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Record W4405808864 · doi:10.1007/s12035-024-04675-7

CD38 Coordinates with NF-κB to Promote Cochlear Inflammation in Noise-Induced Hearing Loss: the Protective Effect of Apigenin

2024· article· en· W4405808864 on OpenAlexaff
Da Liu, Na Sai, Ying Zhou, Ning Yu, Qing‐qing Jiang, Wei Sun, Weiwei Guo

Bibliographic record

VenueMolecular Neurobiology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCalcium signaling and nucleotide metabolism
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsNoise-induced hearing lossHearing lossInflammationMedicineAuditory fatigueCD38Hair cellSignal transductionCochleaPharmacologyChemistryCancer researchAudiologyImmunologyCell biologyBiologyNoise exposureStem cell

Abstract

fetched live from OpenAlex

Noise exposure is one of the most common causes of sensorineural hearing loss. Although many studies considered inflammation to be a major contributor to noise-induced hearing loss, the process of cochlear inflammation is still unclear. Studies have found that activation of the NF-κB signaling pathway results in the accumulation of macrophages in the inner ear plays an important role in hair cell damage. In this study, tandem mass tag (TMT) technique was used to analyze the changes in basilar membrane proteome expression before and after acoustic injury. After noise exposure, the nicotinamide adenine dinucleotide (NAD) metabolism level was decreased, and the NF-κB signaling pathway was activated. The expression of CD38, the main NAD hydrolase in mammals, may directly lead to inflammation onset. Then, anakinra, an IL-1 receptor blocker, and apigenin, a CD38 inhibitor, were administered to animals to protect against noise-induced hearing loss. Our results showed that anakinra had little influence on the hearing threshold shift, while apigenin significantly reduce the threshold shift of hearing by inhibiting the expression of NF-κB and CD38 can be a promising target for protecting against noise-induced hearing loss.

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 score0.003
Threshold uncertainty score0.607

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 head0.005
GPT teacher head0.241
Teacher spread0.236 · 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

Citations12
Published2024
Admission routes1
Has abstractyes

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