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Autophagy is an important protein quality control mechanism for cells under stress conditions to promote cell survival. Modulation of autophagy on biomaterial substrates is rarely reported. In this study, the autophagy of adipose-derived stem cells (ADSCs) cultured on chitosan (CS) substrates was examined. Compared to the traditional monolayer culture, ADSCs cultured on CS substrates showed spheroid formation as well as a prolonged upregulation of autophagosomal marker-microtubule-associated protein 1 light chain 3 (LC3) II protein expression. In addition, the green fluorescent protein tagged-LC3 (GFP-LC3) expressing ADSCs also revealed more GFP-LC3 puncta on CS substrates. The enhanced autophagy on CS substrates was associated with Ca2+, while ethylene glycol tetraacetic acid (EGTA), a Ca2+ chelator, repressed the autophagy in a dose-dependent manner. Moreover, ADSC spheroids on CS substrates demonstrated a higher survival rate and autophagy response upon H2O2 treatment. The upstream components of autophagy signal pathway-UNC51-like kinase 1 (Ulk1), autophagy-related protein 13 (Atg13), and autophagy/beclin-1 regulator 1 (Ambra1) genes were more highly expressed in ADSC spheroids before and after adding H2O2 than those in the conventional culture. EGTA also decreased the cell viability and autophagy-associated gene expression for ADSC spheroids on CS substrates after H2O2 treatment. Therefore, we suggest that three-dimensional (3D) cell culture on CS may confer ADSCs the ability to increase the autophagic flux in response to stimulations in a Ca2+-dependent manner.

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During the COVID-19 pandemic, people were advised to keep a social distance from others. People’s behaviors will also be noticed, such as lying down because of illness, regarded as abnormal conditions. This paper proposes a visual anomaly analysis system based on deep learning to identify individuals with various anomaly types. In the study, two types of anomaly detections are concerned. The first is monitoring the anomaly in the case of falling in an open public area. The second is measuring the social distance of people in the area to warn the individuals under a short distance. By implementing a deep model named You Only Look Once, the related anomaly can be identified accurately in a wide range of open spaces. Experimental results show that the detection accuracy of the proposed method is 91%. In the social distance, the actual social distance is calculated by calculating the plane distance to ensure that everyone can meet the specification. Integrating the two functions and implementing the environmental monitoring system will make it easier to monitor and manage the disease-related abnormalities on the site.