Graphene and graphene oxide with anticancer applications: Challenges and future perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Graphene-based materials have shown immense pertinence for sensing/imaging, gene/drug delivery, cancer therapy/diagnosis, and tissue engineering/regenerative medicine. Indeed, the large surface area, ease of functionalization, high drug loading capacity, and reactive oxygen species induction potentials have rendered graphene- (G-) and graphene oxide (GO)-based (nano)structures promising candidates for cancer therapy applications. Various techniques namely liquid-phase exfoliation, Hummer's method, chemical vapor deposition, chemically reduced GO, mechanical cleavage of graphite, arc discharge of graphite, and thermal fusion have been deployed for the production of G-based materials. Additionally, important criteria such as biocompatibility, bio-toxicity, dispersibility, immunological compatibility, and inflammatory reactions of G-based structures need to be systematically assessed for additional clinical and biomedical appliances. Furthermore, surface properties (e.g., lateral dimension, charge, corona influence, surface structure, and oxygen content), concentration, detection strategies, and cell types are vital for anticancer activities of these structures. Notably, the efficient accumulation of anticancer drugs in tumor targets/tissues, controlled cellular uptake properties, tumor-targeted drug release behavior, and selective toxicity toward the cells are crucial criteria that need to be met for developing future anticancer G-based nanosystems. Herein, important challenges and future perspectives of cancer therapy using G- and GO-based nanosystems have been highlighted, and the recent advancements are deliberated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it