The role of interleukin-21 in COVID-19 vaccine–induced B cell–mediated immune responses in patients with kidney disease and kidney transplant recipients
Bibliographic record
Abstract
T-cell-mediated help to B cells is required for the development of humoral responses, in which the cytokine interleukin (IL)-21 is key. Here, we studied the mRNA-1273 vaccine-induced SARS-CoV-2-specific memory T-cell IL-21 response, memory B cell response, and immunoglobulin (Ig)G antibody levels in peripheral blood at 28 days after the second vaccination by ELISpot and the fluorescent bead-based multiplex immunoassay, respectively. We included 40 patients with chronic kidney disease (CKD), 34 patients on dialysis, 63 kidney transplant recipients (KTR), and 47 controls. We found that KTR, but not patients with CKD and those receiving dialysis, showed a significantly lower number of SARS-CoV-2-specific IL-21 producing T cells than controls (P < .001). KTR and patients with CKD showed lower numbers of SARS-CoV-2-specific IgG-producing memory B cells when compared with controls (P < .001 and P = .01, respectively). The T-cell IL-21 response was positively associated with the SARS-CoV-2-specific B cell response and the SARS-CoV-2 spike S1-specific IgG antibody levels (both Pearson r = 0.5; P < .001). In addition, SARS-CoV-2-specific B cell responses were shown to be IL-21 dependent. Taken together, we show that IL-21 signaling is important in eliciting robust B cell-mediated immune responses in patients with kidney disease and KTR.
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".