Dyes Encapsulated By Single Walled Carbon Nanotubes: A Raman Nanoprobe of Cancer Detection
Bibliographic record
Abstract
Carbon nanotubes are among the most promising platforms for biological applications such as cancer targeting, medical imaging and drug delivery. Herein we present a novel approach to modify single-walled carbon nanotubes (SWCNTs) into Raman nanoprobes capable of targeting biomarkers on cancer cells. The process involves cleaning and shortening in acids followed by encapsulation of specific organic dyes. Subsequently the as-synthesized Raman nanoprobes are then covalently attached with modified NH 2 -PEG-COOH to make a generic nanoprobe that is bio-compatible and highly dispersed in aqueous or buffer media. When covalently linked to specific antibodies anti-E-cadherin (monoclonal mouse anti-human) and CK19 (cytokeratin), such nanoprobes can be used to detect there bio-markers in the membrane of breast cancer cell lines. Here, we will first discuss a modified synthetic approach to densify the covalent pegylation used to link bio-molecules on the SWCNTs and show results with nanoprobes incubated with T47D antibody (ab) and MDA231 cell lines. The immunofluorescence and Raman imaging data shows that dyes@SWCNT/PEG/ab composites can specifically target cancer cells overexpressing high affinity receptors of the specific biomarkers linked to the probes. By densifying the covalently attached PEGs, the results show higher stability and improved selectivity of the Raman nanoprobes towards bio-molecules on cancer cells.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".