Crosstalk between neutrophils and dendritic cells: a context-dependent process
Bibliographic record
Abstract
Neutrophils are massively and rapidly recruited following infection. They migrate to the site of acute infection and also transiently to dLNs. In addition to their well-established role as microbial killers, accumulating evidence shows that neutrophils can play an immunoregulatory role. Neutrophils were recently shown to influence the activation of different leukocyte types including NK cells, B cells, and DCs. DCs are professional APCs playing a key role to the launching and regulation of the immune response; thus, crosstalk between neutrophils and resident or newly recruited DCs may have a direct impact on the development of the antigen-specific immune response and thereby, on the outcome of infection. Neutrophils may regulate DC recruitment and/or activation. We will review here recent progress in the field, including those presented during the first international symposium on "Neutrophil in Immunity", held in Québec, Canada, in June 2012, and discuss how neutrophil regulatory action on DCs may differ depending on the type of invading microorganism and local host factors.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".