Genetic ablation of smooth muscle K <sub>IR</sub> 2.1 is inconsequential to the function of mouse cerebral arteries
Bibliographic record
Abstract
Cerebral blood flow is a finely tuned process dependent on coordinated changes in arterial tone. These changes are strongly tied to smooth muscle membrane potential and inwardly rectifying K + (K IR ) channels are thought to be a key determinant. To elucidate the role of K IR 2.1 in cerebral arterial tone development, this study examined the electrical and functional properties of cells, vessels and living tissue from tamoxifen-induced smooth muscle cell (SMC)-specific K IR 2.1 knockout mice. Patch-clamp electrophysiology revealed a robust Ba 2+ -sensitive inwardly rectifying K + current in cerebral arterial myocytes irrespective of K IR 2.1 knockout. Immunolabeling clarified that K IR 2.1 expression was low in SMCs while K IR 2.2 labeling was remarkably abundant at the membrane. In alignment with these observations, pressure myography revealed that the myogenic response and K + -induced dilation were intact in cerebral arteries post knockout. At the whole organ level, this translated to a maintenance of brain perfusion in SMC K IR 2.1 −/− mice, as assessed with arterial spin-labeling MRI. We confirmed these findings in superior epigastric arteries and implicated K IR 2.2 as more functionally relevant in SMCs. Together, these results suggest that subunits other than K IR 2.1 play a significant role in setting native current in SMCs and driving arterial tone.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".