Non-invasive Monitoring of α-Synuclein in Saliva for Parkinson’s Disease Using Organic Electrolyte-Gated FET Aptasensor
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Bibliographic record
Abstract
Parkinson's disease (PD) currently affects more than 1 million people in the US alone, with nearly 8.5 million suffering from the disease worldwide, as per the World Health Organization. However, there remains no fast, pain-free, and effective method of screening for the disease in the ageing population, which also happens to be the most susceptible to this neurodegenerative disease. αSynuclein (αSyn) is a promising PD biomarker, demonstrating clear delineations between levels of the αSyn monomer and the extent of αSyn aggregation in the saliva of PD patients and healthy controls. In this work, we have demonstrated a laboratory prototype of a soft fluidics integrated organic electrolyte-gated field-effect transistor (OEGFET) aptasensor platform capable of quantifying levels of αSyn aggregation in saliva. The aptasensor relies on a recently reported synthetic aptamer which selectively binds to αSyn monomer as the bio-recognition molecule within the integrated fluidic channel of the biosensor. The produced saliva sensor is label-free, fast, and reusable, demonstrating good selectivity only to the target molecule in its monomer form. The novelty of these devices is the fully isolated organic semiconductor, which extends the shelf life, and the novel fully integrated soft microfluidic channels, which simplify saliva loading and testing. The OEGFET aptasensor has a limit of detection of 10 fg/L for the αSyn monomer in spiked saliva supernatant solutions, with a linear range of 100 fg/L to 10 μg/L. The linear range covers the physiological range of the αSyn monomer in the saliva of PD patients. Our biosensors demonstrate a desirably low limit of detection, an extended linear range, and fully integrated microchannels for saliva sample handling, making them a promising platform for non-invasive point-of-care testing of PD.
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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 | 0.001 |
| 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 it