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Record W1729149818 · doi:10.1002/ar.22715

Evaluation of Hippocampal Injury and Cognitive Function Induced by Embolization in the Rat Brain

2013· article· en· W1729149818 on OpenAlexfundno aff
Hengai Zhang, Mei Gao, Bainian Chen, Lili Shi, Qiaoyun Wang, Xiaoyan Yu, Zhao-Hong Xuan, Li Gao, Guanhua Du

Bibliographic record

VenueThe Anatomical Record · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaMcGill University
KeywordsHippocampal formationCognitionEmbolizationNeuroscienceFunction (biology)Brain functionMedicinePsychologySurgeryBiologyCell biology

Abstract

fetched live from OpenAlex

Embolism is responsible for at least 20% of all stroke and half of cerebral infarctions. A number of animal models have been developed to mimic thromboembolic stroke. However, little aimed directly at hippocampal damage and cognitive function. In the present study, three sizes of emboli (150-178 μm, 74-124 μm, and 48-74 μm) were employed to induce thromboembolic stroke model in rats. Results showed that the diameter of the particle was critical for animal behavioral and histopathological consequences. Hematoxylin-eosin (HE) staining revealed that CA1 and CA2-3, which are two of the main hippocampal subdivisions were injured seriously, especially induced by emboli(48-74 μm) . At 24 hr, the neurological deficit scores showed that emboli injection could cause significant neurological deficit, and the increase of neurological deficit scores correlated well to the diameter of emboli. At 60 days, emboli(150-178 μm) and emboli(48-74 μm) lead to obvious cognitive impairment, which correlated well to the hippocampal CA1 injury. Our research might be helpful to choose suitable size of emboli to induce animal model to research subcortical ischemia and vascular dementia. However, cognitive alterations and cerebral injury following different sizes of emboli injection in rats remains a topic for future investigation.

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)0.001
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 score0.529
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.047
GPT teacher head0.306
Teacher spread0.259 · 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

Citations12
Published2013
Admission routes1
Has abstractyes

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