Analysis and Significance of Anti-Latent Membrane Protein-1 Antibodies in the Sera of Patients with EBV-Associated Diseases
Bibliographic record
Abstract
Anti-latent membrane protein-1 (LMP-1) is an EBV-encoded type III integral membrane protein with oncogenic potential that is expressed most consistently in various EBV-associated malignancies. Unlike many other EBV proteins, LMP-1 Abs have rarely been demonstrated in EBV-associated disease conditions. We established a high level LMP-1-expressing cell clone and used it for the detection, quantitation, and characterization of these Abs in various human sera in immunoblots and ELISA. Our results demonstrate that, in contrast to the commonly held notion, LMP-1 induces significant humoral immune responses in EBV-associated malignant conditions especially in nasopharyngeal carcinoma (NPC) patients in whom >70% sera are positive for these Abs, and their titers correlate with the clinical condition of the tumors. Interestingly, anti-LMP-1 Abs of IgA isotype were found only in NPC patients. These Abs were absent from the sera of infectious mononucleosis and chronic EBV infection patients, whereas a small fraction ( approximately 5%) of the healthy, EBV-seropositive individuals were positive for them; however, their OD values were much lower than those of NPC patients. These studies demonstrate, for the first time, the potential significance of LMP-1-specific Abs for the diagnosis and prognosis of EBV-associated malignancies, especially of NPC.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".