Comparison of Aptamer and Antibody Bioreceptors in the OEGFET Biosensor Platform for Detecting α-Synuclein, a Parkinson's Biomarker
Bibliographic record
Abstract
Early diagnosis of Parkinson's disease (PD) allows for timely interventions that can slow the development and progression of disease pathogenesis; however, at present, there is no population screening method for PD. We have previously demonstrated an organic electrolyte gated field effect transistor (OEGFET) aptasensor (apta-OEGFET) using α-Synuclein (αSyn) dilutions in deionized water and saliva supernatant samples. In this letter, for the first time, we display the potential of the apta-OEGFET in quantifying monomeric αSyn dilutions in serum samples collected from wild-type mice. The apta-OEGFET produced a linear sensing range of 10 pg/mL–100 fg/mL in serum, along with a limit of detection (LOD) of 100 fg/mL estimated using a dilution series in water. To improve robustness of the biosensor for PD screening, we present a novel antibody-based OEGFET (AB-OEGFET) sensor in efforts to expand the pool of potential PD biomarkers. The AB-OEGFET sensor presented the characteristic decreasing current with increasing concentration behavior previously reported of the apta-OEGFET, proving the independence of the biosensor architecture from any single biorecognition method. The AB-OEGFET demonstrated a linear response range of 1 pg/mL–100 fg/mL, with LOD of 100 fg/mL estimated through dilution series in water. The reduced linear range is a factor of the differences in antibody size and, therefore, available binding sites on the surface. Our biosensors demonstrated comparable behavior for the antibody and aptamer bioreceptors and were sensitive for αSyn concentration in all tested solution matrices, making it a promising platform for incorporating multitude of biomarkers for noninvasive testing of PD.
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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".