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Record W3094297752 · doi:10.1002/syn.22192

Traffic noise exposure, cognitive decline, and amyloid‐beta pathology in an AD mouse model

2020· article· en· W3094297752 on OpenAlexafffund
Hadil Karem, Jogender Mehla, Bryan Kolb, Majid H. Mohajerani

Bibliographic record

VenueSynapse · 2020
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversity of Lethbridge
FundersAlzheimer Society of B.C.Natural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsCognitionInternal medicineEndocrinologyAnxietyDementiaCorticosteroneCognitive declineMedicineAmyloid betaOxidative stressEffects of sleep deprivation on cognitive performanceDiseaseNeurosciencePathologyPsychologyPhysiologyHormonePsychiatry

Abstract

fetched live from OpenAlex

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 APP NL‐G‐F/NL‐G‐F mice was exposed to CTNE (70 dB A , 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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.634

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 head0.051
GPT teacher head0.363
Teacher spread0.313 · 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 designObservational
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

Citations9
Published2020
Admission routes2
Has abstractyes

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