Osteosarcoma, a highly aggressive malignant tumor of the bone, usually occurs in children and young adults. However, although the considerable achievement in the clinical treatment of osteosarcoma recent years, the overall survival of osteosarcoma patients has not been obviously improved. Cancer cells preferentially use glycolysis instead of oxidative phosphorylation to meet their increased energetic and biosynthetic demands, a phenomenon known as the Warburg effect. Glycolysis is a driving factor in multiple cancers and is emerging as a new cancer target treatment. In the present study, we established a model to screen for glycolysis-associated genes in osteosarcoma. This risk score of the model were correlated with clinical characteristics osteosarcoma patients. Besides, a functional assay identified that STC2 enhanced the glycolysis of osteosarcoma cells. Modulation of STC2 changes glucose consumption and lactate production as well as GLUT1 expression in osteosarcoma. Furthermore, we identified that change in the expression levels of STC2 affected the proliferation, invasion, and migration of osteosarcoma cells. Our findings showed STC2 as a new tumor-promoting factor of osteosarcoma cells through enhancing glycolysis.Ketone bodies can be increased in the blood under certain physiological conditions, but their role under such conditions remains to be clarified. In the present study, we found the increment and usage of β-hydroxybutyrate (BHB) in the prefrontal cortex (PFC) during acute stress. BHB levels increased in the blood and PFC after 30-min acute immobilization stress, and BHB dehydrogenase 1 increased in the PFC simultaneously, but not in the hippocampus. Moreover, increased levels of acetyl-CoA, pyruvate carboxylase, and glutamate dehydrogenase 1 were found in the PFC, implicating the metabolism of increased BHB in the brain. Thus, we checked the levels of glutamate, glutamine, and GABA and found increased levels of glutamate and glutamine in the stressed group compared with that in the control group in the PFC. Exogenous administration of BHB enhanced struggling behaviors under stressful conditions. Our results suggest that the metabolism of BHB from peripheral blood in the PFC may contribute to acute stress responses to escape stressful conditions.Human induced pluripotent stem cells (hiPSCs) are important starting materials for cell therapy products (CTPs) used for transplantation. During cell culture, hiPSCs often spontaneously undergo morphological changes and lose pluripotency. Such cells are called 'deviated cells', which are deviated from the undifferentiated state of hiPSCs, lack the expression of hiPSC markers and become positive for the early differentiation marker SSEA1 (stage-specific embryonic antigen 1, Lewis X glycan). Previously, we identified fibronectin (FN) as a predominant carrier protein of SSEA1 secreted from deviated cells, but not hiPSCs. A sandwich assay using antibodies (Abs) against FN and SSEA1 was developed for non-destructive quantitative evaluation of deviated cells present in hiPSC cultures. In this study, a novel technology was developed to specifically eliminate deviated cells using an anti-FN Ab along with a near-infrared (NIR) photoabsorber, IRDye700DX N-hydroxysuccinimide ester (IR700), which has been used for cancer photoimmunotherapy. The anti-FN Ab conjugated with the IR700 dye (IR700-αFN) bound to and induced the death of deviated cells upon NIR irradiation. In contrast, IR700-αFN failed to stain the hiPSCs, and IR700-αFN/NIR had little or no effect on survival. Metabolism agonist Finally, IR700-αFN/NIR irradiation induced selective removal of deviated cells from a mixed culture with hiPSCs, demonstrating that the proposed method is suitable for the removal of unwanted deviated cells present in hiPSC culture for the production of CTPs.In this paper, the heartbeat parameters of small model organisms, i.e. Drosophila melanogaster (fruit fly) and Danio rerio (zebrafish), were quantified in-vivo in intact larvae using microfluidics and a novel MATLAB-based software. Among different developmental stages of flies and zebrafish, the larval stage is privileged due to biological maturity, optical accessibility, and the myogenic nature of the heart. Conventional methods for parametric quantification of heart activities are complex and mostly done on dissected, irreversibly immobilized, or anesthetized larvae. Microfluidics has helped with reversible immobilization without the need for anesthesia, but heart monitoring is still done manually due to challenges associated with the movement of floating organs and cardiac interruptions. In our MATLAB software applied to videos recorded in microfluidic-based whole-organism assays, we have used image segmentation to automatically detect the heart and extract the heartbeat signal based on pixel intensity variations of the most contractile region of the heart tube. The smoothness priors approach (SPA) was applied to remove the undesired low-frequency noises caused by environmental light changes or heart movement. Heart rate and arrhythmicity were automatically measured from the detrended heartbeat signal while other parameters including end-diastolic and end-systolic diameters, shortening distance, shortening time, fractional shortening, and shortening velocity were quantified for the first time in intact larvae, using M-mode images under bright field microscopy. The software was able to detect more than 94% of the heartbeats and the cardiac arrests in intact Drosophila larvae. Our user-friendly software enables in-vivo quantification of D. melanogaster and D. rerio larval heart functions in microfluidic devices, with the potential to be applied to other biological models and used for automatic screening of drugs and alleles that affect their heart.Corona Virus Disease (COVID-19) has been announced as a pandemic and is spreading rapidly throughout the world. Early detection of COVID-19 may protect many infected people. Unfortunately, COVID-19 can be mistakenly diagnosed as pneumonia or lung cancer, which with fast spread in the chest cells, can lead to patient death. The most commonly used diagnosis methods for these three diseases are chest X-ray and computed tomography (CT) images. In this paper, a multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer from a combination of chest x-ray and CT images is proposed. This combination has been used because chest X-ray is less powerful in the early stages of the disease, while a CT scan of the chest is useful even before symptoms appear, and CT can precisely detect the abnormal features that are identified in images. In addition, using these two types of images will increase the dataset size, which will increase the classification accuracy. To the best of our knowledge, no other deep learning model choosing between these diseases is found in the literature.