Functional expression of Kir2.x in human aortic endothelial cells: the dominant role of Kir2.2
Bibliographic record
Abstract
Inward rectifier K(+) channels (Kir) are a significant determinant of endothelial cell (EC) membrane potential, which plays an important role in endothelium-dependent vasodilatation. In the present study, several complementary strategies were applied to determine the Kir2 subunit composition of human aortic endothelial cells (HAECs). Expression levels of Kir2.1, Kir2.2, and Kir2.4 mRNA were similar, whereas Kir2.3 mRNA expression was significantly weaker. Western blot analysis showed clear Kir2.1 and Kir2.2 protein expression, but Kir2.3 protein was undetectable. Functional analysis of endothelial inward rectifier K(+) current (I(K)) demonstrated that 1) I(K) current sensitivity to Ba(2+) and pH were consistent with currents determined using Kir2.1 and Kir2.2 but not Kir2.3 and Kir2.4, and 2) unitary conductance distributions showed two prominent peaks corresponding to known unitary conductances of Kir2.1 and Kir2.2 channels with a ratio of approximately 4:6. When HAECs were transfected with dominant-negative (dn)Kir2.x mutants, endogenous current was reduced approximately 50% by dnKir2.1 and approximately 85% by dnKir2.2, whereas no significant effect was observed with dnKir2.3 or dnKir2.4. These studies suggest that Kir2.2 and Kir2.1 are primary determinants of endogenous K(+) conductance in HAECs under resting conditions and that Kir2.2 provides the dominant conductance in these cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".