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Record W2329522280 · doi:10.1152/ajpheart.00934.2001

Differential distribution of Kir2.1 and Kir2.3 subunits in canine atrium and ventricle

2002· article· en· W2329522280 on OpenAlexaff
Peter Melnyk, Liming Zhang, Alvin Shrier, Stanley Nattel

Bibliographic record

VenueAmerican Journal of Physiology-Heart and Circulatory Physiology · 2002
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsUniversité de MontréalMcGill UniversityMontreal Heart Institute
Fundersnot available
KeywordsVentricleImmunocytochemistryAtrium (architecture)Western blotInternal medicinePatch clampProtein subunitImmunohistochemistryBiologyEndocrinologyChemistryMedicineElectrophysiologyAtrial fibrillationBiochemistry

Abstract

fetched live from OpenAlex

Ventricular inward rectifier K(+) current (I(K1)) is substantially larger than atrial, producing functionally important action potential differences. To evaluate possible molecular mechanisms, we recorded I(K1) with patch-clamp techniques and studied Kir2.1 and Kir2.3 subunit expression. I(K1) density was >10-fold larger in the canine ventricle than atrium. Kir2.1 protein expression (Western blot) was 78% greater (P < 0.01) in the ventricle, but Kir2.3 band density was 228% greater (P < 0.01) in the atrium. Immunocytochemistry showed transverse tubular localization of Kir2.1 in 89% (17 of 19) of ventricular and 26% (5 of 19, P < 0.0001) of atrial cells. Both exhibited a weakly positive Kir2.1 signal at intercalated disks. Kir2.3 was strongly expressed at the intercalated disks in all cells and in the transverse tubular regions in 78% (14 of 18) of atrial and 22% (4 of 18, P < 0.001) of ventricular cells. Tissue immunohistochemical results qualitatively resembled isolated cell data. We conclude that the expression density and subcellular localization of Kir2.1 and Kir2.3 subunits differ in the canine atrium versus ventricle. Overall protein density differences are insufficient to explain I(K1) discrepancies, which may be related to differences in subcellular distribution.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.982
Threshold uncertainty score0.669

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 studies0.0000.001
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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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

Citations104
Published2002
Admission routes1
Has abstractyes

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