Traffic noise exposure, cognitive decline, and amyloid‐beta pathology in an AD mouse model
Bibliographic record
Abstract
Abstract Concerns are growing that exposure to environmental pollutants, such as traffic noise, might cause cognitive impairments and predispose individuals toward the development of Alzheimer's disease (AD) dementia. In this study in a knock‐in mouse model of AD, we investigated how chronic traffic noise exposure (CTNE) impacts cognitive performance and amyloid‐beta (Aβ) pathology. A group of APPNL‐G‐F/NL‐G‐F mice was exposed to CTNE (70 dBA, 8 hr/day for 1 month) and compared with nonexposed counterparts. Following CTNE, an increase in hypothalamic–pituitary–adrenal (HPA) axis responsivity was observed by corticosterone assay of the blood. One month after CTNE, the CTNE group demonstrated impairments in cognitive and motor functions, and indications of anxiety‐like behavior, relative to the control animals. The noise‐exposed group also showed elevated Aβ aggregation, as inferred by a greater number of plaques and larger average plaque size in various regions of the brain, including regions involved in stress regulation. The results support that noise‐associated dysregulation of the neuroendocrine system as a potential risk factor for developing cognitive impairment and Aβ pathology, which should be further investigated in human studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".