Monoclonal Antibodies Against Human Cardiac Troponin I for Immunoassays II
Bibliographic record
Abstract
Human cardiac troponin I (cTnI) is one of the most specific biomarkers for detection of acute myocardial infarction (AMI). To formulate immunoassay kits for rapid immunodiagnosis of AMI, monoclonal antibodies with high affinity and specificity were generated against cTnI and subsequently tested through a series of experiments. C57BL/6 mice were immunized with cTnI as the immunogen and cell fusions with myeloma cells of BALB/c origin were performed to generate hybridomas. The supernatants of the hybridoma cell culture were routinely screened for antibody secretions against intact cTnI and synthetic peptides from the N-terminal half of cTnI (amino acid residues N1-30, N24-40, N59-79, and N80-95). Monoclonal antibodies specific to different epitope regions were then determined and selected, according to their respective affinity and specificity, for formulation of enzyme immunoassay kits. The results of this study found that most of the selected antibodies revealed comparable binding affinity to cTnI and to the corresponding synthetic peptides. Optimal sandwich enzyme immunoassays with high sensitivity could be achieved through proper combinations of the epitope-distinct monoclonal antibodies in different capture-detection pairs; signal enhancements were frequently observed when a mixture of epitope-distinct anti-cTnI monoclonal antibodies was used for coating. This indicates that a combination of epitope-distinct anti-cTnI monoclonal antibodies recognizing the N-terminal half of cTnI yield reliable detection and greater sensitivity for cTnI in AMI patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".