Neutrophil Extracellular Vesicles and Airway Smooth Muscle Proliferation in the Natural Model of Severe Asthma in Horses
Bibliographic record
Abstract
Extracellular vesicles (EVs) contribute to intercellular communication through the transfer of their rich cargo to recipient cells. The EVs produced by LPS-stimulated neutrophils from healthy humans and horses increase airway smooth muscle (ASM) proliferation, but the roles of neutrophil EVs in asthma are largely unexplored. The aim of this study was to determine whether neutrophil-derived EVs isolated during the remission or exacerbation of asthma influence ASM proliferation differentially. Peripheral blood neutrophils were collected during remission and exacerbation in eight horses affected by severe asthma. The cells were cultured (±LPS), and their EVs were isolated by ultracentrifugation and characterized by laser scattering microscopy and proteomic analysis. The proliferation of ASM co-incubated with EVs was monitored in real time by electrical impedance. Two proteins were significantly upregulated during disease exacerbation in neutrophil EVs (MAST4 and Lrch4), while LPS stimulation greatly altered the proteomic profile. Those changes involved the upregulation of neutrophil degranulation products, including proteases known to induce myocyte proliferation. In agreement with the proteomic results, EVs from LPS-stimulated neutrophils increased ASM proliferation, without an effect of the disease status. The inhalation of environmental LPS could contribute to asthma pathogenesis by activating neutrophils and leading to ASM hyperplasia.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".