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Abnormal activation of the gut mucosal immune system and a highly dysregulated gut microbiota play essential roles in the progression of inflammatory bowel disease (IBD). The clinical treatment of IBD remains highly challenging, with first‐line drugs showing limited efficacy and significant side effects. A reactive oxygen species (ROS)‐activated CO versatile nanomedicine (CMPs) capable of remodeling the gut immune‐microbiota microenvironment via potent anti‐oxidant, anti‐inflammatory, and antimicrobial effects is developed. CORM‐401‐loaded mannose‐modified peptide dendrimer nanogel: CMPs preferentially congregate on the surface of damaged colon mucosa after rectal administration and are subsequently internalized by activated immune cells. CORM‐401 can release numerous CO molecules in response to high ROS levels in cells and at the site of IBD, resulting in multiple therapeutic effects. In vitro and in vivo studies have demonstrated that CMPs scavenge ROS, suppress inflammatory responses, eliminate pathogens, and alleviate colitis in mouse models. RNA sequencing reveals that CMPs successfully remodel gut mucosal immune homeostasis by scavenging ROS, inhibiting NF‐κB/p38MAPK, activating PI3K‐Akt, and inhibiting HIF‐1‐induced glycolysis. 16S ribosomal RNA sequencing shows that CMPs can remodel the gut flora composition by restraining detrimental bacteria and augmenting beneficial bacteria. This study develops a promising and versatile nanomedicine for the management of IBD.

The recognition of stereotyped action is one of the core diagnostic criteria of Autism Spectrum Disorder (ASD). However, it mainly relies on parent interviews and clinical observations, which lead to a long diagnosis cycle and prevents the ASD children from timely treatment. To speed up the recognition process of stereotyped actions, a method based on skeleton data and Long Short-Term Memory (LSTM) is proposed in this paper. In the first stage of our method, the OpenPose algorithm is used to obtain the initial skeleton data from the video of ASD children. Furthermore, four denoising methods are proposed to eliminate the noise of the initial skeleton data. In the second stage, we track multiple ASD children in the same scene by matching distance between current skeletons and previous skeletons. In the last stage, the neural network based on LSTM is proposed to classify the ASD children’s actions. The performed experiments show that our proposed method is effective for ASD children’s action recognition. Compared to the previous traditional schemes, our scheme has higher accuracy and is almost non-invasive for ASD children.