Neutrophils in Dendritic Cell-Based Cancer Vaccination: The Potential Roles of Neutrophil Extracellular Trap Formation
Bibliographic record
Abstract
Neutrophils have conflicting roles in the context of cancers, where they have been associated with contributing to both anti-tumor and pro-tumor responses. Their functional heterogenicity is plastic and can be manipulated by environmental stimuli, which has fueled an area of research investigating therapeutic strategies targeting neutrophils. Dendritic cell (DC)-based cancer vaccination is an immunotherapy that has exhibited clinical promise but has shown limited clinical efficacy. Enhancing our understanding of the communications occurring during DC cancer vaccination can uncover opportunities for enhancing the DC vaccine platform. There have been observed communications between neutrophils and DCs during natural immune responses. However, their crosstalk has been poorly studied in the context of DC vaccination. Here, we review the dual functionality of neutrophils in the context of cancers, describe the crosstalk between neutrophils and DCs during immune responses, and discuss their implications in DC cancer vaccination. This discussion will focus on how neutrophil extracellular traps can influence immune responses in the tumor microenvironment and what roles they may play in promoting or hindering DC vaccine-induced anti-tumor efficacy.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".