Ultrasensitive detection of vital biomolecules based on a multi-purpose BioMEMS for Point of care testing: digoxin measurement as a case study
Bibliographic record
Abstract
Abstract Rapid and label-free detection of very low concentrations of biomarkers in disease diagnosis or therapeutic drug monitoring is essential to prevent disease progression in Point of Care Testing. For this purpose, we propose a multi-purpose optical Bio-Micro-Electro-Mechanical-System (BioMEMS) sensing platform which can precisely measure very small changes of biomolecules concentrations in plasma-like buffer samples. This is realized by the development of an interferometric detection method on highly sensitive MEMS transducers (cantilevers). This approach facilitates the precise analysis of the obtained results to determine the analyte type and its concentrations. Furthermore, the proposed multi-purpose platform can be used for a wide range of biological assessments in various concentration levels by the use of an appropriate bioreceptor and the control of its coating density on the cantilever surface. In this study, the present system is prepared for the identification of digoxin medication in its therapeutic window for therapeutic drug monitoring as a case study. The experimental results represent the repeatability and stability of the proposed device as well as its capability to detect the analytes in less than eight minutes for all samples. In addition, according to the experiments carried out for very low concentrations of digoxin in plasma-like buffer, the detection limit of LOD = 300 fM and the maximum sensitivity of S = 5.5 × 10 12 AU/M are achieved for the implemented biosensor. The present ultrasensitive multi-purpose BioMEMS sensor can be a fully-integrated, cost-effective device to precisely analyze various biomarker concentrations for various biomedical applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".