Proteomics and Mass Spectrometry: What Have We Learned About The Heart?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The emergence of new platforms for the discovery of innovative therapeutics has provided a means for diagnosing cardiac disease in its early stages. Taking into consideration the global health burden of cardiac disease, clinicians require innovations in medical diagnostics that can be used for risk stratification. Proteomic based studies offer an avenue for the discovery of proteins that are differentially regulated during disease; such proteins could serve as novel biomarkers of the disease state. For instance, in clinical practice, the abundance of such biomarkers in blood could be correlated with the severity of the disease state. As such, early detection of biomarkers would enable an improvement in patient prognosis. In this review, we outline advancements in various proteomic platforms used to study the disease proteome and their applications to the field of clinical medicine. Specifically, we highlight the contributions of proteomic-based profiling experiments to the analysis of cardiovascular diseases.
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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 | 0.001 |
| 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 it