S100A1: A Multifaceted Therapeutic Target in Cardiovascular Disease
Bibliographic record
Abstract
Cardiovascular disease is the leading cause of death worldwide, showing a dramatically growing prevalence. It is still associated with a poor clinical prognosis, indicating insufficient long-term treatment success of currently available therapeutic strategies. Investigations of the pathomechanisms underlying cardiovascular disorders uncovered the Ca(2+) binding protein S100A1 as a critical regulator of both cardiac performance and vascular biology. In cardiomyocytes, S100A1 was found to interact with both the sarcoplasmic reticulum ATPase (SERCA2a) and the ryanodine receptor 2 (RyR2), resulting in substantially improved Ca(2+) handling and contractile performance. Additionally, S100A1 has been described to target the cardiac sarcomere and mitochondria, leading to reduced pre-contractile passive tension as well as enhanced oxidative energy generation. In endothelial cells, molecular analyses revealed a stimulatory effect of S100A1 on endothelial NO production by increasing endothelial nitric oxide synthase activity. Emphasizing the pathophysiological relevance of S100A1, myocardial infarction in S100A1 knockout mice resulted in accelerated transition towards heart failure and excessive mortality in comparison with wild-type controls. Mice lacking S100A1 furthermore displayed significantly elevated blood pressure values with abrogated responsiveness to bradykinin. On the other hand, numerous studies in small and large animal heart failure models showed that S100A1 overexpression results in reversed maladaptive myocardial remodeling, long-term rescue of contractile performance, and superior survival in response to myocardial infarction, indicating the potential of S100A1-based therapeutic interventions. In summary, elaborate basic and translational research established S100A1 as a multifaceted therapeutic target in cardiovascular disease, providing a promising novel therapeutic strategy to future cardiologists.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".