Tailoring the Efficacy of Multifunctional Biopolymeric Graphene Oxide Quantum Dot-Based Nanomaterial as Nanocargo in Cancer Therapeutic Application
Bibliographic record
Abstract
Nanotechnology has acquired an immense recognition in cancer theranostics. Considerable progress has been made in the development of targeted drug delivery system for potent delivery of anticancer drugs to tumor-specific sites. Recently, multifunctional nanomaterials have been explored and used as nanovehicles to carry drug molecules with enhanced therapeutic efficacy. In this present work, graphene oxide quantum dot (GOQD) was conjugated with folic acid functionalized chitosan (FA-CH) to develop a nanocargo (FA-CH-GOQD) for drug delivery in cancer therapy. The synthesized nanomaterials were characterized using Fourier transform infrared spectroscopy, ultraviolet-visible spectroscopy, scanning electron microscopy, transmission electron microscopy, and dynamic light scattering. Photoluminescence spectroscopy was also employed to characterize the formation of GOQD. To validate the efficacy of FA-CH-GOQD as nanocarriers, doxorubicin (DOX) drug was chosen for encapsulation. The in vitro release pattern of DOX was examined in various pH ranges. The drug release rate in a tumor cell microenvironment at pH 5.5 was found higher than that under a physiological range of pH 6.5 and 7.4. An MTT assay was performed to understand the cytotoxic behavior of GOQD and FA-CH-GOQD/DOX. Cytomorphological micrographs of the A549 cell exhibited the various morphological arrangements subject to apoptosis of the cell. Cellular uptake studies manifested that FA-CH-GOQD could specifically transport DOX within a cancerous cell. Further anticancer efficacy of this nanomaterial was corroborated in a breast cancer cell line and demonstrated through 4',6-diamidino-2-phenylindole dihydrochloride staining micrographs.
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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 | 0.000 | 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 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".