Evolution of Anti-RBD IgG Avidity following SARS-CoV-2 Infection
Bibliographic record
Abstract
SARS-CoV-2 infection rapidly elicits anti-Spike antibodies whose quantity in plasma gradually declines upon resolution of symptoms. This decline is part of the evolution of an immune response leading to B cell differentiation into short-lived antibody-secreting cells or resting memory B cells. At the same time, the ongoing class switch and antibody maturation processes occurring in germinal centers lead to the selection of B cell clones secreting antibodies with higher affinity for their cognate antigen, thereby improving their functional activity. To determine whether the decline in SARS-CoV-2 antibodies is paralleled with an increase in avidity of the anti-viral antibodies produced, we developed a simple assay to measure the avidity of anti-receptor binding domain (RBD) IgG elicited by SARS-CoV-2 infection. We longitudinally followed a cohort of 29 convalescent donors with blood samples collected between 6- and 32-weeks post-symptoms onset. We observed that, while the level of antibodies declines over time, the anti-RBD avidity progressively increases and correlates with the B cell class switch. Additionally, we observed that anti-RBD avidity increased similarly after SARS-CoV-2 mRNA vaccination and after SARS-CoV-2 infection. Our results suggest that anti-RBD IgG avidity determination could be a surrogate assay for antibody affinity maturation and, thus, suitable for studying humoral responses elicited by natural infection and/or 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 | 0.000 | 0.000 |
| 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".