Gold Nanoparticles Uptake in Monoculture vs Co-culture of PancreaticCancer Cells
Bibliographic record
Abstract
Despite the advancements in medicine in the last decade, pancreatic cancer is still one of the deadliest types of cancers with a survival rate of less than 7%. The location of pancreas, late detection, the metastatic nature of the disease, and the condensed desmoplasia and stroma of the tumour mass render existing therapeutics inadequate for many patients. Nanotechnology has emerged as a possible solution for hard-to-treat cancers. High atomic number materials such as gold nanoparticles (GNPs) have radiosensitizing properties and could also be used as a drug carrier to precisely deliver drugs to their target. Most recent studies have focused on the radiosensitizing effects of gold in monocultures of cancer cells. However, co-culture systems differ from monoculture systems in the cell-to-cell intercommunication between the different cell lines used. Cancer associated fibroblasts (CAFs) are another very important components of the tumour microenvironment that plays a negative role in radio-and chemo-resistance. They communicate directly with cancer cells to help promote tumour growth. In this paper, separation of cancer cells from CAFs, and GNPs uptake in co-cultures of cancer cells and CAFs vs monocultures were investigated. Successful separation between the two cell lines used was attainable with high accuracy using magnetic beads separation. Our data show that GNP uptakes is roughly the same between cells of the same type whether grown in monoculture or in co-culture. The results also reveal a much higher uptake of GNPs by CAFs compared to tumour cells. We conclude that targeting tumour cells and CAFs in co-culture using radiosensitizing GNPs is feasible and is expected to yield encouraging outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".