This study aims to probe how children with and without autism spectrum disorders (ASD) attribute false belief to a social robot and predict its action accordingly. Twenty 5- to 7-year-old children with ASD and 20 age- and IQ-matched typically developing (TD) children participated in two false belief tasks adapted for robot settings (change-of-location task and the unexpected-contents task). The results showed that most TD children are capable of attributing false belief to the social robot, that is, they could infer higher level mental states in robots, which extends our understanding in TD children’s perception and cognition on social robots. Conversely, children with ASD still show difficulty in interpreting robots’ mental states compared to their TD peers, which would greatly interfere with their interactions and communications with social robots and might impact on efficiency of robot-based intervention and education approaches. This group difference in attributing false belief to social robots could not be explained by the different perception and categorization of the robot. Our study implies that although children with ASD appear to be highly attracted by social robots, they still have difficulty in understanding mental states when socially interacting with robots, which should be taken into consideration when designing the robot-based intervention approach targeting to improve social behaviors of ASD.
Objective
This study aims to build a prognostic model of hepatocellular carcinoma (HCC) with ferroptosis-associated genes and explore their molecular function.
Methods
Gene expression data and clinical information were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases and the International Cancer Genome Consortium (ICGC). A ferroptosis-associated gene set was obtained from the FerrDb database to identify differentially expressed genes. Then, we performed pathway enrichment analysis and immune infiltration analysis. A combined model based on ferroptosis-associated genes for predicting the overall survival of HCC was built by univariate and multivariate Cox regression analyses. Quantitative real-time polymerase chain reaction, Western blotting, colony formation, CCK-8, and EdU incorporation assays were performed to clarify the function of CAPG in the regulation of cell proliferation in human HCC. Ferroptosis was evaluated by glutathione (GSH), malondialdehyde (MDA), and total iron detection.
Results
Forty-nine ferroptosis-related genes were significantly correlated with HCC, 19 of which had prognostic significance. CAPG, SLC7A11 and SQSTM1 were used to construct a novel risk model. The areas under the curves (AUCs) were 0.746 and 0.720 (1 year) in the training and validation groups, respectively. The survival analysis indicated that patients with high risk scores exhibited worse survival in the training and validation groups. The risk score was also identified as an independent prognostic factor of overall survival (OS), which established and validated the predictive abilities of the nomogram. The risk score was also significantly correlated with the expression of immune checkpoint genes. In vitro data showed that CAPG knockdown dramatically suppressed HCC cell proliferation, and the underlying molecular mechanisms might be that the silencing of CAPG reduced the expression of SLC7A11 and promoted ferroptosis.
Conclusion
The established risk model can be used to predict the prognosis of HCC. At the mechanistic level, CAPG may drive HCC progression by regulating SLC7A11, and ferroptosis activation in HCC patients with high CAPG expression may serve as a potential therapeutic strategy.
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