Enzymatic detection of troponin C and melittin bee
Bibliographic record
Abstract
One of the major goals of biosensor technology is to detect and quantify in detail analytes with very high accuracy. To achieve this, much of the emphasis in sensor fabrication has been laid on antibody-antigen interaction. The consequence of this focus of enzyme biosensor studies is the development of critical techniques which can be extended in the detection of Acute Myocardial Infarction (AMI). Biosensors for AMI have attracted considerable interest in the last few years since the monitoring of a specific substance is central in enzymatic reactions. This interest has led to the investigation of biochemical markers of myocardial injury. These biomarkers facilitate the diagnosis and treatment of patients with AMI. Serial measurements of biochemical markers are now universally accepted as an important determinant in AMI diagnosis. Due to their high sensitivity and specificity over other biomarkers, the troponins are the markers of choice for the diagnosis or exclusion of AMI. The present techniques used in the identification of the troponins are lengthy and require large amount of specimen solution. The present research is directed towards the identification of optical detection procedures that are compatible to the miniaturization. In the present study an effort has been made to study the antigen-antibody reaction of rabbit skeletal muscle troponin C (TnC) and bee venom melittin (ME). Fluorescence energy transfer experiments were done to investigate the Ca 2+ -dependant interaction of TnC-ME in a 1:1 complex. Experiments were also conducted on TnC-ME binding at different ratios. These results validate the biosensor technology and illustrate how a biosensor can be developed based on the study of interaction between monoclonal antibody and antigen reaction in real time. The reported experimental results provide valuable information that will be useful in the development of a biosensor for the detection of AMI.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".