New Insights into the Biosensing of Parkinson's Disease Biomarkers:A Concise Review
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is a long-term, degenerative, and neurological disease in which a person loses control of certain body functions. The formulation of novel effective therapeutics for PD as a neurodegenerative disease requires accurate and efficient diagnosis at the early stages. OBJECTIVE: Analyzing data gathered by measurable signals converted from biological reactions allows for qualitative and quantitative evaluations. Among various approaches reported so far, biosensors are powerful analytical tools that have been used in detecting the biomarkers of PD. METHODS: Biosensor's biological recognition components include antibodies, receptors, microorganisms, nucleic acids, enzymes, cells and tissues, and biomimetic structures. This review introduces electrochemical, optical, and optochemical detection of PD biomarkers based on recent advances in nanotechnology and material science, which resulted in the development of high-performance biosensors in this field. RESULTS: PD biomarkers such as α-synuclein protein, dopamine (DA), urate, ascorbic acid, miRNAs, and their biological roles are summarized. Additionally, the advantages and disadvantages of the usual standard methods are reviewed. We compared electrochemical, optical, and optochemical biosensors' properties and novel strategies for higher sensitivity and selectivity. CONCLUSION: The development of novel biosensors is required for the early diagnosis of PD as sensitive, rapid, reliable, and cost-effective systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".