Healing Diabetic Ulcers with MoO3-X Nanodots Possessing Intrinsic ROS-Scavenging and Bacteria-Killing Capacities.

Diabetic ulcers (DUs) appearing as chronic wounds are difficult to heal due to the oxidative stress in the wound microenvironment and their high susceptibility to bacterial infection. A routine treatment combining surgical debridement with anti-infection therapy is widely used for treating DUs in the clinic, but hardly offers a satisfying wound healing outcome. It is known that a long-term antibiotic treatment may also lead to the drug resistance of pathogens. To address these challenges, new strategies combining both reactive oxygen species (ROS) scavenging and bacterial sterilization have been proposed for fighting against DUs. Following this idea, oxygen deficient molybdenum-based nanodots (MoO3-X ) for healing the DUs are reported. The ROS scavenging ability of MoO3-X nanodots is investigated and the antibacterial property of the nanodots is also demonstrated. The systematic cell and animal experimental results indicate that the MoO3-X nanodots can effectively reduce inflammation, promote epithelial cell regeneration, accelerate angiogenesis, and facilitate DUs recovery. Most importantly, they present excellent capacity to diminish infection of methicillin-resistant Staphylococcus aureus, manifesting the potent application prospect of MoO3-X nanodots for diabetic wound therapy.

Time-Aware Explainable Recommendation via Updating Enabled Online Prediction.

There has been growing attention on explainable recommendation that is able to provide high-quality results as well as intuitive explanations. However, most existing studies use offline prediction strategies where recommender systems are trained once while used forever, which ignores the dynamic and evolving nature of user-item interactions. There are two main issues with these methods. First, their random dataset split setting will result in data leakage that knowledge should not be known at the time of training is utilized. Second, the dynamic characteristics of user preferences are overlooked, resulting in a model aging issue where the model's performance degrades along with time. In this paper, we propose an updating enabled online prediction framework for the time-aware explainable recommendation. Specifically, we propose an online prediction scheme to eliminate the data leakage issue and two novel updating strategies to relieve the model aging issue. Moreover, we conduct extensive experiments on four real-world datasets to evaluate the effectiveness of our proposed methods. Compared with the state-of-the-art, our time-aware approach achieves higher accuracy results and more convincing explanations for the entire lifetime of recommendation systems, i.e., both the initial period and the long-term usage.

Fake news detection for epidemic emergencies via deep correlations between text and images.

In recent years, major emergencies have occurred frequently all over the world. When a major global public heath emergency like COVID-19 broke out, an increasing number of fake news in social media networks are exposed to the public. Automatically detecting the veracity of a news article ensures people receive truthful information, which is beneficial to the epidemic prevention and control. However, most of the existing fake news detection methods focus on inferring clues from text-only content, which ignores the semantic correlations across multimodalities. In this work, we propose a novel approach for Fake News Detection by comprehensively mining the Semantic Correlations between Text content and Images attached (FND-SCTI). First, we learn image representations via the pretrained VGG model, and use them to enhance the learning of text representation via hierarchical attention mechanism. Second, a multimodal variational autoencoder is exploited to learn a fused representation of textual and visual content. Third, the image-enhanced text representation and the multimodal fusion eigenvector are combined to train the fake news detector. Experimental results on two real-world fake news datasets, Twitter and Weibo, demonstrate that our model outperforms seven competitive approaches, and is able to capture the semantic correlations among multimodal contents.