A novel multiplex electrochemiluminescent immunoassay for detection and quantification of anti-SARS-CoV-2 IgG and anti-seasonal endemic human coronavirus IgG
Bibliographic record
Abstract
BACKGROUND: Multiplex immunoassays capture a comprehensive profile of the humoral response against SARS-CoV-2 and human endemic coronaviruses. We validated a multiplex panel (V-PLEX Panel 2) from Meso Scale Diagnostics targeting antibodies against nine coronavirus antigens. Performance was compared against alternative single- and multi-antigen immunoassays. METHODS: Sera collected for clinical or public health testing from 2018 to 2020 (n = 135) were used to compare all tested platforms, and inter-test agreement was assessed by Cohen's kappa coefficient. Sample category (positive/negative) was assigned based on collection date relative to the index case in Canada, and SARS-CoV-2 PCR and serology results. 117 out of the 135 samples (31 positive, 86 negative) were assigned a category and were used to calculate sensitivity and specificity, with MSD's test results based upon manufacturer-set cut-offs. RESULTS: We observed SARS-CoV-2 target sensitivities of 100% and specificities >94% for all antigens (RBD, Nucleocapsid, Spike) in V-PLEX Panel 2. When targets were combined, we found a SARS-CoV-2 sensitivity of 100% and specificity of 98.8% with no difference in performance compared to clinical assays, and Cohen's kappa ranging from 0.798 to 0.945 compared to surface plasmon resonance imaging (SPRi). Quantitative measurements of antibodies against the Spike protein of endemic human coronaviruses were concordant with SPRi. CONCLUSION: Meso Scale Diagnostics' V-PLEX Coronavirus Panel 2 allows for highly sensitive and specific detection of anti-coronavirus IgG, and is concordant with other serological assays for detection of antibodies against SARS-CoV-2 and the endemic human coronaviruses, making it a good tool for humoral response characterization after both infection and vaccination.
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 | 0.002 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".