Anti-Scarring Properties of Different Tryptophan Derivatives
Bibliographic record
Abstract
Hypertrophic scars are associated with prolonged extracellular matrix (ECM) production, aberrant ECM degradation and high tissue cellularity. Routinely used antifibrotic strategies aim to reduce ECM deposition and enhance matrix remodeling. Our previous study investigating the antifibrotic effects of indoleamine2, 3 dioxygenase (IDO) led to the identification of kynurenine (Kyn) as an antiscarring agent. A topical antifibrogenic therapy using Kyn is very attractive; however, it is well established that Kyn passes the blood brain barrier (BBB) which causes complications including excitatory neuronal death. Here we investigated the antiscarring properties of kynurenic acid (KynA), a downstream end product of Kyn that is unlikely to pass the BBB, as an effective and safe replacement for Kyn. Our results indicated that while not having any adverse effect on dermal cell viability, KynA significantly increases the expression of matrix metalloproteinases (MMP1 and MMP3) and suppresses the production of type-I collagen and fibronectin by fibroblasts. Topical application of cream containing KynA in fibrotic rabbit ear significantly decreased scar elevation index (1.13±0.13 vs. 1.61±0.12) and tissue cellularity (221.38±21.7 vs. 314.56±8.66 cells/hpf) in KynA treated wounds compared to controls. KynA treated wounds exhibited lower levels of collagen deposition which is accompanied with a significant decrease in type-I collagen and fibronectin expression, as well as an increase in MMP1 expression compared to untreated wounds or wounds treated with cream only. The results of this study provided evidence for the first time that KynA is promising candidate antifibrogenic agent to improve healing outcome in patients at risk of hypertrophic scarring.
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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) | 0.002 | 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".