Neutralization against Omicron variant in transplant recipients after three doses of mRNA vaccine
Bibliographic record
Abstract
The SARS-CoV-2 virus Omicron variant has now supplanted wild-type virus as the dominant circulating strain globally. Three doses of mRNA COVID-19 vaccine are recommended for transplant recipients as their primary vaccine series. However, the immunogenicity of mRNA vaccines as they specifically relate to the Omicron variant are not well studied. We analyzed Omicron-specific neutralization in transplant recipients after three-doses of mRNA-1273 vaccine. Neutralization was determined using a SARS-CoV-2 spike pseudotyped lentivirus assay with constructs for Omicron and Delta variants. A total of 60 transplant patients (kidney, kidney-pancreas, lung, heart, liver) were analyzed 1 month and 3 months after completion of three doses of mRNA-1273. At 1 month, 11/60 (18.3%) patients had detectable neutralizing antibody responses to Omicron (log 10 ID50 of 2.38 [range 1.34–3.57]). At 3 months, 8/51 (15.7%) were positive (median log 10 ID50 [1.68; range 1.12–3.61; approximate fivefold reduction over time]). The proportion of positive patients was lower for Omicron versus wild-type, and Omicron vs. Delta ( p < .001). No demographic variables were found to be significantly associated with Omicron response. Many patients with a positive anti-RBD response still had undetectable Omicron-specific neutralizing antibody. In conclusion, three doses of mRNA vaccine results in poor neutralizing responses against the Omicron variant in transplant patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".