NEDD4 Transcript Variant 3 and IGF-1 as Molecular Markers in the Development and Prognosis of Keloids
Bibliographic record
Abstract
Background: Keloids are benign fibrous growths resulting from abnormal wound healing, commonly affecting individuals with darker skin tones. Genetic factors, particularly the Neural Precursor Cell Expressed Developmentally Down-Regulated Protein 4 (NEDD4) gene transcript variant 3 (NEDD4-TV3) and growth factors like insulin-like growth factor-1 (IGF-1), are implicated in keloid formation. Objective: This study aimed to assess the expression levels of NEDD4-TV3 and IGF-1 in keloid tissue and their potential role in keloid pathogenesis. Patients and methods: This case-control study was conducted involving 30 keloid patients and 20 individuals of matched age, sex and BMI as a control group. Comprehensive history, examination, and laboratory investigations were performed, including PCR for NEDD4-TV3 and IGF-1 gene expression. Results: NEDD4-TV3and IGF-1 gene expressions were significantly higher in keloid patients compared to controls (P ≤ 0.001). NEDD4-TV3 ≥75852 predicted keloid formation with 95% sensitivity, 95% specificity, 97.4% PPV, 90.5% NPV, and 95% accuracy (AUC = 0.983, 95% CI: 0.95-1.0). IGF ≥8490 had 77.5% sensitivity, 70% specificity, 97.4% PPV and 90.5% NPV. NEDD4-TV3 significantly correlated positively with pigmentation score, vascularity score, height score, total Vancouver scale (P ≤ 0.001) for all, and IGF-1 expressions (P = 0.014). IGF-1 significantly correlated with pigmentation score, vascularity score, and total Vancouver scale (P = 0.03, 0.016, 0.005) respectively. Conclusions: Significantly elevated NEDD4-TV3 and IGF-1 gene expressions were associated with increased susceptibility to keloid formation, suggesting their potential role in keloid etiopathogenesis and their predictive roles.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".