Fluorescent Heterotelechelic Single-Chain Polymer Nanoparticles: Synthesis, Spectroscopy, and Cellular Imaging
Bibliographic record
Abstract
The folding and collapse of individual polymer chains into single-chain polymer nanoparticles (SCPNs) is a versatile and emerging platform for biological applications such as diagnostics, imaging, and therapy, where components for two or more of these functions can be combined onto a single polymer carrier. Here, we prepare heterotelechelic polymers with three sets of separately addressable chemical handles at their α-terminus and ω-terminus, and along their backbone. As a model system, the α- and ω-termini are conjugated with a targeting ligand (folic acid or biotin) and therapeutic drug cargo (camptothecin), respectively, and the backbone is grafted with pendant fluorescent dye molecules, poly(ethylene glycol) oligomers, and benzene-1,3,5-tricarboxamide. These polymers fold in water to give fluorescent SCPNs, which are characterized with respect to their physical and photophysical properties. The latter reveals a relationship between polymer folding, quantum yield, and resistance to photobleaching. The SCPNs are then shown to be useful for immunolabeling of SK-BR-3 breast cancer cells and exhibit little or no acute cytotoxicity. This work demonstrates that SCPNs can be used as a viable platform for bioconjugation and cell labeling, helps establish a set of design criteria for optimizing future biological applications, and opens the door to the development of SCPNs for a broader range of theranostic 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) | 0.000 | 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".