Insights into the role of derailed endocytic trafficking pathway in cancer: From the perspective of cancer hallmarks.

The endocytic trafficking pathway is a highly organized cellular program responsible for the regulation of membrane components and uptake of extracellular substances. Molecules internalized into the cell through endocytosis will be sorted for degradation or recycled back to membrane, which is determined by a series of sorting events. Many receptors, enzymes, and transporters on the membrane are strictly regulated by endocytic trafficking process, and thus the endocytic pathway has a profound effect on cellular homeostasis. However, the endocytic trafficking process is typically dysregulated in cancers, which leads to the aberrant retention of receptor tyrosine kinases and immunosuppressive molecules on cell membrane, the loss of adhesion protein, as well as excessive uptake of nutrients. Therefore, hijacking endocytic trafficking pathway is an important approach for tumor cells to obtain advantages of proliferation and invasion, and to evade immune attack. Here, we summarize how dysregulated endocytic trafficking process triggers tumorigenesis and progression from the perspective of several typical cancer hallmarks. The impact of endocytic trafficking pathway to cancer therapy efficacy is also discussed.

Differentiation of umbilical cord mesenchymal stem cells into hepatocytes with CYP450 metabolic enzyme activity induced by a liver injury microenvironment.

The aim of this study was to observe the effect of a simulated liver tissue injury microenvironment on the directed differentiation of umbilical cord mesenchymal stem cells into hepatocytes with CYP450 metabolic activity in vitro, and to explore the mechanisms underlying this directed differentiation. Normal and damaged liver tissue homogenate supernatants (LHS and CCl4-LHS, respectively) were used as induction fluids. After induction for different durations, Western blot and RT-PCR were used to measure the protein and gene expression of the hepatocellular proteins AFP, CK18, ALB, and the CYP450 family. Simultaneously, the metabolic activity of CYP450 in hepatocytes was determined. Compared with the LHS and CCl4-LHS controls, the LHS and CCl4-LHS induction groups showed a significantly elevated protein and gene expression of AFP, CK18, ALB, CYP1A1/2, CYP2A6, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4 (P < 0.05). The metabolic activity of CYP450 in hepatocytes was increased (P < 0.05). In addition, compared with the LHS group, the CCl4-LHS group induced cell differentiation more rapidly and with a higher efficiency. The results suggested that a liver injury microenvironment is conducive for the directed differentiation of umbilical cord mesenchymal stem cells into hepatocytes with metabolic enzyme activity.

Evaluation of the coracoid bone tunnel placement on Dog Bone™ button fixation for acromioclavicular joint dislocation: a cadaver study combined with finite element analysis.

BACKGROUND: Dog Bone™ button fixation is frequently used to treat acromioclavicular joint (ACJ) dislocation. However, various studies have reported complications after fixation. OBJECTIVE: To investigate the effect of the coracoid bone tunnel location on the treatment of ACJ dislocation through single-tunnel coracoclavicular (CC) ligament fixation with the Dog Bone™ button. METHODS: Six cadaveric shoulders were used. Each specimen was subjected to five testing conditions in the following order: (1) normal ACJ (Gn); (2) acromioclavicular and CC ligaments were removed (G0); (3) CC ligament reconstruction was performed using the Dog Bone™ technique, and the coracoid bone tunnel was at the center of the coracoid base (G1); (4) reconstruction was performed at 5 mm distal from the G1 site, along the axis of the coracoid (G2); (5) reconstruction was performed at 10 mm distal from the G1 site, along the axis of the coracoid (G3). The angles of pronation and supination of the clavicle under the same load (30 N) were measured. Next, a finite element (FE) model was created using computed tomography (CT) images of the normal shoulder. Model 1 (M1), model 2 (M2), and model 3 (M3) correspond to G1, G2, and G3, respectively. A force of 70 N was applied as a vertical upward load to the distal clavicle. Subsequently, the von Mises stress, the strain LE along the FiberWire, and the displacement nephogram of the three models were obtained. RESULTS: After single-tunnel CC ligament fixation using the Dog Bone™ technique, the clavicle in the G2 group (20.50 (19.50, 21.25) °, 20.00 (18.75, 21.25) °) had the best rotational stability. The peak von Mises stress, the strain LE along the FiberWire, and the maximum displacement were smaller in M2 than in M1 and M3. CONCLUSIONS: When the coracoid bone tunnel was located 5 mm anterior to the center of the coracoid base (along the axis of the coracoid), the clavicle showed greater rotational stability.

Mechanistic insights into the interfacial adsorption behaviors of Cr(VI) on ferrihydrite: Effects of pH and naturally coexisting anions in the environment.

Interfacial interaction of hexavalent chromium (Cr[VI]) with ferrihydrite (Fh) plays a key role in the behavior of Cr(VI) in the environment. In this study, H2PO4-, SO42-, NO3-, Cl-, and HCO3- were chosen as coexisting anions to explore their inhibition of the capacity of Fh to adsorb Cr(VI). We employed X-ray diffraction, scanning electron microscopy, attenuated total reflection Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy to thoroughly characterize Fh reaction products before and after adsorption of Cr(VI). The results clearly revealed that pH has a marked effect on the extent of Cr(VI) adsorption onto Fh, and this process is also highly dependent on the types of anions present. H2PO4- exhibited the most evident inhibition of Cr(VI) adsorption, even at low concentrations. Similarly, the inhibition of Cr(VI) adsorption by HCO3- increased markedly with increasing pH. In contrast, SO42- only slightly competed with Cr(VI) for reactive Fh surface sites. The anions Cl- and NO3- exhibited almost no inhibitory effect on Cr(VI) adsorption. The differential order of adsorptive affinity of all six anions for Fh was as follows: H2PO4- > HCO3- > SO42- ≈ HCrO4- > NO3- ≈ Cl-. Based on these results, we further provide mechanistic insights into the complexities of Cr(VI) adsorption/desorption behaviors on Fh surfaces. Using Fh as a geosorbent, these interfacial properties could be exploited to mediate the immobilization and release of chromate from and/or into contaminated environments such as aquifers.

Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network.

With the continuous development of intelligent vehicles, people's demand for services has also rapidly increased, leading to a sharp increase in wireless network traffic. Edge caching, due to its location advantage, can provide more efficient transmission services and become an effective method to solve the above problems. However, the current mainstream caching solutions only consider content popularity to formulate caching strategies, which can easily lead to cache redundancy between edge nodes and lead to low caching efficiency. To solve these problems, we propose a hybrid content value collaborative caching strategy based on temporal convolutional network (called THCS), which achieves mutual collaboration between different edge nodes under limited cache resources, thereby optimizing cache content and reducing content delivery latency. Specifically, the strategy first obtains accurate content popularity through temporal convolutional network (TCN), then comprehensively considers various factors to measure the hybrid content value (HCV) of cached content, and finally uses a dynamic programming algorithm to maximize the overall HCV and make optimal cache decisions. We have obtained the following conclusion through simulation experiments: compared with the benchmark scheme, THCS has improved the cache hit rate by 12.3% and reduced the content transmission delay by 16.7%.

A Comprehensive Review of Digital Twin from the Perspective of Total Process: Data, Models, Networks and Applications.

With the rapid development of industrial digitalization and intelligence, there is an urgent need to accurately depict the physical world in digital space, and, in turn, regulate and optimize the behavior of physical entities based on massive data collection and analysis. As a technology that combines virtual space and physical space, digital twin can satisfy all of the above needs, and has attracted widespread attention. Due to the promising application prospects of digital twins, both academia and industry have launched research in this field, and related studies have been conducted from different perspectives. Accordingly, some articles summarizing the existing work have also been published, but they are all from a single perspective, lacking a systematic introduction and summary. Based on this, this paper conducts a comprehensive review of the existing work on digital twins from four perspectives: data, model, network and application, and strives to gain a better understanding of the development of the field from the physical to the virtual and back to the physical. Meanwhile, current research challenges and future directions for the development of digital twins are all discussed.

Intrusion Detection Method for Internet of Vehicles Based on Parallel Analysis of Spatio-Temporal Features.

The problems with network security that the Internet of Vehicles (IoV) faces are becoming more noticeable as it continues to evolve. Deep learning-based intrusion detection techniques can assist the IoV in preventing network threats. However, previous methods usually employ a single deep learning model to extract temporal or spatial features, or extract spatial features first and then temporal features in a serial manner. These methods usually have the problem of insufficient extraction of spatio-temporal features of the IoV, which affects the performance of intrusion detection and leads to a high false-positive rate. To solve the above problems, this paper proposes an intrusion detection method for IoV based on parallel analysis of spatio-temporal features (PA-STF). First, we built an optimal subset of features based on feature correlations of IoV traffic. Then, we used the temporal convolutional network (TCN) and long short-term memory (LSTM) to extract spatio-temporal features in the IoV traffic in a parallel manner. Finally, we fused the spatio-temporal features extracted in parallel based on the self-attention mechanism and used a multilayer perceptron to detect attacks in the Internet of Vehicles. The experimental results show that the PA-STF method reduces the false-positive rate by 1.95% and 1.57% on the NSL-KDD and UNSW-NB15 datasets, respectively, with the accuracy and F1 score also being superior.

Superior photo-Fenton degradation of acetamiprid by α- Fe2O3-pillared bentonite/L-cysteine complex: Synergy of L-cysteine and visible light.

Acetamiprid is a potential threat to human health, aquatic life, soil microorganisms and beneficial insects as a recalcitrant pollutant in wastewater treatment plant effluents. In this work, the synthesized α-Fe2O3-pillared bentonite (FPB) was used to degrade acetamiprid in the photo-Fenton process with the assistance of L-cysteine (L-cys) existing in natural aquatic environment. The kinetic constant k of acetamiprid degradation by FPB/L-cys in the photo-Fenton process was far more than that in the Fenton process of FPB/L-cys lacking light and the photo-Fenton process of FPB without L-cys. The positive linear correlation between k and ≡Fe(II) content indicated the synergy of L-cys and visible light accelerated the cycle of Fe(III) to Fe(II) in FPB/L-cys during the degradation of acetamiprid by elevating the visible light response of FPB, and promoting the interfacial electron transfer from the active sites of FPB to hydrogen peroxide and photo-generated electron transfer from conduction band of α-Fe2O3 to the active sites of FPB. The boosting •OH and 1O2 were predominantly responsible for acetamiprid degradation. Acetamiprid could be efficiently degraded into less toxic small molecules in the photo-Fenton process via C-N bond breaking, hydroxylation, demethylation, ketonization, dechlorination, and ring cleavage.

A Semantic-Enhancement-Based Social Network User-Alignment Algorithm.

User alignment can associate multiple social network accounts of the same user. It has important research implications. However, the same user has various behaviors and friends across different social networks. This will affect the accuracy of user alignment. In this paper, we aim to improve the accuracy of user alignment by reducing the semantic gap between the same user in different social networks. Therefore, we propose a semantically enhanced social network user alignment algorithm (SENUA). The algorithm performs user alignment based on user attributes, user-generated contents (UGCs), and user check-ins. The interference of local semantic noise can be reduced by mining the user's semantic features for these three factors. In addition, we improve the algorithm's adaptability to noise by multi-view graph-data augmentation. Too much similarity of non-aligned users can have a large negative impact on the user-alignment effect. Therefore, we optimize the embedding vectors based on multi-headed graph attention networks and multi-view contrastive learning. This can enhance the similar semantic features of the aligned users. Experimental results show that SENUA has an average improvement of 6.27% over the baseline method at hit-precision30. This shows that semantic enhancement can effectively improve user alignment.

A Secrecy Transmission Protocol with Energy Harvesting for Federated Learning.

In federated learning (FL), model parameters of deep learning are communicated between clients and the central server. To better train deep learning models, the spectrum resource and transmission security need to be guaranteed. Toward this end, we propose a secrecy transmission protocol based on energy harvesting and jammer selection for FL, in which the secondary transmitters can harvest energy from the primary source. Specifically, a secondary transmitter STi is first selected, which can offer the best transmission performance for the secondary users to access the primary frequency spectrum. Then, another secondary transmitter STn, which has the best channel for eavesdropping, is also chosen as a friendly jammer to provide secrecy service. Furthermore, we use outage probability (OP) and intercept probability (IP) as metrics to evaluate performance. Meanwhile, we also derive closed-form expressions of OP and IP of primary users and OP of secondary users for the proposed protocol, respectively. We also conduct a theoretical analysis of the optimal secondary transmission selection (OSTS) protocol. Finally, the performance of the proposed protocol is validated through numerical experiments. The results show that the secrecy performance of the proposed protocol is better than the OSTS and OCJS, respectively.

A Multiple Salient Features-Based User Identification across Social Media.

Identifying users across social media has practical applications in many research areas, such as user behavior prediction, commercial recommendation systems, and information retrieval. In this paper, we propose a multiple salient features-based user identification across social media (MSF-UI), which extracts and fuses the rich redundant features contained in user display name, network topology, and published content. According to the differences between users' different features, a multi-module calculation method is used to obtain the similarity between various redundant features. Finally, the bidirectional stable marriage matching algorithm is used for user identification across social media. Experimental results show that: (1) Compared with single-attribute features, the multi-dimensional information generated by users is integrated to optimize the universality of user identification; (2) Compared with baseline methods such as ranking-based cross-matching (RCM) and random forest confirmation algorithm based on stable marriage matching (RFCA-SMM), this method can effectively improve precision rate, recall rate, and comprehensive evaluation index (F1).

A Comprehensive Review on Edge Caching from the Perspective of Total Process: Placement, Policy and Delivery.

With the explosive growth of smart devices and mobile applications, mobile core networks face the challenge of exponential growth in traffic and computing demand. Edge caching is one of the most promising solutions to the problem. The main purpose of edge caching is to place popular content that users need at the edge of the network, borrow free space to reduce user waiting time, and lighten the network load by reducing the amount of duplicate data. Due to the promising advantages of edge caching, there have been many efforts motivated by this topic. In this paper, we have done an extensive survey on the existing work from our own perspectives. Distinguished from the existing review articles, our work not only investigates the latest articles in this area, but more importantly, covers all the researches of the total process of edge caching from caching placement optimization, policy design, to the content delivery process. In particular, we discuss the benefits of caching placement optimization from the perspective of different stakeholders, detail the delivery process, and conduct in-depth discussions from the five phases, i.e., requested content analysis, user model analysis, content retrieval, delivery, and update. Finally, we put forward several challenges and potential future directions, and hope to bring some ideas for the follow-up researches in this area.

Toxicologic evaluation of repetitive 4-week intravenous injections of midkine antisense oligonucleotide nanoliposomes in rats.

Midkine antisense oligonucleotide (MK-ASODN) nanoliposomes have previously been shown to have inhibitory activity against hepatocellular carcinoma growth. Herein we report the 4-week sub-chronic toxicity of MK-ASODN nanoliposomes in SD rats. The adverse effects included loss of body weight gain and food consumption, peri-rhinal bleeding, piloerection, peri-anal filth, and kidney, liver, spleen, thymus, lung, and injection site lesions at high doses. Macroscopic changes were observed in the kidneys of the high-dose group, accompanied by a variation in urine protein and white blood cells, blood urea nitrogen, and serum creatinine. The increased spleen and liver coefficient, and the variation in circulating white blood cells, lymphocytes, and eosinophils in the high-dose group demonstrated that inflammation was caused by MK-ASODN nanoliposomes and was consistent with the macroscopic changes in the spleen and liver. The main necropsy findings of the animals that died were macroscopic changes in the lung. No severe toxic effects or mortalities occurred in the low- and medium-dose groups. However, a No Adverse Effect Level (NOAEL) was not identified since there were changes in organs deemed to be adverse at all dose levels. Thus, the maximum tolerated dose of MK-ASODN nanoliposomes for rats was considered to be 6 mg/kg/day.

An AutoEncoder and LSTM-Based Traffic Flow Prediction Method.

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

An Energy-Efficient Routing Protocol for Reliable Data Transmission in Wireless Body Area Networks.

Wireless body area networks will inevitably bring tremendous convenience to human society in future development, and also enable people to benefit from ubiquitous technological services. However, one of the reasons hindering development is the limited energy of the network nodes. Therefore, the energy consumption in the selection of the next hop must be minimized in multi-hop routing. To solve this problem, this paper proposes an energy efficient routing protocol for reliable data transmission in a wireless body area network. The protocol takes multiple parameters of the network node into account, such as residual energy, transmission efficiency, available bandwidth, and the number of hops to the sink. We construct the maximum benefit function to select the next hop node by normalizing the node parameters, and dynamically select the node with the largest function value as the next hop node. Based on the above work, the proposed method can achieve efficient multi-hop routing transmission of data and improve the reliability of network data transmission. Compared with the priority-based energy-efficient routing algorithm (PERA) and modified new-attempt routing protocol (NEW-ATTEMPT), the simulation results show that the proposed routing protocol uses the maximum benefit function to select the next hop node dynamically, which not only improves the reliability of data transmission, but also significantly improves the energy utilization efficiency of the node and prolongs the network lifetime.

A Survey of Routing Protocols in WBAN for Healthcare Applications.

The emergence of wireless body area network (WBAN) technology has brought hope and dawn to solve the problems of population aging, various chronic diseases, and medical facility shortage. The increasing demand for real-time applications in such networks, stimulates many research activities. Designing such a scheme of critical events while preserving the energy efficiency is a challenging task, due to the dynamic of the network topology, severe constraints on the power supply, and the limited computation power. The design of routing protocols becomes an essential part of WBANs and plays an important role in the communication stacks and has a significant impact on the network performance. In this paper, we briefly introduce WBAN and focus on the analysis of the routing protocol, classify, and compare the advantages and disadvantages of various routing protocols. Lastly, we put forward some problems and suggestions, which provides ideas for the follow-up routing design.

Celastrol inhibits cancer metastasis by suppressing M2-like polarization of macrophages.

In recent years, a large amount of clinical and experimental data has shown that M2-like polarized tumor-associated macrophages (TAMs) play an important role in cancer metastasis. Therefore, TAMs, especially M2-like TAMs is a promising target for anti-tumor metastasis therapy. Here, we found that celastrol dose-dependently suppressed IL-13 induced CD206 expression both in RAW264.7 and in primary macrophages. Consistently, celastrol also inhibited the expression of M2-like specific genes, including MRC1, Arg1, Fizz1, Mgl2 and CD11c. Further, by the employment of 4T1 breast cancer model, we found that celastrol significantly prevented cancer metastasis in vivo. Mechanistically, celastrol completely ameliorated STAT6 phosphorylation, which is the key signal molecule responsible for M2 polarization. Our research puts forward a new application of celastrol in anti-cancer metastasis, by intervening M2-like polarization through inhibiting STAT6.

Imatinib prevents lung cancer metastasis by inhibiting M2-like polarization of macrophages.

Although M2-like tumor-associated macrophages (TAMs) have been considered as a vital therapeutic target in cancer therapy due to their role in promoting tumor progression and metastasis, very few compounds have been identified to inhibit M2-like polarization of TAMs. Here, we showed that Imatinib significantly prevented macrophage M2-like polarization induced by IL-13 or IL-4 in vitro, as illustrated by reduced expression of cell surface marker CD206 and M2-like genes, including Arg1, Mgl2, Mrc1, CDH1, and CCL2. Further, the migration of lung cancer cells promoted by the conditioned medium from M2-like macrophages could be restrained by Imatinib. Mechanistically, Imatinib inhibited STAT6 phosphorylation and nuclear translocation, resulting in the macrophage M2-like polarization arrest. Furthermore, Imatinib reduced the number of metastasis of Lewis lung cancer without affecting tumor growth. Both in tumor and lung tissues, the percentage of M2-like macrophages decreased after the administration of Imatinib for one week. Taken together, these data suggest that Imatinib is able to inhibit macrophage M2-like polarization, which plays a vital role in Imatinib suppressed metastasis of Lewis lung cancer.

CRISPR-Cas9 mediated gene knockout in human coronary artery endothelial cells reveals a pro-inflammatory role of TLR2.

Endothelial inflammatory responses promote the development and progression of atherosclerosis. It was reported that Toll-like receptors 2 (TLR2) is associated with endothelial inflammation. However, the effect of TLR2 on inflammatory responses in human coronary artery endothelial cells (HCAECs) remains largely unknown. Here, we tested the hypothesis that TLR2 can enhance inflammatory reactions in HCAECs after stimulated by TLR2 agonist. First, we used CRISPR-Cas9 technology to knockout TLR2 gene in HCAECs. Then, TLR2-KO and wild type HCAECs were treated with TLR2 agonist peptidoglycan (PGN). The expression levels of intercellular cell adhesion molecule-1 (ICAM-1), interleukin-6 (IL-6), and interleukin-8 (IL-8) were analyzed by real-time PCR, Western blot, and ELISA. The expression status of myeloid differentiation primary response gene 88 (MyD88), phosphorylated IRAK-1 (pIRAK-1) and phosphorylated NF-κB (pNF-κB) were detected by Western blot. Our results show that after treated with TLR2 agonist, the expression levels of ICAM-1, IL-6, and IL-8 were downregulated in TLR2-KO cells compared to those of wild type cells. Further, Western blots of MyD88, pIRAK-1, and pNF-κB show that the expression levels of these pro-inflammatory molecules were much lower in TLR2-KO cells compared to that of wild type cells by stimulating with TLR2 agonist. We suggest that TLR2 may affect inflammatory reaction in HCAECs by introducing pro-inflammatory molecules like MyD88, pIRAK-1, and pNF-κB.

Interleukin-6 induced by YAP in hepatocellular carcinoma cells recruits tumor-associated macrophages.

Tumor-associated macrophages (TAMs) has been regarded as the most prominent component in tumor microenvironment. The correlation between TAM density and poor prognosis in Hepatocellular carcinoma (HCC) patients suggests a supportive role for TAMs in tumor progression. Here we employed a co-culture system to interrogate the molecular link between Yes-Associated Protein (YAP) and TAMs chemotaxis in HCC cells. We found that YAP activation was critical for the recruitment of TAMs towards HCC cells. Furthermore, cytokine array and quantitative RT-PCR analyses showed that IL-6 secreted by YAP-activated HCC cells might induce the TAMs recruitment. Interrupting YAP function by statins, the inhibitors of hydroxymethylglutaryl-CoA reductase, could robustly suppress the chemotaxis of TAMs. Together with our findings that the expression levels ofIL-6inhumanHCC tumors were highly correlated with the prognosis of HCC patients, the current study highlight the possibility of improving HCC treatment by targeting YAP-IL-6 mediated TAMs recruitment.