MétaCan
Menu
Back to cohort
Record W2007276643 · doi:10.1002/mrm.20008

MR properties of excised neural tissue following experimentally induced inflammation

2004· article· en· W2007276643 on OpenAlexafffund
Greg J. Stanisz, Stephanie Webb, Catherine A. Munro, Teresa Pun, Rajiv Midha

Bibliographic record

VenueMagnetic Resonance in Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersCanadian Institutes of Health Research
KeywordsInflammationHistopathologyMagnetization transferSciatic nerveBrain tissueChemistryPathologyNuclear magnetic resonanceNecrosisAnatomyMedicineMagnetic resonance imagingInternal medicinePhysicsRadiology

Abstract

fetched live from OpenAlex

Changes in the MR parameters of inflamed neural tissue were measured in vitro. Tumor necrosis factor-alpha (TNF-alpha) was injected into rat sciatic nerves to induce inflammation with negligible axonal loss and demyelination. The MR parameters, such as T1/T2 relaxation and magnetization transfer (MT), were measured 2 days after TNF-alpha injection and were found to be substantially different from those of normal nerves. The average T1/T2 relaxation times increased, whereas the MT ratio (MTR) and the quantitative MT parameter M0B (which describes the semisolid pool of protons) decreased. The MR parameters correlated very well with the extracellular volume fraction (EM) of neural tissue evaluated by quantitative histopathology. The multicomponent T2 relaxation was shown to provide the best quantitative assessment of changes in neural tissue microstructure, and allowed us to distinguish between the processes of inflammation and demyelination. In comparison, the MT measurements were less successful due to competing contributions of demyelination and pH-sensitive changes in the MT effect.

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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.074
GPT teacher head0.355
Teacher spread0.281 · 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

Citations141
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueMagnetic Resonance in MedicineSame topicAdvanced Neuroimaging Techniques and ApplicationsFrench-language works237,207