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Record W2049332135 · doi:10.1038/ncomms7235

Targeting protein tyrosine phosphatase σ after myocardial infarction restores cardiac sympathetic innervation and prevents arrhythmias

2015· article· en· W2049332135 on OpenAlexfundno aff
Ryan T. Gardner, Lianguo Wang, Bradley T. Lang, Jared M. Cregg, Claire Dunbar, William R. Woodward, Jerry Silver, Crystal M. Ripplinger, Beth A. Habecker

Bibliographic record

VenueNature Communications · 2015
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteMcGill UniversityNational Institute of Neurological Disorders and StrokeAmerican Heart Association
KeywordsMyocardial infarctionMedicineReinnervationProtein tyrosine phosphataseInternal medicineCardiologySudden cardiac deathStimulationAdrenergicEndocrinologyDenervationAdrenomedullinReceptorSympathetic nervous systemAnatomy

Abstract

fetched live from OpenAlex

Millions of people suffer a myocardial infarction (MI) every year, and those who survive have increased risk of arrhythmias and sudden cardiac death. Recent clinical studies have identified sympathetic denervation as a predictor of increased arrhythmia susceptibility. Chondroitin sulfate proteoglycans present in the cardiac scar after MI prevent sympathetic reinnervation by binding the neuronal protein tyrosine phosphatase receptor σ (PTPσ). Here we show that the absence of PTPσ, or pharmacologic modulation of PTPσ by the novel intracellular sigma peptide (ISP) beginning 3 days after injury, restores sympathetic innervation to the scar and markedly reduces arrhythmia susceptibility. Using optical mapping we observe increased dispersion of action potential duration, supersensitivity to β-adrenergic receptor stimulation and Ca(2+) mishandling following MI. Sympathetic reinnervation prevents these changes and renders hearts remarkably resistant to induced arrhythmias.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.014
GPT teacher head0.278
Teacher spread0.263 · 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 designObservational
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

Citations119
Published2015
Admission routes1
Has abstractyes

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