Longitudinal Innate and Heterologous Adaptive Immune Responses to SARS‐CoV‐2 JN.1 in Transplant Recipients With Prior Omicron Infection: Limited Neutralization but Robust CD4 <sup>+</sup> T‐Cell Activity
Bibliographic record
Abstract
BACKGROUND: Solid organ transplant (SOT) recipients are at increased risk for severe COVID-19 and often exhibit reduced vaccine efficacy due to chronic immunosuppression. As new SARS-CoV-2 variants emerge, understanding immune responses following natural infection remains critical for informing protection strategies in this vulnerable population. We conducted a longitudinal study of SOT recipients who had recovered from Omicron BA.1 or BA.2 infection, evaluating immune responses to the JN.1 subvariant at 4-6 weeks and 1 year postinfection. METHODS: Neutralizing antibodies to JN.1 were measured using a pseudovirus neutralization assay, and JN.1-specific T-cell responses were assessed by flow cytometry. Frequencies of bulk T-cells and innate immune cells, identified via flow cytometry, and their correlation with adaptive responses were also analyzed. RESULTS: At 4-6 weeks, 30% of participants had detectable JN.1-neutralizing antibodies, rising to 43% at one year, although titers remained low. In contrast, CD4⁺ T-cell responses were robust and detected in 75%-83% of participants at 4-6 weeks, increasing to 75%-93% by 1 year. CD8⁺ T-cell responses were observed less frequently. Exploratory correlations between innate and bulk T-cell subsets with heterologous adaptive immune responses were investigated but did not reveal statistically significant relationships. CONCLUSION: T-cell responses may help mitigate severe disease following exposure to JN.1-derived variants, which continue to dominate the SARS-CoV-2 landscape.
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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 | 0.000 | 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".