Maternal Epstein-Barr Virus-Specific Antibodies and Risk of Infection in Ugandan Infants
Bibliographic record
Abstract
BACKGROUND: Epstein-Barr virus (EBV) infection is a major cause of malignancy worldwide. Maternal antibody is thought to prevent EBV infection because it is uncommon in early infancy. Maternal HIV infection is associated with an increased incidence of EBV infection in exposed infants, which we hypothesized results from impaired transfer of EBV-neutralizing maternal antibodies. METHODS: Among Ugandan infants followed for EBV acquisition from birth, we measured antibody binding to EBV glycoproteins (gp350, gH/gL) involved in B-cell and epithelial-cell entry, as well as viral neutralization and antibody-dependent cellular cytotoxicity (ADCC) activity in plasma samples prior to infection. These serologic data were analyzed for differences between HIV-exposed uninfected (HEU) and HIV-unexposed (HUU) infants, and for associations with incident infant EBV infection. RESULTS: HEU infants had significantly higher titers than HUU infants for all EBV-binding and neutralizing antibodies measured (P < .01) but not ADCC activity, which was similar between groups. No antibody measure was associated with a decreased risk of EBV acquisition in the cohort. CONCLUSIONS: Our findings indicate that in this cohort maternal antibody did not protect infants against EBV infection through viral neutralization. The identification of protective nonneutralizing antibody functions would be invaluable for the development of an EBV vaccine.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".