Type I Interferon-Mediated Regulation of Antiviral Capabilities of Neutrophils
Bibliographic record
Abstract
Interferons (IFNs) are induced by viruses and are the main regulators of the host antiviral response. They balance tissue tolerance and immune resistance against viral challenges. Like all cells in the human body, neutrophils possess the receptors for IFNs and contribute to antiviral host defense. To combat viruses, neutrophils utilize various mechanisms, such as viral sensing, neutrophil extracellular trap formation, and antigen presentation. These mechanisms have also been linked to tissue damage during viral infection and inflammation. In this review, we presented evidence that a complex cross-regulatory talk between IFNs and neutrophils initiates appropriate antiviral immune responses and regulates them to minimize tissue damage. We also explored recent exciting research elucidating the interactions between IFNs, neutrophils, and severe acute respiratory syndrome-coronavirus-2, as an example of neutrophil and IFN cross-regulatory talk. Dissecting the IFN-neutrophil paradigm is needed for well-balanced antiviral therapeutics and development of novel treatments against many major epidemic or pandemic viral infections, including the ongoing pandemic of the coronavirus disease that emerged in 2019.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".