Membrane proteomic profiling of the heart: past, present, and future
Bibliographic record
Abstract
Cardiovascular diseases remain the most rapidly rising contributing factor of all-cause mortality and the leading cause of inpatient hospitalization worldwide, with costs exceeding $30 billion annually in North America. Cell surface and membrane-associated proteins play an important role in cardiomyocyte biology and are involved in the pathogenesis of many human heart diseases. In cardiomyocytes, membrane proteins serve as critical signaling receptors, Ca2+ cycling regulators, and electrical propagation regulators, all functioning in concert to maintain spontaneous and synchronous contractions of cardiomyocytes. Membrane proteins are excellent pharmaceutical targets due to their uniquely exposed position within the cell. Perturbations in cardiac membrane protein localization and function have been implicated in the progression and pathogenesis of many heart diseases. However, previous attempts at profiling the cardiac membrane proteome have yielded limited results due to poor technological developments for isolating hydrophobic, low-abundance membrane proteins. Comprehensive mapping and characterization of the cardiac membrane proteome thereby remains incomplete. This review will focus on recent advances in mapping the cardiac membrane proteome and the role of novel cardiac membrane proteins in the healthy and the diseased heart. Listen to this article's corresponding podcast at https://ajpheart.podbean.com/e/membrane-proteomic-profiling-of-the-heart/ .
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".