Novel therapy modalities combining photodynamic therapy and liposomal cisplatin for pancreatic cancer treatments
Bibliographic record
Abstract
• CDDP encapsulation in liposome to form lipid-platinum chloride nanoparticles (LPC NPs) has shown great potential for killing cancer cells with less toxicity. • Combined photodynamic therapy (PDT) with LPC NPs showed inhibited pancreatic tumor growth with increased body weights in animal models. • Histological studies showed the minimal side effects and no renal damage with the treatment of LPC alone or PDT+LPC group. • PDT enhances the LPC NPs therapeutic outcome in pancreatic cancer animal models with potential to emerge as an effective modality. Combination therapy is the mainstream cancer treatment to achieve the best clinical benefits for patients. Photodynamic therapy (PDT) can improve vascular permeability and enhance nanoparticle uptake in tumors with drug delivery permeability and retention effects. PDT can also minimize tissue damage compared with traditional chemotherapy. Therefore, it is beneficial to use PDT with chemotherapeutic administrations to obtain these benefits. Cisplatin (CDDP) is a widely used chemotherapeutic drug with strong toxic side effects such as nephrotoxicity and neurotoxicity. Our novel study is the first original study to investigate PDT with novel lipid-platinum chloride (LPC) nanoparticles to prove the results of greater efficacy and minimal side effects. In the in vitro study, CDDP and LPC nanoparticles enhanced higher amounts of cell death as the concentration increased. The treatment effects of PDT+LPC NPs using pancreatic MIAPaCa-2 and PANC-1 tumor models revealed that PDT+LPC significantly inhibited tumor growth. The body weight of all animals increased with animals showing no side effects. IHC assays for Ki-67, CD31, and cleaved caspase-3, TUNEL assays and western blot assays showed consistent results. Collectively, the combined PDT with LPC nanoparticles showed significant therapeutic outcomes with promising potential for clinical applications. A novel pancreatic cancer treatment – Combined treatment of photodynamic therapy (PDT) and lipid-platinum chloride nanoparticles (LPC NPs). Results suggest that the combined therapy inhibited the tumor growth, increased cytotoxicity, and increased the expression of TP53, P-P53(ser15) and cleaved caspase-3 of effective anticancer property significantly.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".