Synthesis and Evaluation of Glycopolymeric Decorated Gold Nanoparticles Functionalized with Gold-Triphenyl Phosphine as Anti-Cancer Agents
Bibliographic record
Abstract
In this study, statistical glyco-dithiocarbamate (DTC) copolymers were synthesized by reversible addition-fragmentation chain transfer polymerization (RAFT) and subsequently used to prepare glyconanoparticles and conjugated glyconanoparticles with the anticancer drug, gold(I) triphenylphosphine. These glyconanoparticles and the corresponding conjugates were then tested for their in vitro cytotoxicity in both normal and cancer cell lines using Neutral Red assay. The glyconanoparticles and their Au(I)PPh3 conjugates were all active against MCF7 and HepG2 cells, but galactose-functionalized glyconanoparticles {P(GMA-EDAdtc(AuPPh3)-st-LAEMA)AuNP} were found to be the most cytotoxic to HepG2 cells (IC50 ∼ 4.13 ± 0.73 μg/mL). The p(GMA-EDAdtc(AuPPh3)-st-LAEMA)AuNP was found to be a 4-fold more potent antitumor agent in HepG2 cells, and the overexpressed asialoglycoprotein (ASGPR) receptors revealed to play an important role in the cytotoxicity, presumably by the enhanced uptake. In addition, the glyconanoparticles Au(I) conjugates are found to be significantly more toxic as compared to the standard chemotherapeutic reagents such as cisplatin and cytarabine.
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) | 0.000 | 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".