Development and evaluation of a new non-competitive Luminex immunoassay detecting antibodies against human papillomavirus types 6, 11, 16 and 18
Bibliographic record
Abstract
Serum antibody levels can be used to measure the humoral immune response against human papillomaviruses (HPV). We developed and validated a rapid, technically simple and relatively inexpensive multiplex non-competitive Luminex-based immunoassay (ncLIA) to measure total IgG antibody levels against four HPV types. For the assay’s solid phase, virus-like particles (VLPs) of HPV6, 11, 16 and 18 were bound to heparin-coated beads. HPV serum antibody levels binding to the VLPs were quantified using a phycoerithrin-conjugated secondary polyclonal donkey anti-human IgG antibody. Standardization and validation of the ncLIA were performed using 96 paired serum and genital samples from participants in the HITCH cohort study, including young women (aged 18–24 years) and their male sexual partners (aged 18+) in Montreal, Canada. Results from the ncLIA were compared to a validated Luminex immunoassay from PPD laboratories using Pearson’s correlation coefficients, receiver operating characteristic curves and logistic regression. Our assay had good inter- and intra-assay variability. The correlation of serum antibody levels between the ncLIA and validation assay was highest for HPV16 and HPV11 (r=0.90), followed by HPV6 (r=0.86) and HPV18 (r=0.67). The ncLIA was better able to predict HPV DNA positivity in genital samples than the validation assay for HPV16 [area under the curve (AUC) 0.65 versus 0.52, P=0.001] and HPV18 [AUC 0.71 versus 0.57, P=0.024]. AUCs for HPV6 and HPV11 were similar between the two assays (0.70 versus 0.71, P=0.59, and 0.88 versus 0.96, P=0.08, respectively). The developed ncLIA is useful for measuring total IgG antibody response following natural infection or vaccination against four HPV VLPs included in the quadrivalent vaccine.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".