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Record W2785123319 · doi:10.1073/pnas.1719177115

Motile cilia of human airway epithelia contain hedgehog signaling components that mediate noncanonical hedgehog signaling

2018· article· en· W2785123319 on OpenAlexaff
Suifang Mao, Alok S. Shah, Thomas O. Moninger, Lynda S. Ostedgaard, Lin Lü, Xiao Xiao Tang, Ian M. Thornell, Leah R. Reznikov, Sarah E. Ernst, Philip H. Karp, Ping Tan, Shaf Keshavjee, Mahmoud H. Abou Alaiwa, Michael J. Welsh

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHedgehog Signaling Pathway Studies
Canadian institutionsUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institutes of HealthRoy J. Carver Charitable TrustNational Institute of Environmental Health SciencesUniversity of Southern CaliforniaHoward Hughes Medical Institute
KeywordsCiliumSmoothenedIntraflagellar transportCell biologyBiologyCiliogenesisHedgehog signaling pathwaySonic hedgehogMotile ciliumHedgehogSignal transductionAdenylyl cyclaseAirwayMucociliary clearanceFlagellumInternal medicineLungBiochemistryMedicine

Abstract

fetched live from OpenAlex

Significance Previous studies localized the hedgehog (HH) signaling system to primary cilia. We discovered that motile cilia on airway epithelia also contain HH signaling proteins, indicating that like primary cilia, these motile cilia have an important sensory function. However, in contrast to the function of HH signaling in most primary cilia, sonic hedgehog (SHH) elicits noncanonical signaling, reducing cellular levels of cAMP. These findings suggest that airway SHH may quiet airway defenses. Involvement of SHH in lung disease and positioning of motile cilia where they sample SHH and other ligands in the airway lumen suggest that noncanonical HH signaling might modulate airway responses to the environment in health and disease.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.902

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.067
GPT teacher head0.323
Teacher spread0.256 · 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

Citations42
Published2018
Admission routes1
Has abstractyes

Explore more

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