Discrimination of gastric cancer from normal by serum RNA based on surface‐enhanced Raman spectroscopy (SERS) and multivariate analysis
Bibliographic record
Abstract
PURPOSE: Here, the authors explore the feasibility of discriminating cancer patients from healthy controls by serum RNA detection based on surface-enhanced Raman spectroscopy (SERS) and multivariate analysis. METHODS: MgSO(4)-aggregated silver nanoparticles (Ag NP) as the SERS-active substrate presented strong SERS signals to RNA. SERS measurements were performed on two groups of serum RNA samples: one group from patients (n = 31) with gastric cancer and the other group from healthy volunteers (n = 34). RESULTS: Tentative assignments of the Raman bands in the normalized SERS spectra demonstrated that there are differential expressions of circulating RNA between the gastric cancer group and the control group. Principal component analysis (PCA) combined with linear discriminate analysis (LDA) was introduced to differentiate gastric cancer from normal and achieved sensitivity of 100% and specificity of 94.1%. CONCLUSIONS: This exploratory study demonstrated potential for developing serum RNA SERS analysis into a useful clinical tool for noninvasive screening and detection of cancer.
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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".