Priming of Alveolar Macrophages by Leukotriene D4: Potentiation of Inflammation
Bibliographic record
Abstract
Cysteinyl leukotrienes (LTs), including LTC(4), LTD(4), and LTE(4), are well known to induce bronchoconstriction and increase bronchial hyperreactivity, mucus secretion, and vascular permeability. Interestingly, alveolar macrophages (AMs) express LTD(4) high-affinity receptor. These cells represent a major source of inflammatory mediators implicated in the pathophysiology of asthma. Thus, we investigated the immunomodulatory effects of LTD(4) on the production of inflammatory mediators such as macrophage inflammatory protein (MIP)- 1alpha, tumor necrosis factor (TNF), and nitric oxide (NO) by AMs. NR8383 cells, an AM cell line, were pretreated with LTD(4) (10(-11) M) for different periods of time and stimulated or not with lipopolysaccharide (LPS) for 2 h. Although LTD(4) treatment did not modulate the release of MIP-1alpha and TNF, this treatment (6 h) significantly increased the release of these mediators when AMs were further stimulated with LPS (increases of 47 and 21%, respectively). Further, LTD(4) pretreatment increased messenger RNA (mRNA) levels of MIP-1alpha and TNF. These effects of LTD(4) were abrogated by the presence of a LTD(4) receptor antagonist, Verlukast (MK-679), showing the specificity of LTD(4). Interestingly, LTD(4) treatment significantly increased the release of NO by LPS-stimulated AMs without modulating mRNA levels of the inducible NO synthase. Our data suggest that LTD(4) primes AMs to release more MIP-1alpha, TNF, and NO after stimulation. Thus, in addition to its potent bronchoconstrictor effect, LTD(4) may participate in the inflammatory process seen in asthma by potentiating the production of proinflammatory mediators by AMs during immunologic stimuli.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".