Electrochemical biosensor for point of care cancer and disease detection
Bibliographic record
Abstract
The first sign of growth of a malignant tumour within the body is indicated by the presence of protein markers in the bloodstream. Current detection methods, based on turbidity and fluorescent parameters, require the use of bench top optical read out systems which offer no portability. By combining electrochemical techniques and electronic miniaturization, low cost, on site, biosensors are being realized. Protein detection is based on our ability to identify selective molecular binding between the targeted protein and the biorecognition element. When the reaction occurs and the targeted protein binds to the biorecognition element, several attributes of the molecular chain change. It is this change that is indicative of the cancer marker’s presence. Correctly identifying the occurrence of this reaction is highly dependent on the selectivity of the molecules binding to the targeted proteins in question. In this work we plan to achieve high specificity and reliability of protein detection by using aptamers. An aptamer is a nucleic acid receptor that can bind tightly to its target molecule and with high selectivity due to its three dimensional shape. When the target molecule has bound to the aptamer, the electrochemical current path of the molecular chain is changed, and this modulation can be measured directly using cyclic voltammetry. We have built a potentiostat circuit for this purpose, which sweeps a voltage across two electrodes while measuring the induced current across a third electrode. We are exploring different miniaturization methods of the electrodes which will be addressed in the presentation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".