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Record W4237732428 · doi:10.1172/jci0213359

Dual gene therapy with SERCA1 and Kir2.1 abbreviates excitation without suppressing contractility

2002· article· en· W4237732428 on OpenAlexfundno aff
Irene L. Ennis, Ronald A. Li, Anne M. Murphy, Eduardo Marbán, Hanne Nuss

Bibliographic record

VenueJournal of Clinical Investigation · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsnot available
FundersNational Institutes of HealthUniversidad Nacional de La PlataHeart and Stroke Foundation of CanadaJohns Hopkins UniversityAmerican Heart Association
KeywordsContractilityRepolarizationHeart failureMedicineCalciumHeartbeatInternal medicineCardiologyEndocrinologyBiologyElectrophysiologyComputer science

Abstract

fetched live from OpenAlex

Heart failure is characterized by depressed contractility and delayed repolarization. The latter feature predisposes the failing heart to ventricular arrhythmias and represents a logical target for gene therapy. Unfortunately, unopposed correction of the delay in repolarization will decrease the time available for calcium cycling during each heartbeat, potentially aggravating the depression of contractility. Here we describe the development and application of a novel gene therapy strategy designed to abbreviate excitation without depressing contraction. The calcium ATPase SERCA1 was coexpressed with the potassium channel Kir2.1 in guinea pig hearts. Myocytes from the hearts had bigger calcium transients and shorter action potentials. In vivo, repolarization was abbreviated, but contractile function remained unimpaired. Dual gene therapy of the sort described here can be generalized to exploit opposing or synergistic therapeutic principles to achieve a tailored phenotype.

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)0.001
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 score0.141
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.000
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 head0.112
GPT teacher head0.397
Teacher spread0.285 · 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

Citations48
Published2002
Admission routes1
Has abstractyes

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