Synthesis and Evaluation of Polymeric Gold Glyco-Conjugates as Anti-Cancer Agents
Bibliographic record
Abstract
The antitumor activity of organo-gold compounds is a focus of research from the past two decades. A variety of gold stabilizing ligands such as vitamins and xanthanes have been prepared and explored for their 'chelating effect' as well as for their antitumor activity. Dithiocarbamates (DTC) compounds and their metallic conjugates have been well explored for their antiproliferative activities. In this study, glycopolymer based DTC-conjugates are prepared by reversible addition-fragmentation chain transfer polymerization (RAFT) and subsequently modified with gold(I) phosphine. These polymer-DTC derivatives and their gold compounds are tested for their in vitro toxicity in both normal and cancer cell lines. The Au(I) phosphine conjugated cationic glycopolymers of 10 kDa and 30 kDa are evaluated for their cytotoxicity profiles using MTT assay. Au(I) compounds are well-known for their mitochondrial toxicity, hence hypoxic cell lines bearing unusually enlarged mitochondria are subjected to these anticancer compounds. It is concluded that these polymeric DTC derivatives and their gold conjugates indeed show higher accumulation as well as cytotoxicity to cancer cells under hypoxic conditions in comparison to the normoxic ones. Hypoxic MCF-7 cells showed significant sensitivity toward the low molecular weight (10 kDa) glycopolymer-Au(I) complexes.
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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) | 0.001 | 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".