Omicron BA.1, BA.5, BQ.1.1, and XBB.1.5 Neutralizing Antibodies Following BNT162b2 BA.4/5 vs. mRNA-1273 BA.1 Bivalent Vaccination in Hemodialysis Patients and Kidney Transplant Recipients
Bibliographic record
Abstract
Background: Bivalent COVID-19 vaccines are recommended, however differences in neutralization of emerging Omicron subvariants by vaccine type have not been evaluated in patients with kidney disease. Methods: This was a prospective observational cohort study at three centres in Toronto, Canada from July 25, 2022 to November 30, 2022 in 98 patients receiving hemodialysis or with a kidney transplant. Participants received either the BNT162b2 (original and Omicron BA.4/BA.5) or mRNA-1273 (original and Omicron BA.1) COVID-19 vaccine. Neutralizing antibodies against wild-type, Omicron BA.1, BA.5, BQ.1.1, XBB.1.5 subvariants were measured prior to and one month following the receipt of a bivalent vaccine. Results: Neutralizing antibodies against BA.1, BA.5, BQ.1.1, and XBB.1.5 increased 8-fold one month following bivalent vaccination. In comparison to wild-type, neutralizing antibodies against Omicron-specific variants were 7.3-fold lower against BA.1, 8.3-fold lower against BA.5, 45.8-fold lower against BQ.1.1, and 48.2-fold lower against XBB.1.5. Viral neutralization did not differ by bivalent vaccine type: wild-type (p=0.48), BA.1 (p=0.21), BA.5 (p=0.07), BQ.1.1 (p=0.10), XBB.1.5 (p=0.10). Conclusions: The BNT162b2 and mRNA-1273 bivalent vaccines induced similar neutralization against all Omicron subvariants in hemodialysis and kidney transplant recipients, suggesting that bivalent vaccines confer protection against emerging Omicron subvariants even if they are antigenically different from the circulating variant. Funding: Government Support - Non-U.S.Neutralizing antibody levels stratified by a) bivalent vaccine type b) anti-nucleocapsid IgG seropositivity c) and hemodialysis versus kidney transplant recipients.
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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 | 0.001 | 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".