A hair-follicle reconstructed in vitro immunocompetent skin model for prediction of the sensitizing potential of chemicals
Bibliographic record
Abstract
Abstract The development of immunocompetent skin models represents a significant advancement in in vitro methods for detecting skin sensitizers, adhering to the 3R principles aimed at reducing, refining and replacing animal testing. In the present study, an advanced skin model from hair follicle-derived cells was constructed and enriched with two key immune cell types, namely Langerhans cells and T-lymphocytes, named ImmuSkin-MT. The model features a physiologically relevant epidermis and dermis, integration of monocyte-derived Langerhans cells (MoLCs) beneath the dermal layer, and co-cultivation with CD4+-T cells in the lower chamber of a transwell system. This setup closely mimics the native interplay between skin-resident immune cells and T-cells, marking a significant advancement in in vitro toxicology. When exposed to known sensitizers of varying potency, the model demonstrated a robust ability to predict the sensitizing potential of chemicals. By addressing different key events in skin sensitization, a differentiation between extreme, moderate and even weak sensitizers was achieved. The results showed that the MoLCs migrated, and upregulated CD86 expression in response to contact sensitizers. Additionally, proliferation of CD4+ T-lymphocytes was increased in response to the treatment. These results highlight the potential of the ImmuSkin-MT construct to serve as a valuable tool for mechanistic studies and future regulatory applications in the assessment of skin sensitization.
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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) | 0.001 | 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".