Editorial: EBV-Associated Carcinomas: Presence, Role, and Prevention Strategies
Bibliographic record
Abstract
This special issue addresses an important topic related to the role of Epstein-Barr virus (EBV) in human carcinomas initiation and progression, which is one of the most common viral infections worldwide. Today, the relationship between EBV infection and several types of human lymphomas is clearly established, including Hodgkin and Burkitt's lymphoma; meanwhile, it was recently pointed out that EBV is present in nasopharyngeal carcinomas as well as other epithelial cancers (1). EBV is ubiquitous human herpesvirus 4, its genome codes more than 85 proteins of which only few are well-understood; More specifically, six nuclear antigens (EBNA: 1, 2, 3A, 3B, 3C, and LP); three latent membrane proteins/genes (LMP: 1, 2A, 2B) as well as small non-polyadenylated RNAs, EBERs 1 and 2 in addition to few microRNAs have been identified so far, as key regulators, of the oncogenic activity of this virus (2, 3). Present estimates indicate that EBV causes 200,000 new cancer cases annually, accounting for ~2% of cancers worldwide (Cancer Research UK). On the other hand, it is important to emphasize that recent investigations have revealed the possible involvement of EBV in other cancers such as cervical, gliomas, and breast, which are highlighted in this issue.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".