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Pancreatic cancer has a very poor prognosis. Early diagnosis and treatment are especially critical for improving its prognosis. Nanotechnology has been widely used in the diagnosis and treatment of pancreatic cancer. Relying on the unique physicochemical properties of nanoparticles and their rich surface modifications, effective enrichment of tumor sites can be achieved. Magnetic iron oxide nanoparticles (MIONPs) is one of the commonly used nanomaterials in the diagnosis and treatment of pancreatic cancer, and has good biocompatibility. Through special surface modification, it can be used in targeted diagnosis and treatment of pancreatic cancer. MIONPs can be used as a contrast agent for MRI, and by modifying the surface, they also can be used in targeted imaging of pancreatic cancer. And they can also be modified as a drug delivery system to achieve targeted delivery of drugs and improve therapeutic effects. However, the application of MIONPs in pancreatic cancer diagnosis and treatment still faces some challenges, such as nanotoxicity and cost issues. With the development of technology, MIONPs are expected to play an important role in the personalized diagnosis and treatment of pancreatic cancer.

The paper is devoted to the couple-group L2-L∞ consensus problem of nonlinear multi-agent systems affected by external disturbances. The interaction topologies among agents obey a continuous-time Markovian process with unknown transition probabilities. By a system transformation, the problem of couple-group L2-L∞ consensus is converted into a L2-L∞ control issue. Then, by Lyapunov stability theory and graph theory, sufficient conditions for the couple-group L2-L∞ consensus are obtained. The control gains can be acquired via the solutions of a group of linear matrix inequalities. Moreover, the present method is extended to the multi-group L2-L∞ consensus. Finally, an example is provided to illustrate the effectiveness of the results. [ABSTRACT FROM AUTHOR]

Objectives: Gemcitabine is a standard treatment for advanced pancreatic cancer patients but can cause chemoresistance during treatment. The chemoresistant cells have features of cancer stem cells (CSCs). Resveratrol has been reported to overcome the resistance induced by gemcitabine. However, the mechanism by which resveratrol enhances chemosensitivity remains elusive. Here, we explored the mechanism by which resveratrol enhanced chemosensitivity and the role of sterol regulatory element binding protein 1 (SREBP1) in gemcitabine‐induced stemness. Materials and methods: The pancreatic cancer cell lines MiaPaCa‐2 and Panc‐1 were treated under different conditions. Methyl thiazolyl tetrazolium and colony formation assays were performed to evaluate effects on proliferation. Flow cytometry was conducted to detect apoptosis. Oil red O staining was performed to examine lipid synthesis. The sphere formation assay was applied to investigate the stemness of cancer cells. Immunohistochemistry was performed on tumour tissue obtained from treated KPC mice. Results: Resveratrol enhanced the sensitivity of gemcitabine and inhibited lipid synthesis via SREBP1. Knockdown of SREBP1 limited the sphere formation ability and suppressed the expression of CSC markers. Furthermore, suppression of SREBP1 induced by resveratrol reversed the gemcitabine‐induced stemness. These results were validated in a KPC mouse model. Conclusions: Our data provide evidence that resveratrol reverses the stemness induced by gemcitabine by targeting SREBP1 both in vitro and in vivo. Thus, resveratrol can be an effective chemotherapy sensitizer, and SREBP1 may be a rational therapeutic target. [ABSTRACT FROM AUTHOR]

Background: There is no clear description of the evolution of the progression of abdominal adhesions over time. Method: The optimized model was selected using different adhesion scoring systems. Then, this model was used to observe the progression of abdominal adhesions. Visualized observation of abdominal adhesion evolution was performed by laparoscopy and computed tomography. The inflammatory cell infiltration and collagen fibers in adhesion tissues at different times were evaluated by hematoxylin-eosin and picrosirius red staining. RNA sequencing was used to predict potential key targets of abdominal adhesions at different times. Results: The abdominal adhesion model showed the highest reproducibility when it was established using a circular tool and an electric brush. Based on this model, we found that the inflammatory response was activated early in the process of adhesion formation, peaking on day 3 and then gradually decreasing until stabilization on day 7. Collagen and fibronectin formed on day 1 and gradually increased until remaining stable on day 7. In addition, the characteristic changes in the adhesion zone from initial congestion, edema and fragile tissue to later dense and stable tissue could be vividly observed in live mice by laparoscopy and artificial pneumoperitoneum CT. The RNA sequencing results revealed that Hck on day 1, Ndufs3 and Ndufs8 on day 3 and Aif1 on day 7 might play key roles in abdominal adhesion formation. Conclusion: The construction of a standard process for describing the evolution of abdominal adhesions based on an optimized mouse model will help to facilitate subsequent adhesion-related studies.

Background: Pancreatic cancer is an aggressive tumor with a low 5-year survival rate and primary resistance to most therapy. Amino acid (AA) metabolism is highly correlated with tumor growth, crucial to the aggressive biological behavior of pancreatic cancer; nevertheless, the comprehensive predictive significance of genes that regulate AA metabolism in pancreatic cancer remains unknown. Methods: The mRNA expression data downloaded from The Cancer Genome Atlas (TCGA) were derived as the training cohort, and the GSE57495 cohort from Gene Expression Omnibus (GEO) database was applied as the validation cohort. Random survival forest (RSF) and the least absolute shrinkage and selection operator (LASSO) regression analysis were employed to screen genes and construct an AA metabolism-related risk signature (AMRS). Kaplan-Meier analysis and receiver operating characteristic (ROC) curve were performed to assess the prognostic value of AMRS. We performed genomic alteration analysis and explored the difference in tumor microenvironment (TME) landscape associated with KRAS and TP53 mutation in both high- and low-AMRS groups. Subsequently, the relationships between AMRS and immunotherapy and chemotherapy sensitivity were evaluated. Results: A 17-gene AA metabolism-related risk model in the TCGA cohort was constructed according to RSF and LASSO. After stratifying patients into high- and low-AMRS groups based on the optimal cut-off value, we found that high-AMRS patients had worse overall survival (OS) in the training cohort (a median OS: 13.1 months vs. 50.1 months, p < 0.0001) and validation cohort (a median OS: 16.2 vs. 30.5 months, p = 1e-04). Genetic mutation analysis revealed that KRAS and TP53 were significantly more mutated in high-AMRS group, and patients with KRAS and TP53 alterations had significantly higher risk scores than those without. Based on the analysis of TME, low-AMRS group displayed significantly higher immune score and more enrichment of T Cell CD8 + cells. In addition, high-AMRS-group exhibited higher TMB and significantly lower tumor immune dysfunction and exclusion (TIDE) score and T Cells dysfunction score, which suggested a higher sensitive to immunotherapy. Moreover, high-AMRS group was also more sensitive to paclitaxel, cisplatin, and docetaxel. Conclusion: Overall, we constructed an AA-metabolism prognostic model, which provided a powerful prognostic predictor for the clinical treatment of pancreatic cancer., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Hao, Zhou, Wang, Ma, Wu, Lv and Wu.)