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Record W4288060432 · doi:10.3791/64073

Viral Transgene Expression in Rodent Hearts and the Assessment of Cardiac Arrhythmia Risk

2022· article· en· W4288060432 on OpenAlexafffund
Aizhu Lu, Jerry Wang, Ying Xia, Ruonan Gu, Kyoung-Han Kim, Erin E. Mulvihill, Darryl R. Davis, Rob Beanlands, Wenbin Liang

Bibliographic record

VenueJournal of Visualized Experiments · 2022
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsMyocardial infarctionTransgeneHeart failureGenetically modified mouseCardiac arrhythmiaMedicineCardiac electrophysiologyHeart diseaseInternal medicineCardiologyDiseaseBiologyElectrophysiologyAtrial fibrillation

Abstract

fetched live from OpenAlex

Heart disease is the leading cause of morbidity and mortality worldwide. Due to their low cost, ease of handling, and abundance of transgenic strains, rodents have become essential models for cardiovascular research. However, spontaneous lethal cardiac arrhythmias that often cause mortality in heart disease patients are rare in rodent models of heart disease. This is primarily due to the species differences in cardiac electrical properties between human and rodents and poses a challenge to the study of cardiac arrhythmias using rodents. This protocol describes an approach to enable efficient transgene expression in mouse and rat ventricular myocardium using echocardiography-guided intramuscular injections of recombinant virus (adenovirus and adeno-associated virus). This work also outlines a method to enable reliable assessment of cardiac susceptibility to arrhythmias using isolated, Langendorff-perfused mouse and rat hearts with both adrenergic and programmed electrical stimulations. These techniques are critical for studying heart rhythm disorders associated with adverse cardiac remodeling after injuries, such as myocardial infarction.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.027
GPT teacher head0.454
Teacher spread0.427 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2022
Admission routes2
Has abstractyes

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