Challenges in the characterization of neutrophil extracellular traps: The truth is in the details
Bibliographic record
Abstract
Neutrophil extracellular traps play a key role in defense against extracellular pathogens. The release of these chromatin structures, that contain a combination of cytoplasmic and granule proteins, is known as NETosis, a regulated cell death modality typical of neutrophils. NETosis is induced by pathogens as well as other stimuli such as activated platelets. Our understanding of the molecular events underlying this phenomenon remains incomplete. The currently used experimental approaches to study NETs are semi-quantitative, subjective in nature, and low throughput, rendering it difficult to compare results between laboratories. This is highlighted in two articles published in this issue of the European Journal of Immunology which present what appear to be contradicting results on NET formation. Considering the extensive research on NETosis and the importance of this phenomenon in the immune response, we find it timely to briefly review the lacunae in the most commonly used methods to investigate NETosis. The impact these technical difficulties have on the advancement of our knowledge in this field as well as potential solutions are also discussed.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".