Accelerated age-related decline in hippocampal neurogenesis in mice with noise-induced hearing loss is associated with hippocampal microglial degeneration
Bibliographic record
Abstract
Large-scale epidemiological surveys suggest that hearing loss (HL) is a significant risk factor for dementia. We previously showed that noise-induced HL (NIHL) impairs hippocampal cognitive function and decreases hippocampal neurogenesis and neuronal complexity, suggesting a causal role of HL in dementia. To further investigate the influence of acquired peripheral HL on hippocampal neurogenesis with the aging process as well as the underlying mechanism, we produced NIHL in male CBA/J mice and assessed hippocampal neurogenesis and microglial morphology in the auditory brain and hippocampus at 4 days post-noise exposure (DPN) or 1, 3, 6, or 12 months post-noise exposure (MPN) by immunofluorescence labeling. We found that the age-related decline in hippocampal neurogenesis was accelerated in mice with NIHL. Furthermore, in mice with NIHL, prolonged microglial activation occurred from 1 MPN to 12 MPN across multiple auditory nuclei, while aggravated microglial deterioration occurred in the hippocampus and correlated with the age-related decline in hippocampal neurogenesis. These results suggest that acquired peripheral HL accelerates the age-related decline in hippocampal neurogenesis and that hippocampal microglial degeneration may contribute to the development of neurodegeneration following acquired peripheral HL.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".