Receptor and nonreceptor tyrosine kinases in vascular biology of hypertension
Bibliographic record
Abstract
PURPOSE OF REVIEW: Extensive evidence indicates that receptor tyrosine kinases and nonreceptor tyrosine kinases underlie vascular damage in hypertension. However, recent clinical studies using vascular endothelial growth factor (VEGF) receptor inhibitors (bevacizumab, axitinib) revealed the unexpected finding of increased blood pressure. Whether this is a generalized receptor tyrosine kinase phenomenon or a VEGF receptor-specific effect is unclear. The present review focuses on current findings regarding the role of tyrosine kinases and signaling in vascular pathobiology of hypertension. RECENT FINDINGS: Multiple complex and interacting signaling pathways activated by receptor and nonreceptor tyrosine kinases are upregulated and have been implicated in vascular alterations associated with high blood pressure. Experimental evidence suggests that receptor tyrosine kinase activation by direct ligand binding as well as by ligand-independent mechanisms through transactivation by G protein-coupled receptors plays a role in vascular signaling and cardiovascular diseases. SUMMARY: Cellular mechanisms and signaling pathways mediated by tyrosine kinases involved in hypertensive vascular damage are currently the subject of intensive investigation. The unexpected finding of hypertension as a side effect in patients treated with VEGF receptor inhibitors suggests that some tyrosine kinases negatively regulate vascular function. Further characterization of these processes will provide greater understanding of the role of tyrosine kinases in vascular pathobiology in hypertension and should provide new insights on novel therapeutic targets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".