Ratiometric pH Sensing in Living Cells Using Carbon Dots
Bibliographic record
Abstract
Abstract The ability to precisely sense physiological pH changes in the cellular environment is exceedingly difficult. Novel technologies are thus required to address this challenge. Fluorescent nanomaterials can be exploited to this effect because their optical properties can exhibit strong pH dependence. Herein, an intracellular pH‐sensing probe is developed via a facile microwave‐reaction synthesis method for the preparation of carbon dots (CDs) using glutathione and formamide. The CDs possess unique optical properties allowing for concomitant fluorescence in the blue and red regions of the spectrum. These dots are investigated as pH‐sensors using the red fluorescence signatures at 650 and 680 nm. The two fluorescence bands respond differently following pH changes in their environment and could thus be used for ratiometric measurements. Cytotoxicity studies of the CDs in glioblastoma cells show no decrease in cell viability up to 100 μg mL−1 (24 h). Fluorescence imaging reveals that the dots localize in lysosomal compartments. Moreover, they can sense changes in lysosomal pH in response to serum and amino acid starvation, as well as administration of diclofenac and metformin, drugs currently in clinical trials for combination treatments of cancer. These CDs offer a new self‐referencing approach for live intracellular pH sensing in 2D‐ and 3D‐cell models.
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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 | 0.001 | 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".