Catching NETs—development of a flow cytometric assay to detect canine neutrophil extracellular traps
Bibliographic record
Abstract
Neutrophil extracellular traps (NETs) contribute to the pathophysiology of various diseases via host tissue damage, hyperinflammation, and thrombosis across multiple species. Immune-mediated hemolytic anemia (IMHA), an autoimmune disorder of dogs, has a high mortality rate due to thrombotic events, but the exact role NETs play in the pathophysiology is still unclear and requires further investigation. It has been hypothesized that NETs, through their prothrombotic and proinflammatory properties, contribute to mortality in canine IMHA. However, a readily accessible specific test for canine NETs is lacking, thus exploring the role of NETs in IMHA pathogenesis has been limited. The most specific assay used to study NETosis is time- and labor-intensive immunofluorescence microscopy, which can be subjective and biased due to human error. Other assays, such as ELISAs, measure markers of NETs but are not specific for NETs. Flow cytometry is a quick and more objective method to specifically identify and quantitate NETs. There have been several protocols developed for use in humans and mice, using a variety of stains and gating schemes to identify NETs, but no such protocol has been developed for use in companion animals such as dogs. We have developed a flow-cytometry based assay to detect canine NETs using the DNA stain SYTOX Green, an anti-citrullinated H3 histone antibody, and an antibody for a neutrophil marker, CD11b. In isolated canine neutrophils, we were able to demonstrate that we could detect neutrophils using CD11b, we could detect PMA-stimulated increases in citH3 histone and extracellular DNA positive CD11b events, and we could detect significantly increased SYTOX-citH3-CD11b (+) events in PMA-stimulated samples versus the non-stimulated samples.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".