Saliva analysis combining membrane protein purification with surface-enhanced Raman spectroscopy for nasopharyngeal cancer detection
Bibliographic record
Abstract
A method for saliva analysis combining membrane protein purification with silver nanoparticle-based surface-enhanced Raman spectroscopy (SERS) for non-invasive nasopharyngeal cancer detection was present in this paper. In this method, cellulose acetate membrane was used to obtain purified whole proteins from human saliva while removing other native saliva constituents and exogenous substances. The purified proteins were mixed with silver nanoparticle for SERS analysis. A diagnostic accuracy of 90.2% can be achieved by principal components analysis combined with linear discriminate analysis, for saliva samples obtained from patients with nasopharyngeal cancer (n = 62) and healthy volunteers (n = 30). This exploratory study demonstrated the potential for developing non-invasive, rapid saliva SERS analysis for nasopharyngeal cancer detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".