Serotonin and catecholamines in the development and progression of heart valve diseases
Bibliographic record
Abstract
Heart valve diseases (HVDs) arise from a number of different processes that affect both the structure and function of the valve apparatus. Despite diverse aetiologies, treatments for HVDs are limited to percutaneous or surgical interventions. The search for medical therapies to prevent or slow the progression of HVDs has been hampered by our poor understanding of the progression from subclinical to symptomatic phases, and our limited knowledge of the molecular signals that control the susceptibility of valve interstitial cells to pathological remodeling. Clinical evidence has suggested a link between certain neurotransmitters and valvular diseases of the heart. The fenfluramine-phentermine appetite suppressants popular in the 1980s were linked to mitral valve dysfunction, and ergot-derived dopamine agonists for Parkinson's disease have been associated with an increased risk of mitral and aortic valve regurgitation. The effect does not appear to be limited to medications, as valvular pathologies have also been observed in patients with carcinoid tumours of serotonin-producing enterochromaffin cells. The role of neurotransmitter molecules in valve pathology has not been adequately characterized and may represent a target for future medical therapies. Here we present current evidence from both clinical and basic science suggesting a link between neurotransmitters and HVDs, opening the door to future research in this area.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".