LSC 2012 abstract - Lung tissue engineering: generation and characterization of decellularized lung scaffolds for stem cell differentiation
Bibliographic record
Abstract
The interaction of stem cells with the surrounding matrix environment is crucial for cell fate. The development of biomatrices that recapitulate the in vivo environment is a key component to driving differentiation of pluripotent cells into lung endoderm precursors. We investigate whether decellularized lungs with intact matrix composition can promote the differentiation of embryonic stem cells (ESC) into distal lung epithelial cells. Rat cadaveric lungs were decellularized by sequential tracheal lavages and retrograde pulmonary arterial perfusion using a range of physical, chemical, and enzymatic treatments. Histological staining, immunofluorescence, electron microscopy, and tensile testing have confirmed decellularization and preservation of matrix proteins. Murine ESC (Foxa2/CD4; Bry/GFP cells) were seeded onto scaffolds following endoderm induction using activin, and analysed for lung lineage marker expression. Seeded ES cells maintained FoxA2 expression and adopted an epithelial-like tubular organization. This demonstrates the ability of acellular lung scaffolds to support the adherence, proliferation, and potential differentiation of murine embryonic stem cells. Current studies are analysing their potential as viable scaffolds for the unidirectional differentiation of human endoderm-induced ESC (Hes2 cell line) into distal lung epithelial cells.
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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) | 0.003 | 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".