Plasma Proteomic Approach in Patients with Heart Failure: Insights into Pathogenesis of Disease Progression and Potential Novel Treatment Targets
Bibliographic record
Abstract
AIMS: To provide insights into pathogenesis of disease progression and potential novel treatment targets for patients with heart failure by investigation of the plasma proteome using network analysis. METHODS AND RESULTS: The plasma proteome of 50 patients with heart failure who died or were rehospitalised were compared with 50 patients with heart failure, matched for age and sex, who did not have an event. Peptides were analysed on two-dimensional liquid chromatography coupled to tandem mass spectrometry (2D LC ESI-MS/MS) in high definition mode (HDMSE). We identified and quantified 3001 proteins, of which 51 were significantly up-regulated and 46 down-regulated with more than two-fold expression changes in those who experienced death or rehospitalisation. Gene ontology enrichment analysis and protein-protein interaction networks of significant differentially expressed proteins discovered the central role of metabolic processes in clinical outcomes of patients with heart failure. The findings revealed that a cluster of proteins related to glutathione metabolism, arginine and proline metabolism, and pyruvate metabolism in the pathogenesis of poor outcome in patients with heart failure who died or were rehospitalised. CONCLUSIONS: Our findings show that in patients with heart failure who died or were rehospitalised, the glutathione, arginine and proline, and pyruvate pathways were activated. These pathways might be potential targets for therapies to improve poor outcomes in patients with heart failure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".