Search

Simple Summary: In recent years, much research has focused on how exercise improves cancer prognosis and how the protein myokine, which is associated with exercise, inhibits cancer progression. However, there have been few detailed studies on oral cancer. This study is the first to show that exercise inhibits the progression of oral cancer. While other studies have shown that exercise suppresses growth, decreases the tumor formation rate, or improves survival in patients with other organ cancers, this study showed that exercise inhibits tumor formation and growth in oral cancer and prolongs survival. Furthermore, while many studies have demonstrated an indirect immune-mediated mechanism by which exercise suppresses cancer, this study suggests the additional possibility that myokines released by exercise directly affect oral cancer cells. This study has the above novelties. Major advances have been made in cancer treatment, but the prognosis for elderly cancer patients with sarcopenia and frailty remains poor. Myokines, which are thought to exert preventive effects against sarcopenia, have been reported to be associated with the prognosis of various cancers, but their effect on head and neck squamous cell carcinoma (HNSCC) is unknown. The aim of this study was to clarify the influence of exercise on the control of HNSCC and to examine the underlying mechanism involved. Mice were injected with HSC-3-M3 cells, a human cell line of highly metastatic and poorly differentiated tongue cancer, at the beginning of the study. Just prior to transplantation, blood was collected from the mice, and the levels of myokines were measured by ELISA. Oncostatin M (OSM), a selected myokine, was added to HSC-3-M3 cells, after which the cell proliferation ability, cell cycle, and protein expression were analyzed in vitro. Tumor cell viability was lower (control: 100%, exercise: 75%), tumors were smaller (control: 26.2 mm3, exercise: 6.4 mm3), and survival was longer in the exercise group than in the control group in vivo. OSM inhibited HSC-3-M3 cell proliferation in a concentration-dependent manner in vitro. The addition of OSM increased the proportion of cells in the G0/G1 phase, decreased the proportion of cells in the G2/M phase, and increased the expression of the CDK inhibitors p21 and p27. These results indicate that exercise may directly inhibit the proliferation of HNSCC cell lines via OSM. [ABSTRACT FROM AUTHOR]

Dopamine (DA)'s relationship with addiction is complex, and the related pathways in the mesocorticolimbic system are used to deliver DA, regulating both behavioral and perceptual actions. Specifically, the mesolimbic pathway connecting the ventral tegmental area (VTA) and the nucleus accumbens (NAc) is crucial in regulating memory, emotion, motivation, and behavior due to its responsibility to modulate dopamine. To better investigate the relationship between DA and addiction, more advanced mapping methods are necessary to monitor its production and propagation accurately and efficiently. In this study, we incorporate dLight1.2 adeno-associated virus (AAV) into our latest CMOS (complementary metal-oxide semiconductor) imaging platform to investigate the effects of two pharmacological substances, morphine and cocaine, in the NAc using adult mice. By implanting our self-fabricated CMOS imaging device into the deep brain, fluorescence imaging of the NAc using the dLight1.2 AAV allows for the visualization of DA molecules delivered from the VTA in real time. Our results suggest that changes in extracellular DA can be observed with this adapted system, showing potential for new applications and methods for approaching addiction studies. Additionally, we can identify the unique characteristic trend of DA release for both morphine and cocaine, further validating the underlying biochemical mechanisms used to modulate dopaminergic activation. [ABSTRACT FROM AUTHOR]

In this research, we combined our ultralight micro-imaging device for calcium imaging with microdialysis to simultaneously visualize neural activity in the dorsal raphe nucleus (DRN) and measure serotonin release in the central nucleus of the amygdala (CeA) and the anterior cingulate cortex (ACC). Using this platform, we observed brain activity following nociception induced by formalin injection in the mouse's hind paw. Our device showed that DRN fluorescence intensity increased after formalin injection, and the increase was highly correlated with the elevation in serotonin release in both the CeA and ACC. The increase in calcium fluorescence intensity occurred during the acute and inflammatory phases, which suggests the biphasic response of nociceptive pain. Furthermore, we found that the increase in fluorescence intensity was positively correlated with mouse licking behavior. Lastly, we compared the laterality of pain stimulation and found that DRN fluorescence activity was higher for contralateral stimulation. Microdialysis showed that CeA serotonin concentration increased only after contralateral stimulation, while ACC serotonin release responded bilaterally. In conclusion, our study not only revealed the inter-regional serotonergic connection among the DRN, the CeA, and the ACC, but also demonstrated that our device is feasible for multi-site implantation in conjunction with a microdialysis system, allowing the simultaneous multi-modal observation of different regions in the brain. [ABSTRACT FROM AUTHOR]

Sleep-stage classification is essential for sleep research. Various automatic judgment programs, including deep learning algorithms using artificial intelligence (AI), have been developed, but have limitations with regard to data format compatibility, human interpretability, cost, and technical requirements. We developed a novel program called GI-SleepNet, generative adversarial network (GAN)-assisted image-based sleep staging for mice that is accurate, versatile, compact, and easy to use. In this program, electroencephalogram and electromyography data are first visualized as images, and then classified into three stages (wake, NREM, and REM) by a supervised image learning algorithm. To increase its accuracy, we adopted GAN and artificially generated fake REM sleep data to equalize the number of stages. This resulted in improved accuracy, and as little as one mouse's data yielded significant accuracy. Due to its image-based nature, the program is easy to apply to data of different formats, different species of animals, and even outside sleep research. Image data can be easily understood; thus, confirmation by experts is easily obtained, even when there are prediction anomalies. As deep learning in image processing is one of the leading fields in AI, numerous algorithms are also available.