Integration of bioprinting advances and biomechanical strategies for in vitro lung modelling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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