In‐depth examination of hyperproliferative healing in two breeds of <i>Sus scrofa domesticus</i> commonly used for research
Bibliographic record
Abstract
Background: Wound healing can result in various outcomes, including hypertrophic scar (HTS). Pigs serve as models to study wound healing as their skin shares physiologic similarity with humans. Yorkshire (Yk) and Duroc (Dc) pigs have been used to mimic normal and abnormal wound healing, respectively. The reason behind this differential healing phenotype was explored here. Methods: Excisional wounds were made on Dc and Yk pigs and were sampled and imaged for 98 days. PCR arrays were used to determine differential gene expression. Vancouver Scar Scale (VSS) scores were given. Re-epithelialization was analyzed. H&E, Mason's trichrome, and immunostains were used to determine cellularity, collagen content, and blood vessel density, respectively. Results: Yk wounds heal to a "port wine" HTS, resembling scarring in Fitzpatrick skin types (FST) I-III. Dc wounds heal to a dyspigmented, non-pliable HTS, resembling scarring in FST IV-VI. Gene expression during wound healing was differentially regulated versus uninjured skin in 40/80 genes, 15 of which differed between breeds. Yk scars had a higher VSS score at all time points. Yk and Dc wounds had equivalent re-epithelialization, collagen disorganization, and blood vessel density. Conclusions: Our findings demonstrate that Dc and Yk pigs can produce HTS. Wound creation and healing were consistent among breeds, and differences in gene expression were not sufficient to explain differences in resulting scar phenotype. Both pig breeds should be used in animal models to investigate novel therapeutics to provide insight into a treatment's effectiveness on various skin types.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".