Antimicrobial carbon dots/pectin-based hydrogel for promoting healing processes in multidrug-resistant bacteria-infected wounds.

Multidrug-resistant (MDR) bacterial infections have become a significant threat to global healthcare systems. Here, we developed a highly efficient antimicrobial hydrogel using environmentally friendly garlic carbon dots, pectin, and acrylic acid. The hydrogel had a porous three-dimensional network structure, which endowed it with good mechanical properties and compression recovery performance. The hydrogel could adhere closely to skin tissues and had an equilibrium swelling ratio of 6.21, indicating its potential as a wound dressing. In particular, the bactericidal efficacy following 24-h contact against two MDR bacteria could exceed 99.99 %. When the hydrogel was applied to epidermal wounds infected with methicillin-resistant Staphylococcus aureus (MRSA) on mice, a remarkable healing rate of 93.29 % was observed after 10 days. This was better than the effectiveness of the traditionally used antibiotic kanamycin, which resulted in a healing rate of 70.36 %. In vitro cytotoxicity testing and hemolysis assay demonstrated a high biocompatibility. This was further proved by the in vivo assay where no toxic side effects were observed on the heart, liver, spleen, lung, or kidney of mice. This eco-friendly and easy-to-prepare food-inspired hydrogel provides an idea for the rational use of food and food by-products as a wound dressing to control MDR bacterial infections.

Adaptive convolution kernel network for change detection in hyperspectral images.

Feature extraction is a key step in hyperspectral image change detection. However, many targets with great various sizes, such as narrow paths, wide rivers, and large tracts of cultivated land, can appear in a satellite remote sensing image at the same time, which will increase the difficulty of feature extraction. In addition, the phenomenon that the number of changed pixels is much less than unchanged pixels will lead to class imbalance and affect the accuracy of change detection. To address the above issues, based on the U-Net model, we propose an adaptive convolution kernel structure to replace the original convolution operations and design a weight loss function in the training stage. The adaptive convolution kernel contains two various kernel sizes and can automatically generate their corresponding weight feature map during training. Each output pixel obtains the corresponding convolution kernel combination according to the weight. This structure of automatically selecting the size of the convolution kernel can effectively adapt to different sizes of targets and extract multi-scale spatial features. The modified cross-entropy loss function solves the problem of class imbalance by increasing the weight of changed pixels. Study results on four datasets indicate that the proposed method performs better than most existing methods.

Deep robust residual network for super-resolution of 2D fetal brain MRI.

Spatial resolution is a key factor of quantitatively evaluating the quality of magnetic resonance imagery (MRI). Super-resolution (SR) approaches can improve its spatial resolution by reconstructing high-resolution (HR) images from low-resolution (LR) ones to meet clinical and scientific requirements. To increase the quality of brain MRI, we study a robust residual-learning SR network (RRLSRN) to generate a sharp HR brain image from an LR input. Due to the Charbonnier loss can handle outliers well, and Gradient Difference Loss (GDL) can sharpen an image, we combined the Charbonnier loss and GDL to improve the robustness of the model and enhance the texture information of SR results. Two MRI datasets of adult brain, Kirby 21 and NAMIC, were used to train and verify the effectiveness of our model. To further verify the generalizability and robustness of the proposed model, we collected eight clinical fetal brain MRI 2D data for evaluation. The experimental results have shown that the proposed deep residual-learning network achieved superior performance and high efficiency over other compared methods.

Synthetic aircraft RS image modelling based on improved conditional GAN joint embedding network.

Developing an efficient and quality remote sensing (RS) technology using volume and efficient modelling in different aircraft RS images is challenging. Generative models serve as a natural and convenient simulation method. Because aircraft types belong to the fine class under the rough class, the issue of feature entanglement may occur while modelling multiple aircraft classes. Our solution to this issue was a novel first-generation realistic aircraft type simulation system (ATSS-1) based on the RS images. It realised fine modelling of the seven aircraft types based on a real scene by establishing an adaptive weighted conditional attention generative adversarial network and joint geospatial embedding (GE) network. An adaptive weighted conditional batch normalisation attention block solved the subclass entanglement by reassigning the intra-class-wise characteristic responses. Subsequently, an asymmetric residual self-attention module was developed by establishing a remote region asymmetric relationship for mining the finer potential spatial representation. The mapping relationship between the input RS scene and the potential space of the generated samples was explored through the GE network construction that used the selected prior distribution z, as an intermediate representation. A public RS dataset (OPT-Aircraft_V1.0) and two public datasets (MNIST and Fashion-MNIST) were used for simulation model testing. The results demonstrated the effectiveness of ATSS-1, promoting further development of realistic automatic RS simulation.

Vitamin D Supplementation is Beneficial for Children with Autism Spectrum Disorder: A Meta-analysis.

OBJECTIVE: We conducted a meta-analysis of randomized controlled trials to explore whether vitamin D supplementation is beneficial for symptom improvement in children with autism spectrum disorder. METHODS: We systematically searched the PubMed database, EMBASE, Cochrane Library, Web of Science, Sino-Med, Wanfang Data, and China National Knowledge Infrastructure mainly up to September 2019. Using a fixed effects model, we calculated the standard mean difference with 95% confidence interval. Furthermore, we analyzed baseline serum 25-hydroxyvitamin D levels and outcome scores including the Social Responsiveness Scale and Child Autism Rating Scale scores after vitamin D supplementation. RESULTS: There was no significant difference in baseline serum 25-hydroxyvitamin D levels among 203 children included from three studies in the meta-analysis. After vitamin D supplementation, the outcome scores in the experimental group were dramatically elevated compared with those in the control group (p = 0.03). CONCLUSION: Vitamin D supplementation improves the typical symptoms of autism spectrum disorder, as indicated by reduced Social Responsiveness Scale and Child Autism Rating Scale scores; thus, it is beneficial for children with autism spectrum disorder.

Immunoglobulin M profile of viral and atypical pathogens among children with community acquired lower respiratory tract infections in Luzhou, China.

BACKGROUND: Community-acquired lower respiratory tract infections (CA-LRTIs) are the primary cause of hospitalization among children globally. A better understanding of the role of atypical pathogen infections in native conditions is essential to improve clinical management and preventive measures. The main objective of this study was to detect the presence of 7 respiratory viruses and 2 atypical pathogens among hospitalized infants and children with community-acquired lower respiratory tract infections in Luzhou via an IgM test. METHODS: Overall, 6623 cases of local hospitalized children with 9 pathogen-IgM results from 1st July 2013 to 31st Dec 2016 were included; multidimensional analysis was performed. RESULTS: 1) Out of 19,467 hospitalized children with lower respiratory tract infections, 6623 samples were collected, for a submission ratio of 33.96% (6623 /19467). Of the total 6623 serum samples tested, 5784 IgM stains were positive, for a ratio of 87.33% (5784 /6623). Mycoplasma pneumoniae (MP) was the dominant pathogen (2548 /6623, 38.47%), with influenza B (INFB) (1606 /6623, 24.25%), Legionella pneumophila serogroup 1 (LP1) (485 /6623, 7.32%) and parainfluenza 1, 2 and 3(PIVs) (416 /6623, 6.28%) ranking second, third and fourth, respectively. 2) The distribution of various pathogen-IgM by age group was significantly different (χ2 = 455.039, P < 0.05). 3) Some pathogens were found to be associated with a certain age of children and seasons statistically. CONCLUSIONS: The dominant positive IgM in the area was MP, followed by INFB, either of which prefers to infect children between 2 years and 5 years in autumn. The presence of atypical pathogens should not be underestimated clinically as they were common infections in the respiratory tract of children in the hospital.