Bioenzymatic detection of troponin C using micro-opto-electro-mechanical systems
Bibliographic record
Abstract
Diagnosis and monitoring of critical diseases such as acute myocardial infarction (AMI) require a quantitative analysis of biological molecules. A high-throughput identification of these biological molecules can be generated by using micro-electro-mechanical systems (MEMS) structures like simple cantilever beams, which respond to the intermolecular forces resulting from binding these molecules. Biochemical markers like troponin C are considered the primary markers for myocardial injury and have generated considerable interest. A 26-residue lytic membrane protein of bee venom melittin (ME) is chosen to interact with rabbit skeletal muscle troponin C (TnC) on the surface of the cantilever beams. An optical beam deflection method is employed to identify the enzymatic reaction on the surface of the cantilever due to these proteins. Identification of these proteins is also done using fluorescence spectroscopy (FS) to compliment the optical monitored deflection method. A second set of proteins like horse radish peroxide (HRP) and hydrogen peroxide (H2O2) are applied to atomic force microscopy (AFM) cantilever beams to study their behavior under the enzymatic reactions of proteins. Identification of these proteins is done using Fourier transform infrared spectroscopy (FTIR). An analytical model of the cantilever beam is developed, and its mode shapes are studied by employing orthogonal polynomials in the classic Rayleigh-Ritz method. The surface stress caused by the enzymatic reaction of the proteins that leads to pure bending on the top surface of the cantilever is evaluated. The information provided by the experimental and analytical modeling reported in this work will be useful in the development of a portable biosensor for the detection of AMI.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".