Association between SARS-CoV-2 RNAemia and dysregulated immune response in acutely ill hospitalized COVID-19 patients
Bibliographic record
Abstract
Abstract Severe/critical COVID-19 is associated with immune dysregulation and plasmatic SARS-CoV-2 detection (i.e. RNAemia). We detailed the association of SARS-CoV-2 RNAemia with immune responses in COVID-19 patients at the end of the first week of disease. We enrolled patients hospitalized in acute phase of ascertained SARS-CoV-2 pneumonia, and evaluated SARS-CoV-2 RNAemia, plasmatic cytokines, activated/pro-cytolytic T-cells phenotypes, SARS-CoV-2-specific cytokine-producing T-cells (IL-2, IFN-γ, TNF-α, IL-4, IL-17A), simultaneous Th1-cytokines production (polyfunctionality) and amount (iMFI). The humoral responses were assessed with anti-S1/S2 IgG, anti-RBD total-Ig, IgM, IgA, IgG1 and IgG3, neutralization and antibody-dependent cellular cytotoxicity (ADCC). Out of 54 patients, 27 had detectable viremia (viremic). Albeit comparable age and co-morbidities, viremic more frequently required ventilatory support, with a trend to higher death. Viremic displayed higher pro-inflammatory cytokines (IFN-α, IL-6), lower activated T-cells (HLA-DR+CD38+), lower functional SARS-CoV-2-specific T-cells (IFN-γ+CD4+, TNF-α+CD8+, IL-4+CD8+, IL-2+TNF-α+CD4+, and IL-2+TNF-α+CD4+ iMFI) and SARS-CoV-2-specific Abs (anti-S IgG, anti-RBD total-Ig, IgM, IgG1, IgG3; ID50, %ADCC). These data suggest a link between SARS-CoV-2 RNAemia at the end of the first stage of disease and immune dysregulation. Whether high ab initium viral burden and/or intrinsic host factors contribute to immune dysregulation in severe COVID-19 remains to be elucidated, to further inform strategies of targeted therapeutic interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".