Biomimetic In Vitro Model of Cell Infiltration into Skin Scaffolds for Pre-Screening and Testing of Biomaterial-Based Therapies
Bibliographic record
Abstract
Due to great clinical need, research where different biomaterials are tested as 3D scaffolds for skin tissue engineering has increased. In vitro studies use a cell suspension that is simply pipetted onto the material and cultured until the cells migrate and proliferate within the 3D scaffold, which does not mimic the in vivo reality. Our aim was to engineer a novel biomimetic in vitro model that mimics the natural cell infiltration process occurring in wound healing, thus offering a realistic approach when pre-screening and testing new skin substitutes. Our model consists of porous membrane cell culture inserts coated with gelatin and seeded with human dermal fibroblasts, inside which two different commercially available dermal substitutes were placed. Several features relevant to the wound healing process (matrix contraction, cell infiltration and proliferation, integration of the biomaterial with the surrounding tissue, and secretion of exogenous cytokines and growth factors) were evaluated. Our results showed that cells spontaneously infiltrate the materials and that our engineered model is able to induce and detect subtle differences between different biomaterials. The model allows for room for improvements or "adds-on" and miniaturization and can contribute to the development of functional and efficient skin substitutes for burns and chronic wounds.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".