Zipper‐interacting protein kinase is a key regulator of vascular smooth muscle tone with implications in development of hypertension (676.18)
Bibliographic record
Abstract
Essential hypertension is linked to increased contractile tone in the resistance vasculature and affects ~25% of adults, increasing their risk for more severe cardiovascular disease. Recently, zipper‐interacting kinase (ZIPK) was linked to essential hypertension in animal models of the disease. We possess a novel inhibitor of ZIPK that lacks off‐target effects against other contractile kinases and is uniquely situated to investigate the role of ZIPK in normal and pathological vascular smooth muscle function. Hypothesis: ZIPK contributes to the Ca2+ sensitization of vascular smooth muscle, which is in turn accentuated in hypertensive disease states. We used isolated ex vivo vessels with pressure myography, along with the ZIPK inhibitor HS38, to investigate cerebral vessels isolated from human biopsies and spontaneously hypertensive rats (SHR). ZIPK was expressed in human cerebral vessels and contributes to the myogenic response to pressure. Treatment with HS38 resulted in a ~60% reduction in the magnitude of myogenic contractions over the 60‐120mmHg pressure range. Moreover, when early‐stage SHR were compared, we found the myogenic response was enhanced with an increase in the contribution of ZIPK. Based on these findings, we conclude that ZIPK is a critical factor in the development of essential hypertension, and represents a unique and viable, therapeutic target in humans.
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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 | 0.000 |
| 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".