Digital Immunoassay for Sensitive Quantification of Blood Biomarkers using Solid-State Nanopores
Bibliographic record
Abstract
Digital immunoassays enable highly sensitive detection of biomolecules, offering absolute quantification rather than relying on bulk signal intensity. We adapt a digital immunoassay scheme for a nanopore sensor – a versatile platform for single-molecule counting. Current nanopore sensors have demonstrated great progress when counting nucleic acids but struggle with proteins due to variability in translocation behaviour and limited recognition strategies. While recent advancements have highlighted the promise of nanopore platforms for protein studies, precise quantification remains a challenge. Here, building on previous work, we present a nanopore-based digital immunoassay that employs gold nanoparticle-mediated molecular amplification with single-molecule readout. This approach translates protein recognition into quantifiable DNA, enabling a precise digital assay. This assay employs a DNA NanoLock probe combined with a paramagnetic bead based immunocapture, where the target proteins trigger a structural transformation of the NanoLock, converting their presence into a binary DNA-based signal. By incorporating AuNPs carrying hundreds of DNA proxy reporters, we effectively amplify the detectable signal by two orders of magnitude, significantly improving sensitivity. We validate the performance of this system by detecting glial fibrillary acidic protein (GFAP), a biomarker for traumatic brain injury (TBI) and neurodegenerative diseases, in plasma samples and demonstrate high femtomolar-level sensitivity (~40 pg/mL). Using the NanoLock probe, we further mitigate previous challenges, with reduced assay times (hours), and extended dynamic range (3-log). The self-calibrating nature of this digital approach offers robust, reproducible measurements across different nanopores, eliminating inter-device variability and paving the way towards scalable point-of-care detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".