Association of Oral or Intravenous Vitamin C Supplementation with Mortality: A Systematic Review and Meta-Analysis.

Mortality is the most clinically serious outcome, and its prevention remains a constant struggle. This study was to assess whether intravenous or oral vitamin C (Vit-C) therapy is related to reduced mortality in adults. Data from Medline, Embase, and the Cochrane Central Register databases were acquired from their inception to 26 October 2022. All randomized controlled trials (RCTs) involving intravenous or oral Vit-C against a placebo or no therapy for mortality were selected. The primary outcome was all-cause mortality. Secondary outcomes were sepsis, COVID-19, cardiac surgery, noncardiac surgery, cancer, and other mortalities. Forty-four trials with 26540 participants were selected. Although a substantial statistical difference was observed in all-cause mortality between the control and the Vit-C-supplemented groups (p = 0.009, RR 0.87, 95% CI 0.78 to 0.97, I2 = 36%), the result was not validated by sequential trial analysis. In the subgroup analysis, mortality was markedly reduced in Vit-C trials with the sepsis patients (p = 0.005, RR 0.74, 95% CI 0.59 to 0.91, I2 = 47%), and this result was confirmed by trial sequential analysis. In addition, a substantial statistical difference was revealed in COVID-19 patient mortality between the Vit-C monotherapy and the control groups (p = 0.03, RR 0.84, 95% CI 0.72 to 0.98, I2 = 0%). However, the trial sequential analysis suggested the need for more trials to confirm its efficacy. Overall, Vit-C monotherapy does decrease the risk of death by sepsis by 26%. To confirm Vit-C is associated with reduced COVID-19 mortality, additional clinical random control trials are required.

Novel skewed usage of B-cell receptors in COVID-19 patients with various clinical presentations.

B cell-mediated immune responses play important roles in controlling SARS-CoV infection. Here, we performed the single-cell B cell receptor sequencing (scBCR-seq) of the PBMC samples from eleven healthy controls, five asymptomatic subjects and 33 symptomatic COVID-19 patients with various clinical presentations, and subsequently analyzed the abundance and diversity of the BCR repertoires in different groups, respectively. We revealed the skewed usage of the IGHV, IGLV and IGKV genes and identified a number of heavy or light chain VDJ gene pairs and combinational preference in each group, such as IGKV3-7 and IGKV2-24 enriched in the asymptomatic subjects, whereas IGHV3-13, IGHV3-23-IGHJ4, IGHV1-18-IGLV3-19, IGHV1-18-IGLV3-21, and IGHV1-18-IGLV3-25 enriched in the recovery patients with severe diseases. We also observed the differential expression of IGHV3-23 in various B cell clusters by analysis of the scRNA-seq data. Additional dock analysis indicated that IGHV3-13 could bind to the spike protein of SARS-CoV-2. These findings may advance our understanding of the humoral immune responses in COVID-19 patients and help develop novel vaccine candidates as well as therapeutical antibodies against SASR-CoV-2 infections.

Identification of TCR repertoires in asymptomatic COVID-19 patients by single-cell T-cell receptor sequencing.

The T cell-mediated immune responses associated with asymptomatic infection (AS) of SARS-CoV-2 remain largely unknown. The diversity of T-cell receptor (TCR) repertoire is essential for generating effective immunity against viral infections in T cell response. Here, we performed the single-cell TCR sequencing of the PBMC samples from five AS subjects, 33 symptomatic COVID-19 patients and eleven healthy controls to investigate the size and the diversity of TCR repertoire. We subsequently analyzed the TCR repertoire diversity, the V and J gene segment deference, and the dominant combination of αß VJ gene pairing among these three study groups. Notably, we revealed significant TCR preference in the AS group, including the skewed usage of TRAV1-2-J33-TRBV6-4-J2-2 and TRAV1-2-J33-TRBV6-1-J2-3. Our findings may shed new light on understanding the immunopathogenesis of COVID-19 and help identify optimal TCRs for development of novel therapeutic strategies against SARS-CoV-2 infection.

Identification of Distinct Immune Cell Subsets Associated With Asymptomatic Infection, Disease Severity, and Viral Persistence in COVID-19 Patients.

The cell-mediated protective and pathogenic immune responses to SARS-CoV-2 infection remain largely elusive. Here we identified 76 distinct cell subsets in the PBMC samples that were associated with various clinical presentations of COVID-19 using scRNA-seq technology coupled with a deep and comprehensive analysis of unique cell surface markers and differentially expressed genes. We revealed that (TRAV1-2+CD8+)MAIT cells and (NCAM1hiCD160+)NK cells significantly enriched in the asymptomatic subjects whereas (LAG3+CD160+CD8+)NKT cells increased in the symptomatic patients. We also observed that (CD68-CSF1R-IL1BhiCD14+)classical monocytes were positively correlated with the disease severity. Moreover, (CD33-HLA-DMA-CD14+)classical monocytes and (CLEC10A-S100A9lo)pDC were associated with the viral persistence. The GO and KEGG analyses identified enriched pathways related to immune responses, inflammation, and apoptosis. These findings may enhance our understanding of the immunopathogenesis of COVID-19 and help develop novel strategies against SARS-CoV-2 infection.

Distinct miRNAs associated with various clinical presentations of SARS-CoV-2 infection.

MicroRNAs (miRNAs) have been shown to play important roles in viral infections, but their associations with SARS-CoV-2 infection remain poorly understood. Here, we detected 85 differentially expressed miRNAs (DE-miRNAs) from 2,336 known and 361 novel miRNAs that were identified in 233 plasma samples from 61 healthy controls and 116 patients with COVID-19 using the high-throughput sequencing and computational analysis. These DE-miRNAs were associated with SASR-CoV-2 infection, disease severity, and viral persistence in the patients with COVID-19, respectively. Gene ontology and KEGG pathway analyses of the DE-miRNAs revealed their connections to viral infections, immune responses, and lung diseases. Finally, we established a machine learning model using the DE-miRNAs between various groups for classification of COVID-19 cases with different clinical presentations. Our findings may help understand the contribution of miRNAs to the pathogenesis of COVID-19 and identify potential biomarkers and molecular targets for diagnosis and treatment of SARS-CoV-2 infection.