Humoral response to natural influenza infection in solid organ transplant recipients
Bibliographic record
Abstract
The humoral immune response of transplant recipients to influenza vaccination has been studied in detail. In contrast, the hemagglutinin inhibiting (HI) antibody response evoked by natural influenza infection and its impact on viral kinetics is unknown. In this prospective, multicenter, cohort study of natural influenza infection in transplant recipients, we measured HI antibody titers at presentation and 4 weeks later. Serial nasopharyngeal viral loads were determined using a quantitative influenza A polymerase chain reaction (PCR). We analyzed 196 transplant recipients with influenza infection. In the cohort of organ transplant patients with influenza A (n = 116), seropositivity rates for strain-specific antibodies were 44.0% (95% confidence interval [CI] 31.5-53.2%) at diagnosis and 64.7% (95% CI 55.4-72.9%) 4 weeks postinfection. Seroconversion was observed in 32.8% (95% CI 24.7-41.9%) of the cases. Lung transplant recipients were more likely to seroconvert (P = .002) and vaccine recipients were less likely to seroconvert (P = .024). A subset of patients (n = 30) who were unresponsive to prior vaccination were also unresponsive to natural infection. There was no correlation between viral kinetics and antibody response. This study provides novel data on the seroresponse to influenza infection in transplant patients and its relationship to a number of parameters including a prior vaccination status, virologic measures, and clinical variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".