NADH-Cytochrome B5 reductase 2 suppresses retinal vascular dysfunction through regulation of vascular endothelial growth factor A in diabetic retinopathy.

Diabetic retinopathy (DR) is a progressive vascular complication of diabetes mellitus (DM) and is related to retinal vascular abnormalities. NADH-Cytochrome B5 Reductase 2 (CBR2) has been implicated in angiogenesis, but the effect of CBR2 on angiogenesis and endothelial cell biological behavior in DR remains unclear. Here, we aimed to explore the effect of CBR2 on retinal vascular dysfunction under diabetic conditions. The histological analyses were performed to explore the effect of CBR2 on pathological change in streptozotocin (STZ)-induced diabetic rat retinas. The effect of CBR2 on endothelial cell function was explored by CCK-8, scratch wound, transwell, tube formation, and immunofluorescence assays in high glucose (HG)-stimulated human retinal microvascular endothelial cells (HRMECs). CBR2 expression was significantly downregulated in DM rat retinas and HG-stimulated HRMECs. Intravitreal injection of CBR2-expressing lentivirus under diabetic conditions reduced retinal angiogenesis, acellular capillary formation, and pericyte loss, along with decreased expression of hypoxia-inducible factor-1α (HIF-1α), cluster of differentiation 31 (CD31), and vascular endothelial growth factor A (VEGFA) in vivo. Moreover, CBR2 overexpression inhibited cell growth and tube formation and led to decreased expression of HIF-1α and VEGFA in HG-induced HRMECs. Interestingly, the repressive effects of CBR2 on cell proliferation, migration, and tube formation under HG conditions were strongly reversed when VEGFA was overexpressed. Overall, the key findings of our study suggested that CBR2 might alleviate retinal vascular dysfunction and abnormal endothelial proliferation during the process of DR by regulating VEGFA, providing a piece of potent evidence for DR therapy.

Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark.

Since real-world objects and their interactions are often multi-modal and multi-typed, heterogeneous networks have been widely used as a more powerful, realistic, and generic superclass of traditional homogeneous networks (graphs). Meanwhile, representation learning (a.k.a. embedding) has recently been intensively studied and shown effective for various network mining and analytical tasks. In this work, we aim to provide a unified framework to deeply summarize and evaluate existing research on heterogeneous network embedding (HNE), which includes but goes beyond a normal survey. Since there has already been a broad body of HNE algorithms, as the first contribution of this work, we provide a generic paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms. Moreover, existing HNE algorithms, though mostly claimed generic, are often evaluated on different datasets. Understandable due to the application favor of HNE, such indirect comparisons largely hinder the proper attribution of improved task performance towards effective data preprocessing and novel technical design, especially considering the various ways possible to construct a heterogeneous network from real-world application data. Therefore, as the second contribution, we create four benchmark datasets with various properties regarding scale, structure, attribute/label availability, and etc. from different sources, towards handy and fair evaluations of HNE algorithms. As the third contribution, we carefully refactor and amend the implementations and create friendly interfaces for 13 popular HNE algorithms, and provide all-around comparisons among them over multiple tasks and experimental settings. By putting all existing HNE algorithms under a unified framework, we aim to provide a universal reference and guideline for the understanding and development of HNE algorithms. Meanwhile, by open-sourcing all data and code, we envision to serve the community with an ready-to-use benchmark platform to test and compare the performance of existing and future HNE algorithms (https://github.com/yangji9181/HNE).

Long-term surgical outcomes after resection of presacral tumours and risk factors associated with recurrence.

AIM: The incidence of presacral tumours is low and pertinent data on the management and outcomes after surgery are sparse. The aim of this study was to identify the risk factors for recurrence in patients with presacral tumours undergoing surgery at our institution. METHOD: Patients undergoing resection of a presacral tumour between 2009 and 2019 were identified from a prospectively maintained database. Demographics, clinicopathological features, preoperative imaging, operative details, morbidity, mortality, recurrence and survival were investigated. RESULTS: A total of 122 patients were identified. There were 95 women (77.9%) and the median age was 34 years. The most common presenting symptoms included pelvic pain (n = 60, 49.2%) and recurrent abscesses or fistulas (n = 40, 32.8%). The accuracy of preoperative magnetic resonance imaging (MRI) in distinguishing malignant from benign tumours was 93.9%. Six patients underwent three-dimensional computed tomography angiography (3D-CTA) and preoperative interventional embolization. Procedures were performed using transabdominal (n = 9), posterior (n = 99) and combined abdominal and posterior (n = 14) approaches. There were 21 (17.2%) malignant and 101 (82.8%) benign tumours. The local recurrence rate was 33.3% for malignant tumours and 9.9% for benign tumours. Multivariate analysis revealed that recurrence of malignant tumours was associated with R1 resection while recurrence of benign tumours was associated with secondary resections and intraoperative lesion rupture. CONCLUSION: Presacral tumours continue to be a diagnostic and therapeutic challenge. A multidisciplinary team, informed by modern imaging modalities, is essential for the management of presacral tumours.

Abstract Cost Models for Distributed Data-Intensive Computations.

We consider data analytics workloads on distributed architectures, in particular clusters of commodity machines. To find a job partitioning that minimizes running time, a cost model, which we more accurately refer to as makespan model, is needed. In attempting to find the simplest possible, but sufficiently accurate, such model, we explore piecewise linear functions of input, output, and computational complexity. They are abstract in the sense that they capture fundamental algorithm properties, but do not require explicit modeling of system and implementation details such as the number of disk accesses. We show how the simplified functional structure can be exploited to reduce optimization cost. In the general case, we identify a lower bound that can be used for search-space pruning. For applications with homogeneous tasks, we further demonstrate how to directly integrate the model into the makespan optimization process, reducing search-space dimensionality and thus complexity by orders of magnitude. Experimental results provide evidence of good prediction quality and successful makespan optimization across a variety of operators and cluster architectures.

Celecoxib Down-Regulates the Hypoxia-Induced Expression of HIF-1α and VEGF Through the PI3K/AKT Pathway in Retinal Pigment Epithelial Cells.

BACKGROUND/AIMS: The goal of this study was to detect the expression of hypoxia-inducible factor 1α (HIF-1α) and vascular endothelial growth factor (VEGF) in human retinal pigmented epithelial (RPE) cells treated with celecoxib, a selective cyclooxygenase-2 (COX-2) inhibitor, under hypoxic and normoxic conditions and to explore the signaling mechanism involved in regulating the hypoxia-induced expression of HIF-1α and VEGF in RPE cells. METHODS: D407 cells were cultured in normoxic or hypoxic conditions, with or without celecoxib or a PI3K inhibitor (LY294002). The anti-proliferative effect of celecoxib was assessed using the MTT assay. RT-PCR, Western blotting and ELISA were performed to detect the levels of PI3K, phosphorylated AKT (p-AKT), HIF-1α, VEGF and COX-2. RESULTS: Celecoxib inhibited the proliferation of RPE cells in a dose-dependent manner. Celecoxib suppressed the expression of VEGF at both the mRNA and protein levels and decreased HIF-1α protein expression. HIF-1α activation was regulated by the PI3K/AKT pathway. The celecoxib-induced down-regulation of HIF-1α and VEGF required the suppression of the hypoxia-induced PI3K/AKT pathway. However, the down-regulation of COX-2 did not occur in cells treated with celecoxib. CONCLUSIONS: The antiangiogenic effects of celecoxib in RPE cells under hypoxic conditions resulted from the inhibition of HIF-1α and VEGF expression, which may be partly mediated by a COX-2-independent, PI3K/AKT-dependent pathway.

Predicting drug target interactions using meta-path-based semantic network analysis.

BACKGROUND: In the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and links are different. In order to take into account the heterogeneity of the semantic network, meta-path-based topological patterns were investigated for link prediction. RESULTS: Supervised machine learning models were constructed based on meta-path topological features of an enriched semantic network, which was derived from Chem2Bio2RDF, and was expanded by adding compound and protein similarity neighboring links obtained from the PubChem databases. The additional semantic links significantly improved the predictive performance of the supervised learning models. The binary classification model built upon the enriched feature space using the Random Forest algorithm significantly outperformed an existing semantic link prediction algorithm, Semantic Link Association Prediction (SLAP), to predict unknown links between compounds and protein targets in an evolving network. In addition to link prediction, Random Forest also has an intrinsic feature ranking algorithm, which can be used to select the important topological features that contribute to link prediction. CONCLUSIONS: The proposed framework has been demonstrated as a powerful alternative to SLAP in order to predict DTIs using the semantic network that integrates chemical, pharmacological, genomic, biological, functional, and biomedical information into a unified framework. It offers the flexibility to enrich the feature space by using different normalization processes on the topological features, and it can perform model construction and feature selection at the same time.

Rofecoxib inhibits retinal neovascularization via down regulation of cyclooxygenase-2 and vascular endothelial growth factor expression.

BACKGROUND: To explore the anti-angiogenesis mechanism of Rofecoxib and determine whether Rofecoxib can be a therapeutic agent for the prevention of retinal neovascularization using a model of retinopathy of prematurity (ROP). METHODS: ROP was induced by exposing mice to 75% oxygen from postnatal day 7 (P7 ) to P12 , then to room air from P12 to P17 . Sixteen mice were in each of the three groups: untreated ROP group as positive control, Rofecoxib-treated ROP group and the normal group (age-matched mice maintained in room air from birth to P17 as negative control). The localized expression of cyclooxygenase-2 (COX-2) and vascular endothelial growth factor (VEGF) protein and mRNA in retinal blood vessels was assessed using immunohistochemistry, Western blot analysis and reverse transcription polymerase chain reaction. RESULTS: Mice in the Rofecoxib-treated group had a significantly reduced retinal neovascular tufts compared with those in the untreated ROP group. COX-2 and VEGF protein and mRNA expression levels were increased in the untreated ROP group, compared with the normal group. Rofecoxib decreased retinal angiogenesis by inhibiting COX-2 and VEGF expression. The expression levels of VEGF and COX-2 were positively correlated at mRNA and protein levels. CONCLUSIONS: COX-2 and VEGF expressions were both involved in the regulation of angiogenesis and had the same cellular localization. Expression of COX-2 correlated positively with VEGF in retinal neovascularization. Rofecoxib attenuated retinal angiogenesis by inhibiting the expression of COX-2 and VEGF mRNA and protein.

Prediction of systemic lupus erythematosus-related genes based on graph attention network and deep neural network.

Systemic lupus erythematosus (SLE) is an autoimmune disorder intricately linked to genetic factors, with numerous approaches having identified genes linked to its development, diagnosis and prognosis. Despite genome-wide association analysis and gene knockout experiments confirming some genes associated with SLE, there are still numerous potential genes yet to be discovered. The search for relevant genes through biological experiments entails significant financial and human resources. With the advancement of computational technologies like deep learning, we aim to identify SLE-related genes through deep learning methods, thereby narrowing down the scope for biological experimentation. This study introduces SLEDL, a deep learning-based approach that leverages DNN and graph neural networks to effectively identify SLE-related genes by capturing relevant features in the gene interaction network. The above steps transform the identification of SLE related genes into a binary classification problem, ultimately solved through a fully connected layer. The results demonstrate the superiority of SLEDL, achieving higher AUC (0.7274) and AUPR (0.7599), further validated through case studies.

Network properties determine neural network performance.

Machine learning influences numerous aspects of modern society, empowers new technologies, from Alphago to ChatGPT, and increasingly materializes in consumer products such as smartphones and self-driving cars. Despite the vital role and broad applications of artificial neural networks, we lack systematic approaches, such as network science, to understand their underlying mechanism. The difficulty is rooted in many possible model configurations, each with different hyper-parameters and weighted architectures determined by noisy data. We bridge the gap by developing a mathematical framework that maps the neural network's performance to the network characters of the line graph governed by the edge dynamics of stochastic gradient descent differential equations. This framework enables us to derive a neural capacitance metric to universally capture a model's generalization capability on a downstream task and predict model performance using only early training results. The numerical results on 17 pre-trained ImageNet models across five benchmark datasets and one NAS benchmark indicate that our neural capacitance metric is a powerful indicator for model selection based only on early training results and is more efficient than state-of-the-art methods.

Effects of chronic cold stress on tissue structure, antioxidant response, and key gene expression in the warm-water bivalve Chlamys nobilis.

As ectothermic invertebrates, mollusks are regarded as good environmental indicator species for determining the adverse effects of climate change on marine organisms. In the present study, the effects of cold stress on the tissue structure, antioxidant activity, and expression levels of genes were evaluated in the warm-water noble scallop Chlamys nobilis by simulating natural seawater cooled down during winter from 17 °C to 14 °C, 12 °C, 10 °C, and 9 °C. Firstly, the gill was severely damaged at 10 °C and 9 °C, indicating that it could be used as a visually indicative organ for monitoring cold stress. The methylenedioxyamphetamine (MDA) content significantly increased with the temperatures decreasing, meanwhile, the antioxidant enzyme activities superoxide dismutase (SOD) and catalase (CAT) showed a similar pattern, suggesting that the scallop made a positive response. More importantly, 6179 genes related to low temperatures were constructed in a module-gene clustering heat map including 10 modules. Furthermore, three gene modules about membrane lipid metabolism, amino acid metabolism, and molecular defense were identified. Finally, six key genes were verified, and HEATR1, HSP70B2, PI3K, and ATP6V1B were significantly upregulated, while WNT6 and SHMT were significantly downregulated under cold stress. This study provides a dynamic demonstration of the major gene pathways' response to various low-temperature stresses from a transcriptomic perspective. The findings shed light on how warm-water bivalves can tolerate cold stress and can help in breeding new strains of aquatic organisms with low-temperature resistance.

Selenium in the treatment of mild-to-moderate Graves' orbitopathy: a 5-year prospective controlled cohort study.

PURPOSE: Graves' orbitopathy (GO) is the main extrathyroidal manifestation of Graves' disease. However, limited studies have investigated the actual efficacy of selenium in GO therapy. This longitudinal study explored the effect of selenium on QOL and prognosis of patients with mild-to-moderate GO. METHODS: We conducted a 5-year prospective controlled cohort clinical trial to determine the effect of selenium on 74 patients with mild-to-moderate GO. Patients received selenium yeast or placebo orally for 6 months and were followed up at 6 months and at 5 years by biochemical examination, ophthalmologist evaluation and QOL questionnaire to assess oculopathy and QOL. RESULTS: (1) During a follow-up period of 3-6 months, in the selenium group, the symptoms of tearing, grittiness and conjunctival congestion improved (P < 0.01); clinical activity scores and total GO-QOL scores increased relative to baseline (P < 0.01); TRAb was decreased at the 6-month evaluation (P = 0.003); and patients treated with selenium had a higher rate of improvement and a lower rate of worsening than patients treated with placebo (P < 0.05). (2) Exploratory evaluations at 6 months after drug withdrawal confirmed the earlier results; further changes included alleviation of blurred vision and double vision symptoms in the selenium group (P < 0.01). (3) At the 5-year follow-up, compared with baseline, proptosis, clinical activity scores, TRAb level and total GO-QOL scores in both the selenium and placebo groups were significantly improved (P < 0.01). CONCLUSION: Six months of selenium supplementation may effectively change the early course of mild-to-moderate GO, but this regimen makes no difference in long-term outcomes.

Searching for spin glass ground states through deep reinforcement learning.

Spin glasses are disordered magnets with random interactions that are, generally, in conflict with each other. Finding the ground states of spin glasses is not only essential for understanding the nature of disordered magnets and many other physical systems, but also useful to solve a broad array of hard combinatorial optimization problems across multiple disciplines. Despite decades-long efforts, an algorithm with both high accuracy and high efficiency is still lacking. Here we introduce DIRAC - a deep reinforcement learning framework, which can be trained purely on small-scale spin glass instances and then applied to arbitrarily large ones. DIRAC displays better scalability than other methods and can be leveraged to enhance any thermal annealing method. Extensive calculations on 2D, 3D and 4D Edwards-Anderson spin glass instances demonstrate the superior performance of DIRAC over existing methods. The presented framework will help us better understand the nature of the low-temperature spin-glass phase, which is a fundamental challenge in statistical physics. Moreover, the gauge transformation technique adopted in DIRAC builds a deep connection between physics and artificial intelligence. In particular, this opens up a promising avenue for reinforcement learning models to explore in the enormous configuration space, which would be extremely helpful to solve many other hard combinatorial optimization problems.

Learning molecular dynamics: predicting the dynamics of glasses by a machine learning simulator.

Many-body dynamics of atoms such as glass dynamics is generally governed by complex (and sometimes unknown) physics laws. This challenges the construction of atom dynamics simulations that both (i) capture the physics laws and (ii) run with little computation cost. Here, based on graph neural network (GNN), we introduce an observation-based graph network (OGN) framework to "bypass all physics laws" to simulate complex glass dynamics solely from their static structure. By taking the example of molecular dynamics (MD) simulations, we successfully apply the OGN to predict atom trajectories evolving up to a few hundred timesteps and ranging over different families of complex atomistic systems, which implies that the atom dynamics is largely encoded in their static structure in disordered phases and, furthermore, allows us to explore the capacity of OGN simulations that is potentially generic to many-body dynamics. Importantly, unlike traditional numerical simulations, the OGN simulations bypass the numerical constraint of small integration timestep by a multiplier of ≥5 to conserve energy and momentum until hundreds of timesteps, thus leapfrogging the execution speed of MD simulations for a modest timescale.

Predicting metabolomic profiles from microbial composition through neural ordinary differential equations.

Characterizing the metabolic profile of a microbial community is crucial for understanding its biological function and its impact on the host or environment. Metabolomics experiments directly measuring these profiles are difficult and expensive, while sequencing methods quantifying the species composition of microbial communities are well-developed and relatively cost-effective. Computational methods that are capable of predicting metabolomic profiles from microbial compositions can save considerable efforts needed for metabolomic profiling experimentally. Yet, despite existing efforts, we still lack a computational method with high prediction power, general applicability, and great interpretability. Here we develop a method - mNODE (Metabolomic profile predictor using Neural Ordinary Differential Equations), based on a state-of-the-art family of deep neural network models. We show compelling evidence that mNODE outperforms existing methods in predicting the metabolomic profiles of human microbiomes and several environmental microbiomes. Moreover, in the case of human gut microbiomes, mNODE can naturally incorporate dietary information to further enhance the prediction of metabolomic profiles. Besides, susceptibility analysis of mNODE enables us to reveal microbe-metabolite interactions, which can be validated using both synthetic and real data. The presented results demonstrate that mNODE is a powerful tool to investigate the microbiome-diet-metabolome relationship, facilitating future research on precision nutrition.

Multi-object detection at night for traffic investigations based on improved SSD framework.

Despite significant progress in vision-based detection methods, the task of detecting traffic objects at night remains challenging. Visual information of medium and small stationary objects is deteriorated due to poor lighting conditions. And the visual information is important for traffic investigations. For meeting the needs of night traffic investigations, this study focuses on presenting a nighttime multi-object detection framework based on Single Shot MultiBox Detector (SSD). Considering the need of traffic investigations, the applicable detection framework is presented for detecting traffic objects, especially medium and small stationary objects. In the framework, the Dense Convolutional Network (DenseNet) and deconvolutional layers are introduced to enhance the feature reuse, and the effectiveness of the optimization is finally verified. In this paper, qualitative and quantitative experiments are presented. The results show that our presented framework has better detection performance for medium and small stationary objects. Moreover, the results show that presented framework has better performance for nighttime traffic investigations at intersections.

Effects of Ultrasound Combined with Preheating Treatment to Improve the Thermal Stability of Coconut Milk by Modifying the Physicochemical Properties of Coconut Protein.

In the food industry, coconut milk has a unique flavor and rich nutritional value. However, the poor emulsifying properties of coconut proteins restrict its development. In this study, the effect of ultrasound combined with preheating on coconut globulin and coconut milk was evaluated by physicochemical properties and structural characteristics. The results showed that ultrasound and 90 °C preheating gave coconut protein better emulsifying and thermal properties, demonstrated by higher solubility (45.2% to 53.5%), fewer free sulfhydryl groups (33.24 to 28.05 µmol/g) and higher surface hydrophobicity (7658.6 to 10,815.1). Additionally, Fourier transform infrared spectroscopy and scanning electron microscopy showed obvious changes in the secondary structure. Furthermore, the change in the physicochemical properties of the protein brought a higher zeta potential (-11 to -23 mV), decreased the thermal aggregation rate (148.5% to 13.4%) and increased the viscosity (126.9 to 1103.0 m·Pa·s) of the coconut milk, which indicates that ultrasound combined with preheating treatment provided coconut milk with better thermal stability. In conclusion, ultrasound combined with preheating will have a better influence on modifying coconut globulin and increasing the thermal stability of coconut milk. This study provides evidence that ultrasound and other modification technologies can be combined to solve the problems encountered in the processing of coconut protein products.

Multivariate time-series classification with hierarchical variational graph pooling.

In recent years, multivariate time-series classification (MTSC) has attracted considerable attention owing to the advancement of sensing technology. Existing deep-learning-based MTSC techniques, which mostly rely on convolutional or recurrent neural networks, focus primarily on the temporal dependency of a single time series. Based on this, complex pairwise dependencies among multivariate variables can be better described using advanced graph methods, where each variable is regarded as a node in the graph, and their dependencies are regarded as edges. Furthermore, current spatial-temporal modeling (e.g., graph classification) methodologies based on graph neural networks (GNNs) are inherently flat and cannot hierarchically aggregate node information. To address these limitations, we propose a novel graph-pooling-based framework, MTPool, to obtain an expressive global representation of MTS. We first convert MTS slices into graphs using the interactions of variables via a graph structure learning module and obtain the spatial-temporal graph node features via a temporal convolutional module. To obtain global graph-level representation, we design an "encoder-decoder"-based variational graph pooling module to create adaptive centroids for cluster assignments. Then, we combine GNNs and our proposed variational graph pooling layers for joint graph representation learning and graph coarsening, after which the graph is progressively coarsened to one node. Finally, a differentiable classifier uses this coarsened representation to obtain the final predicted class. Experiments on ten benchmark datasets showed that MTPool outperforms state-of-the-art strategies in the MTSC task.

Accuracy of Google Translate in translating the directions and counseling points for top-selling drugs from English to Arabic, Chinese, and Spanish.

PURPOSE: A study was conducted to evaluate the accuracy of Google Translate (Google LLC, Mountain View, CA) when used to translate directions for use and counseling points for the top 100 drugs used in the United States into Arabic, Chinese (simplified), and Spanish. METHODS: Directions for use and common counseling points for the top 100 drugs were identified by 2 clinicians. This information was translated from English to Arabic, Chinese (simplified), and Spanish using Google Translate. Two nonclinician, bilingual native speakers of each language back-translated the Google Translate translation into English and determined if the sentence made sense in their native language. Two clinicians reviewed the back-translations to determine the clinical significance of each inaccurate translation. RESULTS: For the top 100 drugs, 38 unique directions for use and 170 unique counseling points were identified for translation. For the 38 directions for use, 29 (76.3%) of the Arabic translations were accurate, 34 (89.5%) of the Chinese (simplified) translations were accurate, and 27 (71%) of the Spanish translations were accurate. For the 170 counseling points, 92 (54.1%) of the Arabic translations were accurate, 130 (76.5%) of the Chinese (simplified) translations were accurate, and 65 (38.2%) of the Spanish translations were accurate. Of the 247 inaccurate translations, 72 (29.1%) were classified as highly clinically significant or potentially life-threatening. CONCLUSION: Certified translators should be used to translate directions for use and common counseling points for prescription medications into Arabic, Chinese (simplified), and Spanish. Clinicians should be aware of the risk of inaccurate translation when Google Translate is used.

Finding key players in complex networks through deep reinforcement learning.

Finding an optimal set of nodes, called key players, whose activation (or removal) would maximally enhance (or degrade) certain network functionality, is a fundamental class of problems in network science1,2. Potential applications include network immunization3, epidemic control4, drug design5, and viral marketing6. Due to their general NP-hard nature, those problems typically cannot be solved by exact algorithms with polynomial time complexity7. Many approximate and heuristic strategies have been proposed to deal with specific application scenarios1,2,8-12. Yet, we still lack a unified framework to efficiently solve this class of problems. Here we introduce a deep reinforcement learning framework FINDER, which can be trained purely on small synthetic networks generated by toy models and then applied to a wide spectrum of influencer finding problems. Extensive experiments under various problem settings demonstrate that FINDER significantly outperforms existing methods in terms of solution quality. Moreover, it is several orders of magnitude faster than existing methods for large networks. The presented framework opens up a new direction of using deep learning techniques to understand the organizing principle of complex networks, which enables us to design more robust networks against both attacks and failures.

Inhibition of ß-elemene on the expressions of HIF-lα, VEGF and iNOS in diabetic rats model.

AIM: To evaluate the effect of ß-elemene on the expressions of hypoxia-inducible factor (HIF)-lα, vascular endothelial growth factor (VEGF) and inducible nitric oxide synthase (iNOS) in a streptozotocin (STZ) induced diabetic Sprague-Dawley (SD) rat model. METHODS: SD rats were administered an abdominal injection of STZ and induced to a diabetic model. After 6wk course of diabetes, the treatment groups were given ß-elemene through periocular and intravitreous injection separately and the control groups were given blank emulsion injection. HE staining was used to observe the morphology of retina. The mRNA expressions of HIF-1α, VEGF and iNOS was assayed by real-time polymerase chain reaction (PCR) and the protein expression was measured by Western blot and immunocytochemistry methods. RESULTS: The results indicated that the protein and mRNA expressions of HIF-1α, VEGF and iNOS after treated by ß-elemene periocularly and intravitreally injections were all found to be reduced compared with the levels in the diabetic rats group (P<0.05). The inhibitory effect of intravitreal injection was more remarkable. CONCLUSION: The results show ß-elemene protect the retina of diabetic rats from high glucose damage by downregulating the expression of HIF-1α, VEGF and iNOS.