The viable bioengineered allogeneic cellularized construct StrataGraft® synthesizes, deposits, and organizes human extracellular matrix proteins into tissue type-specific structures and secretes soluble factors associated with wound healing
Bibliographic record
Abstract
StrataGraft® (allogeneic cultured keratinocytes and dermal fibroblasts in murine collagen–dsat) is an FDA-approved viable bioengineered allogeneic cellularized construct for adult patients with deep partial-thickness burns requiring surgery. We characterized the structural and functional properties of StrataGraft to improve product understanding by evaluating extracellular matrix (ECM) molecule distribution and secreted protein factor expression in vitro. ECM protein expression was determined using indirect immunofluorescence on construct cross sections using commercial antibodies against collagen III, IV, VI, laminin-332, and decorin. Human collagen I expression was verified by enzyme-linked immunosorbent assay (ELISA) for collagen I C-terminal propeptide. Soluble protein factor secretion was quantified by multiplex biomarker assays and singleplex ELISA in conditioned media from meshed constructs. StrataGraft cellular components produced collagen I, collagen III, collagen VI, and decorin in patterns indicating an organized ECM. Distributions of collagen IV and laminin-332 indicated formation of basement membranes and dermal-epidermal junctions. Soluble protein factors were observed in the pg/cm2/h range from 1 h to the experiment end at 168 h. The organization of the ECM proteins was like human skin and the viable cellular components provided sustained secretion of soluble wound healing factors, making StrataGraft an attractive option for treating severe burns.
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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 | 0.001 | 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) | 0.002 | 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".