Optimization of Cardiac Troponin I Pull-Down by IDM Affinity Beads and SELDI
Bibliographic record
Abstract
Cardiac troponin I (cTnI) is a key regulator of cardiac muscle contraction. Upon myocardial cell injury, cTnI is lost from the cardiac myocyte and can be detected in serum, in some cases with specific disease-induced modifications, making it an important diagnostic marker for acute myocardial injury. Presently, hospital laboratories use enzyme-linked immunosorbent assays to detect cTnI, but this type of analysis lacks information about modified forms of protein (degradation or phosphorylation) that may give a more specific diagnosis from either serum or biopsies. Because cardiac and serum tissues are widely used for proteomic analysis, it is important to detect these cTnI posttranslational modifications. Therefore, we have chosen to optimize the enrichment and detection of cTnI protein by IDM Affinity Bead pull-down and surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS or SELDI) analyses. By adjusting the chemical compositions of the buffers, we have retained antibody specificity and enriched for different forms of cTnI and its associated proteins.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".