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Objective To explore the effect of OASL expression on the proliferation and migration of pancreatic cancer cells. Methods The GEPIA database was used to analyze the differences in OASL expression in pancreatic cancer tissues and normal pancreatic tissues. The TIMER database was used to analyze the relationship between OASL expression and patient survival. The TCGA database was used to analyze the correlation of OASL expression with the clinicopathological parameters of pancreatic cancer. shRNA was used to knock down the expression of OASL gene in pancreatic cancer panc-1 cells. Lentiviruses were used to overexpress the OASL gene in pancreatic cancer cells. MTT assay was used to evaluate their proliferation ability, and scratch and Transwell experiments were used to evaluate their migration ability. Western blot experiments were used to detect changes in proteins related to tumor proliferation, migration, and invasion. Results OASL expression in the pancreatic cancer group was significantly higher than that in normal pancreatic tissue (P < 0.05), and patients with high OASL expression in pancreatic cancer patients had worse OS than patients with low expression (P < 0.05). After OASL gene knockdown, the proliferation and migration abilities of panc-1 cells were inhibited, whereas the overexpression of OASL gene promoted the proliferation and migration ability of panc-1 cells. Western blot experiments showed that after OASL knockdown, p-STAT3 protein expression increased, whereas STAT3 and BAK protein expressions decreased. After OASL overexpression, p-STAT3 protein expression decreased, and STAT3 and BAK protein expression increased. Conclusion OASL may affect the proliferation and migration of pancreatic cancer cells through the STAT3 signaling pathway while affecting BAK expression to induce cell death.

Osteosarcoma, a common invasive malignant bone disease, presents therapeutic challenges due to the persistent problem of incomplete resection during surgical treatment. This often results in postoperative tumor recurrence and metastasis, and large‐scale bone defects are difficult to self‐repair, seriously affecting patient health. In this study, a dual‐ion doped organic‐inorganic composited SOH1(CP)1 injectable hydrogel system is successfully designed and constructed. This system consists of sericin protein grafted with hydrazide bonds, oxidized chondroitin sulfate, Se and Mg co‐doped HAp nanorods, and polydopamine‐coated CaO2 nanospheres. The system displays strong anti‐tumor activity due to its mild photothermal effects combined with the chemotherapeutic efficacy of SeO32−. Because the degradation behavior of hydrogel matches the bone repair cycle, including the nutritional support of hydrogel skeleton degradation products to promote bone cell proliferation, and the positive regulation of Ca2+, Mg2+, and PO43− released via the degradation of inorganic nanoparticles to promote bone differentiation, the system shows excellent bone defect repair efficacy. Importantly, this system achieves 100% tumor inhibition after 18 days, while ensuring complete bone repair after 12 weeks. Hence, the SOH1(CP)1 injectable hydrogel system, which displays both high anti‐osteosarcoma efficacy and strong bone repair properties, can serve as a new tool for osteosarcoma‐related bone defect repair. [ABSTRACT FROM AUTHOR]

In real hyperspectral scenes, heterogeneous spatial details and noises make a single subspace assumptions unrealistic. In this letter, a novel transferable multiple tensor subspace learning scheme is proposed for super-resolution enhancement of hyperspectral image (HSI). The intrinsic assumption is that the nonlocal patch tensors extracted from HSIs are derived from multiple tensor low-rank subspaces, which is compatible with practical data distribution and may better characterize the complex structures underlying HSIs. The transferable subspace structures are embedded into both nonblind and semi-blind HSI super-resolution. The alternating direction method of multipliers (ADMMs) algorithm is derived for model learning. The superiority of our method is demonstrated by comprehensive experiments on both synthetic and real datasets. [ABSTRACT FROM AUTHOR]