The effect of midkine on growth factors and oxidative status in an experimental wound model in diabetic and healthy rats
Bibliographic record
Abstract
Wound healing is important for longevity. Midkine is a cytokine involved in controlling tissue repair and new tissue development, and in regulating inflammation. We investigated the effect of midkine on wound healing in rats. In total, 108 Wistar albino rats were used: 12 as healthy and diabetic controls; 96 were split into 4 groups: healthy, saline treated; healthy, midkine (10 ng/kg, 48 h intervals) treated; diabetic, saline treated; and diabetic, midkine treated. Following wound creation, 6 rats per group were euthanized on days 3, 7, 14, and 28; the wounded skin was removed. Levels of epidermal growth factor (EGF), matrix metalloproteinase-8 (MMP-8), transforming growth factor beta (TGF-β), platelet-derived growth factor (PDGF), vascular endothelial growth factor (VEGF), and thiobarbituric acid reactive substances (TBARS) were measured. MMP-8 and PDGF levels fluctuated in all groups; TGF-β fluctuated in the diabetic groups and was significantly higher in the HM group than other groups after 14 days. EGF and VEGF levels were increased in the HM group after 3 days. TBARS levels were highest in the diabetic groups. Macroscopically, the midkine-treated groups healed better. Midkine can accelerate wound healing by influencing growth factors and oxidative status in wound tissues.
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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 | 0.001 | 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) | 0.001 | 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".