Dual drug delivery collagen vehicles for modulation of skin fibrosis in vitro
Bibliographic record
Abstract
Abstract Single molecule drug delivery systems have failed to yield functional therapeutic outcomes, triggering investigations into multi-molecular drug delivery vehicles. In the context of skin fibrosis, although multi-drug systems have been assessed, no system has assessed molecular combinations that directly and specifically reduce cell proliferation, collagen synthesis and transforming growth factor β1 (TGFβ1) expression. Herein, a core–shell collagen type I hydrogel system was developed for the dual delivery of a TGFβ trap, a soluble recombinant protein that inhibits TGFβ signalling, and Trichostatin A (TSA), a small molecule inhibitor of histone deacetylases. The antifibrotic potential of the dual delivery system was assessed in an in vitro skin fibrosis model induced by macromolecular crowding (MMC) and TGFβ1. Sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) and high performance liquid chromatography analyses revealed that ∼50% of the TGFβ trap and ∼30% of the TSA were released from the core and shell compartments, respectively, of the hydrogel system after 10 d (longest time point assessed) in culture. As a direct consequence of this slow release, the core (TGFβ trap)/shell (TSA) hydrogel system induced significantly (p < 0.05) lower than the control group (MMC and TGFβ1) collagen type I deposition (assessed via SDS-PAGE and immunocytochemistry), α smooth muscle actin (αSMA) expression (assessed via immunocytochemistry) and cellular proliferation (assessed via DNA quantification) and viability (assessed via calcein AM and ethidium homodimer-I staining) after 10 d in culture. On the other hand, direct TSA-TGFβ supplementation induced the lowest (p < 0.05) collagen type I deposition, αSMA expression and cellular proliferation and viability after 10 d in culture. Our results illustrate the potential of core–shell collagen hydrogel systems for sustained delivery of antifibrotic molecules.
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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".