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Record W2084921078 · doi:10.1073/pnas.0307333101

Cardiac hypertrophy and sudden death in mice with a genetically clamped renin transgene

2004· article· en· W2084921078 on OpenAlexfundno aff
Kathleen M. Caron, Leighton R. James, Hyung‐Suk Kim, Josh Knowles, Rick Uhlir, Lan Mao, John R. Hagaman, Wayne E. Cascio, Howard A. Rockman, Oliver Smithies

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2004
Typearticle
Languageen
FieldMedicine
TopicRenin-Angiotensin System Studies
Canadian institutionsnot available
FundersNational Institutes of HealthNational Heart, Lung, and Blood InstituteKidney Foundation of Canada
KeywordsTransgeneInternal medicineMuscle hypertrophyEndocrinologyRenin–angiotensin systemHeart failureAtrial natriuretic peptideBiologyAdrenomedullinGenetically modified mouseCardiologyGeneReceptorMedicineGeneticsBlood pressure

Abstract

fetched live from OpenAlex

Several mouse models have already proved valuable for investigating hypertrophic responses to cardiac stress. Here, we characterize one caused by a well defined single copy transgene, RenTgMK, that genetically clamps plasma renin and thence angiotensin II at high levels. All of the transgenic males develop concentric cardiac hypertrophy with fibrosis but without dilatation. Over half die suddenly aged 6-8 months. Telemetry showed disturbances in diurnal rhythms a few days before death and, later, electrocardiographic disturbances comparable to those in humans with congestive heart failure. Expression of seven hypertrophy-related genes in this and two categorically different models (lack of atrial natriuretic peptide receptor A; overexpression of calsequestrin) were compared. Statistical analyses show that ventricular expressions of the genes coding for atrial natriuretic peptide, beta myosin heavy chain, medium chain acyl-CoA dehydrogenase, and adrenomedullin correlate equally well with the degree of hypertrophy, although their ranges of expression are, respectively, 50-, 30-, 10-, and 3-fold.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.036
GPT teacher head0.290
Teacher spread0.254 · 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

Citations49
Published2004
Admission routes1
Has abstractyes

Explore more

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