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Record W2022675535 · doi:10.1371/journal.pone.0049550

Silent Damage of Noise on Cochlear Afferent Innervation in Guinea Pigs and the Impact on Temporal Processing

2012· article· en· W2022675535 on OpenAlexafffund
Lijie Liu, Hui Wang, Lijuan Shi, Awad M. Almuklass, Tingting He, Steven J. Aiken, Manohar Bance, Shankai Yin, Jian Wang

Bibliographic record

VenuePLoS ONE · 2012
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health ResearchNational Natural Science Foundation of China
KeywordsSpiral ganglionGuinea pigHearing lossInner earAfferentNoise-induced hearing lossSynapseNoise (video)BiologyRibbon synapseAuditory fatigueCochleaNoise exposureMedicineAudiologyAnatomyNeuroscienceEndocrinologySynaptic vesicle

Abstract

fetched live from OpenAlex

Noise-exposure at levels low enough to avoid a permanent threshold shift has been found to cause a massive, delayed degeneration of spiral ganglion neurons (SGNs) in mouse cochleae. Damage to the afferent innervation was initiated by a loss of synaptic ribbons, which is largely irreversible in mice. A similar delayed loss of SGNs has been found in guinea pig cochleae, but at a reduced level, suggesting a cross-species difference in SGN sensitivity to noise. Ribbon synapse damage occurs "silently" in that it does not affect hearing thresholds as conventionally measured, and the functional consequence of this damage is not clear. In the present study, we further explored the effect of noise on cochlear afferent innervation in guinea pigs by focusing on the dynamic changes in ribbon counts over time, and resultant changes in temporal processing. It was found that (1) contrary to reports in mice, the initial loss of ribbons largely recovered within a month after the noise exposure, although a significant amount of residual damage existed; (2) while the response threshold fully recovered in a month, the temporal processing continued to be deteriorated during this period.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.408

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 head0.102
GPT teacher head0.300
Teacher spread0.198 · 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

Citations85
Published2012
Admission routes2
Has abstractyes

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Same venuePLoS ONESame topicHearing, Cochlea, Tinnitus, GeneticsFrench-language works237,207