(Invited) Multi-Architectured Lanthanide Doped Nanoparticles for Theranostics
Bibliographic record
Abstract
Light triggered theranostic (therapy and diagnostic) nanoplatforms have gained a considerable attention in recent years. In theranostics, light as an external trigger stands out due to its non-invasiveness, high local precision and temporal resolution. Many such nanoplatforms employ high-energy (visible or UV) light to initiate the individual therapeutic and diagnostic modalities. However, light at these wavelengths suffers from inherent drawbacks such as having little to no penetration in living tissue, inducing autofluorescence from inherent fluorophores or chromophores in tissues and causing photodamage. The use of near-infrared (NIR) light for excitation mitigates such drawbacks associated with high-energy excitation, for example, little to no background autofluorescence from the specimen under investigation as well as no incurred photodamage. Moreover, one of the biggest limitations is that of penetration and NIR light can penetrate tissues much better than high-energy light especially when these wavelengths lie within the three biological windows where tissues are optically transparent. At the forefront of NIR excited nanomaterials are lanthanide doped nanoparticles, which can undergo conventional (Stokes) luminescence and emit in the NIR biological windows. However, unlike other classes of nanoparticles, they can also undergo a multiphoton excitation process where the NIR excitation light is converted to higher energies resulting in anti-Stokes luminescence spanning the UV-visible-NIR regions (known as upconversion). Thus, it now becomes possible to generate upconverted high-energy light (UV or visible) in situ to trigger other light activated therapeutic modalities (i.e. drug release) while using the NIR emission for diagnostics (i.e. bioimaging, nanothermometry). Here, we demonstrate how the luminescence properties (upconversion and NIR) of various lanthanide doped core/shell (and multishell) nanoparticles can be exploited for potential use in theranostics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".