A new tool for detection of extracellular traps
Bibliographic record
Abstract
Extracellular traps ejected by various immune cells (neutrophils, macrophages, eosinophils and mast cells) have several immune functions, either protective against pathogens or deleterious in some autoimmune or inflammatory disorders. Since their first description in 2004, the mechanisms of extracellular traps formation have been extensively investigated though still not fully understood. We describe here a new tool for the detection of extracellular traps by fluorescence microscopy in a single-step staining protocol, which does not require any wash. The approach uses the GreenGlo™ DNA dye, which can differentiate between nuclear DNA and extracellular DNA (extracellular traps) released from cells using different fluorescence excitation wavelengths. GreenGlo™ staining is suitable for adherent and non-adherent cells and is expected to be extendable to extracellular traps from other cells types (i.e. eosinophils, mast cells and monocytes).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".