SARS-CoV-2 Dysregulates Neutrophil Degranulation and Reduces Lymphocyte Counts
Bibliographic record
Abstract
SARS-CoV-2, the virus that causes COVID-19, has given rise to one of the largest pandemics, affecting millions worldwide. High neutrophil-to-lymphocyte ratios have been identified as an important correlate to poor recovery rates in severe COVID-19 patients. However, the mechanisms underlying this clinical outcome and the reasons for its correlation to poor prognosis are unclear. Furthermore, the mechanisms involved in healthy neutrophils acquiring a SARS-CoV-2-mediated detrimental role are yet to be fully understood. In this study, we isolated circulating neutrophils from healthy donors for treatment with supernates from infected epithelial cells and direct infection with SARS-CoV-2 in vitro. Infected epithelial cells induced a dysregulated degranulation of primary granules with a decrease in myeloperoxidase (MPO), but slight increase in neutrophil elastase release. Infection of neutrophils resulted in an impairment of both MPO and elastase release, even though CD16 receptor shedding was upregulated. Importantly, SARS-CoV-2-infected neutrophils had a direct effect on peripheral blood lymphocyte counts, with decreasing numbers of CD19+ B cells, CD8+ T cells, and CD4+ T cells. Together, this study highlights the independent role of neutrophils in contributing to the aberrant immune responses observed during SARS-CoV-2 infection that may be further dysregulated in the presence of other immune cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".