Investigation of Nano-Bio Interactions within a Pancreatic Tumor Microenvironment for the Advancement of Nanomedicine in Cancer Treatment
Bibliographic record
Abstract
Pancreatic cancer is one of the deadliest types of cancer, with a five-year survival rate of only 10%. Nanotechnology offers a novel perspective to treat such deadly cancers through their incorporation into radiotherapy and chemotherapy. However, the interaction of nanoparticles (NPs) with cancer cells and with other major cell types within the pancreatic tumor microenvironment (TME) is yet to be understood. Therefore, our goal is to shed light on the dynamics of NPs within a TME of pancreatic origin. In addition to cancer cells, normal fibroblasts (NFs) and cancer-associated fibroblasts (CAFs) were examined in this study due to their important yet opposite roles of suppressing tumor growth and promoting tumor growth, respectively. Gold nanoparticles were used as the model NP system due to their biocompatibility and physical and chemical proprieties, and their dynamics were studied both quantitatively and qualitatively in vitro and in vivo. The in vitro studies revealed that both cancer cells and CAFs take up 50% more NPs compared to NFs. Most importantly, they all managed to retain 70-80% of NPs over a 24-h time period. Uptake and retention of NPs within an in vivo environment was also consistent with in vitro results. This study shows the paradigm-changing potential of NPs to combat the disease.
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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 | 0.000 |
| 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 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".