Preparation, Cytotoxicity, and In Vitro Bioimaging of Water Soluble and Highly Fluorescent Palladium Nanoclusters
Bibliographic record
Abstract
Fluorescent probes offer great potential to identify and treat surgical tumors by clinicians. To this end, several molecular probes were examined as in vitro and in vivo bioimaging probes. However, due to their ultra-low extinction coefficients as well as photobleaching problems, conventional molecular probes limit its practical utility. To address the above mentioned challenges, metal nanoclusters (MNCs) can serve as an excellent alternative with many unique features such as higher molar extinction coefficients/light absorbing capabilities, good photostability and appreciable fluorescence quantum yields. Herein, we reported a green synthesis of water soluble palladium nanoclusters (Pd NCs) and characterized them by using various spectroscopic and microscopic characterization techniques. These nanoclusters showed excellent photophysical properties with the characteristic emission peak centered at 500 nm under 420 nm photoexcitation wavelength. In vitro cytotoxicity studies in human cervical cancer cells (HeLa) cells reveal that Pd NCs exhibited good biocompatibility with an IC50 value of >100 µg/mL and also showed excellent co-localization and distribution throughout the cytoplasm region with a significant fraction translocating into cell nucleus. We foresee that Pd NCs will carry huge potential to serve as a new generation bioimaging nanoprobe owing to its smaller size, minimal cytotoxicity, nucleus translocation capability and good cell labelling properties.
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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".