Fabrication and<i>in vitro</i>characterization of gadolinium-based nanoclusters for simultaneous drug delivery and radiation enhancement
Bibliographic record
Abstract
We report the synthesis of a gadolinium hydroxide (Gd(OH)3) nanorod based doxorubicin (Dox) delivery system that can enhance both magnetic resonance imaging contrast and radiation sensitivity. A simple and cost effective wet-chemical method was utilized in the presence of manganese (Mn) ions and Dox to produce the Gd(OH)3:Mn·Dox nanocluster structure. The Gd(OH)3:Mn·Dox nanocluster was composed of Mn-doped Gd(OH)3 nanorods arranged in parallel with Dox as a linker molecule between the adjacent nanorods. No other studies have utilized Dox as both the linker and therapeutic molecule in a nanostructure to date. The Gd(OH)3 nanorod is reported to have no significant cellular or in vivo toxicity, which makes it an ideal base material for this biomedical application. The Gd(OH)3:Mn·Dox nanocluster exhibited paramagnetic behavior and was stable in a colloidal solution. The nanocluster also enabled high Dox loading capacity and specifically released Dox in a sustained and pH-dependent manner. The positively charged Gd(OH)3:Mn·Dox nanoclusters were readily internalized into MDA-MB-231 breast cancer cells via endocytosis, which resulted in intracellular release of Dox. The released Dox in cells was effective in conferring cytotoxicity and inhibiting proliferation of cancer cells. Furthermore, a synergistic anticancer effect could be observed with radiation treatment. Overall, the Gd(OH)3:Mn·Dox nanocluster drug delivery system described herein may have potential utility in clinics as a multifunctional theranostic nanoparticle with combined benefits in both diagnosis and therapy in the management of cancer.
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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".