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Modification of alterations in cardiac function and sarcoplasmic reticulum by vanadate in ischemic-reperfused rat hearts

2005· article· en· W2143432361 on OpenAlexaff
Satoshi Takeda, Seibu Mochizuki, Harjot K. Saini, Vijayan Elimban, Naranjan S. Dhalla

Bibliographic record

VenueJournal of Applied Physiology · 2005
Typearticle
Languageen
FieldChemistry
TopicVanadium and Halogenation Chemistry
Canadian institutionsSt. Boniface Hospital
Fundersnot available
KeywordsVanadateSuperoxide dismutaseCatalaseInternal medicineIschemiaXanthine oxidaseChemistryEndocrinologyEndoplasmic reticulumCardiac function curveReperfusion injuryAntioxidantBiochemistryMedicineEnzymeHeart failure

Abstract

fetched live from OpenAlex

To study the cardioprotective effects of vanadate on ischemia-reperfusion (I/R) injury, isolated rat hearts perfused at constant flow were subjected to global ischemia for 30 min followed by reperfusion for 30 min. In this experimental model, I/R markedly decreased ventricular developed pressure and increased end-diastolic pressure. Pretreatment of hearts with 4 microM vanadate attenuated I/R-induced cardiac dysfunction. The reduction in sarcoplasmic reticulum (SR) Ca2+ uptake and Ca2+ release, as well as SR protein contents for Ca2+-pump ATPase and Ca2+-release channel, was also prevented by vanadate. Pretreatment of hearts with an antioxidant mixture containing superoxide dismutase + catalase exerted effects similar to those of vanadate in I/R hearts. Postischemic treatment of hearts with vanadate or superoxide dismutase + catalase also had beneficial effects on I/R-induced changes in cardiac performance and SR function. Alterations in cardiac function and SR Ca2+ transport due to an oxyradical-generating system (xanthine + xanthine oxidase) or an oxidant (H2O2) were attenuated by treatment with vanadate. These results suggest that vanadate may exert beneficial effects on cardiac performance and SR function in I/R hearts because of its antioxidant action.

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 score0.004
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.218
Teacher spread0.211 · 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

Citations12
Published2005
Admission routes1
Has abstractyes

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