Recent advances in synergistic use of GQD-based hydrogels for bioimaging and drug delivery in cancer treatment
Bibliographic record
Abstract
Graphene quantum dot (GQD) integration into hydrogel matrices has become a viable approach for improving drug delivery and bioimaging in cancer treatment in recent years. Due to their distinct physicochemical characteristics, graphene quantum dots (GQDs) have attracted interest as adaptable nanomaterials for use in biomedicine. When incorporated into hydrogel frameworks, these nanomaterials exhibit enhanced stability, biocompatibility, and responsiveness to external stimuli. The synergistic pairing of hydrogels with GQDs has created new opportunities to tackle the problems related to drug delivery and bioimaging in cancer treatment. Bioimaging plays a pivotal role in the early detection and monitoring of cancer. GQD-based hydrogels, with their excellent photoluminescence properties, offer a superior platform for high-resolution imaging. The tunable fluorescence characteristics of GQDs enable real-time visualization of biological processes, facilitating the precise diagnosis and monitoring of cancer progression. Moreover, the drug delivery landscape has been significantly transformed by GQD-based hydrogels. Because hydrogels are porous, therapeutic compounds may be placed into them and released in a controlled environment. The large surface area and distinct interactions of graphene quantum dots (GQDs) with medicinal molecules boost loading capacity and release dynamics, ultimately improving therapeutic efficacy. Moreover, GQD-based hydrogels' stimulus-responsiveness allows for on-demand medication release, which minimizes adverse effects and improves therapeutic outcomes. The ability of GQD-based hydrogels to specifically target certain cancer cells makes them notable. Functionalizing GQDs with targeting ligands minimizes off-target effects and delivers therapeutic payloads to cancer cells selectively. Combined with imaging capabilities, this tailored drug delivery creates a theranostic platform for customized cancer treatment. In this study, the most recent advancements in the synergistic use of GQD-based hydrogels are reviewed, with particular attention to the potential revolution these materials might bring to the area of cancer theranostics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".