Epstein-Barr virus infection and its lifelong autoimmune ramifications in developed and developing nations
Bibliographic record
Abstract
Background. Epstein-Barr virus (EBV) infection is a major cause of malignancy worldwide. Maternal antibodies are thought to prevent EBV infection because infection is uncommon in early infancy. Additionally, maternal HIV infection is associated with an increased incidence of EBV infection in exposed infants possibly resulting from impaired transfer of EBV maternal antibodies. EBV, possibly in its acute form manifesting as infectious mononucleosis (IM), is also the highest environmental risk factor for the development of multiple sclerosis (MS), a devastating neurologic disease caused by autoimmune destruction of myelin. IM can be diagnosed by the presence of heterophile antibodies which target an antigen that is akin to certain myelin glycans in the brain. We hypothesized that maternal antibodies can protect against EBV acquisition by studying infants with and without maternal HIV exposure. Furthermore, , since the degeneration of myelin is a hallmark of MS, we hypothesized that EBV infection, specifically IM, primes the immune system to cross-react and attack myelin. Methods. Ugandan infants were followed for EBV acquisition from birth and measured antibody binding to EBV glycoproteins involved in B cell and epithelial cell entry, as well as viral neutralization and antibody-dependent cellular cytotoxicity (ADCC). Serum antibodies against major brain glycans were measured using a Luminex based glycan array for 6 infant samples (pre-post EBV infection) and 9 adults with IM. Results. HIV-exposed uninfected infants had significantly higher titers than HIV-unexposed uninfected infants for all EBV-binding and neutralizing antibodies measured (p<0.01), but not ADCC activity, which was similar between groups. No antibody measure was associated with a decreased risk of EBV acquisition in the cohort. IM-positive sera had 10 times the anti-glycan antibodies to major brain glycans compared to pre and post-EBV infected infants, while there was no significant difference between pre-post EBV infected infant samples. Conclusions. Our findings indicate that in this cohort maternal antibody did not protect infants against EBV infection through viral neutralization. The increase in anti-myelin antibodies in individuals with IM suggests that the development of cross-reactivity towards myelin may be a mechanistic link between IM and MS.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".