Colorectal cancer detection by gold nanoparticle based surface-enhanced Raman spectroscopy of blood serum and statistical analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The capabilities of using gold nanoparticle based surface-enhanced Raman spectroscopy (SERS) to obtain blood serum biochemical information for non-invasive colorectal cancer detection were presented in this paper. SERS measurements were performed on two groups of blood serum samples: one group from patients (n = 38) with pathologically confirmed colorectal cancer and the other group from healthy volunteers (control subjects, n = 45). Tentative assignments of the Raman bands in the measured SERS spectra suggested interesting cancer specific biomolecular changes, including an increase in the relative amounts of nucleic acid, a decrease in the percentage of saccharide and proteins contents in the blood serum of colorectal cancer patients as compared to that of healthy subjects. Both empirical approach and multivariate statistical techniques, including principal components analysis (PCA) and linear discriminant analysis (LDA) were employed to develop effective diagnostic algorithms for classification of SERS spectra between normal and colorectal cancer serum. The empirical diagnostic algorithm based on the ratio of the SERS peak intensity at 725 cm(-1) for adenine to the peak intensity at 638 cm(-1) for tyrosine achieved a diagnostic sensitivity of 68.4% and specificity of 95.6%, whereas the diagnostic algorithms based on PCA-LDA yielded a diagnostic sensitivity of 97.4% and specificity of 100% for separating cancerous samples from normal samples. Receiver operating characteristic (ROC) curves further confirmed the effectiveness of the diagnostic algorithm based on PCA-LDA technique. The results from this exploratory study demonstrated that gold nanoparticle based SERS serum analysis combined with PCA-LDA has tremendous potential for the non-invasive detection of colorectal cancers.
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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 it