Integration of bioprinting advances and biomechanical strategies for in vitro lung modelling
Bibliographic record
Abstract
Abstract The recent occurrence of the Covid-19 pandemic and frequent wildfires have worsened pulmonary diseases and raised the urgent need for investigating host-pathogen interactions and advancing drug and vaccine therapies. Historically, research and experimental studies have relied on two-dimensional cell culture dishes and/or animal models, which suffer from physiological differences from the human lung. More recently, there has been investigation into the use of lung-on-a-chip models and organoids, while the use of bioprinting technologies has also emerged to fabricate three-dimensional constructs or lung models with enhanced physiological relevance. Concurrently, achievements have also been made to develop biomimetic strategies for simulating the in vivo biomechanical conditions induced by lung breathing, though challenges remain with incorporating these strategies with bioprinted models. Bioprinted models combined with advanced biomimetic strategies would represent a promising approach to advance disease discovery and therapeutic development. As inspired, this article briefly reviews the recent progress of both bioprinted in vitro lung models and biomechanical strategies, with a focus on native lung tissue microstructure and biomechanical properties, bioprinted constructs, and biomimetic strategies to mimic the native environment. This article also urges that the integration of bioprinting advances and biomimetic strategies would be essential to achieve synergistic effects for in vitro lung modelling. Key issues and challenges are also identified and discussed along with recommendations for future research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".