Immobilized cellulose nanospheres in lateral flow immunoassay enable rapid nucleocapsid antigen-based diagnosis of SARS-CoV-2 from salivary samples
Bibliographic record
Abstract
Rapid and efficient diagnostic systems are essential in controlling the spread of viral pathogens and efficient patient management. The available technologies for low-cost viral antigen testing have several limitations, including lack of accuracy and sensitivity. Here, we develop sensitive antigen tests based on recently introduced, oppositely charged cellulose II nanoparticles (NPan and NPcat) that are effective in controlling surface protein interactions. Passivation against non-specific adsorption and augmented immobilization of sensing antibodies are achieved by adjusting the electrostatic charge of the nanoparticles. The interactions affecting the performance of the system are investigated by microgravimetry and confocal imaging. We further demonstrate SARS-CoV-2 nucleocapsid rapid sensing by saliva-wicking channels stencil-printed on flexible paper supports. Therein, NPcat inkjet printed on the channels elicit distinctive optical signals, visible after only a few minutes, allowing faster diagnosis compared to current microfluidic devices designed for saliva sampling.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".