Micro-physiological system of human lung: The current status and application to drug discovery
Bibliographic record
Abstract
Various attempts have been made to elucidate the mechanisms of human lung development, its physiological functions, and diseases, in the hope of new drug discovery. Recent technological advancements in experimental animals, cell culture, gene editing, and analytical methods have provided new insights and therapeutic strategies. However, the results obtained from animal experiments are often inconsistent with those obtained from human data because of reproducibility issues caused by structural and physiological differences between mice and humans. In addition, it is not possible to accurately reproduce the internal environment of the human lung structure using conventional two-dimensional (2D) or three-dimensional (3D) cell culture methods. As a result, the micro-physiological system (MPS) technology, such as "lung-on-a-chip" that can culture human cells in a state close to human body environment have been developed, and its applications to disease models, toxicological studies, and drug discovery are accelerated worldwide. Here, we focus on the mimetics of the lung, including "lung-on-a-chip" technology, and review their recent progress, achievements and challenges. Finally, we discuss the role of these chips in drug discovery for refractory lung diseases.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".