Single-Cell Sequencing Reveals Circadian Sensitivity of Noise-Induced Hearing Loss Mediated by Macrophage-Driven NLRP3 Inflammasome Activation
Bibliographic record
Abstract
Circadian sensitivity significantly influences the severity of noise-induced hearing loss (NIHL), but the underlying mechanisms remain unclear. Here, we applied single-cell RNA sequencing to 97,043 cochlear cells, identifying macrophages as the primary immune responders to acoustic trauma, with a notable increase in their proportion in the cochlea. Immunofluorescence confirmed significant recruitment and activation of cochlear macrophages following noise exposure, while in vivo macrophage depletion resulted in the recovery of hearing. Furthermore, analyses of differentially-expressed genes and pathways revealed pronounced activation of NLRP3 inflammasome signaling in macrophages during night-time noise exposure. Measurements of elevated IL-1β and IL-18 expression in cochlear macrophages by multiplex immunohistochemistry correlated with heightened inflammation in the night-time exposure group. These findings were further confirmed by the administration of the selective NLRP3 inhibitor CY-09, which mitigated inflammasome activation, preserved synaptic integrity, and protect against hearing loss. In conclusion, our findings underscore the role of macrophage-driven NLRP3 inflammasome activation in mediating circadian variations in cochlear damage, offering a potential therapeutic target for mitigating NIHL.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".