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Noninvasive assessment of cardiac ischemic injury using87Rb and23Na MR imaging,31P MR, and optical spectroscopy

2000· article· en· W1977666516 on OpenAlexaff
V.V. Kupriyanov, George Dai, Raymond A. Shaw, J. Sun, Olga Jilkina, Z Luo, Henry H. Mantsch, R. Deslauriers

Bibliographic record

VenueMagnetic Resonance in Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsNational Research Council Institute for BiodiagnosticsUniversity of Manitoba
Fundersnot available
KeywordsIschemiaAnterior wallOcclusionChemistryMagnetic resonance imagingIschemic injuryNuclear medicineNuclear magnetic resonanceMedicineCardiologyRadiology

Abstract

fetched live from OpenAlex

The aim of the study was to compare and analyze different noninvasive indices of cell damage in the isolated pig heart model of regional ischemia. We used (23)Na and (87)Rb MR imaging to evaluate Na(+)/K(+) balance, (31)P MR spectroscopy to measure energetics, and optical spectroscopy to assess oxymyoglobin (MbO(2)). Hearts were subjected to 120-min occlusion of the left anterior descending artery and were then reperfused for 120 min. Reperfusion resulted in an increase in (23)Na (37 +/- 18% of the posterior wall) and decrease in (87)Rb (55 +/- 15%) image intensities, partial recovery of PCr, ATP, the total phosphates, and MbO(2) in the anterior wall. The above changes are consistent with the irreversible cell damage in the anterior wall, confirmed by lack of staining with triphenyltetrazolium chloride. Changes in Na(+) and Rb(+) in the infarct area inversely correlated and their ratio is a more sensitive index of cell injury than either of them alone.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.010
GPT teacher head0.339
Teacher spread0.329 · 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 designObservational
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
Published2000
Admission routes1
Has abstractyes

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