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Record W2094588478 · doi:10.1117/1.1751125

Detection of myocardial cell damage in isolated rat hearts with near-infrared spectroscopy

2004· article· en· W2094588478 on OpenAlexafffund
Stephen P. Nighswander‐Rempel, Raymond A. Shaw, Bozena Kuzio, V.V. Kupriyanov

Bibliographic record

VenueJournal of Biomedical Optics · 2004
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsNational Research Council CanadaNational Research Council Institute for Biodiagnostics
FundersCanadian Institutes of Health Research
KeywordsDigitoninMembraneBiophysicsIntracellularCell membranePerfusionChemistryWashoutMembrane potentialBiochemistryMedicineInternal medicineBiology

Abstract

fetched live from OpenAlex

One hallmark of cell death resulting from prolonged ischemia is cell membrane disruption. We apply optical spectroscopy to gauge membrane disruption in isolated rat hearts by monitoring (1) the washout of myoglobin (Mb) and (2) the accumulation of an exogenous contrast agent in permeabilized cells. The contrast agent, a neodymium (Nd) chelate, has several absorptions in the visible and near-IR, and when present in the perfusate, it cannot penetrate cellular membranes. When membrane integrity is disrupted, however, it is expected to accumulate within the intracellular space; moreover, cellular Mb is expected to wash out. To test this hypothesis, rat hearts (n=12) are perfused with Krebs-Henseleit buffer (KHB), followed by perfusion with KHB in which a 5 mM Nd-DTPA solution is present. Membrane damage is then induced by infusion of digitonin into the Nd-KHB perfusate to provide a digitonin concentration of 2.5, 5, or 10 microg/mL. After 30 min of infusion, Mb levels fall to 46+/-14% of baseline levels and Nd-DTPA rises to 161+/-19% of predigitonin levels. No apparent dependence of total membrane disruption on digitonin concentration over the concentration range studied is found, although higher concentrations do lead to more rapid membrane disruption.

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)0.000
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 score0.038
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.264
Teacher spread0.258 · 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

Citations4
Published2004
Admission routes2
Has abstractyes

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