Plasma concentrations of matrix metalloproteinase-2, tissue inhibitor of metalloproteinase-1 and osteopontin reflect severity of heart failure in DOCA-salt hypertensive rat
Bibliographic record
Abstract
The matrix metalloproteinases (MMPs) and their endogenous inhibitors, the tissue inhibitors of metalloproteinases (TIMPs) play a key role in extracellular matrix maintenance and are altered in the failing heart, both in experimental models and in chronic end-stage heart failure in humans. As the common diagnostic markers of heart failure, atrial natriuretic peptide (ANP) and B-type natriuretic peptide (BNP) primarily reflect increased pressure loading, determination of soluble, heart-derived MMPs and TIMPs in plasma, as well as the determination of the emerging fibrosis marker osteopontin (OPN) might be valuable tools for detecting heart fibrosis. In this study the effect of spironolactone treatment on plasma MMP-2, TIMP-1 and OPN levels was assessed in a heart failure animal model. Unilaterally nephrectomized Sprague Dawley rats received subcutaneous injection of 100 mg deoxycorticosterone acetate (DOCA) once a week and 1% (w/v) NaCl in drinking water. Blood pressure was monitored weekly and blood samples were collected after 1, 2 and 4 weeks. After 6 weeks, left ventricular contractility (LVC) and heart weight-to-body weight ratio (HW/BW) were assessed. DOCA treatment increased plasma MMP-2, TIMP-1 and OPN concentrations. Alterations of plasma marker levels were correlated with changes of HW/BW and paralleled impaired LVC. Furthermore, beneficial effects of spironolactone treatment were observed. In DOCA-salt hypertensive rats, plasma concentrations of MMP-2, TIMP-1 and OPN reflected heart failure associated with haemodynamic, functional and morphological changes. Based on these findings, it appears reasonable to use plasma markers of fibrosis to monitor the development of heart failure.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".