Cohort-specific serological recognition of SARS-CoV-2 variant RBD antigens
Bibliographic record
Abstract
Abstract Background Estimating the response of different cohorts (e.g. vaccinated or critically ill) to new SARS-CoV-2 variants is important to customize measures of control. Thus, our goal was to evaluate binding of antibodies from sera of infected and vaccinated people to different antigens expressed by SARS-CoV-2 variants. Methods We compared sera from vaccinated donors with sera from four patient/donor cohorts: critically ill patients admitted to an intensive care unit (split in sera collected between 2 and 7 days after admission and more than ten days later), a NIBSC/WHO reference panel of SARS-CoV-2 positive individuals, and ambulatory or hospitalized (but not critically ill) positive donors. Samples were tested with an anti-SARS-CoV-2 IgG serological assay designed with microplates coated with a SARS-CoV-2 RBD recombinant antigen. The same sample sets were also tested with microplates coated with antigens harbouring RBD mutations present in eleven of the most widespread variants. Results Sera from vaccinated individuals exhibited higher antibody binding (P<0.001) than sera from infected (but not critically ill) individuals when tested against the WT and each of 11 variants’ RBD. The optical density generated by sera from non-critically ill convalescence individuals upon binding to variant’s antigens was different (P<0.05) from that of the WT in some variants—noteworthy, Beta, Gamma, Delta, and Delta Plus variants. Conclusions Understanding differences in binding and neutralizing antibody titers against WT vs variant RBD antigens from different donor cohorts can help design variant-specific immunoassays and complement other diagnostic and clinical data to evaluate the epidemiology of new variants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".