A Synergetic Approach Utilizing Nanotechnology, Chemotherapy, and Radiotherapy for Pancreatic Cancer Treatment
Bibliographic record
Abstract
<img src=” https://s3.amazonaws.com/production.scholastica/article/90447/large/prnano_1112023ga.jpg?1700670543”> The objective of this study was to assess the anticancer effectiveness of gold nanoparticles (GNPs) and lipid-encapsulated docetaxel prodrug (LNPDTX-P) with radiotherapy (RT). The study utilized a co-culture spheroid model comprising MIA PaCa-2 cancer cells and patient-derived cancer-associated fibroblasts (CAF-98) to mimic pancreatic cancer conditions. The spheroids underwent treatment with GNPs (7.5 μg/mL), LNPDTX-P (99 nM of DTX pro-drug), and 2 Gy of RT. Cell viability of the spheroids was evaluated using the CellTiter-Glo 3D assay. At the same time, DNA double-strand breaks (DSBs) were assessed by examining the expression of the DNA damage marker 53BP1 through an immunofluorescence assay. Alt-hough GNPs/RT and RT/LNPDTX-P showed a reduction in spheroid size and an apparent in-crease in DNA DSB damage, the combination of the two nanoparticles, GNPs, and LNPDTX-P, with RT, significantly enhanced the anticancer efficacy, resulting in a 28% decrease in spheroid size and an estimated 39% increase in DNA DSB. The combination of GNPs and LNPDTX-P with RT showed a synergetic effect due to their radiosensitizing properties, improving the ther-apeutic efficacy of each treatment modality alone. This triple modality offers a hopeful strategy to enhance cancer treatment efficacy while reducing adverse effects.
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 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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".