Semiquantitative Paper-Based Microfluidic Surrogate Virus Neutralization Test for SARS-CoV-2 Neutralizing Antibodies
Bibliographic record
Abstract
Neutralizing antibodies (nAbs) produced from infection or vaccination play an important role in acquired immunity. Determining virus-specific nAb titers is a useful tool for measuring aquired immunity in an individual. The standard methods to do so rely on titrating serum samples against live virus and monitoring viral infection in cultured cells which requires high biosafety level containment. The surrogate virus neutralization test (sVNT) reduces the biohazards and it is suitable for designing rapid test device in a lateral flow assay (LFA) format. Here, we introduce the fabrication and development of a unique paper-based LFA device for determining the level of SARS-CoV-2 nAb in a sample with a semiquantitative direct colorimetric readout. A LFA-based gradient assay design was used to facilitate the sVNT, where the spike glycoprotein receptor binding domain (RBD) and angiotensin-converting enzyme 2 (ACE2) stand in as proxies for viruses and cells, respectively. The gradient assay employed multiple test dots of ACE2 spotted in increasing concentration along the sample flow path and gold nanoparticle-conjugated RBD for readout. In this way, the number of developed spots is inversely proportional to the concentration of nAbs present in the sample. The assay was tested with both standard solutions of nAb as well as human serum samples. We have demonstrated that the device can effectively provide semiquantitative test results of nAbs by direct instrument-free colorimetric 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".