Transplantation of Bioengineered Lung Using Decellularized Mouse Lungs and Primary Human Endothelial Cells
Bibliographic record
Abstract
Lung transplantation is a critical treatment for patients with end-stage lung diseases like idiopathic pulmonary fibrosis, but challenges such as donor shortages and posttransplant complications persist. Bioengineered lungs, integrating patient-specific cells into decellularized animal scaffolds, present a promising alternative. Despite progress in using bioengineered lungs in animal models, functionality and structure remain immature. This protocol addresses a critical barrier in organ bioengineering: the need for a cost-effective experimental platform. By using mouse models instead of larger animals like rats or swine, researchers can significantly reduce the resources required for each experiment, accelerating research progress. The protocol outlines a detailed procedure for lung bioengineering using mouse heart-lung blocks and human primary cells, focusing on isolation strategy for the mouse heart-lung block, decellularization, bioreactor setup, perfusion-based organ culture, and orthotopic transplantation of bioengineered lungs. This mouse-scale platform not only reduces experimental costs but also provides a viable framework for optimizing cell types and numbers for recellularization, testing different cell types using histological and molecular methods, and ensuring blood flow post-transplantation. The method holds potential for broad applications, including studying cell interactions in three-dimensional culture conditions, cell-matrix interactions, and ex vivo cancer modeling, thereby advancing the field of organ bioengineering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".