Characterization of Circulating Low-Density Neutrophils Intrinsic Properties in Healthy and Asthmatic Horses
Bibliographic record
Abstract
Low-density neutrophils (LDNs) are a subset of neutrophils first described in the bloodstream upon pathological conditions, and recently, in the blood of healthy humans. LDNs may have an enhanced pro-inflammatory (low-density granulocytes, LDGs) or an immunosuppressive (Granulocytic myeloid-derived suppressor cells, G-MDSCs) profile. Whether these characteristics are specific to LDNs or related to disease states is unknown. Thus, we sought to investigate the properties of LDNs in both health and disease states, and to compare them to those of autologous normal-density neutrophils (NDNs). We studied 8 horses with severe equine asthma and 11 healthy animals. LDNs were smaller and contained more N-formylmethionine-leucyl-phenylalanine receptors than NDNs, but the myeloperoxidase content was similar in both cell populations. They also had an increased capacity to produce neutrophil extracellular traps, and were more sensitive to activation by phorbol-12-myristate-13-acetate. This profile is suggestive of LDGs. These characteristics were similar in both healthy and diseased animals, indicating that these are intrinsic properties of LDNs. Furthermore, these results suggest that LDNs represent a population of primed and predominantly mature cells. This study is the first to characterize LDNs in health, and to compare their properties with those of NDNs and of animals with a naturally occurring disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".