Epstein-Barr Virus DNA Measured in Nasopharyngeal Brushings in Patients with Nasopharyngeal Carcinoma: Pilot Study
Bibliographic record
Abstract
OBJECTIVE: We measured the amount of tumour-derived Epstein-Barr virus (EBV) deoxyribonucleic acid (DNA) in the nasal brushings of nasopharyngeal carcinoma (NPC) patients to determine the correlation with tumour load and response to treatment. MATERIALS AND METHODS: Twenty-eight patients with NPC were included in the study. Baseline measurements of EBV from nasopharyngeal brushings were obtained from 26 patients prior to treatment. A follow-up sample was available from 11 of these patients post-treatment and from 2 additional patients who did not have a baseline sample. Quantitative real-time polymerase chain reaction (PCR) using SYBR Green I fluorescent dye was used to detect the EBV DNA copy number. RESULTS: Nasopharyngeal brush biopsies showed a high copy number of EBV DNA in most of the pretreatment samples (median 9714 copies/mL). The highest copy number detected was 14536944 copies/mL in one sample. In the post-treatment follow-up samples, the copy number was significantly lower (median 6 copies/mL). CONCLUSIONS: We have demonstrated that EBV DNA can be detected in the brush biopsies from NPC patients using quantitative real-time PCR. These pilot data suggest that nasopharyngeal brushings with PCR detection of EBV may be an effective tool for determining local tumour response. The potential of this technique as an NPC tumour marker for post-treatment follow-up is being validated with larger patient numbers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".