Development and characterization of a decellularized lung ECM-based bioink for bioprinting and fabricating a lung model
Bibliographic record
Abstract
The construction of three-dimensional (3D) in vitro lung tissue models mimicking the physiological structure of the native lung poses a huge challenge in tissue engineering. While advances in bioprinting technology has made fabrication of 3D lung models feasible, the bioinks and printed constructs often fall short in achieving desired mechanical and biological properties. Toward this, we aimed to develop a novel bioink and use it to print and characterize in vitro 3D lung models with living cells. We generated porcine lung extracellular matrix (LdECM) which was then strategically combined with other hydrogels - alginate, carboxymethylcellulose (CMC), and collagen, to synthesize novel bioinks. The printability, mechanical and biological properties of the synthesized bioinks was characterized. We also characterized the rheological properties and identified the bioink composition - 3 % w/v alginate, 0.5 % w/v CMC, 0.5 mg/mL collagen Type 1 and 1 % v/v porcine LdECM was appropriate for bioprinting. To fabricate 3D lung models, we strategically designed and printed constructs featuring spatially organized patterns of MRC-5 human lung fibroblasts and A549-ACE2 human lung epithelial cells along with a cup-shaped structure to confine epithelial cells. Our results demonstrated that the bioinks with viscosities between 60 and 90 Pa.s were appropriate, which resulted in high printing resolution of cell-laden constructs and excellent cell viability. The bioprinted lung constructs also exhibited an elastic modulus of 2-4 kPa comparable to the stiffness of native lung tissues. Our findings establish a foundation for developing lung-specific 3D bioprinted models to address the growing global prevalence of respiratory diseases and for advancing preclinical therapeutic testing.
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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) | 0.000 | 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 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".