The immunology of the porcine skin and its value as a model for human skin
Bibliographic record
Abstract
The porcine skin has striking similarities to the human skin in terms of general structure, thickness, hair follicle content, pigmentation, collagen and lipid composition. This has been the basis for numerous studies using the pig as a model for wound healing, transdermal delivery, dermal toxicology, radiation and UVB effects. Considering that the skin also represents an immune organ of utmost importance for health, immune cells present in the skin of the pig will be reviewed. The focus of this review is on dendritic cells, which play a central role in the skin immune system as they serve as sentinels in the skin, which offers a large surface area exposed to the environment. Based on a literature review and original data we propose a classification of porcine dendritic cell subsets in the skin corresponding to the subsets described in the human skin. The equivalent of the human CD141(+) DC subset is CD1a(-)CD4(-)CD172a(-)CADM1(high), that of the CD1c(+) subset is CD1a(+)CD4(-)CD172a(+)CADM1(+/low), and porcine plasmacytoid dendritic cells are CD1a(-)CD4(+)CD172a(+)CADM1(-). CD209 and CD14 could represent markers of inflammatory monocyte-derived cells, either dendritic cells or macrophages. Future studies for example using transriptomic analysis of sorted populations are required to confirm the identity of these cells.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".