The Role of Latently Infected B Cells in CNS Autoimmunity
Bibliographic record
Abstract
The onset of multiple sclerosis (MS) is caused by both genetic and environmental factors. Among the environmental factors, it is believed that previous infection with Epstein-Barr virus (EBV) may contribute in the development of MS. EBV has been associated with other autoimmune diseases, such as systemic lupus erythematous, and cancers like Burkitt's lymphoma. EBV establishes a life-long latency in B cells with occasional reactivation of the virus throughout the individual's life. The role played by B cells in MS pathology has been largely studied, yet is not clearly understood. In MS patients, Rituximab, a novel treatment that targets CD20(+) B cells, has proven to have successful results in diminishing the number of relapses in remitting relapsing MS; however, the mechanism of how this drug acts has not been clearly established. In this review, we analyze the evidence of how B cells latently infected with EBV might be altering the immune system response and helping in the development of MS. We will also discuss how animal models, such as experimental autoimmune encephalomyelitis (EAE) and murine gammaherpesvirus-68 (γHV-68), can be used as powerful tools in the study of the relationship between EBV, MS, and B cells.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".