Lung tissue engineering and preservation of alveolar microstructure using a novel casting method
Bibliographic record
Abstract
We used a rat model to decellularize and seed alveolar cells on a three-dimensional lung scaffold to preserve alveolar microarchitecture. We verified the preservation of terminal respiratory structure by casting and by scanning electron microscopy (SEM) of the casts after decellularization. Whole lungs were obtained from 12 healthy Sprague-Dawley rats, cannulated through the trachea under sterile conditions, and decellularized using a detergent-based method. Casting of both natural and decellularized lungs was performed to verify preservation of the inner microstructure of scaffolds for further cell seeding. Alveolar cell seeding was performed using green fluorescent protein (GFP) lung cells and non-GFP lung cells, and a peristaltic pump. We assessed cell seeding using histological and immunohistochemical staining, and enzymatic evaluation. All cellular components were removed completely from the scaffolds, and histological staining and SEM of casts were used to verify the preservation of tissue structure. Tensile tests verified conservation of biomechanical properties. The hydroxyproline content of decellularized lungs was similar to native lung. Histological and immunohistochemical evaluations showed effective cell seeding on decellularized matrices. Enzymatic measurement of trypsin and alpha 1 antitrypsin suggested the potential functional properties of the regenerated lungs. Casts produced by our method have satisfactory geometrical properties for further cell seeding of lung scaffolds. Preservation of micro-architecture and terminal alveoli that was confirmed by SEM of lung casts increases the probability of an effective cell seeding process.
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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) | 0.000 | 0.001 |
| 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 | 0.001 | 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".