<p>Applications of Graphene and Graphene Oxide in Smart Drug/Gene Delivery: Is the World Still Flat?</p>
Bibliographic record
Abstract
Graphene, a wonder material, has made far-reaching developments in many different fields such as materials science, electronics, condensed physics, quantum physics, energy systems, etc. Since its discovery in 2004, extensive studies have been done for understanding its physical and chemical properties. Owing to its unique characteristics, it has rapidly became a potential candidate for nano-bio researchers to explore its usage in biomedical applications. In the last decade, remarkable efforts have been devoted to investigating the biomedical utilization of graphene and graphene-based materials, especially in smart drug and gene delivery as well as cancer therapy. Inspired by a great number of successful graphene-based materials integrations into the biomedical area, here we summarize the most recent developments made about graphene applications in biomedicine. In this paper, we review the up-to-date advances of graphene-based materials in drug delivery applications, specifically targeted drug/ gene delivery, delivery of antitumor drugs, controlled and stimuli-responsive drug release, photodynamic therapy applications and optical imaging and theranostics, as well as investigating the future trends and succeeding challenges in this topic to provide an outlook for future researches.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".