Reconstructed Human Skin with Hypodermis Shows Essential Role of Adipose Tissue in Skin Metabolism
Bibliographic record
Abstract
BACKGROUND: Dysregulation of skin metabolism is associated with a plethora of diseases such as psoriasis and dermatitis. Until now, reconstructed human skin (RhS) models lack the metabolic potential of native human skin, thereby limiting their relevance to study human healthy and diseased skin. We aimed to determine whether incorporation of an adipocyte-containing hypodermis into RhS improves its metabolic potential and to identify major metabolic pathways up-regulated in adipose-RhS. METHODS: Primary human keratinocytes, fibroblasts and differentiated adipose-derived stromal cells were co-cultured in a collagen/fibrin scaffold to create an adipose-RhS. The model was extensively characterized structurally in two- and three-dimensions, by cytokine secretion and RNA-sequencing for metabolic enzyme expression. RESULTS: Adipose-RhS showed increased secretion of adipokines. Both RhS and adipose-RhS expressed 29 of 35 metabolic genes expressed in ex vivo native human skin. Addition of the adipose layer resulted in up-regulation of 286 genes in the dermal-adipose fraction of which 7 were involved in phase I (CYP19A1, CYP4F22, CYP3A5, ALDH3B2, EPHX3) and phase II (SULT2B1, GPX3) metabolism. Vitamin A, D and carotenoid metabolic pathways were enriched. Additionally, pro-inflammatory (IL-1β, IL-18, IL-23, IL-33, IFN-α2, TNF-α) and anti-inflammatory cytokine (IL-10, IL-12p70) secretion was reduced in adipose-RhS. CONCLUSIONS: Adipose-RhS mimics healthy native human skin more closely than traditional RhS since it has a less inflamed phenotype and a higher metabolic activity, indicating the contribution of adipocytes to tissue homeostasis. Therefore it is better suited to study onset of skin diseases and the effect of xenobiotics.
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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 | 0.000 | 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) | 0.002 | 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 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".