Label-free surface-enhanced Raman spectroscopy for detection of colorectal cancer and precursor lesions using blood plasma
Bibliographic record
Abstract
Fecal based tests have limited diagnostic values in detecting adenomatous polyps, the precursor lesions to colorectal cancer (CRC). Surface enhanced Raman spectroscopy (SERS) using silver nanoparticles as substrate is a multiplexed analytical technique capable of detecting biomolecules with high sensitivity. This study utilizes SERS to analyze blood plasma for detecting both CRC and adenomatous polyps for the first time. Blood plasma samples are collected from healthy control subjects and patients diagnosed with adenomas and CRC. Using a real-time Raman system, SERS spectra for blood plasma samples are measured in 1 s. The collected SERS spectra are analyzed with partial least squares-discriminant analysis. Classification of normal versus CRC plus adenomatous polyps achieved diagnostic sensitivity of 86.4% and specificity of 80%. The results suggest that blood plasma SERS analysis could be a potential screening test to detect both CRC and adenomas.
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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) | 0.000 | 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 | 0.001 | 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".