OMICS Approaches Evaluating Keloid and Hypertrophic Scars
Bibliographic record
Abstract
Abnormal scar formation during wound healing can result in keloid and hypertrophic scars, which is a major global health challenge. Such abnormal scars can cause significant physiological pain and psychological distress and become a financial burden. Due to the biological complexity of scar formation, the pathogenesis of such scars and how to prevent them from forming remains elusive. In this review paper, we delve into the world of "omics" approaches to study abnormal scars and provide examples of genomics, transcriptomics, proteomics, epigenomics, and metabolomics. The benefits of "omics" approaches are that they allow for high-throughput studies and the analysis of 100s to 1000s of genes and proteins with the accumulation of large quantities of data. Currently in the field, there is a lack of "omics" review articles describing pathological scars. In this review, we summarize genome-wide linkage analysis, genome-wide association studies, and microarray data to name a few omics technologies. Such data can provide novel insights into different molecular pathways and identify novel factors which may not be captured through small-scale laboratory techniques.
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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) | 0.001 | 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 | 0.000 |
| 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".