Decellularized placental matrices for adipose tissue engineering
Bibliographic record
Abstract
A tissue-engineered adipose substitute would be invaluable to plastic surgeons for reconstructive, corrective, and cosmetic procedures. This work involves the design of a scaffold for soft tissue augmentation incorporating the decellularized extracellular matrix (ECM) of human placenta. We have developed a protocol to decellularize an intact, large segment (8 cm by 8 cm) of the human placenta. To facilitate the complete decellularization of the dense matrix, a system was designed to perfuse the required chemicals into the placenta via the existing vasculature. Following processing, the original architecture of the placental ECM was preserved, including an intact vascular network. Histological, immunohistochemical, and scanning electron microscopic analyses confirmed the removal of the cells and cellular debris and characterized the composition and structure of the matrix. In vitro cell culture experimentation showed that the placental decellular matrix (PDM) could facilitate the adhesion of primary human adipose precursor cells at early time points. The PDM has great potential for use as a scaffold for adipose tissue engineering, as the placenta is a rich source of human ECM components that can be readily harvested without harm to the donor.
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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".