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Record W2772528121 · doi:10.1038/s41598-017-17952-4

Conditionally reprogrammed primary airway epithelial cells maintain morphology, lineage and disease specific functional characteristics

2017· article· en· W2772528121 on OpenAlexaff
Kelly M. Martinovich, Thomas Iosifidis, Alysia G. Buckley, Kevin Looi, Kak‐Ming Ling, Erika N. Sutanto, Elizabeth Kicic‐Starcevich, Luke W. Garratt, Nicole C. Shaw, Samuel Montgomery, Francis J. Lannigan, Darryl A. Knight, Anthony Kicic, Stephen M. Stick

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCystic fibrosisPhenotypeBiologyLineage (genetic)PopulationAirwayImmunologyCell biologyGeneticsMedicineGene

Abstract

fetched live from OpenAlex

Current limitations to primary cell expansion led us to test whether airway epithelial cells derived from healthy children and those with asthma and cystic fibrosis (CF), co-cultured with an irradiated fibroblast feeder cell in F-medium containing 10 µM ROCK inhibitor could maintain their lineage during expansion and whether this is influenced by underlying disease status. Here, we show that conditionally reprogrammed airway epithelial cells (CRAECs) can be established from both healthy and diseased phenotypes. CRAECs can be expanded, cryopreserved and maintain phenotypes over at least 5 passages. Population doublings of CRAEC cultures were significantly greater than standard cultures, but maintained their lineage characteristics. CRAECs from all phenotypes were also capable of fully differentiating at air-liquid interface (ALI) and maintained disease specific characteristics including; defective CFTR channel function cultures and the inability to repair wounds. Our findings indicate that CRAECs derived from children maintain lineage, phenotypic and importantly disease-specific functional characteristics over a specified passage range.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.323
Teacher spread0.277 · 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

Citations96
Published2017
Admission routes1
Has abstractyes

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