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Record W2270056461 · doi:10.4172/2368-0512.1000004

Role of intracellular Ca2+ overload in inducing changes in cardiac gene expression

2014· article· en· W2270056461 on OpenAlexafffundvenue
A. Tanju Özçelikay, Donald Chapman

Bibliographic record

VenueCurrent research. Cardiology · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiac Ischemia and Reperfusion
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
FundersCanadian Institutes of Health ResearchHospital Research FoundationAnkara Universitesi
KeywordsIntracellularGene expressionGeneCell biologyChemistryBiologyInternal medicineBiochemistryMedicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Although intracellular Ca2+ overload is believed to cause cardiac abnormalities and subcellular remodelling, its role in inducing alterations in cardiac gene expression has not been investigated. METHODS: Intracellular Ca2+ overload was induced in isolated rat hearts on perfusion with Ca2+-free medium for 5 min followed by reperfusion with medium containing different concentrations of Ca2+ for 30 min (Ca2+paradox). Changes in messenger RNA levels for various subcellular proteins were monitored either by Northern blotting or real-time polymerase chain reaction techniques. RESULTS: Marked depressions in gene expression for sarcolemma Na+- K+-ATPase and Na+-Ca2+ exchanger, sarcoplasmic reticulum Ca2+-pump ATPase, Ca2+ release channel and phospholamban, as well as myofibrillar α- and β-myosin heavy chain proteins were observed in hearts reperfused with 1.25 mM Ca2+ following perfusion with Ca2+-free medium. In contrast, messenger RNA levels for calpain-1 and -2 proteins were elevated in hearts subjected to Ca2+paradox. These changes were dependent on the concentration of Ca2+ in the reperfusion medium. CONCLUSIONS: The results suggest that intracellular Ca2+ overload is an important factor in the induction of defects in gene expression, subcellular remodelling and cardiac dysfunction in heart disease.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.094
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.038
GPT teacher head0.344
Teacher spread0.306 · 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

Citations5
Published2014
Admission routes3
Has abstractyes

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