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Record W2077927186 · doi:10.1002/pmic.201100604

Proteomic analysis of right and left cardiac ventricles under aerobic conditions and after ischemia/reperfusion

2012· article· en· W2077927186 on OpenAlexaff
Virgilio J. J. Cadete, Hanbin Lin, Jolanta Sawicka, Mieczysław Woźniak, Grzegorz Sawicki

Bibliographic record

VenuePROTEOMICS · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsVentricleInternal medicineCardiologyIschemiaReperfusion injuryShock (circulatory)Heat shock proteinMedicineChemistryBiochemistry

Abstract

fetched live from OpenAlex

Ischemia/reperfusion (I/R) injury is a major consequence of a cardiovascular intervention. The study of changes of the left and right ventricle proteomes from hearts subjected to I/R may be a key to revealing the pathological mechanisms underlying I/R-induced heart contractile dysfunction. Isolated rat hearts were perfused under aerobic conditions or subjected to 25 min global ischemia and 30 min reperfusion. At the end of perfusion, right and left ventricular homogenates were analyzed by 2DE. Contractile function and coronary flow were significantly reduced by I/R. 2DE followed by mass spectrometry identified ten protein spots whose levels were significantly different between aerobic left and right ventricles, eight protein spots whose levels were different between aerobic and I/R left ventricle, ten protein spots whose levels were different between aerobic and I/R right ventricle ten protein spots whose levels were different between the I/R groups. Among these protein spots were ATP synthase beta subunit, myosin light chain 2, myosin heavy chain fragments, peroxiredoxin-2, and heat shock proteins, previously associated with cardiovascular disease. These results reveal differences between proteomes of left and right ventricle both under aerobic conditions and in response to I/R that contribute to a better understanding of I/R injury.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.241
Teacher spread0.236 · 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

Citations22
Published2012
Admission routes1
Has abstractyes

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