MétaCan
Menu
Back to cohort
Record W2061474355 · doi:10.1165/ajrcmb.23.3.4090

Proliferation of Rat Pleural Mesothelial Cells in Response to Hepatocyte and Keratinocyte Growth Factors

2000· article· en· W2061474355 on OpenAlexaff
Ian Y. R. Adamson, Julita Bakowska, Heather Prieditis

Bibliographic record

VenueAmerican Journal of Respiratory Cell and Molecular Biology · 2000
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsKeratinocyte growth factorHepatocyte growth factorMesothelial CellCell growthDNA synthesisCell cultureCell biologyGrowth factorBiologyRegeneration (biology)Tumor necrosis factor alphaChemistryCancer researchEndocrinologyPathologyMedicineBiochemistryDNAReceptor

Abstract

fetched live from OpenAlex

The proliferative response of cultured pulmonary mesothelial cells (MCs) to epithelial cell mitogens such as keratinocyte growth factor (KGF) and hepatocyte growth factor (HGF) is investigated. A cell line of rat pleural MCs and freshly prepared rat visceral and parietal MCs were studied. Both KGF and HGF stimulated thymidine uptake in the cell line when cultured for 2 d in serum-free conditions; the growth increase was magnified when tumor necrosis factor (TNF)-alpha was also added to the cultures. Adding asbestos fibers alone to MCs in culture did not enhance DNA synthesis by these cells. The MCs were also shown to synthesize significant amounts of HGF but much less KGF when cultured for 2 d. When freshly prepared MCs were examined, normal cell growth was more rapid in the parietal cells, which also had a more epithelial-type morphology. The addition of HGF and KGF resulted in increased DNA synthesis in each cell type, but no effect of added TNF-alpha was found. The results indicate that pulmonary MCs have the potential to proliferate in response to cytokines such as HGF and KGF that are usually associated with epithelial cell regeneration after injury.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.256
Teacher spread0.249 · 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 designBench or experimental
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

Citations20
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Respiratory Cell and Molecular BiologySame topicTissue Engineering and Regenerative MedicineFrench-language works237,207