The JNK Inhibitor XG-102 Protects against TNBS-Induced Colitis

The c-Jun N-terminal kinase (JNK)-inhibiting peptide D-JNKI-1, syn. XG-102 was tested for its therapeutic potential in acute inflammatory bowel disease (IBD) in mice. Rectal instillation of the chemical irritant trinitrobenzene sulfonic acid (TNBS) provoked a dramatic acute inflammation in the colon of 7–9 weeks old mice. Coincident subcutaneous application of 100 µg/kg XG-102 significantly reduced the loss of body weight, rectal bleeding and diarrhoea. After 72 h, the end of the study, the colon was removed and immuno-histochemically analysed. XG-102 significantly reduced (i) pathological changes such as ulceration or crypt deformation, (ii) immune cell pathology such as infiltration and presence of CD3- and CD68-positive cells, (iii) the production of tumor necrosis factor (TNF)-α in colon tissue cultures from TNBS-treated mice, (iv) expression of Bim, Bax, FasL, p53, and activation of caspase 3, (v) complexation of JNK2 and Bim, and (vi) expression and activation of the JNK substrate and transcription factor c-Jun. A single application of subcutaneous XG-102 was at least as effective or even better depending on the outcome parameter as the daily oral application of sulfasalazine used for treatment of IBD

Wind Power Forecasting Using Optimized Dendritic Neural Model Based on Seagull Optimization Algorithm and Aquila Optimizer

It is necessary to study different aspects of renewable energy generation, including wind energy. Wind power is one of the most important green and renewable energy resources. The estimation of wind energy generation is a critical task that has received wide attention in recent years. Different machine learning models have been developed for this task. In this paper, we present an efficient forecasting model using naturally inspired optimization algorithms. We present an optimized dendritic neural regression (DNR) model for wind energy prediction. A new variant of the seagull optimization algorithm (SOA) is developed using the search operators of the Aquila optimizer (AO). The main idea is to apply the operators of the AO as a local search in the traditional SOA, which boosts the SOA’s search capability. The new method, called SOAAO, is employed to train and optimize the DNR parameters. We used four wind speed datasets to assess the performance of the presented time-series prediction model, called DNR-SOAAO, using different performance indicators. We also assessed the quality of the SOAAO with extensive comparisons to the original versions of the SOA and AO, as well as several other optimization methods. The developed model achieved excellent results in the evaluation. For example, the SOAAO achieved high R2 results of 0.95, 0.96, 0.95, and 0.91 on the four datasets

An Efficient Off-line Signature Identification Method Based On Fourier Descriptor and

Summary This paper presents a novel off-line signature identification method based on Fourier Descriptor ( FDs ) and Chain Codes features. Signature identification classified into two different problems: recognition and verification. In recognition process we used Principle Component Analysis. In verification process we designed a multilayer feed forward artificial neural network. The main steps of constructing a signature identification system are discussed and experiments on real data sets show that the average error rate can reach 3.8%

Leveraging Regression Analysis to Predict Overlapping Symptoms of Cardiovascular Diseases

In medical informatics, deep learning-based models are being used to predict and diagnose cardiovascular diseases (CVDs). These models can detect clinical signs, recognize phenotypes, and pick treatment methods for complicated illnesses. One approach to predicting CVDs is to collect a large dataset of patient medical records and use it to train a deep learning model. This study investigated CVDs for early prediction using deep learning-based regression analysis on a dataset of 2621 medical records from UAE hospitals, including age, symptoms, and CVD information. We propose a long short-term memory-based deep neural network for early prediction of CVDs by leveraging the regression analysis. It can be seen that the accuracy level of the diseases increased when they were simulated in pairs of one disease with another due to the overlapping symptoms. The study’s results suggest that coronary heart disease has been predicted with an 71.5% accuracy level, with 84.4% overlapping with Dyspnea; when accuracy measured with a combination of three conditions the accuracy was 86.7%, Dyspnea, Chest Pain, and Cyanosis, it has been increased up to 88.9%. Weakness, Fatigue, and Emptysis showed a value of 89.8%. In our proposed work, the combinations were Dyspnea, Chest Pain, Cyanosis, Weakness and Fatigue, Emptysis, and discomfort pressure in the chest have shown the ideal value of accuracy measured up to 90.6%, and with Fever, the accuracy is 91%. We show the effectiveness of our proposed method on several evaluation benchmarks

Developmental toxicity of carbon nanoparticles during embryogenesis in chicken

International audienc

The potential ameliorative impacts of cerium oxide nanoparticles against fipronil-induced hepatic steatosis

Abstract Fipronil (FIP) is a phenylpyrazole insecticide that is commonly used in agricultural and veterinary fields for controlling a wide range of insects, but it is a strong environmentally toxic substance. Exposure to FIP has been reported to increase the hepatic fat accumulation through altered lipid metabolism, which ultimately can contribute to nonalcoholic fatty liver disease (NAFLD) development. The present study aimed to examine the function of cerium oxide nanoparticles (CeNPs) in protecting against hepatotoxicity and lipogenesis induced by FIP. Twenty-eight male albino rats were classified into four groups: FIP (5 mg/kg/day per os), CTR, CeNPs (35 mg/kg/day p.o.), and FIP + CeNPs (5 (FIP) + 35 (CeNPs) mg/kg/day p.o.) for 28 consecutive days. Serum lipid profiles, hepatic antioxidant parameters and pathology, and mRNA expression of adipocytokines were assessed. The results revealed that FIP increased cholesterol, height-density lipoprotein, triacylglyceride, low-density lipoprotein (LDL-c), and very-low-density lipoprotein (VLDL-c) concentrations. It also increased nitric oxide (NO) and malondialdehyde (MDA) hepatic levels and reduced glutathione peroxidase (GPx) and superoxide dismutase (SOD) enzyme activities. Additionally, FIP up-regulated the fatty acid-binding protein (FABP), acetyl Co-A carboxylase (ACC1), and peroxisome proliferator-activated receptor-alpha (PPAR-α). Immunohistochemically, a strong proliferation of cell nuclear antigen (PCNA), ionized calcium-binding adapter molecule 1 (Iba-1), cyclooxygenase-2 (COX-2) reactions in the endothelial cells of the hepatic sinusoids, and increased expression of caspase3 were observed following FIP intoxication. FIP also caused histological changes in hepatic tissue. The CeNPs counteracted the hepatotoxic effect of FIP exposure. So, this study recorded an ameliorative effect of CeNPs against FIP-induced hepatotoxicity

Chemo-Protective Potential of Cerium Oxide Nanoparticles against Fipronil-Induced Oxidative Stress, Apoptosis, Inflammation and Reproductive Dysfunction in Male White Albino Rats

Fipronil (FIP) is an insecticide commonly used in many fields, such as agriculture, veterinary medicine, and public health, and recently it has been proposed as a potential endocrine disrupter. The purpose of this study was to inspect the reproductive impacts of FIP and the possible protective effects of cerium nanoparticles (CeNPs) on male albino rats. Rats received FIP (5 mg/kg bwt; 1/20 LD50), CeNPs (35 mg/kg bwt) and FIP+CeNPs per os daily for 28 days. Serum testosterone levels, testicular oxidative damage, histopathological and immunohistochemical changes were evaluated. FIP provoked testicular oxidative damage as indicated by decreased serum testosterone (≈60%) and superoxide dismutase (≈50%), glutathione peroxidase activity (≈46.67%) and increased malondialdehyde (≈116.67%) and nitric oxide (≈87.5%) levels in testicular tissues. Furthermore, FIP induced edematous changes and degeneration within the seminiferous tubules, hyperplasia, vacuolations, and apoptosis in the epididymides. In addition, FIP exposure upregulated interleukin-1β (IL-1β), nitric oxide synthase 2 (NOS), caspase-3 (Casp3) and downregulated the Burkitt-cell lymphomas (BCL-2), inhibin B proteins (IBP), and androgen receptor (Ar) mRNA expressions Casp3, nitric oxide synthase (iNOS), ionized calcium-binding adapter molecule 1(IBA1), and IL-1β immunoreactions were increased. Also, reduction of proliferating cell nuclear antigen (PCNA), mouse vasa homologue (MVH), and SOX9 protein reactions were reported. Interestingly, CeNPs diminished the harmful impacts of FIP on testicular tissue by decreasing lipid peroxidation, apoptosis and inflammation and increasing the antioxidant activities. The findings reported herein showed that the CeNPs might serve as a supposedly new and efficient protective agent toward reproductive toxicity caused by the FIP insecticide in white male rats