Enhancing Wound Healing and Bactericidal Efficacy: A Hydrogel Membrane of Bacterial Cellulose and Sanxan Gel for Accelerating the Healing of Infected Wounds.

Bacterial cellulose is an extracellular polysaccharide produced by microorganisms, offering advantages such as high water-holding capacity, flexibility, and biocompatibility. However, its lack of bactericidal activity hampers its wide application. Usnic acid, a secondary metabolite derived from lichens of the Usnea genus, is recognized for its antibacterial and anti-biofilm efficiency, coupled with anti-inflammatory properties. Its water insolubility presents challenges for wide utilization and stable release. Sanxan gel, a novel polysaccharide, exhibits exceptional freeze-thaw stability, suspension properties, and high elasticity, rendering it effective as a suspending agent to improve the bioavailability of water-insoluble drugs. In this study, a hydrogel membrane is designed by combining bacterial cellulose and usnic acid suspended in sanxan gel through a simple in situ microorganism fermentation. The obtained membranes demonstrate excellent ability for sustained drug release, strong eradication capability against tested bacteria in both in vitro and in vivo experiments, effective inhibition of biofilm formation, and excellent hemocompatibility and cytocompatibility. Additionally, the composite membranes promote wound healing with reduced inflammation and bacterial infection in a full-thickness wound infection model in mice. This study provides innovative insights and strategies for the development of functional dressings for infected wounds in future clinical applications.

Sentiment analysis and prediction model based on Chinese government affairs microblogs.

Existing sentiment analysis research on Chinese government affairs microblogs primarily focuses on the task of sentiment classification on microblogs. There has been a lack of investigation into the correlation of each government affairs microblog with the sentiment values of the corresponding comments below it. This study constructs a large-scale government affairs microblog dataset and explore the correlation of each microblog with the sentiment values of the corresponding comments below it. We proposed a new framework that includes data collection, sentiment analysis and sentiment prediction model training. This sentiment analysis framework is crucial in the government's understanding of the public's real-time sentiments toward policies. It also helps monitor the Internet public sentiment and actively guide the Internet public opinion. We first analyzed the sentiment distribution of government affairs microblogs and the sentiment values on meaningful words. We also discussed the discrepancy in text similarity and sentiment values between microblogs. Furthermore, we investigated the extreme emotional content and discussed the factors influencing the sentiment values of comments. Finally, we designed a collaborative attention regression model to predict the sentiments of microblogs. The sentiment prediction model performed well in the sentiment prediction regression task. The sentiment analysis and the prediction framework for government affairs microblogs in this study can be used as a reference for government-related Internet opinion monitoring.

The Global Changes of N6-methyldeoxyadenosine in Response to Low Temperature in Arabidopsis thaliana and Rice.

N6-methyldeoxyadenosine (6mA) is a recently discovered DNA modification involved in regulating plant adaptation to abiotic stresses. However, the mechanisms and changes of 6mA under cold stress in plants are not yet fully understood. Here, we conducted a genome-wide analysis of 6mA and observed that 6mA peaks were predominantly present within the gene body regions under both normal and cold conditions. In addition, the global level of 6mA increased both in Arabidopsis and rice after the cold treatment. The genes that exhibited an up-methylation showed enrichment in various biological processes, whereas there was no significant enrichment observed among the down-methylated genes. The association analysis revealed a positive correlation between the 6mA level and the gene expression level. Joint analysis of the 6mA methylome and transcriptome of Arabidopsis and rice unraveled that fluctuations in 6mA levels caused by cold exposure were not correlated to changes in transcript levels. Furthermore, we discovered that orthologous genes modified by 6mA showed high expression levels; however, only a minor amount of differentially 6mA-methylated orthologous genes were shared between Arabidopsis and rice under low-temperature conditions. In conclusion, our study provides information on the role of 6mA in response to cold stress and reveals its potential for regulating the expression of stress-related genes.

m6A mRNA modification promotes chilling tolerance and modulates gene translation efficiency in Arabidopsis.

N 6-methyladenosine (m6A), the most prevalent mRNA modification in eukaryotes, is an emerging player of gene regulation at transcriptional and translational levels. Here, we explored the role of m6A modification in response to low temperature in Arabidopsis (Arabidopsis thaliana). Knocking down mRNA adenosine methylase A (MTA), a key component of the modification complex, by RNA interference (RNAi) led to drastically reduced growth at low temperature, indicating a critical role of m6A modification in the chilling response. Cold treatment reduced the overall m6A modification level of mRNAs especially at the 3' untranslated region. Joint analysis of the m6A methylome, transcriptome and translatome of the wild type (WT) and the MTA RNAi line revealed that m6A-containing mRNAs generally had higher abundance and translation efficiency than non-m6A-containing mRNAs under normal and low temperatures. In addition, reduction of m6A modification by MTA RNAi only moderately altered the gene expression response to low temperature but led to dysregulation of translation efficiencies of one third of the genes of the genome in response to cold. We tested the function of the m6A-modified cold-responsive gene ACYL-COA:DIACYLGLYCEROL ACYLTRANSFERASE 1 (DGAT1) whose translation efficiency but not transcript level was reduced in the chilling-susceptible MTA RNAi plant. The dgat1 loss-of-function mutant exhibited reduced growth under cold stress. These results reveal a critical role of m6A modification in regulating growth under low temperature and suggest an involvement of translational control in chilling responses in Arabidopsis.

A Lightweight Sentiment Analysis Framework for a Micro-Intelligent Terminal.

Sentiment analysis aims to mine polarity features in the text, which can empower intelligent terminals to recognize opinions and further enhance interaction capabilities with customers. Considerable progress has been made using recurrent neural networks or pre-trained models to learn semantic representations. However, recently published models with complex structures require increasing computational resources to reach state-of-the-art (SOTA) performance. It is still a significant challenge to deploy these models to run on micro-intelligent terminals with limited computing power and memory. This paper proposes a lightweight and efficient framework based on hybrid multi-grained embedding on sentiment analysis (MC-GGRU). The gated recurrent unit model is designed to incorporate a global attention structure that allows contextual representations to be learned from unstructured text using word tokens. In addition, a multi-grained feature layer can further enrich sentence representation features with implicit semantics from characters. Through hybrid multi-grained representation, MC-GGRU achieves high inference performance with a shallow structure. The experimental results of five public datasets show that our method achieves SOTA for sentiment classification with a trade-off between accuracy and speed.

Query-Efficient Black-Box Adversarial Attack With Customized Iteration and Sampling.

It is a challenging task to fool an image classifier based on deep neural networks under the black-box setting where the target model can only be queried. Among existing black-box attacks, transfer-based methods tend to overfit the substitute model on parameter settings. Decision-based methods have low query efficiency due to fixed sampling and greedy search strategy. To alleviate the above problems, we present a new framework for query-efficient black-box adversarial attack by bridging transfer-based and decision-based attacks. We reveal the relationship between current noise and variance of sampling, the monotonicity of noise compression, and the influence of transition function on the decision-based attack. Guided by the new framework, we propose a black-box adversarial attack named Customized Iteration and Sampling Attack (CISA). CISA estimates the distance from nearby decision boundary to set the stepsize, and uses a dual-direction iterative trajectory to find the intermediate adversarial example. Based on the intermediate adversarial example, CISA conducts customized sampling according to the noise sensitivity of each pixel to further compress noise, and relaxes the state transition function to achieve higher query efficiency. Extensive experiments demonstrate CISA's advantage in query efficiency of black-box adversarial attacks.

Measurement and Analysis of Shock Wave Pressure in Moving Charge and Stationary Charge Explosions.

Shock wave pressure is one of the most important parameters in an explosion. However, there have been few experimental and analytical investigations of moving charge explosions. In this article, we present an experimental method to measure the shock wave pressure from a moving charge explosion. Tests of stationary charges and moving charges with speeds of 580 m/s, 703 m/s and 717 m/s were carried out. The shock wave pressure curves and parameters at different measurement points were obtained and analyzed. The theoretical calculation of the shock wave overpressure was studied and compared with the experimental result. The differences between the stationary charge and moving charge explosions were investigated. The results showed that the shock wave pressure distribution of a moving charge had strong directionality. The shock wave pressure parameters (including overpressure, arrival time, duration and impulse) were influenced by the charge's moving velocity, direction angle and distance from the blast point. The shock wave overpressure value was greater than that of a stationary charge explosion at angles between 0° and 90°. The correlation model based on the velocity vector superposition method could describe the relationship of overpressure between the stationary charge and moving charge explosions.

Automatic liver tumor segmentation used the cascade multi-scale attention architecture method based on 3D U-Net.

PURPOSE: Due to the complex structure of liver tumors and the low contrast with normal tissues make it still a challenging task to accurately segment liver tumors from CT images. To address these problems, we propose an end-to-end segmentation method for liver tumors. METHODS: The method uses a cascade structure to improve the network's extraction of information. First, the Side-output Feature Fusion Attention block is used to fuse features at different levels and combine with attention mechanism to focus on important information. Then, the Atrous Spatial Pyramid Pooling Attention block is used to extract multi-scale semantic features. Finally, the Multi-scale Prediction Fusion block is used to fully fused the features captured at each layer of the network. RESULT: To verify the performance of the proposed model and the effectiveness of each module, we evaluate it on LiTS and 3DIRCADb datasets and obtained Dice per Case of 0.665 and 0.719, respectively, and Dice Global of 0.812 and 0.784, respectively. CONCLUSION: The proposed method is compared with the basic model 3D U-Net, as well as some mainstream methods based on U-Net variants, and our method achieves better performance on the liver tumor segmentation task and is superior to most segmentation algorithms.

SPECT imaging and highly efficient therapy of rheumatoid arthritis based on hyperbranched semiconducting polymer nanoparticles.

Rheumatoid arthritis (RA) is an inflammatory autoimmune disease. Although significant progress has been made in clinical treatment, joint inflammation may continue or worsen, and may even progress to the end-stage that requires joint replacement. Traditional therapy using methotrexate (MTX) would cause serious off-target systemic toxicities. Therefore, it is crucial to effectively and specifically deliver MTX to targeted inflamed joints to decrease its adverse systemic toxicities and improve its therapeutic index. Herein, we develop multifunctional nanocarriers for diagnostic radioisotope (99mTc) labeling and therapeutic targeted drug (MTX) delivery by using PEGylated hyperbranched semiconducting polymer nanoparticles (HSP-PEG-NPs) as carriers. Upon intravenous administration, the nanoparticles can extravasate through the turbulent blood-joint barrier and access the inflamed joints. In vivo SPECT/CT imaging shows high accumulation in the inflamed joints of mice with RA after intravenous injection of HSP-PEG-NPs with 99mTc labeling (99mTc-HSP-PEG). In vivo therapeutic evaluations suggest that MTX@HSP-PEG-NPs significantly alleviate RA with a high therapeutic index and relatively low adverse systemic toxicities in comparison with free MTX at the same dose. Our study shows that HSP-PEG-NPs could serve as multifunctional vehicles to deliver radioisotopes for in vivo imaging, and MTX for RA treatment, highlighting the innovative development of the nanoparticle-based RA treatment strategy for clinical applications.

[Effect of acupuncture on serum lipid and cerebral neurogrowth factor levels in hyperlipemia rats with concurrent cerebral ischemia].

OBJECTIVE: To observe the effect of electroacupuncture (EA) on serum lipid and cerebral neurogrowth factor (NGF) in rats with hyperlipemia and cerebral ischemia (CI). METHODS: A total of 36 SD rats were evenly randomized into control, model, EA-I and EA-II groups with 9 cases in each group. Hyperlipemia plus CI model was established by feeding the rats with high-fat foodstuff (6 weeks) and FeCI3-evoked middle-cerebral-artery (MCA) occlusion method. EA (15 Hz, 1-3 mA) was applied to "Sanyinjiao" (SP 6) and "Fenglong" (ST 40) for 20 min, once daily, continuously for 7 days, additionally,"Baihui" (GV 20) and "Shuigou"(GV 26) were punctured and stimulated with hand, which was conducted first before MCA occlusion for rats of group EA-I and after MCA occlusion for rats of group EA-II separately. Fasting blood samples (2 mL/rat) were taken for detecting serum total cholesterol (TC), triglyceride (TG), low density lipid cholesterol (LDL-C) and high density lipid cholesterol (HDL-C) levels. After decapitation of the rats, cerebral tissues were taken, homogenated for detecting NGF content with ABC-ELISA (enzyme-linked immunosorbent assay) and some other cerebral tissues were cut into sections (6 microm)for observing structural changes of the brain after staining with haematoxylin and cosin (HE) method. RESULTS: Compared with control group, serum TC, TG and LDL-C contents of model, EA-I and EA-II groups increased significantly on day 42 and 59 after modeling (P < 0.01), and HDL-C content of model, EA-I and EA-II groups decreased markedly on day 42 after modeling (P < 0.01). Compared with model group, TC contents of both EA-I and EA-II groups, and TG and LDL-C of EA-I group decreased significantly on day 59 after establishing the model (P < 0.05, 0.01). No significant differences were found between EA-I and EA-II groups in the 4 indexes of blood lipid (P > 0.05). HE staining showed that in model group, neuronal ischemic injury including cellular swelling, edema and nuclear fragmentation in the striatum and the cortex of parietal lobe, widening of the cellular interspace. vacuolation, irregularity of the kytoplasm and karyolemma, etc in CA3 region of hippocampus was apparent, while the situation in EA groups was lighter. Regarding the changes of NGF, compared with control group, NGF contents of model group and EA-I group were significantly lower (P < 0.01); while compared with model group, NGF content of EA-I was significantly higher (P < 0.01); and that of EA-lI group was markedly lower than that of EA-I group (P < 0.05). CONCLUSION: EA can lower the levels of serum TC, LDL-C and TG and suppress cerebral ischemia plus hyperlipemia induced decrease of NGF level in the brain, which may contribute to its effect in relieving ischemic cerebral injury.