Who and how, DNA sensors in NETs-driven inflammation
Bibliographic record
Abstract
During infections, neutrophil extracellular traps act like a meshwork of molecules that captures microbes. In contrast, during sterile inflammation the presence of NETs is usually associated with tissue damage and uncontrolled inflammation. In this context, DNA acts both as activator of NETs formation and immunogenic molecule fueling inflammation within the injured tissue microenvironment. Pattern recognition receptors that specifically bind to and get activated by DNA such as Toll-like receptor-9 (TLR9), cyclic GMP-AMP synthase (cGAS), Nod-like receptor protein 3 (NLRP3) and Absence in Melanoma-2 (AIM2) have been reported to play a role in NETs formation and detection. However, how these DNA sensors contribute to NETs-driven inflammation is not well understood. Whether these DNA sensors have unique roles or on the contrary they are mostly redundant is still elusive. In this review, we summarize the known contribution of the above DNA sensors to the formation and detection of NETs in the context of sterile inflammation. We also highlight scientific gaps needed to be addressed and propose future direction for therapeutic targets.
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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 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".