ß-Amyloid as a Novel Target Biomarker for the OEGFET Biosensor, Revolutionizing Noninvasive Alzheimer's Screening
Bibliographic record
Abstract
Early detection of neurodegenerative diseases has the potential to slow disease progression by timely interventions and effective management. Alzheimer's Disease (AD), the most common form of dementia globally, has a preclinical phase that lasts decades prior to the prodromal stage. Preclinical stage neurological changes are accompanied by changes in biomarker concentrations such as Amyloid Beta peptides (Aß), however, at present there are no cost-effective and non-invasive biomarker quantification methods suitable for population screening. To meet this need, we have tailored our proven Organic Electrolyte Gated Field Effect Transistor (OEGFET) biosensor for the detection of Aß in serum and saliva samples. This was achieved by incorporating a covalently bound Aß antibody (AB) within the soft-fluidic microchannel of the biosensor, a significant advancement that demonstrates the robustness of our sensor system towards different types of bioreceptors and target biomarkers. Furthermore, the AB-OEGFET was created using flexible substrates and polymers due to their impressive biocompatibility. We observed the characteristic OEGFET device current to Aß concentration correlation behavior in all tested media, including serum and saliva specimens. The response of AB-OEGFET in buffer was recorded from 10μg/ml to 100fg/ml, and the limit of detection (LOD) of Aß was achieved in the range of 100 ng/ml for the spiking tests in saliva and serum specimens. The device specificity was investigated in serum samples spiked with a non-binding protein analyte, α-synuclein. The predictable behavior of the sensor in targeting the Aß was observed in all tested solutions including saliva and serum that are crowded with numerous other proteomic biomarkers. The background ionic concentration in higher concentration serum and saliva was observed to promote some non-specific binding to the bioreceptor adding a competing matrix effect. Irrespective of these exceptions, our first demonstration of the integration of antibody receptors with OEGFET electronics, as explored in our study, holds substantial promise for the advancement of disposable, non-invasive biosensors for AD.
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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".