Tβ4–Ac-SDKP pathway: Any relevance for the cardiovascular system?
Bibliographic record
Abstract
-acetyl-seryl-aspartyl-lysyl-proline (Ac-SDKP) pathway as a new source of future therapeutic tools to treat cardiovascular and renal diseases. In this review article, we attempted to shed light on the numerous experimental findings pertaining to the many promising cardiovascular therapeutic avenues for Tβ4 and (or) its N-terminal derivative, Ac-SDKP. Specifically, Ac-SDKP is endogenously produced from the 43-amino acid Tβ4 by 2 successive enzymes, meprin α and prolyl oligopeptidase. We also discussed the possible mechanisms involved in the Tβ4-Ac-SDKP-associated cardiovascular biological effects. In infarcted myocardium, Tβ4 and Ac-SDKP facilitate cardiac repair after infarction by promoting endothelial cell migration and myocyte survival. Additionally, Tβ4 and Ac-SDKP have antifibrotic and anti-inflammatory properties in the arteries, heart, lungs, and kidneys, and stimulate both in vitro and in vivo angiogenesis. The effects of Tβ4 can be mediated directly through a putative receptor (Ku80) or via its enzymatically released N-terminal derivative Ac-SDKP. Despite the localization and characterization of Ac-SDKP binding sites in myocardium, more studies are needed to fully identify and clone Ac-SDKP receptors. It remains promising that Ac-SDKP or its degradation-resistant analogs could serve as new therapeutic tools to treat cardiac, vascular, and renal injury and dysfunction to be used alone or in combination with the already established pharmacotherapy for cardiovascular diseases.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".