Viral Causes of Lymphoma: The History of Epstein-Barr Virus and Human T-Lymphotropic Virus 1
Bibliographic record
Abstract
In 1964, Epstein, Barr, and Achong published a report outlining their discovery of viral particles in lymphoblasts isolated from a patient with Burkitt lymphoma. The Epstein-Barr virus (EBV) was the first human cancer virus to be described, and its discovery paved the way for further investigations into the oncogenic potential of viruses. In the decades following the discovery of EBV, multinational research efforts led to the discovery of further viral causes of various human cancers. Lymphomas are perhaps the cancer type that is most closely associated with oncogenic viruses: infection with EBV, human T-lymphotropic virus 1 (HTLV-1), human immunodeficiency virus (HIV), Kaposi sarcoma-associated herpesvirus/human herpesvirus 8, and hepatitis C virus have all been associated with lymphomagenesis. Lymphomas have also played an important role in the history of oncoviruses, as both the first human oncovirus (EBV) and the first human retrovirus (HTLV-1) were discovered through isolates taken from patients with unique lymphoma syndromes. The history of the discovery of these 2 key oncoviruses is presented here, and their impact on further medical research, using the specific example of HIV research, is briefly discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".