Fluorescence Activation Imaging of Cytochrome c Released from Mitochondria Using Aptameric Nanosensor
Bibliographic record
Abstract
We have developed an aptameric nanosensor for fluorescence activation imaging of cytochrome c (Cyt c). Fluorescence imaging tools that enable visualization of key molecular players in apoptotic signaling are essential for cell biology and clinical theranostics. Cyt c is a major mediator in cell apoptosis. However, fluorescence imaging tools allowing direct visualization of Cyt c translocation in living cells have currently not been realized. We report for the first time the realization of a nanosensor tool that enables direct fluorescence activation imaging of Cyt c released from mitochondria in cell apoptosis. This strategy relies on spatially selective cytosolic delivery of a nanosensor constructed by assembly of a fluorophore-tagged DNA aptamer on PEGylated graphene nanosheets. The cytosolic release of Cyt c is able to dissociate the aptamer from graphene and trigger an activated fluorescence signal. The nanosensor is shown to exhibit high sensitivity and selectivity, rapid response, large signal-to-background ratio for in vitro, and intracellular detection of Cyt c. It also enables real-time visualization of the Cyt c release kinetics and direct identification of the regulators for apoptosis. The developed nanosensor may provide a very valuable tool for apoptotic studies and catalyze the fundamental interrogations of Cyt c-mediated biology.
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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".