Surface Imprinted Electroimpedance Biosensor for Detecting α-Synuclein for Parkinson's Disease
Bibliographic record
Abstract
Surface Imprinted Polymers (SIP) are a unique category of molecular imprinted polymers that do not require specific chemical reactions to create the nanoimprint desired for detecting target analytes with high specificity. In this work, we demonstrate a soft-printed and low-temperature processed SIP bio-recognition nanomaterial for label-free detection of high impact analyte, α-Synuclein (αSyn), using electroimpedance spectroscopy. αSyn, a target selected due to its viability for monitoring early onset and progression of the Parkinson's Disease (PD), was detected over a broad concentration range of 10 fg/L to 10 μg/L. The high specificity of the biosensor was demonstrated by analyzing a biomolecule, β-Synuclein, which is highly homologous to αSyn, but is not a factor towards PD. The simplistic, soft-printed SIP EIS biosensor is a label-free, and specific method for quantifying αSyn over broad concentration range that could revolutionize PD testing in the future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".