Recent breakthroughs in graphene quantum dot-enhanced sonodynamic and photodynamic therapy
Bibliographic record
Abstract
Water-soluble graphene quantum dots (GQDs) have recently exhibited considerable potential for diverse biomedical applications owing to their exceptional optical and chemical properties. However, the pronounced heterogeneity in the composition, size, and morphology of GQDs poses challenges for a comprehensive understanding of the intricate correlation between their structural attributes and functional properties. This variability also introduces complexities in scaling the production processes and addressing safety considerations. Light and sound have firmly established their role in clinical applications as pivotal energy sources for minimally invasive therapeutic interventions. Given the limited penetration depth of light, photodynamic therapy (PDT) predominantly targets superficial conditions such as dermatological disorders, head and neck malignancies, ocular ailments, and early-stage esophageal cancer. Conversely, ultrasound-based sonodynamic therapy (SDT) capitalizes on its superior ability to propagate and focus ultrasound within biological tissues, enabling a diverse range of therapeutic applications, including the management of gliomas, breast cancer, hematological tumors, and modulation of the blood-brain barrier (BBB). Considering the advancements in theranostic and precision therapies, reevaluating these conventional energy sources and their associated sensitizers is imperative. This review introduces three prevalent treatment modalities that harness light and sound stimuli: PDT, SDT, and a synergistic approach that integrates PDT and SDT. This study delineated the therapeutic dynamics and contemporary designs of sensitizers tailored to these modalities. By exploring the historical context of the field and elucidating the latest design strategies, this review underscores the pivotal role of GQDs in propelling the evolution of PDT and SDT. This aspires to stimulate researchers to develop "multimodal" therapies integrating both light and sound stimuli.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".