Alzheimer's disease and cerebrovascular pathology alter inward rectifier potassium (K<sub>IR</sub>2.1) channels in endothelium of mouse cerebral arteries
Bibliographic record
Abstract
Background and Purpose Inward rectifier potassium (KIR) channels are key effectors of vasodilatation in neurovascular coupling (NVC). KIRchannels expressed in cerebral endothelial cells (ECs) have been confirmed as essential modulators of NVC. Alzheimer's disease (AD) and cerebrovascular disease (CVD) impact on EC‐KIRchannel function, but whether oxidative stress or inflammation explains this impairment remains elusive. Experimental Approach We evaluated KIRchannel function in intact and EC‐denuded pial arteries of wild‐type (WT) and transgenic mice overexpressing a mutated form of the human amyloid precursor protein (APP mice, recapitulating amyloid β‐induced oxidative stress seen in AD) or a constitutively active form of TGF‐β1 (TGF mice, recapitulating inflammation seen in cerebrovascular pathology). The benefits of antioxidant (catalase) or anti‐inflammatory (indomethacin) drugs also were investigated. Vascular and neuronal components of NVC were assessed in vivo. Key Results Our findings show that (i) KIRchannel‐mediated maximal vasodilatation in APP and TGF mice reaches only 37% and 10%, respectively, of the response seen in WT mice; (ii) KIRchannel dysfunction results from KIR2.1 subunit impairment; (iii) about 50% of K+‐induced artery dilatation is mediated by EC‐KIRchannels; (iv) oxidative stress and inflammation impair KIRchannel function, which can be restored by antioxidant and anti‐inflammatory drugs; and (v) inflammation induces KIR2.1 overexpression and impairs NVC in TGF mice. Conclusion and Implications Therapies targeting both oxidative stress and inflammation are necessary for full recovery of KIR2.1 channel function in cerebrovascular pathology caused by AD and CVD.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".