Blood Plasma Stabilized Gold Nanoclusters for Personalized Tumor Theranostics
Bibliographic record
Abstract
Personalized cancer theranostics has a potential to increase efficiency of early cancer diagnostics and treatment, and to reduce negative side-effects. Protein-stabilized gold nanoclusters may serve as theranostic agents. To make gold nanoclusters personalized and highly biocompatible, the clusters were stabilized with human plasma proteins. Optical properties of synthesized nanoclusters were investigated spectroscopically, and possible biomedical application was evaluated using standard cell biology methods. The spectroscopic investigations of human plasma proteins stabilized gold nanoclusters revealed that a wide photoluminescence band in the optical tissue window is suitable for cancer diagnostics. High-capacity generation of singlet oxygen and other reactive oxygen species was also observed. Furthermore, the cluster accumulation in cancer cells and the photodynamic effect were evaluated. The results demonstrate that plasma proteins stabilized gold nanoclusters that accumulate in breast cancer cells and are non-toxic in the dark, while appear phototoxic under irradiation with visible light. The results positively confirm the utility of plasma protein stabilized gold nanoclusters for the use in cancer diagnostics and treatment.
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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".