Identification of New Targets Used by 15-deoxy-delta-12,14-Prostaglandin J2 to Stimulate Skeletal Muscle Cell Proliferation
Bibliographic record
Abstract
Introduction: Clinical conduct can influence the healing of injured tissue. Eradication of inflammation seemed a promising strategy to promote musculoskeletal healing until studies showed a delayed/incomplete recovery from partial or complete elimination of inflammation. Endogenous lipid mediators biosynthesized from omega-3 and some from -6 fatty acids are molecules potentially playing important roles in the resolution of inflammation. Using such lipid mediators to treat injuries represents an attractive approach due to their anti-inflammatory and pro-resolving roles. Our goal was to identify the intracellular and/or extracellular targets used by 15-deoxy-delta-12,14- Prostaglandin J2 (15¿-PGJ2 ) to stimulate myoblast proliferation.\nMethods: Expression of D prostanoid (DP) 1 and 2 receptors was evaluated by western blotting. Proliferation of L6 myoblasts incubated with agonists and antagonists of prostaglandin (PG) D2 receptors DP1 and DP2 and of the peroxisome proliferator-activated receptor (PPAR) d was assessed. Intracellular and extracellular concentrations of 15¿-PGJ2 following L6 cell activation with protease-activated receptor (PAR)-2 agonist were measured by liquid chromatography coupled to tandem mass spectrometry.\nResults: Both DP1 and DP2 receptors are present in myoblasts. DP1 agonist did not modulate L6 myoblast proliferation, but DP2 and PPARd agonists induced an increase. DP1 and DP2 antagonists both significantly inhibited 15¿-PGJ2 -induced stimulating effect of L6 cell proliferation (60% and 75%, respectively). 15¿-PGJ2 was present in the intracellular and extracellular compartments under basal conditions, but was not modulated by PAR-2 receptor activation.\nConclusion: L6 muscle cell can produce 15¿-PGJ2 and its effect on cell proliferation likely relies on both DP1 and DP2 receptor activation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".