SARS-CoV-2–specific memory B cells can persist in the elderly who have lost detectable neutralizing antibodies
Bibliographic record
Abstract
Memory B cells (MBCs) can provide a recall response able to supplement waning antibodies (Abs) with an affinity-matured response better able to neutralize variant viruses. We studied a cohort of elderly care home residents and younger staff (median age of 87 years and 56 years, respectively), who had survived COVID-19 outbreaks with only mild or asymptomatic infection. The cohort was selected because of its high proportion of individuals who had lost neutralizing antibodies (nAbs), thus allowing us to specifically investigate the reserve immunity from SARS-CoV-2-specific MBCs in this setting. Class-switched spike and receptor-binding domain (RBD) tetramer-binding MBCs persisted 5 months after mild or asymptomatic SARS-CoV-2 infection, irrespective of age. The majority of spike- and RBD-specific MBCs had a classical phenotype, but we found that activated MBCs, indicating possible ongoing antigenic stimulation or inflammation, were expanded in the elderly group. Spike- and RBD-specific MBCs remained detectable in the majority of individuals who had lost nAbs, although at lower frequencies and with a reduced IgG/IgA isotype ratio. Functional spike-, S1 subunit of the spike protein- (S1-), and RBD-specific recall was also detectable by enzyme-linked immune absorbent spot (ELISPOT) assay in some individuals who had lost nAbs, but was significantly impaired in the elderly. Our findings demonstrate that a reserve of SARS-CoV-2-specific MBCs persists beyond the loss of nAbs but highlight the need for careful monitoring of functional defects in spike- and RBD-specific B cell immunity in the elderly.
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 | 0.001 |
| 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".