Localized Controlled Release of Kynurenic Acid Encapsulated in Synthetic Polymer Reduces Implant—Induced Dermal Fibrosis
Bibliographic record
Abstract
Excessive fibrosis following surgical procedures is a challenging condition with serious consequences and no effective preventive or therapeutic option. Our group has previously shown the anti-fibrotic effect of kynurenic acid (KynA) in vitro and as topical cream formulations or nanofiber dressings in open wounds. Here, we hypothesized that the implantation of a controlled release drug delivery system loaded with KynA in a wound bed can prevent fibrosis in a closed wound. Poly (lactic-co-glycolic acid) (PLGA), and a diblock copolymer, methoxy polyethylene glycol-block-poly (D, L-lactide) (MePEG-b-PDLLA), were used for the fabrication of microspheres which were evaluated for their characteristics, encapsulation efficiency, in vitro release profile, and in vivo efficacy for reduction of fibrosis. The optimized formulation exhibited high encapsulation efficiency (>80%), low initial burst release (~10%), and a delayed, gradual release of KynA. In vivo evaluation of the fabricated microspheres in the PVA model of wound healing revealed that KynA microspheres effectively reduced collagen deposition inside and around PVA sponges and α-smooth muscle actin expression after 66 days. Our results showed that KynA can be efficiently encapsulated in PLGA microspheres and its controlled release in vivo reduces fibrotic tissue formation, suggesting a novel therapeutic option for the prevention or treatment of post-surgical 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) | 0.000 | 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".