MEMS based cardiac bio-enzyme detection for the acute myocardial syndrome recognition
Bibliographic record
Abstract
Abstract:- Early detection through enzymatic identification and confirmation is essential for diagnosis and prevention as in the case of Acute Myocardial Syndrome (AMS). Biochemical markers continue to be an important clinical tool for the enzymatic detection. The advent of Micro Electro Mechanical Systems (MEMS) devices can enable the use of various microstructures for the detection of enzymes. In this study, the concept of MEMS is applied for the detection of enzyme reaction, in which micro-cantilevers undergo changes in mechanical behavior that can be optically detected when enzyme molecules react on their surface, inducing geometric modifications. This paper presents the static behavior of micro-cantilevers under antigen-antibody reaction of rabbit skeletal muscle troponin C (TnC) and bee venom melittin (ME). The same troponin C is detected in the blood stream in humans a couple of hours form the acute myocardial infarction. The reported experimental results provide valuable information that will be useful in the development of MEMS sensors for enzymatic detection. The surface stress produced due to enzyme reactions results in the bending of cantilevers as similar to the influencing of laminar stress in the cantilevers. A possible design of such a system is provided in the paper.
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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) | 0.003 | 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".