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Record W2119672343 · doi:10.1177/0748233709105593

Verapamil but not calpain or creatine alters arsenate-induced cardiac cell death

2009· article· en· W2119672343 on OpenAlexaff
MW Luong, Rabkin Sw

Bibliographic record

VenueToxicology and Industrial Health · 2009
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsArsenateVerapamilArsenicProgrammed cell deathToxicityChemistryCreatineCalpainCreatine kinasePharmacologyCalciumBiochemistryBiologyApoptosisEnzyme

Abstract

fetched live from OpenAlex

The objective of this study was to examine the potential of arsenate to induce cardiomyocyte cell death and to explore the cellular mechanisms of arsenate toxicity. Isolated cardiomyocytes in culture from embryonic chick hearts were treated with a pentavalent arsenic species (H3AsO4) or arsenate. Arsenate produced a significant (P < 0.01) concentration-dependent increase in cell death with an EC50 about 1 mM. Cardiomyocytes manifested a loss of actin structure, reduced size, and damaged nuclei. Creatine 0.1-100 uM did not significantly modify arsenate-induced cell death. In contrast, verapamil, 0.01-1 uM, produced a significant concentration-dependent accentuation of arsenate-induced cell death. The effect of verapamil was evident at low concentrations of arsenate, which produced only a small increase in cell death, and at high concentrations of arsenate, which induced a large amount of cell death. Verapamil alone did not alter cardiomyocyte cell death. By comparison, calpain inhibitor II did not modify arsenate-induced cardiomyocyte cell death. These data suggest that cardiomyocytes are vulnerable to the effects of verapamil to increase the cellular toxicity of arsenate. Two potential cellular mechanisms of arsenate toxicity, however, are likely not involved in arsenate toxicity namely calpain activation and reduction of creatine phosphate production.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.100
GPT teacher head0.359
Teacher spread0.259 · 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.

Study designOther design
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

Citations8
Published2009
Admission routes1
Has abstractyes

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