Magnetic Photoluminescent Nanoplatform Built from Large-Pore Mesoporous Silica
Bibliographic record
Abstract
Integrating multiple components to realize cancer diagnosis and therapy in a single theranostic nanoplatform has drawn considerable attention. Herein, a multifunctional theranostic nanoplatform (mSiO 2 @PbS/CdS-Fe 3 O 4 ) was successfully fabricated by carefully designing thiol-modified large-pore mesoporous silica nanospheres (mSiO 2 ), followed by coordination-driven embedding of Fe 3 O 4 nanoparticles (NPs) and PbS/CdS quantum dots (QDs) inside. The excellent feature of near-infrared (NIR) excitation and NIR emission of PbS/CdS QDs enables deep-tissue photoluminescence imaging, which was demonstrated ex vivo with tissue as thick as 14 mm. Meanwhile, owing to the presence of superparamagnetic Fe 3 O 4 NPs, mSiO 2 @PbS/CdS-Fe 3 O 4 can be rapidly confined under an external magnetic field (MF), and exhibit a significantly high T 2 relaxivity in T 2 -weighted magnetic resonance (MR) images in vivo. When mSiO 2 @PbS/CdS-Fe 3 O 4 was exposed to external physical stimuli of MF and/or NIR laser, they produced strong local heating through magnetothermal/photothermal effects. Owing to the unique mesoporous structure of mSiO 2 @PbS/CdS-Fe 3 O 4, doxorubicin (DOX) was readily loaded into them and the drug-release profile was subsequently evaluated under multistimuli (pH/MF/NIR). The release of DOX was significantly enhanced at lower pH, and higher temperatures caused by magnetothermal/photothermal effects. Our results pave the road toward developing a highly powerful nanoplatform for bimodal imaging (NIR deep-tissue photoluminescence and MR imaging), and simultaneously for integrating synergistic treatment capabilities of hyperthermia and pH/MF/NIR-responsive drug release.
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.
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) | 0.001 | 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".