Rapid and label-free exosome analysis by surface-enhanced Raman spectroscopy using silver nanoparticle substrate based on selective laser ablation and melting of silver nanowire
Bibliographic record
Abstract
Abstract Early diagnostics of breast cancer is crucial to reduce the risk of cancer metastasis and late relapse. Exosome, which contains distinct information of its origin, can be the target object as a liquid biopsy. However, its low sensitivity and inadequate diagnostic tools interfere with the point-of-care testing (POCT) of the exosome. Recently, Surface-enhanced Raman spectroscopy (SERS), which enables the detection of Raman scattering, has been proved as a promising tool for exosome detection but the fabrication process of SERS probe or substrate is still inefficient and far from large-scale production. This study proposes rapid and label-free detection of breast cancer-derived exosomes by statistical analysis of Raman spectra using silver nanoparticle SERS substrate fabricated by selective laser ablation and melting. Employing silver nanowire and optimizing pulse repetition rate power enable rapid and energy-efficient fabrication of SERS substrate. The functionality including sensitivity, reproducibility, stability, and renewability was evaluated using rhodamine 6G as a probe molecule. Then, the feasibility of POCT was examined by the statistical analysis of Raman spectra of exosomes from malignant breast cancer cells and non-tumorigenic breast epithelial cells. The presented framework is anticipated to be utilized in other biomedical applications, facilitating cost-effective and large-scale production performance.
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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) | 0.001 | 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".