Human Neutrophils Present Mild Activation by Zika Virus But Reduce the Infection of Susceptible Cells
Bibliographic record
Abstract
The emergence of the Zika virus (ZIKV) has highlighted the need for a deeper understanding of virus-host interactions in order to pave the way for the development of antiviral therapies. The present work aimed to address the response of neutrophils during ZIKV infection. Neutrophils are important effector cells in innate immunity implicated in the host’s response to neurotropic arboviruses. Our results indicate that human neutrophils were not permissive to Asian or African ZIKV strain replication. In fact, after stimulation with ZIKV, neutrophils were mild primed against the virus as evaluated through CD11b and CD62L modulation, secretion of inflammatory cytokines and granule content, production of reactive oxygen species, and neutrophil extracellular traps formation. Overall, neutrophils did not affect ZIKV infectivity. Moreover, in vitro ZIKV infection of primary innate immune cells did not trigger neutrophil migration. However, neutrophils co-cultured with ZIKV susceptible cell lineages resulted in lower cell infection frequencies, possibly due to cell-to-cell contact. In vivo, neutrophil depletion in immunocompetent mice did not affect ZIKV spreading to the draining lymph nodes. The data suggest that human neutrophils do not play an antiviral role against ZIKV per se, but these cells might participate in an infected environment shaping the ZIKV infection in other target cells.
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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 | 0.000 |
| 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) | 0.006 | 0.001 |
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".