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Record W6966627480 · doi:10.48448/xanz-fd60

The effects of maternal immune activation on early developmental milestones and behaviour in outbred CD-1 mice.

2021· other· en· W6966627480 on OpenAlexaff

Bibliographic record

VenueUnderline Science Inc. · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOffspringImmune systemDevelopmental MilestoneAutismOpen fieldLipopolysaccharideHoming (biology)Cerebral palsy

Abstract

fetched live from OpenAlex

Maternal immune activation (MIA) in mice can be used to investigate the link between perinatal infection and early-life developmental disorders such as cerebral palsy and autism spectrum disorder. Studies have shown that adult mice exposed to a bacterial or viral immune response during early- to mid-embryonic development display reduced social communication and interaction, and increased repetitive stereotypic behaviour. To date however, few studies have looked at the effects of MIA on early behaviours and developmental milestones in mice. In the current study, pregnant CD-1 dams were injected at embryonic day 11.5 with either lipopolysaccharide (LPS) or polyinosinic:polycytidylic acid (PolyIC) to stimulate aspects of a bacterial or viral immune response, respectively. Offspring were then assessed from post-natal day 2-21 using a modified Fox developmental behavioural test battery to assess reflexive and morphological developmental milestones. Additionally, grooming, homing to maternal bedding, and activity/exploration in the open field were evaluated. Overall, accelerated development and alterations in behaviour were observed in the MIA mice compared to sham-injected controls. These results suggest that MIA results in differential physiological and behavioural characteristics that emerge during early post-natal development, and further highlight the need for research that focuses on the effects of MIA during this developmental period.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.266
Teacher spread0.254 · 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 designBench or experimental
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

Citations0
Published2021
Admission routes1
Has abstractyes

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Same venueUnderline Science Inc.French-language works237,207