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Glioblastoma multiforme (GBM) is a cancer of the central nervous system with limited therapeutic outcomes. Infiltrating cancer cells are the contributing factor to high GBM malignancy. The intracranial brain cancer cell infiltration is a complex cascade involving adhesion, migration, and invasion. An arsenal of natural products has been under exploration to overcome GBM malignancy. This study applied the antimicrobial peptide tilapia piscidin 3 (TP3) to GBM8401, U87MG, and T98G cells. The cellular assays and microscopic observations showed that TP3 significantly attenuated cell adhesion, migration, and invasion. A live-cell video clip showed the inhibition of filopodia protrusions and cell attachment. Probing at the molecular levels showed that the proteolytic activities (from secretion), the mRNA and protein expression levels of matrix metalloproteinases-2 and -9 were attenuated. This result strongly evidenced that both invasion and metastasis were inhibited, although metastatic GBM is rare. Furthermore, the protein expression levels of cell-mobilization regulators focal adhesion kinase and paxillin were decreased. Similar effects were observed in small GTPase (RAS), phosphorylated protein kinase B (AKT) and MAP kinases such as extracellular signal-regulated kinases (ERK), JNK, and p38. Overall, TP3 showed promising activities to prevent cell infiltration and metastasis through modulating the tumor microenvironment balance, suggesting that TP3 merits further development for use in GBM treatments. K E Y W O R D Scell mobility, glioblastoma multiforme, infiltration, TP3, tumor microenvironment 3920 | CHEN Et al. 7.2. Prior to the experiments, the TP3 stock solution was protected from light and stored at −20°C.

Human pose estimation is an important task for several applications, such as video surveillance systems. However, color (i.e., RGB) images may not always be available under certain conditions, such as privacy issues and lack of illumination. In these scenarios, thermal images are more prominent than color images. We introduce in this study ThermalPose, which is a neural network system that parses thermal images and extracts accurate 2D human poses. ThermalPose uses lightweight neural network models that can be easily matched to the design requirements for Internet-of-Things applications. The performance of ThermalPose in visible scenes only slightly decreases compared with that of the state-of-the-art vision-based pose estimator. Meanwhile, in complex scenes with masking background textures or lack of illumination, ThermalPose does not degenerate in terms of performance, whereas the vision-based system completely fails. INDEX TERMS Pose estimation, deep convolutional neural networks, thermal images