A study of the effect of natural brown cotton gauze on the healing of infected wounds.

BACKGROUND: Medical dressings are designed to promote wound healing and reduce infection. The aim of project is to investigate the effect of natural brown colored cotton dressings on the healing of infected wounds in E.coli animals. MATERIALS AND METHODS: In this study, degreased white cotton gauze was used as the control group, with degreased brown cotton gauze and degreased bleached brown cotton gauze as the experimental group 1 and experimental group 2, to investigate the effect on the repair of post-infectious wound damage in animals by establishing an infected wound model in rats with E.coli as the infecting organism. RESULTS: The ability to promote healing of infected wounds was investigated by analyzing the wound healing status, macroscopic wound healing rate, hematoxylin-eosin staining, Masson staining, secretion of inflammatory factors by Elisa assay. The result showed that at day 14 of wound healing, the macroscopic wound healing rate was greater than 98% for all three groups of dressings; the collagen content reached 49.85 ± 5.84% in the experimental group 1 and 53.48 ± 5.32% in the experimental group 2, which was higher than the control group; brown cotton gauze promotes skin wound healing by shortening the inflammatory period in both groups. The expression of three inflammatory factors THF-α, IL-2, and IL-8 and three cytokines MMP-3, MMP-8, and MMP-9 were lower than that of the control group. CONCLUSIONS: It was found that natural brown cotton gauze has better repairing and promoting healing effect on infected wounds. It opens up the application of natural brown cotton gauze in the treatment of infected wounds.

Microdroplets initiate organic-inorganic interactions and mass transfer in thermal hydrous geosystems.

Organic-inorganic interactions regulate the dynamics of hydrocarbons, water, minerals, CO2, and H2 in thermal rocks, yet their initiation remains debated. To address this, we conducted isotope-tagged and in-situ visual thermal experiments. Isotope-tagged studies revealed extensive H/O transfers in hydrous n-C20H42-H2O-feldspar systems. Visual experiments observed water microdroplets forming at 150-165 °C in oil phases near the water-oil interface without surfactants, persisting until complete miscibility above 350 °C. Electron paramagnetic resonance (EPR) detected hydroxyl free radicals concurrent with microdroplet formation. Here we propose a two-fold mechanism: water-derived and n-C20H42-derived free radicals drive interactions with organic species, while water-derived and mineral-derived ions trigger mineral interactions. These processes, facilitated by microdroplets and bulk water, blur boundaries between organic and inorganic species, enabling extensive interactions and mass transfer. Our findings redefine microscopic interplays between organic and inorganic components, offering insights into diagenetic and hydrous-metamorphic processes, and mass transfer cycles in deep basins and subduction zones.

Effective Signal Extraction Algorithm for Cerebral Blood Oxygen Based on Dual Detectors.

Functional near-infrared spectroscopy (fNIRS) can dynamically respond to the relevant state of brain activity based on the hemodynamic information of brain tissue. The cerebral cortex and gray matter are the main regions reflecting brain activity. As they are far from the scalp surface, the accuracy of brain activity detection will be significantly affected by a series of physiological activities. In this paper, an effective algorithm for extracting brain activity information is designed based on the measurement method of dual detectors so as to obtain real brain activity information. The principle of this algorithm is to take the measurement results of short-distance channels as reference signals to eliminate the physiological interference information in the measurement results of long-distance channels. In this paper, the performance of the proposed method is tested using both simulated and measured signals and compared with the extraction results of EEMD-RLS, RLS and fast-ICA, and their extraction effects are quantified by correlation coefficient (R), root-mean-square error (RMSE), and mean absolute error (MAE). The test results show that even under low SNR conditions, the proposed method can still effectively suppress physiological interference and improve the detection accuracy of brain activity signals.

Effect of virtual reality-based exercise and physical exercise on adolescents with overweight and obesity: study protocol for a randomised controlled trial.

INTRODUCTION: Obesity is a complex and multifactorial disease that has affected many adolescents in recent decades. Clinical practice guidelines recommend exercise as the key treatment option for adolescents with overweight and obesity. However, the effects of virtual reality (VR) exercise on the physical and brain health of adolescents with overweight and obese remain unclear. This study aims to evaluate the effects of physical and VR exercises on physical and brain outcomes and explore the differences in benefits between them. Moreover, we will apply a multiomics analysis to investigate the mechanism underlying the effects of physical and VR exercises on adolescents with overweight and obesity. METHODS AND ANALYSIS: This randomised controlled clinical trial will include 220 adolescents with overweight and obesity aged between 11 and 17 years. The participants will be randomised into five groups after screening. Participants in the exercise groups will perform an exercise programme by adding physical or VR table tennis or soccer classes to routine physical education classes in schools three times a week for 8 weeks. Participants in the control group will maintain their usual physical activity. The primary outcome will be the change in body fat mass measured using bioelectrical impedance analysis. The secondary outcomes will include changes in other physical health-related parameters, brain health-related parameters and multiomics variables. ETHICS AND DISSEMINATION: This study was approved by the Ethics Committee of Shanghai Sixth People's Hospital and registered in the Chinese Clinical Trial Registry. Dissemination of the findings will include peer-reviewed publications, conference presentations and media releases. TRIAL REGISTRATION NUMBER: ChiCTR2300068786.

Research on Emotion Recognition Method of Cerebral Blood Oxygen Signal Based on CNN-Transformer Network.

In recent years, research on emotion recognition has become more and more popular, but there are few studies on emotion recognition based on cerebral blood oxygen signals. Since the electroencephalogram (EEG) is easily disturbed by eye movement and the portability is not high, this study uses a more comfortable and convenient functional near-infrared spectroscopy (fNIRS) system to record brain signals from participants while watching three different types of video clips. During the experiment, the changes in cerebral blood oxygen concentration in the 8 channels of the prefrontal cortex of the brain were collected and analyzed. We processed and divided the collected cerebral blood oxygen data, and used multiple classifiers to realize the identification of the three emotional states of joy, neutrality, and sadness. Since the classification accuracy of the convolutional neural network (CNN) in this research is not significantly superior to that of the XGBoost algorithm, this paper proposes a CNN-Transformer network based on the characteristics of time series data to improve the classification accuracy of ternary emotions. The network first uses convolution operations to extract channel features from multi-channel time series, then the features and the output information of the fully connected layer are input to the Transformer netork structure, and its multi-head attention mechanism is used to focus on different channel domain information, which has better spatiality. The experimental results show that the CNN-Transformer network can achieve 86.7% classification accuracy for ternary emotions, which is about 5% higher than the accuracy of CNN, and this provides some help for other research in the field of emotion recognition based on time series data such as fNIRS.

Preparation and evaluation of liposome with ropivacaine ion-pairing in local pain management.

Local analgesia is one of the most desirable methods for postoperative pain control, while the existing local anesthetics have a short duration of analgesic effect. Nano-drug carriers have been widely used in various fields and provide an excellent strategy for traditional drugs. Although the existing liposomes for local anesthetics have certain advantages, their instability and complexity of the preparation process still cannot be ignored. Here, we developed novel ropivacaine hydrochloride liposomes with improved stability and sustained release performance by combining ropivacaine hydrochloride with sodium oleate in liposomes via hydrophobic ion-pairing (HIP). The liposomes are easy to prepare, inexpensive, and suitable for mass production. The infrared (IR), particle size, and Zeta potential measurements adequately characterized the complex, which showed a diameter of 81.09 nm and a zeta potential of -83.3 mV. Animal behavioral experiments, including the hot plate test and von Frey fiber test, demonstrated that the liposome system had a prolonged analgesic effect of 2 h versus conventional liposome preparations, consistent with the results of in vitro release experiments. In addition, in vitro cytotoxicity evaluations in RAW264.7 cells and in vivo evaluations revealed the biocompatibility and safety of the ropivacaine-sodium oleate ion-paired liposome (Rop-Ole-Lipo) system as a suitable local anesthetic for local pain management. Our findings provide a new idea for the preparation of local anesthetics.

Numerical Simulation of the Shear Capacity of a GFRP-Strengthened Natural Bamboo-Bolt Composite Joint.

As an ecological green building material, natural bamboo has many advantages such as a light weight, high strength, and short growth cycle. Natural bamboo is widely used in landscape architecture and fabricated structures. However, in bamboo building structures, the most common bolted joints often appear cleaved along the grain. In this paper, glass fiber-reinforced polymer (GFRP) is designed to wrap and improve the shear capacity of natural bamboo-bolt composite joints. According to the corresponding material parameters, the finite element model of composite joints is established, and the key influencing variables of the bearing capacity, namely the bolt diameter, bamboo tube outer diameter, and screw end distance, are analyzed. In addition, according to the European analytical yield model of bolted connections, the analytical calculation method of the bearing capacity is proposed and compared with the experimental and simulated values. The results showed that the numerical model and the modified analytical model can suitably describe the bearing capacity of composite joints, and a higher bolt diameter, along with the bamboo outer diameter, will lead to a higher ultimate load of the composite joints. Moreover, the bearing capacity of composite joints has no obvious relationship with the end spacing.

Low-quality training data detection method of EEG signals for motor imagery BCI system.

BACKGROUND: The design and implementation of high-performance motor imagery-based brain computer interface (MI-BCI) requires high-quality training samples. However, fluctuation in subjects' physiological and mental states as well as artifacts can produce the low-quality motor imagery electroencephalogram (EEG) signal, which will damage the performance of MI-BCI system. NEW METHOD: In order to select high-quality MI-EEG training data, this paper proposes a low-quality training data detection method combining independent component analysis (ICA) and weak classifier cluster. we also design and implement a new online BCI system based on motor imagery to verify the online processing performance of the proposed method. RESULT: In order to verify the effectiveness of the proposed method, we conducted offline experiments on the public dataset called BCI Competition IV Data Set 2b. Furthermore, in order to verify the processing performance of the online system, we designed 60 groups of online experiments on 12 subjects. The online experimental results show that the twelve subjects can complete the system task efficiently (the best experiment is 135.6 s with 9 trials of subject S1). CONCLUSION: This paper demonstrated that the proposed low-quality training data detection method can effectively screen out low-quality training samples, so as to improve the performance of the MI-BCI system.

Selenium in wastewater can be adsorbed by modified natural zeolite and reused in vegetable growth.

Modified natural zeolites (MNZ) are widely used in pollutant removal, but how to address these MNZ that have adsorbed pollutants must be considered. Selenium is an essential trace element for metabolism and is also a water pollutant. Selenium is adsorbed in the water by MNZ in this study first. Then the Brassica chinensis L. was planted in the soil which contains the MNZ loaded with selenium (MNZ-Se) to explore selenium uptake. MNZ-Se release tests in water and soil were also considered. The results showed the following: (1) The maximum adsorption capacity of MNZ for selenium is 46.90 mg/g. (2) Water release experiments of MNZ-Se showed that regardless of how the pH of the aqueous solution changes, the trend of the release of selenium from MNZ-Se in aqueous solution is not affected and first decreases before stabilizing. (3) Soil release experiments of MNZ-Se showed that the selenium content in the soil increased and reached the concentration in the standard of selenium-rich soil. Addition amount and soil pH value will affect the release ratio. The release ratio of MNZ-Se in the water was higher than that in the soil. (4) With an increase in the soil MNZ-Se content, the selenium content in the soil and B. c increases. Above all, MZN can be a good medium for water pollutant removal and soil improvement.