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Record W2138850310 · doi:10.1513/pats.201012-067cb

Cell Plasticity in Lung Injury and Repair: Report from an NHLBI Workshop, April 19–20, 2010

2011· article· en· W2138850310 on OpenAlexaff
Zea Borok, Jeffrey A. Whitsett, Peter B. Bitterman, Victor J. Thannickal, Darrell N. Kotton, Susan D. Reynolds, Mark A. Krasnow, Diana W. Bianchi, Edward E. Morrisey, Brigid L.M. Hogan, Jonathan M. Kurie, David C. Walker, Derek C. Radisky, Steve L. Nishimura, Shelia M. Violette, Paul W. Noble, Steve D. Shapiro, Carol J. Blaisdell, Harold A. Chapman

Bibliographic record

VenueProceedings of the American Thoracic Society · 2011
Typearticle
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
FundersNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsMedicineLungTranslation (biology)FibroblastGroup (periodic table)Internal medicineGeneticsBiologyGeneCell culture

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.350
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations39
Published2011
Admission routes1
Has abstractyes

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