A Robust Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)–Specific T- and B-Cell Response Is Associated With Early Viral Clearance in SARS-CoV-2 Omicron-Infected Immunocompromised Individuals
Bibliographic record
Abstract
BACKGROUND: The immunological determinants of delayed viral clearance and intrahost viral evolution that drive the development of new pathogenic virus strains in immunocompromised individuals are unknown. Therefore, we longitudinally studied severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific immune responses in relation to viral clearance and evolution in immunocompromised individuals. METHODS: Among Omicron-infected immunocompromised individuals, we determined SARS-CoV-2-specific T- and B-cell responses, anti-spike immunoglobulin G (IgG) and IgG3 titers, neutralization titers, and monoclonal antibody (mAb) resistance-associated mutations. The 28-day post-enrollment nasopharyngeal specimen defined early (reverse-transcription polymerase chain reaction [RT-PCR] negative ≤28 days) or late (RT-PCR positive >28 days) viral clearance. RESULTS: Of 30 patients included (median age, 61.9 [interquartile range, 47.4-72.3] years; 50% females), 20 (66.7%) received mAb therapy. Thirteen (43.3%) demonstrated early and 17 (56.7%) late viral clearance. Patients with early viral clearance and patients without resistance-associated mutations had significantly higher baseline interferon-γ release, and patients with early viral clearance had a higher frequency of SARS-CoV-2-specific B cells at baseline. In non-mAb-treated patients, day 7 IgG and neutralization titers were significantly higher in those with early versus late viral clearance. CONCLUSIONS: An early robust adaptive immune response is vital for efficient viral clearance and associated with less emergence of mAb resistance-associated mutations in Omicron-infected immunocompromised patients. This emphasizes the importance of early SARS-CoV-2-specific T- and B-cell responses and thereby provides a rationale for development of novel therapeutic approaches.
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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 | 0.001 |
| 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".