Cell Plasticity in Lung Injury and Repair: Report from an NHLBI Workshop, April 19–20, 2010
Bibliographic record
Abstract
In April 2010, a NIH workshop was convened to discuss the current state of understanding of lung cell plasticity, including the responses of epithelial cells to injury, with the objectives of summarizing what is known, what the field needs to know, and how to get there. The proximal stimulus for this workshop is the body of recent evidence suggesting that plasticity is a prominent but incompletely characterized property of lung epithelial cells, and that a focus on understanding this aspect of epithelial cell biology in particular, may be an important window into disease pathobiology and pathogenesis. In addition to their many vital functions in maintaining tissue homeostasis, epithelial cells have emerged as both a central target of disease initiation and an active contributor to disease progression, making a workshop to investigate the role of cell plasticity in lung injury and repair timely. The workshop was organized around four major themes: lung epithelial cell plasticity, signaling control of plasticity, fibroblast plasticity and crosstalk, and translation to human disease. Although this breakdown was recognized to be somewhat artificial, it was felt that this approach would promote cross-fertilization among groups that ordinarily do not communicate and lend itself to the generation of new approaches. The summary reports of individual group discussions below are followed by consensus priorities and recommendations of the workshop participants.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".