A Tissue-Engineered Human Psoriatic Skin Model to Investigate the Implication of cAMP in Psoriasis: Differential Impacts of Cholera Toxin and Isoproterenol on cAMP Levels of the Epidermis
Bibliographic record
Abstract
Pathological and healthy skin models were reconstructed using similar culture conditions according to well-known tissue engineering protocols. For both models, cyclic nucleotide enhancers were used as additives to promote keratinocytes’ proliferation. Cholera toxin (CT) and isoproterenol (ISO), a beta-adrenergic agonist, are the most common cAMP stimulators recommended for cell culture. The aim of this study was to evaluate the impact of either CT or ISO on the pathological characteristics of the dermatosis while producing a psoriatic skin model. Healthy and psoriatic skin substitutes were produced according to the self-assembly method of tissue engineering, using culture media supplemented with either CT (10−10 M) or ISO (10−6 M). Psoriatic substitutes produced with CT exhibited a more pronounced psoriatic phenotype than those produced with ISO. Indeed, the psoriatic substitutes produced with CT had the thickest epidermis, as well as contained the most proliferating cells and the most altered expression of involucrin, filaggrin, and keratin 10. Of the four conditions under study, psoriatic substitutes produced with CT had the highest levels of cAMP and enhanced expression of adenylate cyclase 9. Taken together, these results suggest that high levels of cAMP are linked to a stronger psoriatic phenotype.
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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 | 0.001 |
| 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".