A CD36 Synthetic Peptide Inhibits Bleomycin-Induced Pulmonary Inflammation and Connective Tissue Synthesis in the Rat
Bibliographic record
Abstract
Transforming growth factor (TGF)-beta1 is an important regulator of inflammation and fibrosis. TGF-beta1 is usually secreted as a biologically latent protein called latent TGF-beta1 (L-TGF-beta1). L-TGF-beta1 has no biologic effect unless L-TGF-beta1 is converted to its active form. Using a well-recognized model of lung injury induced by the antineoplastic antibiotic bleomycin (Blm), we demonstrated that 7 d after intratracheal Blm administration, total lung TGF-beta was maximally increased. This induction was due to TGF-beta1 production by alveolar macrophages that, when explanted, generated increased quantities of L-TGF-beta1 complexed with the glycoprotein thrombospondin (TSP)-1. The TSP-1/L-TGF-beta1 complex was associated with CD36, a receptor for TSP-1. The association of TSP-1/L-TGF-beta1 to CD36 was critical for plasmin-mediated release of mature TGF-beta1. In this paper we show that, compared with administration of Blm by itself, when a synthetic peptide of CD36 between amino acids 93 and 110 is given concomitantly with Blm to rats, alveolar macrophages generate markedly less active TGF-beta1, the rats gain weight more rapidly, and there is less inflammation, collagen I and III, and fibronectin synthesis. These findings demonstrate a novel in vivo mechanism of activation of L-TGF-beta1 in lung injury and the importance of alveolar macrophage- derived active TGF-beta1 in the pathogenesis of pulmonary inflammation and fibrosis.
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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 | 0.000 |
| 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 | 0.001 |
| 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".