Multifunctional Self-Assembled Supernanoparticles for Deep-Tissue Bimodal Imaging and Amplified Dual-Mode Heating Treatment
Bibliographic record
Abstract
Developing multifunctional therapeutic and diagnostic (theranostic) nanoplatforms is critical for addressing challenging issues associated with cancers. Here, self-assembled supernanoparticles consisting of superparamagnetic Fe 3 O 4 nanoparticles and photoluminescent PbS/CdS quantum dots whose emission lies within the second biological window (II-BW) are developed. The proposed self-assembled Fe 3 O 4 and PbS/CdS (II-BW) supernanoparticles [SASNs (II-BW)] exhibit outstanding photoluminescence detectable through a tissue as thick as 14 mm, by overcoming severe light extinction and concomitant autofluorescence in II-BW, and significantly enhanced T 2 relaxivity (282 mM –1 s –1, ca . 4 times higher than free Fe 3 O 4 nanoparticles) due to largely enhanced magnetic field inhomogeneity. On the other hand, SASNs (II-BW) possess the dual capacity to act as both magnetothermal and photothermal agents, overcoming the main drawbacks of each type of heating separately. When SASNs (II-BW) are exposed to the dual-mode (magnetothermal and photothermal) heating, the thermal energy transfer efficiency is amplified 7-fold compared with magnetic heating alone. These results, in hand with the excellent photo- and colloidal stability, and negligible cytotoxicity, demonstrate the potential use of SASNs (II-BW) for deep-tissue bimodal (magnetic resonance and photoluminescence) in vivo imaging, while simultaneously providing the possibility of SASNs (II-BW)-mediated amplified dual-mode heating treatment for cancer therapy.
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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".