Treating cancer stem cells and cancer metastasis using glucose-coated gold nanoparticles
Bibliographic record
Abstract
Cancer ranks among the leading causes of human mortality. Cancer becomes intractable when it spreads from the primary tumor site to various organs (such as bone, lung, liver, and then brain). Unlike solid tumor cells, cancer stem cells and metastatic cancer cells grow in a non-attached (suspension) form when moving from their source to other locations in the body. Due to the non-attached growth nature, metastasis is often first detected in the circulatory systems, for instance in a lymph node near the primary tumor. Cancer research over the past several decades has primarily focused on treating solid tumors, but targeted therapy to treat cancer stem cells and cancer metastasis has yet to be developed. Because cancers undergo faster metabolism and consume more glucose than normal cells, glucose was chosen in this study as a reagent to target cancer cells. In particular, by covalently binding gold nanoparticles (GNPs) with thio-PEG (polyethylene glycol) and thio-glucose, the resulting functionalized GNPs (Glu-GNPs) were created for targeted treatment of cancer metastasis and cancer stem cells. Suspension cancer cell THP-1 (human monocytic cell line derived from acute monocytic leukemia patients) was selected because it has properties similar to cancer stem cells and has been used as a metastatic cancer cell model for in vitro studies. To take advantage of cancer cells' elevated glucose consumption over normal cells, different starvation periods were screened in order to achieve optimal treatment effects. Cancer cells were then fed using Glu-GNPs followed by X-ray irradiation treatment. For comparison, solid tumor MCF-7 cells (breast cancer cell line) were studied as well. Our irradiation experimental results show that Glu-GNPs are better irradiation sensitizers to treat THP-1 cells than MCF-7 cells, or Glu-GNPs enhance the cancer killing of THP-1 cells 20% more than X-ray irradiation alone and GNP treatment alone. This finding can help oncologists to design therapeutic strategies to target cancer stem cells and cancer metastasis.
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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".