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Record W2751815819 · doi:10.1038/s41598-017-11458-9

Mild traumatic brain injury induces memory deficits with alteration of gene expression profile

2017· article· en· W2751815819 on OpenAlexafffund
Yawen Luo, Haiyan Zou, Yili Wu, Fang Cai, Si Zhang, Weihong Song

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of British Columbia HospitalUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsTraumatic brain injuryGene expressionGeneNeuroscienceBioinformaticsMedicineBiologyGeneticsPsychiatry

Abstract

fetched live from OpenAlex

Repeated mild traumatic brain injury (rmTBI), the most common type of traumatic brain injuries, can result in neurological dysfunction and cognitive deficits. However, the molecular mechanisms and the long-term consequence of rmTBI remain elusive. In this study, we developed a modified rmTBI mouse model and found that rmTBI-induced transient neurological deficits and persistent impairments of spatial memory function. Furthermore, rmTBI mice had long-lasting detrimental effect on cognitive function, exhibiting memory deficits even 12 weeks after rmTBI. Microarray analysis of whole genome gene expression showed that rmTBI significantly altered the expression level of 87 genes which are involved in apoptosis, stress response, metabolism, and synaptic plasticity. The results indicate the potential mechanism underlying rmTBI-induced acute neurological deficits and its chronic effect on memory impairments. This study suggests that long-term monitoring and interventions for rmTBI individuals are essential for memory function recovery and reducing the risk of developing neurodegenerative diseases.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.106
GPT teacher head0.369
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), 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

Citations34
Published2017
Admission routes2
Has abstractyes

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