Rapid Quantification of Virus-Like Particles via Gold Nanoparticle Sensors and Dark-Field Differential Dynamic Microscopy
Bibliographic record
Abstract
We present a novel diagnostic platform using differential dynamic microscopy (DDM) to quantify viral load in salivary samples. This method leverages gold nanoparticle-based sensors that form heteroaggregates with virus-like particles (VLPs), designed to mimic viruses. The sensors were sequentially functionalized with biotin, streptavidin, and biotinylated angiotensin-converting enzyme 2, while VLPs were functionalized with streptavidin and the spike S1 receptor-binding domain of SARS-CoV-2 as the model virus. Viral load is quantified by tracking changes in the sensor nanoparticle dynamics during their interactions with VLPs. Initially optimized in buffer and subsequently adapted for salivary samples, the assay leverages dark-field DDM to remove possible interference from unbound VLPs. This approach enables the quantification of VLPs that were otherwise undetectable by dark-field DDM alone, by exploiting the slower diffusion of nanosensor–VLP heteroaggregates, achieving a detection limit of 9 × 10 3 VLPs/mL (20 pg/mL), within clinically relevant viral loads. The platform requires minimal sample preparation, a 5 min incubation, and no fluorescent labeling or washing steps. With only a conventional microscope and camera, this rapid assay provides quantitative results in 10–15 min. This proof-of-concept offers an accessible tool for rapid and precise viral load quantification in laboratory settings with potential for point-of-care applications through setup miniaturization. With its simplicity, speed, and sensitivity, this platform represents a promising advancement in infectious disease diagnostics.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".