Erythromycin exerts <i>in vivo</i> anti‐inflammatory activity downregulating cell adhesion molecule expression
Bibliographic record
Abstract
1. Macrolides have long been used as anti-bacterial agents; however, there is some evidence that may exert anti-inflammatory activity. Therefore, erythromycin was used to characterize the mechanisms involved in their in vivo anti-inflammatory activity. 2. Erythromycin pretreatment (30 mg kg(-1) day(-1) for 1 week) reduced the lipopolysaccharide (LPS; intratracheal, 0.4 mg kg(-1))-induced increase in neutrophil count and elastase activity in the bronchoalveolar lavage fluid (BALF) and lung tissue myeloperoxidase activity, but failed to decrease tumor necrosis factor-alpha and macrophage-inflammatory protein-2 augmented levels in BALF. Erythromycin pretreatment also prevented lung P-selectin, E-selectin, intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1) mRNA upregulation in response to airway challenge with LPS. 3. Mesentery superfusion with LPS (1 mug ml(-1)) induced a significant increase in leukocyte-endothelial cell interactions at 60 min. Erythromycin pretreatment abolished the increases in these parameters. 4. LPS exposure of the mesentery for 4 h caused a significant increase in leukocyte rolling flux, adhesion and emigration, which were inhibited by erythromycin by 100, 93 and 95%, respectively. 5. Immunohistochemical analysis showed that LPS exposure of the mesentery for 4 h caused a significant enhancement in P-selectin, E-selectin, ICAM-1 and VCAM-1 expression that was downregulated by erythromycin pretreatment. 6. Flow cytometry analysis indicated that erythromycin pretreatment inhibited LPS-induced CD11b augmented expression in rat neutrophils. 7. In conclusion, erythromycin inhibits leukocyte recruitment in the lung and this effect appears mediated through downregulation of CAM expression. Therefore, macrolides may be useful in the control of neutrophilic pulmonary diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".