Adiponectin, but Not TGF-β1, CTGF, IL-6 or TNF-α, May Be a Potential Anti-Inflammation and Anti-Fibrosis Factor in Keloid
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Bibliographic record
Abstract
INTRODUCTION: Numerous studies have elucidated adiponectin as a negative impact on inflammation and tissue fibrosis. However, little is known about the relevance between adiponectin and inflammatory factors in keloid. METHODS: To clarify whether adiponectin plays a role in the inflammation and fibrosis of keloid, 50 patients with keloid and 50 healthy subjects were enrolled, We examined the serum and mRNA expression levels of adiponectin, TGF-β1, CTGF, IL-6 and TNF-α in normal skin tissues and keloid tissues by ELISA and qPCR, respectively. Correlation analysis between serum concentration of adiponectin with Vancouver Scar Scale (VSS) scores and the age of patients with keloid was evaluated, and the adiponectin concentrations in patients with keloid between different genders were measured. We further examined the effects of adiponectin on TGF-β1 mediated expression of collagen I, FN and MMP-1 in normal fibroblasts (NFs) and keloid fibroblasts (KFs). RESULTS: We discovered that lower serum concentration and mRNA expression of adiponectin, but higher TGF-β1, CTGF, IL-6 and TNF-α levels were measured in patients with keloid compared with those in normal controls. Furthermore, there was a strong inverse correlation between the serum adiponectin levels and VSS scores in patients with keloid, but not in ages, and there was no statistically difference between different genders. Moreover, adiponectin attenuated TGF-β1 mediated expression of collagen I and FN, and upregulated the expression level of MMP-1 in KFs, but not in NFs. In addition, the inhibitory effect of adiponectin on TGF-β1 was attenuated by AMPK inhibitor Compound C, but not PI3K/Akt inhibitor LY294002. DISCUSSION: Adiponectin may exert an anti-inflammation and anti-fibrosis role in the development of keloid. One of the underlying mechanisms may be the activation of the AMPK signaling pathway.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it