The effects of lanthanide-doped upconverting nanoparticles on cancer cell biomarkers
Bibliographic record
Abstract
Lanthanide-doped upconverting nanoparticles (Ln-UCNPs) possess optical and physicochemical properties that are promising for the design of new theranostic platforms. This applies in particular to the treatment of cancer. Towards this goal, oleate-capped-NaLuF4:Tm3+(0.5%)/Yb3+(20%)/Gd3+(30%) with an average size of 35 nm ± 2 nm were synthesized by co-precipitation. Due to their hydrophobic surface, these Ln-UCNPs produced agglomerates under cell culture conditions. To assess the cellular response to Ln-UCNPs at the molecular level, we evaluated several key aspects of tumor cell physiology. Using cancer lines of different origins, we demonstrated Ln-UCNP dependent changes of cancer cell biomarkers. Multiple cellular components that regulate tumorigenesis and cancer cell homeostasis were affected. In particular, Ln-UCNPs reduced the abundance of hsp70s, elevated DNA damage, and diminished nucleolin and B23/nucleophosmin, proteins required for the assembly of ribosomes. Treatment with Ln-UCNPs also decreased the concentration of paxillin, a focal adhesion protein that is involved in directed cell migration. Furthermore, epidermal growth factor (EGFR) levels were decreased by Ln-UCNPs for most cancer cell lines examined. Taken together, we identified several potential cancer cell targets that were affected by Ln-UCNPs. Our work thereby provides the foundation to optimize Ln-UCNPs for the targeted killing of tumor cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".