Immunological challenges associated with artificial skin grafts: available solutions and stem cells in future design of synthetic skin
Bibliographic record
Abstract
The repair or replacement of damaged skins is still an important, challenging public health problem. Immune acceptance and long-term survival of skin grafts represent the major problem to overcome in grafting given that in most situations autografts cannot be used. The emergence of artificial skin substitutes provides alternative treatment with the capacity to reduce the dependency on the increasing demand of cadaver skin grafts. Over the years, considerable research efforts have focused on strategies for skin repair or permanent skin graft transplantations. Available skin substitutes include pre- or post-transplantation treatments of donor cells, stem cell-based therapies, and skin equivalents composed of bio-engineered acellular or cellular skin substitutes. However, skin substitutes are still prone to immunological rejection, and as such, there is currently no skin substitute available to overcome this phenomenon. This review focuses on the mechanisms of skin rejection and tolerance induction and outlines in detail current available strategies and alternatives that may allow achieving full-thickness skin replacement and repair.
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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 | 0.001 | 0.000 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".