Toward Immunocompetent 3D Skin Models
Bibliographic record
Abstract
3D human skin models provide a platform for toxicity testing, biomaterials evaluation, and investigation of fundamental biological processes. However, the majority of current in vitro models lack an inflammatory system, vasculature, and other characteristics of native skin, indicating scope for more physiologically complex models. Looking at the immune system, there are a variety of cells that could be integrated to create novel skin models, but to do this effectively it is also necessary to understand the interface between skin biology and tissue engineering as well as the different roles the immune system plays in specific health and disease states. Here, a progress report on skin immunity and current immunocompetent skin models with a focus on construction methods is presented; scaffold and cell choice as well as the requirements of physiologically relevant models are elaborated. The wide range of technological and fundamental challenges that need to be addressed to successfully generate immunocompetent skin models and the steps currently being made globally by researchers as they develop new models are explored. Induced pluripotent stem cells, microfluidic platforms to control the model environment, and new real-time monitoring techniques capable of probing biochemical processes within the models are discussed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".