Gold Nanoparticle Based Surface-Enhanced Raman Scattering Spectroscopy of Cancerous and Normal Nasopharyngeal Tissues under Near-Infrared Laser Excitation
Bibliographic record
Abstract
The capabilities of using gold nanoparticle based near-infrared surface-enhanced Raman scattering (SERS) to obtain biochemical information with high spatial resolution from human nasopharyngeal tissue were presented in this paper. The gold nanoparticles used have a mean diameter of 43 nm with a standard deviation of 6 nm. The SERS bands of nasopharyngeal tissue were assigned to known molecular vibrations of nucleic acids, amino acids, proteins, and metabolites. We also observed the blinking phenomenon at the tissue level when measuring the nasopharyngeal tissue SERS spectra, most frequently in signal intensity but also occasionally in peak positions. This phenomenon is excitation light intensity dependent. This work demonstrated great potential for using SERS imaging for distinguishing cancerous and normal nasopharyngeal tissues on frozen sections without using any dye labeling or other chemical species as functionalized binding sites.
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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 | 0.000 | 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 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".