Cardiac Natriuretic Peptides Gene Expression and Secretion in Inflammation
Bibliographic record
Abstract
The genetic expression and secretion of the cardiac polypeptide hormones atrial natriuretic factor (ANF or ANP) and brain natriuretic peptide (BNP) have been studied mainly in the context of cardiac diseases associated with neuroendocrine and hemodynamic changes arising from cardiac dysfunction such as in chronic congestive heart failure. In this type of pathology, both ANF and BNP plasma levels change in an approximate coordinated fashion so that the use of these hormones as biomarkers of cardiac disease is, in principle, indistinctive. However, we reported that during an acute cardiac allograft rejection episode, BNP plasma levels can significantly increase in the absence of a similar increase in ANF plasma levels. We tested the hypothesis that these changes were related to cytokines and found that some pro-inflammatory cytokines, including TNFalpha and IL-1beta, selectively promote BNP synthesis and secretion in cultures of neonatal rat ventricular cardiocytes. This effect was found related to increased BNP promoter activity and sensitive to p38 mitogen-activated protein kinase inhibition.In order to determine in vivo if the selective up-regulation of BNP would be observed in inflammatory processes other than acute cardiac allograft rejection, we carried out investigation using the experimental autoimmune myocarditis rat model, which histologically resembles human giant cell myocarditis. It was found that this model is also accompanied by a specific increase in BNP-circulating levels although the cytokines involved seem to differ from those characterized earlier through in vitro studies.Recent studies in humans have found that in sepsis, plasma BNP levels increase in the absence of hemodynamic changes.In conclusion, BNP appears to be regulated uniquely in the setting of an inflammatory process. This sets it apart from ANF in terms of potential roles in the pathogenesis of disease and in its use as a biomarker of cardiac disease.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".