Development and translation of a method of clinical utility for LC-MS/MS analysis to detect SARS-CoV-2 antigens from ONP swabs and saliva
Bibliographic record
Abstract
During the COVID-19 pandemic, development of diagnostic tests was vital to chart the course and to reduce the impact of the infection. Continued testing and surveillance of vaccine escape will continue for years to come which presents an opportunity to integrate such testing into clinical biochemistry laboratories that form part of integrated healthcare testing. Here we describe a protocol for a targeted mass spectrometry based proteomic assay (COVIDCAP) developed to detect SARS-CoV-2 peptides from oro-nasopharyngeal swabs (ONP) and saliva. This uses novel SISCAPA antibodies bound to magnetic beads and subsequent analysis of captured and purified SARS-CoV-2 nucleocapsid (NCAP) peptides. The method involves immediate deactivation of the sample using an ethanolic solution. This simultaneously inactivates the virus and denatures viral proteins at sampling in contrast to the approach for RT-PCR testing, with benefits for the assay as well as for downstream processing. A plate-based preparation of the samples involving acetone precipitation followed by a short tryptic digestion and subsequent immunocapture allows LC-MS detection and quantification of peptides from the NCAP protein, in a 3-minute inject-to-inject assay with an LOD of 20 attomoles from starting sample. For 576 ONP swab samples taken as exemplars here, the sensitivity and specificity of this analysis is shown to be 97.0% and 96.6% respectively.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".