Negative Pressure Cell Delivery Augments Recellularization of Decellularized Lungs
Why this work is in the frame
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Bibliographic record
Abstract
For end-stage lung disease, lung transplantation remains the only treatment but is limited by the availability of organs. Production of bioengineered lungs via recellularization is an alternative but is hindered by inadequate repopulation. We present a cell delivery method via the generation of negative pressure. Decellularized lungs were seeded with human bronchial epithelial cells using gravity-based perfusion or negative pressure (via air removal). After delivery, lungs were maintained in static conditions for 18 h, and cell surface coverage was qualitatively assessed using histology and analyzed by subjective scoring and an image analysis software. Negative pressure seeded lungs had higher cell surface coverage area, and this effect was maintained following 5 days of culture. Enhanced coverage via negative pressure cell delivery was also observed when vasculature seeded with endothelial cells. Our findings show that negative pressure cell delivery is a superior approach for the recellularization of the bioengineered lung. Impact statement New strategies are required to overcome the shortage of organ donors for lung transplantation. Recellularization of acellular biological scaffolds is an exciting potential alternative. Adequate recellularization, however, remains a significant challenge. This proof of concept study describes a novel cell delivery approach, which further enhances the recellularization of decellularized lungs. Organs seeded and cultured with this method possess higher cell surface coverage and number compared to those seeded via traditional gravity-based perfusion approaches.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it