Recent frontiers in biofabrication for respiratory tissue engineering
Bibliographic record
Abstract
Respiratory tissue engineering offers a robust framework for studying cell-cell and host-pathogen interactions in a tissue-like environment and offers a platform for studying lung tissue regeneration and disease mechanisms. However, the challenge of replicating dynamic three-dimensional (3D) microenvironments is a huge obstacle with existing technology. Current animal models and two-dimensional cell culture models do not replicate in vivo conditions seen in human lungs, thus research utilizing these techniques often fails to help alleviate the global burden of respiratory diseases. Respiratory tissue engineering has been drawing significant attention over the past decade. Particularly with the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), many inspiring developments and advances have been reported. This review presents the recent advances of respiratory tissue engineering focusing on 3D bioprinting, organ-on-a-chip, and organoid technologies. It also provides an overview of recent attempts to integrate biomechanical stimulus with the aim of improving the integrity of 3D constructs and enhancing cellular propagation. This review addresses the challenges inherent in existing 3D respiratory models and discusses the future prospects of research in this field, urging continuing innovation and investment toward the success of respiratory tissue engineering and increasing clinical relevance.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".