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Record W2617219790 · doi:10.1002/nbm.3743

Early regional cuprizone‐induced demyelination in a rat model revealed with MRI

2017· article· en· W2617219790 on OpenAlexafffund
Wendy Oakden, Nicholas A. Bock, Alia Al‐Ebraheem, Michael Farquharson, Greg J. Stanisz

Bibliographic record

VenueNMR in Biomedicine · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsUniversity of TorontoMcMaster UniversitySunnybrook Health Science Centre
FundersCanadian Institutes of Health Research
KeywordsNeuroscienceNuclear magnetic resonanceMagnetic resonance imagingMultiple sclerosisMedicinePathologyChemistryPsychologyPhysicsRadiologyImmunology

Abstract

fetched live from OpenAlex

The cuprizone model of demyelination is well established in the mouse as a tool for the study of the mechanisms of both demyelination and remyelination. It is often desirable, however, to have a larger model, such as the rat, especially for imaging‐based studies, yet initial work has failed to show demyelination in cuprizone‐fed rats. Several recent studies have demonstrated demyelination in the rat, but only in the corpus callosum. In this study, we acquired high‐resolution, three‐dimensional images of the whole brain every 2 weeks, using a T 1 ‐weighted magnetization‐prepared rapid acquisition gradient echo imaging sequence, optimized for myelin contrast, in order to assess myelination across the entire rat brain over a period of 8 weeks on a 1% cuprizone diet. We observed a consistent pattern of demyelination, beginning in the cerebellum by 4 weeks and involving more rostral regions of the brain by 8 weeks on the cuprizone diet, with validation using Luxol fast blue histology. This imaging technique permits the effects of cuprizone‐induced demyelination to be followed longitudinally in a single animal, over the entire brain. In turn, this may facilitate the establishment of the cuprizone model of demyelination in the rat.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.604

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 head0.072
GPT teacher head0.308
Teacher spread0.236 · 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

Citations19
Published2017
Admission routes2
Has abstractyes

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