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Record W2166887223 · doi:10.1109/iembs.2008.4650564

Reduced conduction reserve of the propagating cardiac impulse in the diabetic rat heart: A model study

2008· article· en· W2166887223 on OpenAlexaff
Haisam A. Ghaly, Patrick M. Boyle, Edward J. Vigmond, Anders Nygren

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsImpulse (physics)CardiologyInternal medicineThermal conductionMedicineEnvironmental sciencePhysicsThermodynamics

Abstract

fetched live from OpenAlex

Conduction velocity is dependent on two main factors: intercellular electrical coupling and cellular electrical excitability. There is significant redundancy, 'conduction reserve', in these parameters such that significant reduction in the conduction velocity of the action potential requires either a severe change in one of these parameters or a combined change in both parameters. Studies in diabetic rat hearts have shown a significant reduction in the conduction reserve and it was hypothesized that this is mainly due to the lateralization of the gap junction protein connexin 43 (Cx43). To gain a better understanding of the effect of reduced intercellular coupling, a rat ventricle myocyte model was used to simulate propagation along a strand of cells. Simulations were performed to assess the effect of reduction of intercellular conductance on the conduction velocity. As the conductance of the gap junction decreased a significant reduction in the conduction velocity was observed. The relationship between conduction velocity and intercellular coupling became steeper with decreasing coupling, such that conduction velocity became increasingly sensitive to further uncoupling. This is consistent with experimental results, in which application of the gap junction uncoupler heptanol caused a larger conduction slowing in diabetic hearts than in controls.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.049
GPT teacher head0.289
Teacher spread0.240 · 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
Published2008
Admission routes1
Has abstractyes

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