Spatial learning and memory deficits in young adult mice exposed to a brief intense noise at postnatal age
Bibliographic record
Abstract
Noise pollution is a major hazardous factor to human health and is likely harmful for vulnerable groups such as pre-term infants under life-support system in an intensive care unit. Previous studies have suggested that noise exposure impairs children's learning ability and cognitive performance and cognitive functions in animal models in which the effect is mainly attributed to the oxidant stress of noise on the cognitive brain. The potential role of noise induced hearing loss (NIHL), rather than the oxidant stress, has also been indicated by a depression of neurogenesis in the hippocampus long after a brief noise exposure, which produces only a tentative oxidant stress. It is not clear if noise exposure and NIHL during early development exerts a long term impact on cognitive function and neurogenesis towards adulthood. In the present study, a brief noise exposure at high sound level was performed in neonatal C57BL/6J mice (15 days after birth) to produce a significant amount of permanent hearing loss as proved 2 months after the noise. At this age, the noise-exposed animals showed deteriorated spatial learning and memory abilities and a reduction of hippocampal neurogenesis as compared with the control. The averaged hearing threshold was found to be strongly correlated with the scores for spatial learning and memory. We consider the effects observed are largely due to the loss of hearing sensitivity, rather than the oxidant stress, due to the long interval between noise exposure and the observations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".