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Driven by the increasing demand of real-time mobile application processing, Multi-access Edge Computing (MEC) has been envisioned as a promising paradigm for pushing computational resources to network edges. In this paper, we investigate an MEC network enabled by Unmanned Aerial Vehicles (UAV), and consider both the multi-user computation offloading and edge server deployment to minimize the system-wide computation cost under dynamic environment, where users generate tasks according to time-varying probabilities. We decompose the minimization problem by formulating two stochastic games for multi-user computation offloading and edge server deployment respectively, and prove that each formulated stochastic game has at least one Nash Equilibrium (NE). Two learning algorithms are proposed to reach the NEs with polynomial-time computational complexities. We further incorporate these two algorithms into a chess-like asynchronous updating algorithm to solve the system-wide computation cost minimization problem. Finally, performance evaluations based on real-world data are conducted and analyzed, corroborating that the proposed algorithms can achieve efficient computation offloading coupled with proper server deployment under dynamic environment for multiple users and MEC servers.

Unmanned Aerial Vehicles (UAVs) have been utilized to serve on-ground users with various services, e.g., computing, communication and caching, due to their mobility and flexibility. The main focus of many recent studies on UAVs is to deploy a set of homogeneous UAVs with identical capabilities controlled by one UAV owner/company to provide services. However, little attention has been paid to the issue of how to enable different UAV owners to provide services with differentiated service capabilities in a shared area. To address this issue, we propose a multi-agent imitation learning enabled UAV deployment approach to maximize both profits of UAV owners and utilities of on-ground users. Specially, a Markov game is formulated among UAV owners and we prove that a Nash equilibrium exists based on the full knowledge of the system. For online scheduling with incomplete information, we design agent policies by imitating the behaviors of corresponding experts. A novel neural network model, integrating convolutional neural networks, generative adversarial networks and a gradient-based policy, can be trained and executed in a fully decentralized manner with a guaranteed -Nash equilibrium. Performance results show that our algorithm has significant superiority on average profits, utilities and execution time compared with other representative algorithms.

Background: Emerging evidence has linked electrocardiographic parameters with serum adhesion molecules and cognition; however, their interrelationship has not been explored. Objective: We sought to investigate the associations of ventricular depolarization and repolarization intervals with serum adhesion molecules and cognitive function among rural-dwelling older adults. Methods: This population-based study engaged 4,886 dementia-free participants (age ≥60 years, 56.2% women) in the baseline examination (March-September 2018) of MIND-China. Of these, serum intercellular and vascular adhesion molecules (ICAM-1 and VCAM-1) were measured in 1591 persons. We used a neuropsychological test battery to assess cognitive function. Resting heart rate, QT, JT intervals, and QRS duration were assessed with electrocardiogram. Data were analyzed using general linear models adjusting for multiple confounders. Results: Longer JT interval was significantly associated with lower z-scores of global cognition (multivariable-adjusted β= –0.035; 95% confidence interval = –0.055, –0.015), verbal fluency (–0.035; –0.063, –0.007), attention (–0.037; –0.065, –0.010), and executive function (–0.044; –0.072, –0.015), but not with memory function (–0.023; –0.054, 0.009). There were similar association patterns of QT interval with cognitive functions. In the serum biomarker subsample, longer JT and QT intervals remained significantly associated with poorer executive function and higher serum adhesion molecules. We detected statistical interactions of JT interval with adhesion molecules (pinteraction

Arachidonic acid affect the feeding and fecundity of H. oblita .

Image denoising is a fundamental problem in computer vision and multimedia computation. Non-local filters are effective for image denoising. But existing deep learning methods that use non-local computation structures are mostly designed for high-level tasks, and global self-attention is usually adopted. For the task of image denoising, they have high computational complexity and have a lot of redundant computation of uncorrelated pixels. To solve this problem and combine the marvelous advantages of non-local filter and deep learning, we propose a Convolutional Unbiased Regional (CUR) transformer. Based on the prior that, for each pixel, its similar pixels are usually spatially close, our insights are that (1) we partition the image into non-overlapped windows and perform regional self-attention to reduce the search range of each pixel, and (2) we encourage pixels across different windows to communicate with each other. Based on our insights, the CUR transformer is cascaded by a series of convolutional regional self-attention (CRSA) blocks with U-style short connections. In each CRSA block, we use convolutional layers to extract the query, key, and value features, namely Q , K , and V , of the input feature. Then, we partition the Q , K , and V features into local non-overlapped windows and perform regional self-attention within each window to obtain the output feature of this CRSA block. Among different CRSA blocks, we perform the unbiased window partition by changing the partition positions of the windows. Experimental results show that the CUR transformer outperforms the state-of-the-art methods significantly on four low-level vision tasks, including real and synthetic image denoising, JPEG compression artifact reduction, and low-light image enhancement.

The effects in field manipulation experiments are strongly influenced by amplified interannual variation in ambient climate as the experimental duration increases. Soil respiration (SR), as an important part of the carbon cycle in terrestrial ecosystems, is sensitive to climate changes such as temperature and precipitation changes. A growing body of evidence has indicated that ambient climate affects the temperature sensitivity of SR, which benchmarks the strength of terrestrial soil carbon-climate feedbacks. However, whether SR sensitivity to precipitation changes is influenced by ambient climate is still not clear. Additionally, the mechanism driving the above phenomenon is still poorly understood. Here, a long-term field manipulation experiment with five precipitation treatments (-60%, -40%, +0%, +40%, and +60% of annual precipitation) was conducted in a marsh in the Yellow River Delta, China, which is sensitive to soil dying-wetting cycle caused by precipitation changes. Results showed that SR increased exponentially along the experimental precipitation gradient each year and the sensitivity of SR (standardized by per 100 mm change in precipitation under precipitation treatments) exhibited significant interannual variation from 2016 to 2021. In addition, temperature, net radiation, and ambient precipitation all exhibited dramatic interannual variability, however, only ambient precipitation had a significant negative correlation with SR sensitivity. Moreover, the sensitivity of SR was significantly positively related to the sensitivity of belowground biomass (BGB) across six years. Structural equation modeling and regression analysis also showed that precipitation treatments significantly affected SR and its autotrophic and heterotrophic components by altering BGB. Our study demonstrated that ambient precipitation determines the sensitivity of SR to precipitation treatments in marshes. The findings underscore the importance of ambient climate in regulating ecosystem responses in long-term field manipulation experiments.

Evidence remains conflicted on the association between dietary carbohydrate quantity and quality and risk of cardiovascular disease, all-cause, cardiovascular and cancer mortality, and such meta-analyses are lacking. The study aimed to conduct a systematic review and meta-analysis to synthesize the knowledge about their associations and to explore the dose-response relations.We comprehensively searched PubMed, EMBASE, and Web of Science up to March 2022 for observational studies investigating the associations in adults. Random effect model was used to estimate the summary relative risks (RRs) and 95% confidence intervals (CIs) and the dose-response association was explored by restricted cubic splines.We obtained the data from 41 eligible studies. Compared with participants with lowest dietary carbohydrate intake, those with highest intake had an RR of 1.10 (95% CI 1.03-1.17, IIncreased consumption of dietary carbohydrate intake is associated with increased risk of cardiovascular disease, stroke, and all-cause mortality. Linear relation was found for cardiovascular disease and stroke but non-linear relation for all-cause mortality. More studies are warranted to investigate the association of dietary carbohydrate quality using a combined indicator and cardiovascular disease and mortality.

Research on droplet generation technology with a large volume range and high uniformity has great significance in biomedical engineering.We explored the influence of both the liquid level height in the reservoir and the flow resistance distribution on the droplet volumes besides the nozzle size for the microfluidic impact printing (MIP) method. Finally, this droplet generation technology is applied to the preparation of lyophilized microbeads with high uniformity in volume.The coefficient of variation (CV) of the droplet volumes can be down to 0.55%. By adjusting the flow resistance distribution of the microchannels and the nozzle size, the droplet volume generated by the MIP system ranges from 0.4 nL to 610 nL.On-demand droplet generation with high uniformity in the range of subnanoliter to submicroliter is achieved. Furthermore, this droplet generation technology is successfully applied to the preparation of highly uniform lyophilized microbeads.This large volumetric range and highly uniform droplet generation technology has tremendous application potential in fields such as digital PCR detection, pharmaceutic preparation, and the construction of concentration arrays. Furthermore, it can also be used as a high-precision liquid distribution method for processing liquid in a submicroliter.

With the rapid growth of online commerce and fashion-related applications, visual clothing analysis and recognition has become a hotspot in computer vision. In this paper, we propose a novel AABLSTM network, which is based on deep CNN-RNN, to solve the visual fashion analysis of clothing category classification, attribute detection, and landmark localization. The designed fashion model is leveraged with the multi-task driven mechanism as follows: firstly, a bidirectional LSTM (Bi-LSTM) branch is proposed for efficiently mining the semantic association between related attributes so as to improve the precision of clothing category classification and attribute detection; then, an imitated hourglass sub-network of “down-up sampling” is constructed for boosting the accuracy of fashion landmark localization; and finally, a specially designed multi-loss function is constructed to better optimize the network training. Extensive experimental results on large-scale fashion datasets demonstrate the superior performance of our approach.

Although olfactory deficits have been found in patients with early-stage Alzheimer's disease (AD), the underlying mechanisms remain unclear. Here we investigated whether and how human amyloid β (Aβ) oligomers affect neural activity in the piriform cortex (PC) slices of adult mice. We found that oligomeric Aβ1-42 decreased the excitability of pyramidal neurons in the anterior PC. The effect was not blocked by glutamate or GABA

The fat mass and nutritional status play important roles in the onset and progression of cancer cachexia. The present study evaluated the joint prognostic value of the fat mass, as indicated by the triceps skinfold thickness (TSF), and the serum albumin level, for mortality in patients with cancer cachexia.We performed a multicentre cohort study including 5134 patients with cancer cachexia from January 2013 to April 2019. The sum of the TSF (mm) and serum albumin (g/L) was defined as the triceps skinfold-albumin index (TA). Harrell's C index, a time-dependent receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to evaluate the prognostic performance of the TA and other indices. Optimal stratification was used to identify the thresholds to define a low TA, and the association of the TA with all-cause mortality was evaluated using Kaplan-Meier analysis and Cox proportional hazard regression models.The study enrolled 2408 women and 2726 men with a median age of 58.6 years and a median follow-up of 44 months. A total of 607 women (TA 49.9) and 817 men (TA 45.6) were classified as having a low TA. The TA showed better discrimination performance (C index = 0.621, 95% confidence interval [CI] = 0.607-0.636) to predict mortality in patients with cancer cachexia than the handgrip strength, the nutritional risk index, the prognostic nutritional index, the controlling nutritional status index, the systemic immune-inflammation index, the modified Glasgow prognostic score, and the TSF or albumin alone in the study population (all P 0.05). The 1-, 3- and 5-year time-dependent ROC analyses (AUC = 0.647, 0.625 and 0.630, respectively) showed that the TA had the highest prognostic value among all indices investigated (all P 0.05). Univariate analysis showed that a lower TA was associated with an increased death hazard (hazard ratio [HR] = 1.859, 95% CI = 1.677-2.062), regardless of the sex and cancer type. Multivariable survival analysis showed that a lower TA was independently associated with an increased death hazard (HR = 1.381, 95% CI = 1.223-1.560). This association was significantly strengthened in patients who did not receive curative chemotherapy (HR = 1.491, 95% CI = 1.298-1.713), those who had higher serum total protein levels (HR = 1.469, 95% CI = 1.284-1.681) and those with better physical performance (HR = 1.453, 95% CI = 1.271-1.662).This study defined and evaluated a new prognostic index, the TA, which may improve the selection of intervention strategies to optimize the survival of patients with cancer cachexia.

Intelligent Transportation System (ITS) is critical to cope with traffic events, e.g., traffic jams and accidents, and provide services for personal traveling. However, existing researches have not jointly considered the user data safety, utility and system latency comprehensively, to the best of our knowledge. Since both safe and efficient transmissions are significant for ITS, we construct a blockchain-enabled crowdsensing framework for distributed traffic management. First, we illustrate the system model and formulate a multi-objective optimization problem. Due to its complexity, we decompose it into two subproblems, and propose the corresponding schemes, i.e., a Deep Reinforcement Learning (DRL)-based algorithm and a DIstributed Alternating Direction mEthod of Multipliers (DIADEM) algorithm. Extensive experiments are carried out to evaluate the performance of our solutions, and experimental results demonstrate that the DRL-based algorithm can legitimately select active miners and transactions to make a satisfied trade-off between the blockchain safety and latency, and the DIADEM algorithm can effectively select task computation modes for vehicles in a distributed way to maximize their social welfare.

Internet of Vehicles (IoV), as a special application of Internet of Things (IoT), has been widely used for Intelligent Transportation System (ITS), which leads to complex and heterogeneous IoV backbone networks. Network traffic prediction techniques are crucial for efficient and secure network management, such as routing algorithm, network planning, and anomaly and intrusion detection. This article studies the problem of end-to-end network traffic prediction in IoV backbone networks, and proposes a deep learning-based method. The constructed system considers the spatio-temporal feature of network traffic, and can capture the long-range dependence of network traffic. Furthermore, a threshold-based update mechanism is put forward to improve the real-time performance of the designed method by using Q-learning. The effectiveness of the proposed method is evaluated by a real network traffic dataset.

Evidence on the association between air pollution and dementia is accumulating but still inconclusive, and the potential effect modification by genetics is unclear. We investigated the joint effects of air pollution exposure and genetic risk on incident dementia in a prospective cohort study, the UK Biobank study. Land use regression models were used to estimate exposure to ambient particulate matter (PM) in 3 fraction sizes (PM with diameter

Vascular endothelial growth factor（VEGF）, fibroblast growth factor（FGF）, nerve growth factor（NGF）, epidermal growth factor and interferon are important endogenous proteins that regulate cell proliferation, differentiation and regeneration. Biological products targeting growth factors are used in the treatment of ocular diseases such as wet age-related macular degeneration, corneal injury and neurotrophic keratitis. Anti-VEGF drugs can regulate the proliferation of vascular endothelia, reduce the edema and exudation of retinal tissue，which are the main therapeutic agents for wet age-related macular degeneration and diabetic retinopathy. The basic FGF (b-FGF) can promote the proliferation, differentiation, and migration of corneal epithelial cells, accelerating the healing of the corneal injury and reduces corneal inflammation；and bovine b-FGF has been approved for the treatment of corneal injuries. The NGF promotes the growth, development, and differentiation of central and peripheral neurons, thus accelerating the repair of nerve damage；and the European Medicines Agency approved the use of nerve growth factor for the treatment of neurotrophic keratitis in 2017. Recent clinical studies show that patients with moderate or severe neurotrophic keratitis achieved complete corneal healing following 8 weeks of NGF therapy. Epidermal growth factor derivative eye drops have been approved for the treatment of corneal epithelial injuries. Recombinant human interferon has been clinically used in the treatment of ocular viral infections. This article reviews the research progress in the development of new cell growth factor drugs for the treatment of ophthalmic diseases, to provide insights for expanding the application of cell growth factors in ophthalmology.

We study the high dynamic range (HDR) imaging problem in dual-lens systems. Existing methods usually treat the HDR imaging problem as an image fusion problem and the HDR result is estimated by fusing the aligned short exposure image and long exposure image. However, the image fusion pipeline depends highly on the image alignment, which is difficult to be perfect. We propose to transfer the dual-lens HDR imaging problem into the disentangled enhancement of exposure correction and denoising for the short exposure image, guided by the long exposure image. In the guided exposure correction module, we make use of the guidance image and 3D color transformation to propose a guided 3D exposure CNN (GEC) to get the rough HDR result from the short exposure image. Then, in the guided denoising module, we make use of the cross-attention mechanism to propose a guided denoising transformer (GDT) to directly use the long exposure image as guidance to denoise the rough HDR result in a pyramid way. And in both modules, we bypass the difficult image alignment processing. Experimental results demonstrate the superiority of our method over the state-of-the-art ones.

Podocytes are unique, highly specialized, terminally differentiated cells, which are restricted in a post-mitotic state with limited ability to repair or regenerate. Re-entering the mitotic phase causes podocyte mitotic catastrophe, thereby leading to podocyte death and glomerular injury. Myeloid-derived growth factor (MYDGF) is a novel secreted protein and plays an important role in the regulation of cardiovascular function. However, whether MYDGF is expressed in kidney parenchymal cells and whether it has biological functions in the kidney remain unknown. Here, we found that MYDGF was expressed in kidney parenchymal cells and was significantly reduced in podocytes from mice with models of focal segmental glomerulosclerosis and diabetic kidney disease. Podocyte-specific deletion of Mydgf in mice exacerbated podocyte injury and proteinuria in both disease models. Functionally, MYDGF protected podocytes against mitotic catastrophe by reducing accumulation of podocytes in the S phase, a portion of the cell cycle in which DNA is replicated. Mechanistically, MYDGF regulates the expression of the transcription factor RUNX2 which mediates some MYDGF effects. Importantly, a significant reduction of MYDGF was found in glomeruli from patients with glomerular disease due to focal segmental glomerulosclerosis and diabetic kidney disease and the level of MYDGF was correlated with glomerular filtration rate, serum creatinine and podocyte loss. Thus, our studies indicate that MYDGF may be an attractive therapeutic target for glomerular disease.

Recently, Age of Information (AoI) has become an important metric to evaluate the freshness of information, and studies on minimizing AoI in wireless networks have drawn extensive attention. In mobile edge networks, the change of critical levels for distinct information is important for users’ decision making, especially when merely partial observations are available. However, existing researches have not addressed that issue yet. To tackle the above challenges, we first establish the system model, in which the information freshness is quantified by the changes of its critical levels. We formulate the Age-of-Critical-Information (AoCI) minimization issue as an optimization problem, with the purpose of minimizing the average relative AoCI of mobile clients to help them make timely decisions. Then, we propose an information-aware heuristic algorithm that can reach optimal performance with full obsevations in an offline manner. For online scheduling, an imitation learning-based scheduling approach is designed to decide update preferences for mobile clients under partial observations, where policies obtained by the above heuristic algorithm are utilized for expert policies. At last, we demonstrate the superiority of our designed algorithm from both theoretical and experimental perspectives.

Salidroside has been widely used in anti-tumor, cardiovascular, and cerebrovascular protection. However, there are few reports of its use for wound repair. Herein, salidroside inflammation-targeted emulsion gel and non-targeted emulsion gel were developed for wound repair. The inflammation-targeted emulsion gels showed an overall trend of better transdermal penetration and lower potential than non-targeted emulsion gels (−58.7 mV and −1.6 mV, respectively). The apparent improvement of the trauma surface was significant in each administration group. There was a significant difference in the rate of wound healing of the rats between each administration group and the model group at days 7 and 14. Pathological tissue sections showed that inflammatory cells in the epidermis, dermis, and basal layer were significantly reduced, and the granulation tissue was proliferated in the inflammation-targeted emulsion gel group and the non-targeted emulsion gel group. Regarding the expressions of EGF and bFGF, the expressions of bFGF and EGF in the tissues of the inflammation-targeted group at days 7, 14, or 21 were significantly higher than that of the non-targeted emulsion gel group and the model group, both of which were statistically significant compared with the model group (p < 0.05). These results demonstrated that salidroside has the potential as an alternative drug for wound repair.

Aiming to solve the partial discharge problem caused by defects in composite insulators, most existing live detection methods are limited by the subjectivity of human judgment, the difficulty of effective quantification, and the use of a single detection method. Therefore, a composite insulator defect diagnosis model based on acoustic–electric feature fusion and a multi-scale perception multi-input of stacked auto-encoder (MMSAE) network is proposed in this paper. Initially, during the withstanding voltage experiment, the electromagnetic wave spectrometer and ultrasonic detector were used to collect and process the data of six types of composite insulator samples with artificial defects. The electromagnetic wave spectrum, ultrasonic power spectral density, and n-S map were then obtained. Then, the network architecture of MMSAE was built by integrating a stacked auto-encoder and multi-scale perception module; the feature extraction and fusion methods of the electromagnetic wave spectrum and ultrasonic signal were investigated. The proposed method was used to diagnose test samples, and the diagnostic results were compared to those obtained using a single input source and the artificial neural network (ANN) method. The results demonstrate that the detection accuracy of acoustic–electric feature fusion is greater than that of a single feature; the accuracy of the proposed method is 99.17%, which is significantly higher than the accuracy of the conventional ANN method. Finally, composite insulator defect diagnosis software based on PYQT5 and Keras was developed. Ten 500 kV aging composite insulators were used to validate the effectiveness of the proposed method and design software.

Large stocks of carbon and nitrogen stored in permafrost regions can potentially feed back to global biogeochemical cycles under climate warming. To understand the response of microbial communities to environmental changes, this study investigated the spatial distribution of bacterial communities in the upper layers (0-10, 10-20, and 20-30 cm) of seasonally frozen soil on the Tibetan Plateau and their relationships with the environmental factors. A total of 135 soil samples were collected from the soils at depths of 0-10, 10-20, and 20-30 cm in the Lhasa River and Nyang River basins, and the diversity and composition of bacterial communities in them were identified by high-throughput 16S rRNA gene sequencing. Bacterial diversity changed significantly with soil depth in the Nyang River basin (p 0.001), while no obvious change was found in the Lhasa River basin. The whole bacterial composition exhibited small variations across different soil layers (p 0.05). The relative abundance of aerobic bacteria, Sphingomonas and Arthrobacter, decreased with soil depth, while that of the other aerobic, facultative anaerobic, and anaerobic bacteria did not exhibit this trend. Soil pH was the key driving edaphic factor of the whole bacterial composition in all three depth layers, while vegetation also had an important influence on bacterial composition. Arthrobacter, Bradyrhizobium, and Bacillus had obvious correlations with soil nutrients or vegetation, while the other species were not significantly correlated with any environmental factors. Structural equation modeling revealed that vegetation and mean annual temperature had a key direct impact on the bacterial diversity and composition, respectively. Climate also indirectly affected bacterial communities, mainly through shaping soil pH and vegetation. These results indicate that the soil depth has a different impact on the bacterial α-diversity, whole bacterial composition, and specific taxa in the 0-30-cm surface layers of seasonally frozen soil, which were mainly determined by various environmental factors.

Renal tubulointerstitial fibrosis is the hallmark of chronic kidney disease (CKD) and the best predictor of renal survival. However, current treatments for CKD remain extremely limited. Therefore, novel therapeutic targets are urgently needed to either stop or reverse CKD progression. The present study was designed to explore the potential role of GPR87, a member of the G protein-coupled receptors (GPCRs) family, in the pathogenesis of tubulointerstitial fibrosis. It was found that GPR87 was significantly induced in the kidney, especially in tubular areas, from different mouse models of renal fibrosis, including unilateral ureteral obstruction (UUO) nephropathy, aristolochic acid nephropathy, and diabetic nephropathy, respectively. Tubule-specific GPR87 deletion dramatically ameliorated tubulointerstitial fibrosis in UUO mice. Mechanistically, GPR87 accelerated glycolysis and mitochondrial injury by YAP-hexokinase-2 signaling, thereby promoting renal fibrosis. Importantly, the upregulation of GPR87 was also found in the kidney from patients with various CKD, indicating that the induction of GPR87 may be a common feature of human kidney diseases. Collectively, our studies for the first time demonstrate that GPR87 plays a pivotal role in renal fibrosis at least in part by accelerating glycolysis and mitochondrial injury, suggesting that targeting GPR87 may represent a novel therapeutic strategy for patients with CKD.

A novel paradigm named Wireless Powered Mobile Edge Computing (WP-MEC) emerges recently, which integrates Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) technologies. It enables mobile clients to both extend their computing capacities by task offloading, and charge from edge servers via energy transmission. Existing studies generally focus on the centralized design of task scheduling and energy charging in WP-MEC networks. To meet the decentralization requirement of the near-coming 6G network, we propose an online learning algorithm for computation offloading in WP-MEC networks with a distributed execution manner. Specifically, we first define the delay minimization problem by considering task deadline and energy constraints. Then, we transform it into a primal-dual optimization problem based on the Bellman equation. After that, we design a novel neural model that learns both offloading and time division decisions in each time slot to solve the formulated optimization problem. To train and execute the designed algorithm distributivity, we form multiple learning models decentralized on edge servers and they work coordinately to achieve parameter synchronization. At last, both theoretical and performance analyses show that the designed algorithm has significant advantages in comparison with other representative schemes.

Nonalcoholic fatty liver disease (NAFLD) has been previously shown to be associated with diabetes mellitus (DM) which is one of the most decisive risk factors for the faster progression of NAFLD to nonalcoholic steatohepatitis (NASH), fibrosis or advanced cirrhosis. However, the critical molecular pathway involved in the development of diabetic-induced liver injury is unclear. By the proteomic study of liver from high-fat diet (HFD)/streptozotocin(STZ)-induced diabetic mice, we revealed that the upregulation of S100A9 was involved in the development of NAFLD with DM. Moreover, we found that S100A9 silencing decreased proinflammatory response and inhibited the TLR4-NF-κB signaling in in-vitro study. Our findings provide new perspectives into the pivotal role of S100A9 for development of diabetic NAFLD and revealed that S100A9 is a critical molecule that links liver injury to inflammation of NAFLD with DM.

Pre-operative assessment with high-resolution magnetic resonance imaging (MRI) is useful for assessing the risk of local recurrence (LR) and survival in rectal cancer. However, no studies have explored the clinical importance of the morphology of the anterior mesorectum, especially in patients with anterior cancer. Hence, the study aimed to investigate the impact of the morphology of the anterior mesorectum on LR in patients with primary rectal cancer.A retrospective study was performed on 176 patients who underwent neoadjuvant treatment and curative-intent surgery. Patients were divided into two groups according to the morphology of the anterior mesorectum on sagittal MRI: (1) linear type: the anterior mesorectum was thin and linear; and (2) triangular type: the anterior mesorectum was thick and had a unique triangular shape. Clinicopathological and LR data were compared between patients with linear type anterior mesorectal morphology and patients with triangular type anterior mesorectal morphology.Morphometric analysis showed that 90 (51.1%) patients had linear type anterior mesorectal morphology, while 86 (48.9%) had triangular type anterior mesorectal morphology. Compared to triangular type anterior mesorectal morphology, linear type anterior mesorectal morphology was more common in females and was associated with a higher risk of circumferential resection margin involvement measured by MRI (35.6% vs. 16.3%, P = 0.004) and a higher 5-year LR rate (12.2% vs. 3.5%, P = 0.030). In addition, the combination of linear type anterior mesorectal morphology and anterior tumors was confirmed as an independent risk factor for LR (odds ratio = 4.283, P = 0.014).The classification established in this study was a simple way to describe morphological characteristics of the anterior mesorectum. The combination of linear type anterior mesorectal morphology and anterior tumors was an independent risk factor for LR and may act as a tool to assist with LR risk stratification and treatment selection.

High-throughput circular RNA (circRNA) sequencing identified circRNA_001678 (circ_001678) as an upregulated circRNA in non-small cell lung cancer (NSCLC) tissues. Hence, the current study sought to investigate the function and the underlying mechanism of circRNA_001678 in immune escape of NSCLC. Briefly, commercially purchased NSCLC cell lines were adopted for in vitro experiment to evaluate the effects of circ_001678 over-expression or knockdown on cell biological functions, including proliferation, migration and invasive abilities. In addition, the effects of circ_001678 on the in vivo tumorigenicity ability were evaluated for verification. Accordingly, we uncovered that circ_001678 over-expression augmented NSCLC progression in vitro and enhanced tumorigenicity ability in vivo. The interaction between circ_001678 and miR-326 predicted online was verified by means of luciferase and RNA pull-down assays. Furthermore, circ_001678 could sponge miR-326 to up-regulate ZEB1. On the other hand, the tumor-promoting effects of circ_001678 could be inhibited by anti-PD-L1/PD-1 treatment. Mechanistically, circ_001678 led to the activation of the PD-1/PD-L1 pathway to promote CD8+ T cell apoptosis, thereby inducing NSCLC cell immune escape via regulation of the miR-326/ZEB1 axis. To conclude, our findings revealed that circ_001678 sponges miR-326 to up-regulate ZEB1 expression and induce the PD-1/PD-L1 pathway-dependent immune escape, thereby promoting the malignant progression of NSCLC.

Cervical cancer is a common malignant gynecological disease that threatens the health of women all over the world. The abnormal expression of Annexin A5 (ANXA5) is closely related to the biological behavior of various malignant tumors, however, the relationship between ANXA5 and cervical cancer is still unclear. Therefore, the effects of low expression of ANXA5 on the proliferation, apoptosis, migration and invasion of cervical cancer cells (HeLa) and its related mechanism were explored.The cells were divided into three groups: ANXA5-si group, negative control group and blank group. RNA interference was used to suppress ANXA5 expression. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, colony formation assay, flow cytometry and propidium iodide (PI) staining, wound healing assay and transwell assay were employed to detect cell proliferation, apoptosis, migration and invasion respectively. Meanwhile, gene expression was detected by qPCR and Western blotting.ANXA5 suppression lead to the increase of proliferation, migration, invasion and the decrease of apoptosis of cervical cancer HeLa cells. Furthermore, the expression of both pPI3K and pAkt increased.ANXA5 might inhibit Hela cells proliferation and metastasis by regulating PI3K/Akt signal pathway.

Genetically encoded collagen-like protein-based hydrogels have demonstrated remarkable efficacy in promoting the healing process in diabetic patients. However, the current methods for preparing these hydrogels pose significant challenges due to harsh reaction conditions and the reliance on chemical crosslinkers. In this study, we present a genetically encoded approach that allows for the creation of protein hydrogels without the need for chemical additives. Our design involves the genetic encoding of paired-cysteine residues at the C- and N-terminals of a meticulously engineered collagen-like recombination protein. The protein-based hydrogel undergoes a gel-sol transition in response to redox stimulation, achieving a sol-gel transition. We provide evidence that the co-incubation of the protein hydrogel with 3T3 cells not only enhances cell viability but also promotes cell migration. Moreover, the application of the protein hydrogel significantly accelerates the healing of diabetic wounds by upregulating the expression of COL-1alpha and CK-14, while simultaneously reducing oxidant stress in the wound microenvironment. Our study highlights a straightforward and chemical-free strategy for the preparation of redox-responsive protein hydrogels. Importantly, our findings underscore the potential of this hydrogel system for effectively treating diabetic wounds, offering a promising avenue for future therapeutic applications.

Glioblastoma (GBM) is among the most fatal and recurring malignant solid tumors. It arises from the GBM stem cell population. Conventional neurosurgical resection, temozolomide (TMZ)-dependent chemotherapy and radiotherapy have rendered the prognosis of patients unsatisfactory. Radiotherapy and chemotherapy can frequently induce non-specific damage to healthy brain and other tissues, which can be extremely hazardous. There is therefore a pressing need for a more effective treatment strategy for GBM to complement or replace existing treatment options. Cell-based and cell-free immunotherapies are currently being investigated to develop new treatment modalities against cancer. These treatments have the potential to be both selective and successful in minimizing off-target collateral harm in the normal brain. In this review, several aspects of cell-based and cell-free immunotherapies related to GBM will be discussed.