Simple, rapid and sensitive detection of Parkinson's disease related alpha-synuclein using a DNA aptamer assisted liquid crystal biosensor
Bibliographic record
Abstract
Alpha-synuclein (αS) has been proposed as a potential biomarker for the diagnosis of Parkinson's disease (PD). However, the detection of αS using a simple, rapid and sensitive approach is still challenging. Herein, we construct a new type of biosensor for the detection of αS, combining the stimuli-responsiveness of liquid crystals (LCs) and the specific interaction of a DNA aptamer with proteins. In principle, the positively charged surfactant hexadecyltrimethylammonium bromide (CTAB) binds with the negatively charged DNA aptamer via electrostatic interactions; in the presence of αS, the DNA aptamer specifically binds with αS and releases CTAB, which is an amphiphilic molecule and subsequently assembles at the LC-aqueous interface, resulting in a homeotropic alignment of LCs with a dark optical signal. In the absence of αS, CTAB binds with the DNA aptamer without affecting the alignment of LCs, which shows planar anchoring with a bright optical signal. The response time of LCs towards αS is rapid and can be down to minutes. The LC biosensor established here has a good specificity for αS and can recognize αS even from a mixture of proteins. The LC biosensor also exhibits high sensitivity with a limit of detection of αS as low as 10 pM, which is comparable to that of the enzyme-linked immunosorbent assay. This work provides a new strategy for the detection of αS in a simple, rapid and sensitive manner, possessing promising potentials towards early diagnosis and clinical applications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".