Omicron BA.4/5 Neutralization and T-Cell Responses in Organ Transplant Recipients After Booster Messenger RNA Vaccine: A Multicenter Cohort Study
Bibliographic record
Abstract
BACKGROUND: In solid organ transplant (SOT) recipients, the primary vaccination series against Coronavirus Disease 2019 is 3 doses followed by boosters. We determined whether a fourth dose booster induced Omicron BA.4/5 neutralizing antibodies (nAbs) and T cells in a large multicenter cohort study. METHODS: Serum was collected 4-6 weeks post-third and post-fourth doses of messenger RNA vaccine in 222 SOT recipients. nAbs were measured using a pseudovirus neutralization assay that targeted the Omicron BA.4/5 spike protein. A subset underwent T-cell testing. RESULTS: The median age of the cohort was 63 years (interquartile range [IQR], 50-68) with 61.7% men. BA.4/5 nAb detection increased from 26.6% (59 of 222) post-third dose to 53.6% (119 of 222) post-fourth dose (P < .0001). In patients with breakthrough infection prior to the fourth dose (n = 27), nAbs were detected in 77.8% and median nAb titers were significantly higher compared with those with 4 vaccine doses alone (P < .0001). Factors associated with a low BA.4/5 neutralization response after the fourth dose were older age (odds ratio [OR], 0.96; 95% confidence interval [CI], .94-.99), mycophenolate use (OR, 0.39; 95% CI, .20-.77) and prednisone use (OR, 0.34; 95% CI, .18-.63), and vaccine type (OR, 0.72; 95% CI, .51-.99), while breakthrough infection prior to the fourth dose (OR, 3.6; 95% CI, 1.3-9.9) was associated with a greater nAb response. Polyfunctional BA.4/5-specific CD4+ T cells significantly increased after 4 doses and were identified in 76.9% of patients at a median frequency of 213/106 cells (IQR, 98-650). CONCLUSIONS: In summary, a booster significantly increases BA.4/5-specific neutralization and polyfunctional CD4+ T-cell responses, suggesting protection from severe disease even with new Omicron variants. However, SOT recipients who are older and on mycophenolate and prednisone need additional preventative strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".