and evolutionary dynamics of the Omicron variant of SARS-CoV-2 in Morocco

Dynamic lymphocyte-CRP ratio as a predictor: a single-centre retrospective study on disease severity and progression in adult COVID-19 patients

OBJECTIVE

To assess the efficacy of dynamic changes in lymphocyte-C-reactive protein ratio (LCR) on differentiating disease severity and predicting disease progression in adult patients with Coronavirus disease 2019 (COVID-19).

METHODS

This single-centre retrospective study enrolled adult COVID-19 patients categorized into moderate, severe and critical groups according to the Diagnosis and Treatment of New Coronavirus Pneumonia (ninth edition). Demographic and clinical data were collected. LCR and sequential organ failure assessment (SOFA) score were calculated. Lymphocyte count and C-reactive protein (CRP) levels were monitored on up to four occasions. Disease severity was determined concurrently with each LCR measurement.

RESULTS

This study included 145 patients assigned to moderate (n = 105), severe (n = 33) and critical groups (n = 7). On admission, significant differences were observed among different disease severity groups including age, comorbidities, neutrophil proportion, lymphocyte count and proportion, D-Dimer, albumin, total bilirubin, direct bilirubin, indirect bilirubin, CRP and SOFA score. Dynamic changes in LCR showed significant differences across different disease severity groups at different times, which were significantly inversely correlated with disease severity of COVID-19, with correlation coefficients of -0.564, -0.548, -0.550 and -0.429 at four different times.

CONCLUSION

Dynamic changes in LCR can effectively differentiate disease severity and predict disease progression in adult COVID-19 patients.

Temporal Dynamics and Genomic Landscape of SARS-CoV-2 After Four Years of Evolution

Introduction Since its emergence, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has undergone extensive genomic evolution, impacting public health policies, diagnosis, medication, and vaccine development. This study leverages advanced bioinformatics to assess the virus's temporal and regional genomic evolution from December 2019 to October 2023. Methods Our analysis incorporates 16,575 complete SARS-CoV-2 sequences collected from 214 countries. These samples were comparatively analyzed, with a detailed characterization of nucleic mutations, lineages, distribution, and evolutionary patterns during each year, using the Wuhan-Hu-1 strain as the reference. Results Our analysis has identified a total of 21,580 mutations that we classified into transient mutations, which diminished over time, and persistent mutations with steadily increasing frequencies. This mutation landscape led to a notable surge in the evolutionary rate, rising from 13 mutations per sample in 2020 to 96 by 2023, with minor geographic variations. The phylogenetic analysis unveiled three distinct evolutionary branches, each representing unique viral evolution pathways. These lineages exhibited a tendency for a reduced duration of dominance with a shortening prevalence period over time, as dominant strains were consistently replaced by more fit variants. Notably, the emergence of the Alpha and Delta variants in 2021 was followed by the subsequent dominance of Omicron clade variants that have branched into several recombinant variants in 2022, marking a significant shift in the viral landscape. Conclusion This study sheds light on the dynamic nature of SARS-CoV-2 evolution, emphasizing the importance of continuous and vigilant genomic surveillance. The dominance of recombinant lineages, coupled with the disappearance of local variants, underscores the virus's adaptability.