Changes in the Cardiac GHSR1a-Ghrelin System Correlate With Myocardial Dysfunction in Diabetic Cardiomyopathy in Mice
Bibliographic record
Abstract
Ghrelin and its receptor, the growth hormone secretagogue receptor 1a (GHSR1a), are present in cardiac tissue. Activation of GHSR1a by ghrelin promotes cardiomyocyte contractility and survival, and changes in myocardial GHSR1a and circulating ghrelin track with end-stage heart failure, leading to the hypothesis that GHSR1a is a biomarker for heart failure. We hypothesized that GHSR1a could also be a biomarker for diabetic cardiomyopathy (DCM). We used two models of streptozotocin (STZ)-induced DCM: group 1, adult mice treated with 35 mg/kg STZ for 3 days; and group 2, neonatal mice treated with 70 mg/kg STZ at days 2 and 5 after birth. In group 1, mild fasting hyperglycemia (11 mM) was first detected 8 weeks after the last injection, and in group 2, severe fasting hyperglycemia (20 mM) was first detected 1 to 3 weeks after the last injection. In group 1, left ventricular function was slightly impaired as measured by echocardiography, and Western blot analysis showed a significant decrease in myocardial GHSR1a. In group 2, GHSR1a levels were also decreased as assessed by Cy5-ghrelin(1–19) fluorescence microscopy, and there was a significant negative correlation between GHSR1a levels and glucose tolerance. There were significant positive correlations between GHSR1a and ghrelin and between GHSR1a and sarcoplasmic reticulum Ca2+-ATPase 2a (SERCA2a), a marker for contractility, but not between GHSR1a and B-type natriuretic peptide, a marker for heart failure. We conclude that the subclinical stage of DCM is accompanied by alterations in the myocardial ghrelin-GHSR1a system, suggesting the possibility of a biomarker for DCM.
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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 | 0.001 | 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 | 0.000 |
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".