Next‐generation preclinical models of lung development, physiology and disease
Bibliographic record
Abstract
Abstract The incidence of respiratory diseases such as chronic obstructive pulmonary disease and pulmonary cancer is growing significantly around the world, making pulmonary disease one of the leading causes of mortality. However, the development of effective therapeutics for pulmonary diseases has been hindered by the lack of human‐mimetic physiological models that reliably emulate patient responses. Recent advances in technology and cell culture have led to the development of organoids and organ‐on‐a‐chip models that allow us to recapitulate the structure, cellular organization, and organ‐level responses of the target tissue in vitro. Here, we review the advances and milestones of lung organoid and lung‐on‐a‐chip models in the past decade and highlight their applications in mimicking pulmonary system development, physiology, disease, and regeneration. In addition, we discuss the ongoing challenges and the future prospects of integrating lung organoids and lung‐on‐a‐chip models to overcome current limitations and to enhance their physiological relevance. These human‐centric models are likely to provide important insights into pulmonary physiology and pathophysiology for drug discovery that complement and potentially replace traditional animal models.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".