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Record W4383302687 · doi:10.1002/jdn.10285

Maternal music exposure during pregnancy influences reflexive motor behaviors in mice offspring

2023· article· en· W4383302687 on OpenAlexaff
Sara Bidari, Morteza Zendehdel, Shahin Hassanpour, Behrooz Rahmani

Bibliographic record

VenueInternational Journal of Developmental Neuroscience · 2023
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsDalhousie University
FundersUniversity of Tehran
KeywordsOffspringHindlimbPrenatal exposurePregnancyGrip strengthMedicinePhysiologyEndocrinologyBiology

Abstract

fetched live from OpenAlex

Evidence supports that music can modulate many physiological roles, exerting clear effects on the central nervous system. For this effect to be positive, music should be tuned at a frequency of 432 Hz. This study aims to determine the effects of prenatal exposure to music on reflexive motor behaviors in mice offspring. Six pregnant female NMRI mice (8-10 weeks old) were randomly and equally allocated into two groups. Group 1 as control was placed in a normal housing area (average room noise 35 dB), and Group 2 was exposed to music pitched at 432 Hz for 2 h a day played at constant volume (75/80 dB) during pregnancy. Following delivery, four pups from each pregnant mouse were selected, and reflexive motor behaviors including ambulation, hind-limb foot angle, surface righting, grip strength, front- and hind-limb suspension, and negative geotaxis were determined. Based on the findings, prenatal exposure to music significantly increased ambulation score, grip strength, and front- and hind-limb suspension compared to the control group (P < 0.05). Also, prenatal exposure to music significantly decreased hind-limb foot angle, negative geotaxis, and surface righting compared to the control group (P < 0.05). These results suggested that music exposure during pregnancy had a significant positive effect on all tested reflexive motor behaviors in mice offspring.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.060
GPT teacher head0.368
Teacher spread0.309 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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