Enhanced burn wound healing by controlled-release 3D ADMSC-derived exosome-loaded hyaluronan hydrogel.

Adipose mesenchymal stem cell (ADMSC)-derived exosomes (ADMSC-Exos) have shown great potential in regenerative medicine and been evidenced benefiting wound repair such as burns. However, the low yield, easy loss after direct coating, and no suitable loading system to improve their availability and efficacy hinder their clinical application for wound healing. And few studies focused on the comparison of biological functions between exosomes derived from different culture techniques, especially in exosome-releasing hydrogel system. Therefore, we designed a high-performance exosome controllable releasing hydrogel system for burn wound healing, namely loading 3D-printed microfiber culture-derived exosomes in a highly biocompatible hyaluronic acid (HA). In this project, we compared the biological functions in vitro and in a burn model among exosomes derived from the conventional two-dimensional (2D) plate culture (2D-Exos), microcarrier culture (2.5D-Exos), and 3D-printed microfiber culture (3D-Exos). Results showed that compared with 2D-Exos and 2.5D-Exos, 3D-Exos promoted HACATs and HUVECs cell proliferation and migration more significantly. Additionally, 3D-Exos had stronger angiogenesis-promoting effects in tube formation of (HUVECs) cells. Moreover, we found HA-loaded 3D-Exos showed better burn wound healing promotion compared to 2D-Exos and 2.5D-Exos, including accelerated burn wound healing rate and better collagen remodeling. The study findings reveal that the HA-loaded, controllable-release 3D-Exos repair system distinctly augments therapeutic efficacy in terms of wound healing, while concurrently introducing a facile application approach. This system markedly bolsters the exosomal loading efficiency, provides a robust protective milieu, and potentiates the inherent biological functionalities of the exosomes. Our findings provide a rationale for more efficient utilization of high-quality and high-yield 3D exosomes in the future, and a novel strategy for healing severe burns.

MiR-7-5p targeted Rb regulating cell cycle is involved in hydroquinone-induced malignant progression in human lymphoblastoid TK6 cells.

MiR-7-5p has been demonstrated to inhibit tumorigenesis by limiting tumor cell proliferation, migration and invasion. However, its role in countering hydroquinone (HQ)-induced malignant phenotype of TK6 cells has remained unclear. The present study aimed to investigate whether miR-7-5p overexpression could restrain the malignant phenotype in TK6 cells exposed to HQ. The results displayed that HQ suppressed the expression of miR-7-5p and promoted cell cycle progression. Further investigations confirmed that miR-7-5p could decelerate the cell cycle progression by targeting Rb after acute HQ exposure. Through the regulation of the Rb/E2F1 signaling pathway, the overexpression of miR-7-5p mitigated HQ-induced malignant phenotype in TK6 cells by impeding cell cycle progression. In conclusion, miR-7-5p overexpression appears to be involved in HQ-induced malignant transformation by suppressing Rb/E2F1 signaling pathway, resulting in a deceleration of the cell cycle progression.

Pb(II)-inducible proviolacein biosynthesis enables a dual-color biosensor toward environmental lead.

With the rapid development of synthetic biology, various whole-cell biosensors have been designed as valuable biological devices for the selective and sensitive detection of toxic heavy metals in environmental water. However, most proposed biosensors are based on fluorescent and bioluminescent signals invisible to the naked eye. The development of visible pigment-based biosensors can address this issue. The pbr operon from Klebsiella pneumoniae is selectively induced by bioavailable Pb(II). In the present study, the proviolacein biosynthetic gene cluster was transcriptionally fused to the pbr Pb(II) responsive element and introduced into Escherichia coli. The resultant biosensor responded to Pb(II) in a time- and dose-dependent manner. After a 5-h incubation with Pb(II), the brown pigment was produced, which could be extracted into n-butanol. Extra hydrogen peroxide treatment during n-butanol extract resulted in the generation of a stable green pigment. An increased brown signal was observed upon exposure to lead concentrations above 2.93 nM, and a linear regression was fitted from 2.93 to 3,000 nM. Extra oxidation significantly decreased the difference between parallel groups. The green signal responded to as low as 0.183 nM Pb(II), and a non-linear regression was fitted in a wide concentration range from 0.183 to 3,000 nM. The specific response toward Pb(II) was not interfered with by various metals except for Cd(II) and Hg(II). The PV-based biosensor was validated in monitoring bioaccessible Pb(II) spiked into environmental water. The complex matrices did not influence the regression relationship between spiked Pb(II) and the dual-color signals. Direct reading with the naked eye and colorimetric quantification enable the PV-based biosensor to be a dual-color and low-cost bioindicator for pollutant heavy metal.

JNK1 activated pRb/E2F1 and inhibited p53/p21 signaling pathway is involved in hydroquinone-induced pathway malignant transformation of TK6 cells by accelerating the cell cycle progression.

Hydroquinone (HQ) is an important metabolites of benzene in the body, and it has been found to result in cellular DNA damage, mutation, cell cycle imbalance, and malignant transformation. The JNK1 signaling pathway plays an important role in DNA damage repair. In this study, we focused on whether the JNK1 signaling pathway is involved in the HQ-induced cell cycle abnormalities and the underlying mechanism. The results showed that HQ induced abnormal progression of the cell cycle and initiated the JNK1 signaling pathway. We further confirmed that JNK1 suppression decelerated the cell cycle progression through inhibiting pRb/E2F1 signaling pathway and triggering p53/p21 pathway. Therefore, we concluded that JNK1 might be involved in HQ-induced malignant transformation associated with activating pRb/E2F1 and inhibiting p53/p21 signaling pathway which resulting in accelerating the cell cycle progression.

Structure-Aware Feature Disentanglement With Knowledge Transfer for Appearance-Changing Place Recognition.

Long-term visual place recognition (VPR) is challenging as the environment is subject to drastic appearance changes across different temporal resolutions, such as time of the day, month, and season. A wide variety of existing methods address the problem by means of feature disentangling or image style transfer but ignore the structural information that often remains stable even under environmental condition changes. To overcome this limitation, this article presents a novel structure-aware feature disentanglement network (SFDNet) based on knowledge transfer and adversarial learning. Explicitly, probabilistic knowledge transfer (PKT) is employed to transfer knowledge obtained from the Canny edge detector to the structure encoder. An appearance teacher module is then designed to ensure that the learning of appearance encoder does not only rely on metric learning. The generated content features with structural information are used to measure the similarity of images. We finally evaluate the proposed approach and compare it to state-of-the-art place recognition methods using six datasets with extreme environmental changes. Experimental results demonstrate the effectiveness and improvements achieved using the proposed framework. Source code and some trained models will be available at http://www.tianshu.org.cn.

Nrf2 affects hydroquinone-induces cell cycle arrest through the p16/pRb signaling pathway and antioxidant enzymes.

Hydroquinone (HQ), a well-known carcinogenic agent, induces oxidative stress, cell cycle arrest, apoptosis, and malignant transformation. As an antioxidant actor, the nuclear factor erythroid 2-related factor 2 (Nrf2) drives adaptive cellular protection in response to oxidative stress. The human lymphoblastoid cell line (TK6 cells) is widely used as a model for leukemia researches. In the present study, we focused on exploring whether Nrf2 regulatory cell cycle in TK6 cells upon HQ treatment and the underlying mechanisms. The results showed that the cell cycle arrest in TK6 cells induced by hydroquinone was accompanied by activation of the Nrf2 signaling pathway. We further clarified that Nrf2 loss accelerated cell cycle progression from G0/G1 to S and G2/M phases and promoted ROS production by downregulating the expression of SOD and GSH. Western blotting analysis indicated that Nrf2 regulated cell cycle progression via p16/pRb signaling pathways. Therefore, we conclude that Nrf2 is engaged in HQ-induced cell cycle arrest as well through p16/pRb and antioxidant enzymes.

Anthocyanin biosynthetic pathway switched by metalloregulator PbrR to enable a biosensor for the detection of lead toxicity.

Environmental lead pollution mainly caused by previous anthropogenic activities continuously threatens human health. The determination of bioavailable lead is of great significance to predict its ecological risk. Bacterial biosensors using visual pigments as output signals have been demonstrated to have great potential in developing minimal-equipment biosensors for environmental pollutant detection. In this study, the biosynthesis pathway of anthocyanin was heterogeneously reconstructed under the control of the PbrR-based Pb(II) sensory element in Escherichia coli. The resultant metabolic engineered biosensor with colored anthocyanin derivatives as the visual signal selectively responded to concentrations as low as 0.012 µM Pb(II), which is lower than the detection limit of traditional fluorescent protein-based biosensors. A good linear dose-response pattern in a wide Pb(II) concentration range (0.012-3.125 µM) was observed. The color deepening of culture was recognized to the naked eye in Pb(II) concentrations ranging from 0 to 200 µM. Importantly, the response of metabolic engineered biosensors toward Pb(II) was not significantly interfered with by organic and inorganic ingredients in environmental water samples. Our findings show that the metabolic engineering of natural colorants has great potential in developing visual, sensitive, and low-cost bacterial biosensors for the detection and determination of pollutant heavy metals.

Intelligent Logistics System Design and Supply Chain Management under Edge Computing and Internet of Things.

In order to carry out practical innovation of the intelligent logistics system and promote the practicality of the intelligent logistics system and supply chain management process, this study aims to optimize the design of the intelligent logistics system and supply chain management under edge computing (EC) and the Internet of Things (IoT). The flower pollination algorithm performs the positioning function in the intelligent logistics system and supply chain management. Based on the research on the design of the intelligent logistics system and supply chain management under the EC and IoT, this thesis analyzes the positioning of intelligent logistics systems and supply chain management through the flower pollination algorithm. The eXtreme Gradient Boosting (XGBoost) model is used to predict user information in the system of supply chain management information. Finally, the operation of intelligent logistics and supply chain management systems, the prediction model of supply chain management under XGBoost, and the change of supply chain management and material flow are analyzed. The results show that with the increase in the number of iterations, the optimized algorithm improves the comparison distance error by 53.57%, which has high accuracy and can meet the requirements of positioning and tracking of the intelligent logistics system and logistics status query in supply chain management. The waiting time of the intelligent logistics system is shorter than that of the supply chain management system, and the average waiting time of the system increases by 121.252 ms. The XGBoost model can well predict user information under supply chain management. After discussing the changes of the intelligent logistics system from 2018 to 2020, it is found that the operation efficiency of the supply management system is higher with the increase of the system operation days. The intelligent logistics system has a significant impact on the development of the logistics industry. This research gives a reference for establishing the intelligent logistics system and supply chain management system.

Metabolic engineering of the violacein biosynthetic pathway toward a low-cost, minimal-equipment lead biosensor.

Metabolic engineered bacteria have been successfully employed to produce various natural colorants, which are expected to be used as the visually recognizable signals to develop mini-equipment biological devices for monitoring toxic heavy metals. The violacein biosynthetic pathway has been reconstructed in Escherichia coli (E. coli). Here the successful production of four violacein derivatives was achieved by integrating metabolic engineering and synthetic biology. Lead binding to the metalloregulator enables whole-cell colorimetric biosensors capable of assessing bioavailable lead. Deoxyviolacein-derived signal showed the most satisfied biosensing properties among prodeoxyviolacein (green), proviolacein (blue), deoxyviolacein (purple), and violacein (navy). The limit of detection (LOD) of pigment-based biosensors was 2.93 nM Pb(II), which is lower than that of graphite furnace atomic absorption spectrometry. Importantly, a good linear dose-response model in a wide dose range (2.93-6000 nM) was obtained in a non-cytotoxic deoxyviolacein-based biosensor, which was significantly better than cytotoxic violacein-based biosensor (2.93-750 nM). Among ten metal ions, only Cd(II) and Hg(II) exerted a slight influence on the response of the deoxyviolacein-based biosensor toward Pb(II). The deoxyviolacein-based biosensor was validated in detecting bioaccessible Pb(II) in environmental samples. Factors such as low cost and minimal-equipment requirement make this biosensor a suitable biological device for monitoring toxic lead in the environment.

Acute effects of vibration foam rolling and local vibration during warm-up on athletic performance in tennis players.

Athletes are currently fond of vibration foam rollers (VFRs) and commercial portable vibration percussion devices (PVPDs). It is still unknown whether using these devices during warm-up has an immediate impact on athletic performance. A randomized block design was used in this study. The acute effects of VFR and PVPD on tennis players' athletic performance during warm-up were compared. For the countermovement jump (CMJ), reactive strength index (RSI), and hexagon test (HT), the difference in performance between all interventions was significant (p = 0.007-0.034, η2p = 0.266-0.364). Only those who received VFR had significantly different CMJ and HT results when compared to the control group (CMJ height = 53.18 ±4.49 cm, p = 0.03, d = 1.26; HT time = 10.73 ±0.4 s, p = 0.03, d = 1.12). Participants' RSI values were significantly different after VFR (RSI = 2.01 ±0.11 cm·mm-1, p = 0.012, d = 1.76) and PVPD (RSI = 1.99 ±0.11 cm·mm-1, p = 0.025, d = 1.52) compared to the control group. Therefore, when using VFR and PVPD as part of warm-up protocols for tennis players of varying skill levels, VFR could have an immediate positive effect on power, reactive strength, and change of direction performance, while PVPD could immediately improve reactive strength performance.

Talent Cultivation of New Ventures by Seasonal Autoregressive Integrated Moving Average Back Propagation Under Deep Learning.

This study combines the discovery methods and training of innovative talents, China's requirements for improving talent training capabilities, and analyses the relationship between the number of professional enrollments in colleges and universities and the demand for skills in specific places. The research learns the characteristics and training models of innovative talents, deep learning (DL), neural networks, and related concepts of the seasonal difference Autoregressive Moving Average (ARMA) Model. These concepts are used to propose seasonal autoregressive integrated moving average back propagation (SARIMA-BP). Firstly, the SARIMA-BP artificially sets the weight parameter values and analyzes the model's convergence speed, superiority, and versatility. Then, particle swarm optimization (PSO) algorithm is used to pre-process the model and test its independence. The accuracy of the model is checked to ensure its proper performance. Secondly, the model analyzes and predicts the relationship between the number of professional enrollments of 10 colleges and universities in a specific place and the talent demand of local related enterprises. Moreover, the established model is optimized and tested by wavelet denoising. Independent testing is done to ensure the best possible performance of the model. Finally, the weight value will not significantly affect the model's versatility obtained by experiments. The prediction results of professional settings and corporate needs reveal that: there is a moderate correlation between professional locations and corporate needs; colleges and universities should train professional talents for local enterprises and eliminate the practical education concepts.

The Teaching Pattern of Law Majors Using Artificial Intelligence and Deep Neural Network Under Educational Psychology.

With the increasing attention to the cultivation of legal talents, a new teaching model has been explored through artificial intelligence (AI) technology under educational psychology, which focuses on improving learning initiative, teaching methods, and teaching quality of students. First, the application of AI and deep neural network (DNN) algorithms are reviewed in education, and the advantages and disadvantages of traditional learning material recommendation algorithms are summarized. Then, a personalized learning material recommendation algorithm is put forward based on DNN, together with an adaptive learning system based on DNN. Finally, the traditional user-based collaborative filtering (UserCF) model and lifelong topic modeling (LTM) algorithm are introduced as the control group to verify the performance of the proposed recommendation system. The results show that the best learning rate of model training is 0.0001, the best dropout value is 0.5, and the best batch size is 32. The proposed personalized learning resource recommendation method based on deep learning (DL) still has good stability under various training data scales. The personalized test questions of recommended students are moderately difficult. It is easier to recommend materials according to the acquisition of knowledge points and the practicability of the recommended test questions of students. Personalized learning material recommendation algorithm based on AI can timely feedback needs of students, thereby improving the effect of classroom teaching. Using the combination of AI and DL algorithms in teaching design, students can complete targeted personalized learning assignments, which is of great significance to cultivate high-level legal professionals.

Modern Physical Education and Its Influence on Students' Entrepreneurial Psychology in Sports Universities.

This study aims to explore the entrepreneurial psychology of physical Education (PE) students under the "Internet+"environment, to cultivate and improve the entrepreneurial consciousness of PE students, taking the realization of students' sense of self-efficacy as an intermediary factor. The new educational technology in modern PE is analyzed first. Specifically, the motion sensing technology based on human-computer natural interaction can be used for training, so that learners can effectively improve their physical skills. Subsequently, the current entrepreneurial situation of PE majors is discussed, with 188 students from Tianjin University of Sport and Guangzhou Sport University selected as research subjects. It is found that 62.2% of students have never been exposed to online entrepreneurship, and they are more afraid of entrepreneurial risks. In terms of entrepreneurial motivation, most students choose to start a business because of "personal ideals," and only 40 people choose to start a business because of economic factors. There is a significant positive correlation between entrepreneurial self-efficacy and entrepreneurial intention of college students majoring in PE, and the correlation coefficient is 0.488. At present, the teaching mode of sports universities focuses on the teaching of professional courses. However, students generally believe that the professional knowledge learned is not useful for future entrepreneurship. The entrepreneurial self-efficacy of college students tends to be positive, and there are notable differences in the entrepreneurial self-efficacy between boys and girls. The regression analysis of entrepreneurial self-efficacy and entrepreneurial intention of college students shows that entrepreneurial self-efficacy can effectively predict entrepreneurial intention. This research promotes the innovation and development of the sports industry under the background of "Internet+".

A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images.

Hepatocellular carcinoma (HCC), as the most common type of primary malignant liver cancer, has become a leading cause of cancer deaths in recent years. Accurate segmentation of HCC lesions is critical for tumor load assessment, surgery planning, and postoperative examination. As the appearance of HCC lesions varies greatly across patients, traditional manual segmentation is a very tedious and time-consuming process, the accuracy of which is also difficult to ensure. Therefore, a fully automated and reliable HCC segmentation system is in high demand. In this work, we present a novel hybrid neural network based on multi-task learning and ensemble learning techniques for accurate HCC segmentation of hematoxylin and eosin (H&E)-stained whole slide images (WSIs). First, three task-specific branches are integrated to enlarge the feature space, based on which the network is able to learn more general features and thus reduce the risk of overfitting. Second, an ensemble learning scheme is leveraged to perform feature aggregation, in which selective kernel modules (SKMs) and spatial and channel-wise squeeze-and-excitation modules (scSEMs) are adopted for capturing the features from different spaces and scales. Our proposed method achieves state-of-the-art performance on three publicly available datasets, with segmentation accuracies of 0.797, 0.923, and 0.765 in the PAIP, CRAG, and UHCMC&CWRU datasets, respectively, which demonstrates its effectiveness in addressing the HCC segmentation problem. To the best of our knowledge, this is also the first work on the pixel-wise HCC segmentation of H&E-stained WSIs.

AFAP1-AS1 Promotes Proliferation of Pituitary Adenoma Cells through miR-103a-3p to Activate PI3K/AKT Signaling Pathway.

BACKGROUND: We previously found that AFAP1-AS1 regulates the cell growth of pituitary tumor cells; however, the mechanism still remains unclear. Here, we investigated whether AFAP1-AS1 acts as a competing endogenous RNA of miR-103a-3p to regulate pituitary adenoma growth via the PI3K/AKT pathway. METHODS: The bind between AFAP1-AS1 and rno-miR-103a-3p was measured by luciferase reporter assay, and rno-miR-103a-3p expression was measured by quantitative reverse transcription polymerase chain reaction. Proliferation, cell cycle, and apoptosis were measured by cell counting kit 8 and flow cytometry. Rat growth hormone (GH) and prolactin (PRL) levels in culture supernatant of GH3 and MMQ cells were measured by enzyme-linked immunosorbent assay. RESULTS: AFAP1-AS1 binds to rno-miR-103a-3p in rat pituitary adenoma cells. Additionally, rno-miR-103a-3p overexpression suppressed rat pituitary adenoma cell proliferation, induced cell apoptosis, arrested cell cycle in the G/S phase, reduced GH and PLR secretion, and inhibited the PI3K/AKT signaling pathway. Activated PI3K/AKT signaling pathway revised the effect of rno-miR-103a-3p overexpression on proliferation and GH and PLR secretion. Coexpression of both si-AFAP1-AS1 and rno-miR-103a-3p inhibitor promoted cell proliferation and cell cycle progression, reduced cell apoptosis, enhanced GH and PLR secretion, and activated the PI3K/AKT signaling pathway in rat pituitary adenoma cells. CONCLUSION: We found that AFAP1-AS1 and miR-103a-3p could be a potential therapeutic target for pituitary adenoma.