Role of IgM Memory B Cells and Spleen Function in COVID-19
Bibliographic record
Abstract
IgM memory B cells, are a peculiar subset of memory B cells, which probably originates in the spleen and outside germinal centers and provide a rapid line of defence against mucosal infections. Their role in counteracting COVID-19 is still elusive but, recent evidence, mainly boosted by studies on spleen function/involvement in COVID-19, seems to support the notion that this subset of memory B cells could exert a protective role against this virus, along with other coronaviruses, particularly in the acute setting of the infection, as outlined by worst clinical outcomes observed in unvaccinated patients with impaired IgM B memory response and spleen function. Herein we critically summarise the current landscape of studies on IgM memory B cells, focusing on the clinical impact of their depletion, by comparing the COVID-19-related splenic dysfunction with other hypo- and asplenic conditions and by adding recent data on follow-up studies and postulate a mechanistic explanation for their reduced numbers. The early detection of an impaired IgM memory B cell response in patients with COVID-19 may contribute to their improved care through different strategies, such as through tailored vaccine strategies, prompt hospital admission and/or administration of anti-infective treatments, thus resulting in an better prognosis, although at present management algorithms are still unavailable. Moreover, further studies with longer follow-up are needed to assess the evolution of COVID-19-associated/exacerbated immune deficit.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".