Effects of kynurenine on <scp>CD</scp>3+ and macrophages in wound healing
Bibliographic record
Abstract
As prolongation of the inflammation phase in a healing process frequently leads to wound impairment, here we queried whether kynurenine (Kyn) could modulate this phase of wound healing. To address this, a protein microarray, quantitative polymerase chain reaction (qPCR), flow cytometry for immune cells and immune cell proliferation in the presence and absence of Kyn were conducted and compared. The result of a protein microarray revealed that the expression of 12 pro-inflammatory cytokines and chemokines was modulated in Kyn-treated mouse splenocytes as compared with those of control. These findings were then evaluated by conducting a qPCR for the gene expression of these factors and showed a significant reduction in the gene expression of majority of these cytokines and chemokines (interleukin [IL]-2, IL-17, C-X-C motif chemokine ligand [CXCL] 10, CXCL1, C-C motif ligand [CCL] 12, CXCL9, CCL4, CXCL2, and CCL5) in response to Kyn treatment. To test the anti-inflammatory effect of Kyn in an animal model, dorsal surface wounds were generated in a mouse model and wounds received daily topical application of either nothing (control), dermal cream (second control), or Kyn cream using uninjured skin tissue as another control. The wounded tissues were harvested on days 3, 6, and 10 postwounding. As anticipated, the results of fluorescence-activated cell sorting analysis revealed that upon wounding, the number of total infiltrated CD3+ cells and macrophages (CD11b+) significantly increased on day 3, peaked on day 6, and reduced on day 10 post-wounding. Interestingly, as compared with those of uninjured and dermal cream alone-treated wounds, Kyn treatment significantly reduced the number of infiltrated CD3+ cells, but not CD11b+ cells, at different time intervals examined. These findings collectively suggest that Kyn, as a small molecule, can potentially be used to overcome the difficulties associated with persistency of inflammation in healing wounds.
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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".