A High-Throughput NanoBiT-Based Serological Assay Detects SARS-CoV-2 Seroconversion
Bibliographic record
Abstract
High-throughput detection strategies for antibodies against SARS-CoV-2 in patients recovering from COVID-19, or in vaccinated individuals, are urgently required during this ongoing pandemic. Serological assays are the most widely used method to measure antibody responses in patients. However, most of the current methods lack the speed, stability, sensitivity, and specificity to be selected as a test for worldwide serosurveys. Here, we demonstrate a novel NanoBiT-based serological assay for fast and sensitive detection of SARS-CoV-2 RBD-specific antibodies in sera of COVID-19 patients. This assay can be done in high-throughput manner at 384 samples per hour and only requires a minimum of 5 μL of serum or 10 ng of antibody. The stability of our NanoBiT reporter in various temperatures (4-42 °C) and pH (4-12) settings suggests the assay will be able to withstand imperfect shipping and handling conditions for worldwide seroepidemiologic surveillance in the post-vaccination period of the pandemic. Our newly developed rapid assay is highly accessible and may facilitate a more cost-effective solution for seroconversion screening as vaccination efforts progress.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".