Ultrasound-mediated method for rapid delivery of nano-particles into cells for intracellular surface-enhanced Raman spectroscopy and cancer cell screening
Bibliographic record
Abstract
Surface-enhanced Raman spectroscopy (SERS) is a powerful technology for providing finger-printing information of cells. A big challenge has been the long time duration and inefficient uptake of metal nano-particles into living cells as substrate for SERS analysis. Herein, a simple method (based on ultrasound) for the rapid transfer of silver nanoparticles (NPs) into living cells for intracellular SERS spectroscopy was presented. In this study, the ultrasound-mediated method for NP delivery overcame the shortcoming of 'passive uptake', and achieved quick acquisition of reproducible SERS spectra from living human nasopharyngeal carcinoma cell lines (C666 and CNE1) and normal nasopharyngeal cell line (NP69). Tentative assignment of the Raman bands in the measured SERS spectra showed cancer cell specific biomolecular differences, including significantly lower DNA concentrations and higher protein concentrations in cancerous nasopharyngeal cells as compared to those of normal cells. Combined with PCA-LDA multivariate analysis, ultrasound-mediated cell SERS spectroscopy differentiated the cancerous cells from the normal nasopharyngeal cells with high diagnostic accuracy (98.7%), demonstrating great potential for high-throughput cancer cell screening applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".