Immunoassay for LMP1 in nasopharyngeal tissue based on surface-enhanced Raman scattering
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown that Epstein-Barr virus (EBV)-encoded latent membrane protein 1 (LMP1) is closely associated with the occurrence and development of nasopharyngeal carcinoma, and can be used as a tumor marker in screening for the disease. Here we report a new methodology based on highly specific and sensitive surface-enhanced Raman scattering (SERS) technology to detect LMP1 in nasopharyngeal tissue sections directly with no need of tedious procedures as with conventional immunohistochemistry methods. METHODS: LMP1-functionalized 4-mercaptobenzoic acid (4-MBA)-labeled Au/Ag core-shell bimetallic nanoparticles were prepared first and then applied for analyzing LMP1 in formalin-fixed paraffin-embedded nasopharyngeal tissue sections obtained from 34 cancer patients and 20 healthy controls. SERS spectra were acquired from a 25 × 25 spot square area on each tissue section and used to generate SERS images. RESULTS: Data from SERS spectra and images show that this new SERS-based immunoassay detected LMP1 in formalin-fixed paraffin-embedded nasopharyngeal tissue sections with high sensitivity and specificity. The results from the new LMP1-SERS probe method are superior to those of conventional immunohistochemistry staining for LMP1, and in excellent agreement with those of in situ hybridization for EBV-encoded small RNA (EBER). CONCLUSION: This new SERS technique has the potential to be developed into a new clinical tool for detection and differential diagnosis of nasopharyngeal carcinoma as well as for predicting metastasis and immune-targeted treatment of nasopharyngeal carcinoma.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".