Bibliographic record
Abstract
Over the past few years, the power and potential of proteomics has become widely recognized. The use of proteomics for the study of complex diseases is increasing and is particularly applicable to cardiovascular disease, the leading cause of death in developed countries. The ability to investigate the complete proteome provides a critical tool toward elucidating the complex and multif- torial basis of cardiovascular biology, especially disease processes. Proteomics involves the integration of a number of technologies with the aim of analyzing all the proteins expressed by a biological system in response to various stimuli under different pathophysiological conditions. The proteomic approach offers the ability to evaluate simultaneously the changes in protein expression and cell signaling pathways in response to such conditions as atherosclerosis, c- diac hypertrophy, stroke, or heart failure. Cardiovascular Proteomics: Methods and Protocols covers many of the above aspects of the proteomic approach in the cardiovascular field. This v- ume takes the reader through the complete process of proteomic analysis, from the obtention of specific heart proteins (troponin I) to the new techniques of identifying risk biomarkers of atherome plaque rupture, analyzing the secretome of explanted endarterectomies cultured in vitro, or the application of phage display techniques to decipher the molecular diversity of blood vessels
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".