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The influence of acoustic and static stimuli on development of inner ear sensory epithelia

2010· article· en· W1965616015 on OpenAlexafffund
Irena Rot, Boris Kablar

Bibliographic record

VenueInternational Journal of Developmental Neuroscience · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInner earOrgan of CortiCristaHair cellBiologyCochleaAnatomyVestibular systemCell biologySensory systemMYF5MyoDMyogenesisSkeletal muscleNeuroscienceMitochondrion

Abstract

fetched live from OpenAlex

Mechanical stimuli affect differentiation of specific cell types in several organs of mouse fetuses that develop without any skeletal musculature. To that end, we employed Myf5(-/-):MyoD(-/-) mouse embryos that completely lack skeletal musculature, and analyzed the development of sensory fields in the inner ear. Amyogenic fetuses lack skeletal muscles that move the chain of three middle ear ossicles which normally transfers sound vibrations. They also cannot tilt their head, which prevents the perception of angular acceleration. While our findings in the spiral organ of Corti are surprisingly normal, our results show that the development of cristae ampullaris, vestibular sensory fields sensitive to the angular acceleration, was the most affected. In cristae, hair cells and supporting cells were significantly smaller in the mutant embryos, but hair cells completely lacked tenascin, while supporting cells were more numerous. In maculae, supporting cells were significantly smaller but more numerous in the mutants. Here, we propose that our finding of a specific type I hair cell absence in the mutant's crista may now be employed in the identification of a profile of genes specific for the lacking cell type.

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)0.001
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 score0.208
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.017
GPT teacher head0.311
Teacher spread0.294 · 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

Citations5
Published2010
Admission routes2
Has abstractyes

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