Injectable spontaneous hydrogen-releasing hydrogel for long-lasting alleviation of osteoarthritis.

Excessive production of reactive oxygen species (ROS) amplifies pro-inflammatory pathways and exacerbates immune responses, and is a key factor in the progression of osteoarthritis (OA). Therapeutic hydrogen gas (H2) with antioxidative and anti-inflammatory effects, has a potential for OA alleviation, but the targeted delivery and sustained release of H2 are still challenging. Herein, we develop an injectable calcium boride nanosheets (CBN) loaded hydrogel platform (CBN@GelDA hydrogel) as a high-payload and sustainable H2 precursor for OA treatment. The CBN@GelDA hydrogel could maintain constant physiological pH conditions which further promotes more H2 release than the CBN alone and lasts more than one week. The biocompatibility of this hydrogel with macrophages and chondrocytes is effectively enhanced. The experiments show that the CBN@GelDA hydrogel holds the ROS scavenging ability, reducing the expression of related inflammatory cytokines, lessening M1 macrophages but stimulating M2 phenotype, and thereby decreasing chondrocyte apoptosis, which facilitates to breaking of the vicious circle of OA progression. Furthermore, a single-time injection of the CBN@GelDA hydrogel markedly reduces joint destruction in OA rats. From what has been discussed above, this injectable spontaneous H2-releasing hydrogel is promising for OA treatment. STATEMENT OF SIGNIFICANCE: Oxidative stress and inflammation play the key role in the occurrence and development of osteoarthritis (OA). The system of a hydrogel loaded with H2 precursor calcium boride nanosheet (CBN), which is the first to use as an H2 precursor, integrates superior injectable and biocompatible of hydrogel and the selection of antioxidant properties of H2. This system can improve H2 release behavior and achieve a single injection into the articular cavity to alleviate the progression of OA in rats. This study of the combination of a convenient long-acting injectable hydrogel and a safe therapeutic gas is of great value for improving the quality of life of clinical patients.

MalFuzz: Coverage-guided fuzzing on deep learning-based malware classification model.

With the continuous development of deep learning, more and more domains use deep learning technique to solve key problems. The security issues of deep learning models have also received more and more attention. Nowadays, malware has become a huge security threat in cyberspace. Traditional signature-based malware detection methods are not adaptable to the current large-scale malware detection. Thus many deep learning-based malware detection models are widely used in real malware detection scenarios. Therefore, we need to secure the deep learning-based malware detection models. However, model testing currently focuses on image and natural language processing models. There is no related work to test deep learning-based malware detection models specifically. Therefore, to fill this gap, we propose MalFuzz. MalFuzz uses the idea of coverage-guided fuzzing to test deep learning-based malware detection models. To solve the model state representation problem, MalFuzz uses the first and last layer neuron values to approximately represent the model state. To solve the new coverage calculation problem, MalFuzz uses the fast approximate nearest neighbor algorithm to compute the new coverage. The mutation strategy and seed selection strategy in image model or natural language processing model testing is not appropriate in deep learning-based malware detection model testing. Hence MalFuzz designs the seed selection strategy and seed mutation strategy for malware detection model testing. We performed extensive experiments to demonstrate the effectiveness of MalFuzz. Based on MalConv, Convnet, and CNN 2-d, we compared the modified TensorFuzz and MAB-malware with MalFuzz. Experiment results show that MalFuzz can detect more model classification errors. Likewise, the mutation operation of MalFuzz can retain the original functionality of malware with high probability. Moreover, the seed selection strategy of MalFuzz can help us explore the model state space quickly.

Correction to: Predictive biomarkers and mechanisms underlying resistance to PD1/PD-L1 blockade cancer immunotherapy.

After publication of the article [1], authors found out that Fig. 1 was inadvertently replaced.

Predictive biomarkers and mechanisms underlying resistance to PD1/PD-L1 blockade cancer immunotherapy.

Immune checkpoint blockade targeting PD-1/PD-L1 has promising therapeutic efficacy in a variety of tumors, but resistance during treatment is a major issue. In this review, we describe the utility of PD-L1 expression levels, mutation burden, immune cell infiltration, and immune cell function for predicting the efficacy of PD-1/PD-L1 blockade therapy. Furthermore, we explore the mechanisms underlying immunotherapy resistance caused by PD-L1 expression on tumor cells, T cell dysfunction, and T cell exhaustion. Based on these mechanisms, we propose combination therapeutic strategies. We emphasize the importance of patient-specific treatment plans to reduce the economic burden and prolong the life of patients. The predictive indicators, resistance mechanisms, and combination therapies described in this review provide a basis for improved precision medicine.