Coenzyme Q Functionalized CdTe/ZnS Quantum Dots for Reactive Oxygen Species (ROS) Imaging
Bibliographic record
Abstract
Quantum dots (QDs) have been widely used for fluorescent imaging in cells. In particular, surface functionalized QDs are of interest, since they possess the ability to recognize and detect the analytes in the surrounding nanoscale environment based on electron and hole transfer between the analytes and the QDs. Here we demonstrate that fluorescence enhancement/quenching in QDs can be switched by electrochemically modulating electron transfer between attached molecules and QDs. For this purpose, a number of redox-active coenzyme Q (CoQ) disulfide derivatives [CoQC(n)S](2) were synthesized with different alkyl chain lengths (n=1, 5, and 10). The system supremely sensitive to NADH (nicotinamide adenine dinucleotide) and superoxide radical (O(2)(.)(-)), and represents a biomimetic electron-transfer system, modeling part of the mitochondrial respiratory chain. The results of our in situ fluorescence spectroelectrochemical study demonstrate that the reduced state of [CoQC(n)S](2) significantly enhanced the fluorescence intensity of CdTe/ZnS QDs, while the oxidized state of the CoQ conjugates quench the fluorescence to varying degrees. Fluorescence imaging of cells loaded with the conjugate QD-[CoQC(n)S](2) displayed strikingly differences in the fluorescence depending on the redox state of the capping layer, thus introducing a handle for evaluating the status of the cellular redox potential status. Moreover, an MTT assay (MTT=3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) proved that the cytotoxicity of QDs was significantly reduced after immobilization by CoQ derivatives. Those unique features make CoQ derivatived QDs as a promising probe to image redox coenzyme function in vitro and in vivo.
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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".