Infiltration of T Cells into a Three-Dimensional Psoriatic Skin Model Mimics Pathological Key Features
Bibliographic record
Abstract
Psoriasis is an autoimmune chronic dermatosis that is T cell-mediated, characterized by epidermal thickening, aberrant epidermal differentiation and inflammatory infiltrates, with a dominant Th1 and Th17 profile. Additional in vitro models are required to study the complex interactions between activated T cells and skin cells, and to develop new, more effective treatments. We have therefore sought to model this psoriatic inflammation by the generation of tissue-engineered immunocompetent tissues, and we have investigated the response of activated T-cell infiltration in models produced with lesional psoriatic skin cells on major hallmarks of psoriasis. The immunocompetent lesional skin model displayed a delayed onset of epidermal differentiation, an hyperproliferation of the basal keratinocytes, a drastic increase in the secretion of proinflammatory cytokines, and a disturbed expression of key transcription factors, as observed in lesional plaques, suggesting a crucial importance of combining the pathological phenotype of cutaneous cells to T cells in order to generate a relevant model for psoriasis. Finally, we found this skin model to be responsive to methotrexate treatment, making it a valuable tool for drug development.
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 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) | 0.001 | 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 | 0.001 |
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".