Nintedanib Induces the Autophagy-Dependent Death of Gastric Cancer Cells by Inhibiting the STAT3/Beclin1 Pathway.

BACKGROUND: Tyrosine kinase inhibitors are currently the most widely studied targeted therapies for gastric cancer. As a triple tyrosine inhibitor, nintedanib can alleviate the progression of a variety of cancers, but it is poorly studied in gastric cancer. AIMS: To investigate the effect of nintedanib on gastric cancer. METHODS: This study investigated nintedanib's effect on gastric cancer autophagy in vivo and in vitro, and the activity and morphological changes of gastric cancer cells were detected by MTT and HE staining. Proliferation, migration, invasion, and EMT-related marker proteins of AGS and MKN-28 cells were detected. The effects of nintedanib on autophagy in gastric cancer cells were detected by acridine orange, immunofluorescence, and Western blotting assays. The regulation of nintedanib on STAT3 and Beclin1 was detected by qPCR and Western blotting assays. Subsequently, the effects of nintedanib on the tumor STAT3/Beclin1 pathway were verified by stably overexpressing STAT3 in gastric cancer cell lines and tumor-bearing experiments in nude mice. RESULTS: The results showed that nintedanib could inhibit gastric cancer cells' proliferation and EMT process. Meanwhile, autophagy was induced in AGS and MKN-28 cells, and the expression of autophagy-related protein Beclin1 was upregulated, and the phosphorylation level of STAT3 was downregulated. Nintedanib inhibited STAT3 phosphorylation and upregulated Beclin1 to inhibit tumor growth in gastric cancer cell lines with stable STAT3 overexpression and tumor-bearing experiments in nude mice. CONCLUSIONS: By inhibiting STAT3, nintedanib upregulated Beclin1 and caused autophagic death in gastric cancer cells.

Detecting protagonists and antagonists in the voice quality of American cartoon characters: a quantitative LTAS-based analysis.

The voices of heroes and villains in cartoons contribute to their uniqueness and helps shape how we perceive them. However, not much research has looked at the acoustic properties of character voices and the possible contributions these have to cartoon character archetypes. We present a quantitative examination of how voice quality distinguishes between characters based on their alignment as either protagonists or antagonists, performing Principal Component Analysis (PCA) on the Long-term Average Spectra (LTAS) of concatenated passages of the speech of various characters obtained from four different animated cartoons. We then assessed if the categories of "protagonists" and "antagonists" (determined via an a priori classification) could be distinguished using a classification algorithm, and if so, what acoustic characteristics could help distinguish the two categories. The overall results support the idea that protagonists and antagonists can be distinguished by their voice qualities. Support Vector Machine (SVM) analysis yielded an average classification accuracy of 96% across the cartoons. Visualisation of the spectral traits constituting the difference did not yield consistent results but reveals a low-versus-high frequency energy dominance pattern segregating antagonists and protagonists. Future studies can look into how other variables might be confounded with voice quality in distinguishing between these categories.