Evaluation of Lanthanide-Doped Upconverting Nanoparticles for <i>in Vitro</i> and <i>in Vivo</i> Applications
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Because of their unique physicochemical properties, lanthanide-doped upconverting nanoparticles (Ln-UCNPs) have exceptional potential for biological applications. However, the use in biological systems is hampered by the limited understanding of their bionano interactions. Our multidisciplinary study has generated these insights through in-depth and quantitative analyses. The Ln-UCNPs examined here are spherical, monodisperse, and stable in aqueous environments. We show that Ln-UCNPs were associated with HeLa (cervical cancer) and LLC-PK1 (renal proximal tubule) cells and were nontoxic over a wide concentration range. Multiple biomarkers were assessed to monitor the cellular homeostasis in Ln-UCNP-treated cells. To this end, we evaluated the nuclear lamina, nucleoli, and nuclear transport factors. Single-cell analyses quantified the impact on Nrf2 and NF-κB, two transcription factors that control stress and immune responses. Moreover, we measured Ln-UCNP-induced changes in the abundance of molecular chaperones. Collectively, in vitro studies confirmed that Ln-UCNPs are nontoxic and trigger minor cellular stress responses. This lack of toxicity was verified in vivo, using the model organism Caenorhabditis elegans . The compatibility with biological systems prompted us to assess Ln-UCNPs as potential contrast agents for magnetic resonance imaging. We demonstrated that the Ln-UCNPs examined here were especially suitable as T 2 contrast agents; they clearly outperformed the clinically used Gadovist. Taken together, our interdisciplinary work provides robust evidence for the nontoxicity of Ln-UCNPs. This sets the stage for the translation of Ln-UCNP for use in complex biological systems.
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".