Antibody-Based Electrochemical Sensor for Detection of the Full-Length Phosphorylated TDP-43 Protein Biomarker of Amyotrophic Lateral Sclerosis
Bibliographic record
Abstract
Transactive response DNA binding protein (TDP-43) is a biomarker associated with neurodegenerative diseases, specifically amyotrophic lateral sclerosis (ALS). ALS remains without treatment or a cure, and diagnosis relies on the onset of symptoms. Hence, novel methods are needed for the early detection of TDP-43 as an ALS biomarker. Toward this aim, the detection of full-length phosphorylated TDP-43 (pTDP-43) was achieved by using the electrochemical impedance spectroscopy (EIS)-based biosensor. The TDP-43 antibodies (Abs) on gold (Au) surfaces (Ab-Au) were employed as recognition probes for the protein detection. EIS was used to characterize the Ab-Au surface before and after pTDP-43 binding. In the presence of a solution redox probe, [Fe(CN)6]3−/4−, the dramatic changes in the charge-transfer resistance (Rct) values were observed after the pTDP-43 binding and were directly related to the amount of protein present in solution. Sensitivity for pTDP-43 was highly dependent on the antibody used as a recognition probe, and the pTDP-43 was detected at the limit of detection of 11 ± 6 nM with a large dynamic range, and excellent selectivity against the common bovine serum albumin. This study provides the example of a methodology for fabricating an immunosensor as a recognition layer for ALS protein which can be easily extended for the detection of other disease-related biomarkers.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".