pH- and Redox-Responsive Cancer Nanotheranostics Based on Cyclodextrin-Capped Mesoporous Silica
Bibliographic record
Abstract
Versatile chemical modalities for multi-step surface functionalization of mesoporous silica nanoparticle (MSN) open a plethora of possibilities for devising nanosystems for applications in targeted cancer therapy and imaging. Herein, recent research efforts on the development of multifunctionalized MSN-based nanotherapeutics for targeted treatment and imaging of glioblastoma multiforme (GBM) are described. MSN's surface was functionalized with GBM-targeting biomolecules, while further surface functionalization was performed to endow the materials with linkers that are cleavable in the weakly acidic microenvironment of tumors or in the presence of tumor-overexpressed glutathione (GSH). The entrapment of cargo molecules (dyes, anticancer drugs or contrast agents for magnetic resonance imaging (MRI)) within MSN's pores was achieved by binding cyclodextrin analogues to the pH- or GSH-cleavable linkers on the MSN's surface. The release kinetics of cargo molecules was monitored by UV/Vis and fluorescence measurements, and an enhanced cargo release in the weakly acidic environment and upon exposure to GSH was observed. Furthermore, MSNs containing gadolinium-based contrast agents were evaluated for possible imaging of tumor tissue in MRI. Biological activity of MSNs was investigated using U87 (GBM) cell line, while their cellular uptake ability was evaluated by confocal microscopy and flow cytometry. The research results evidence the promising potential of the developed MSNs for their application as GBM- targeted theranostic nanoparticle.
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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".