Green Synthesized of Thymus vulgaris Chitosan Nanoparticles Induce Relative WRKY-Genes Expression in Solanum lycopersicum against Fusarium solani, the Causal Agent of Root Rot Disease

: Fusarium solani is a plant pathogenic fungus that causes tomato root rot disease and yield losses in tomato production. The current study's main goal is testing the antibacterial efficacy of chitosan nanoparticles loaded with Thyme vulgaris essential oil (ThE-CsNPs) against F. solani in vitro and in vivo. GC-MS analysis was used to determine the chemical constituents of thyme EO. ThE-CsNPs were investigated using transmission electron microscopy before being physicochemically characterized using FT-IR. ThE-CsNPs were tested for antifungal activity against F. solani mycelial growth in vitro. A pot trial was conducted to determine the most effective dose of ThE-CsNPs on the morph/physiological characteristics of Solanum lycopersicum, as well as the severity of fusarium root rot. The relative gene expression of WRKY transcript factors and defense-associated genes were quantified in root tissues under all treatment conditions. In vitro results revealed that ThE-CsNPs (1%) had potent antifungal efficacy against F. solani radial mycelium growth. The expression of three WRKY transcription factors and three tomato defense-related genes was upregulated. Total phenolic, flavonoid content, and antioxidant enzyme activity were all increased. The outfindings of this study strongly suggested the use of ThE-CsNPs in controlling fusarium root rot on tomatoes; however, other experiments remain necessary before they are recommended

A new class of glycomimetic drugs to prevent free fatty acid-induced endothelial dysfunction

Background: Carbohydrates play a major role in cell signaling in many biological processes. We have developed a set of glycomimetic drugs that mimic the structure of carbohydrates and represent a novel source of therapeutics for endothelial dysfunction, a key initiating factor in cardiovascular complications. Purpose: Our objective was to determine the protective effects of small molecule glycomimetics against free fatty acid­induced endothelial dysfunction, focusing on nitric oxide (NO) and oxidative stress pathways. Methods: Four glycomimetics were synthesized by the stepwise transformation of 2,5­dihydroxybenzoic acid to a range of 2,5­substituted benzoic acid derivatives, incorporating the key sulfate groups to mimic the interactions of heparan sulfate. Endothelial function was assessed using acetylcholine­induced, endotheliumdependent relaxation in mouse thoracic aortic rings using wire myography. Human umbilical vein endothelial cell (HUVEC) behavior was evaluated in the presence or absence of the free fatty acid, palmitate, with or without glycomimetics (1µM). DAF­2 and H2DCF­DA assays were used to determine nitric oxide (NO) and reactive oxygen species (ROS) production, respectively. Lipid peroxidation colorimetric and antioxidant enzyme activity assays were also carried out. RT­PCR and western blotting were utilized to measure Akt, eNOS, Nrf­2, NQO­1 and HO­1 expression. Results: Ex vivo endothelium­dependent relaxation was significantly improved by the glycomimetics under palmitate­induced oxidative stress. In vitro studies showed that the glycomimetics protected HUVECs against the palmitate­induced oxidative stress and enhanced NO production. We demonstrate that the protective effects of pre­incubation with glycomimetics occurred via upregulation of Akt/eNOS signaling, activation of the Nrf2/ARE pathway, and suppression of ROS­induced lipid peroxidation. Conclusion: We have developed a novel set of small molecule glycomimetics that protect against free fatty acidinduced endothelial dysfunction and thus, represent a new category of therapeutic drugs to target endothelial damage, the first line of defense against cardiovascular disease

Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN

In the world, one in eight women will develop breast cancer. Men can also develop it, but less frequently. This condition starts with uncontrolled cell division brought on by a change in the genes that regulate cell division and growth, which leads to the development of a nodule or tumour. These tumours can be either benign, which poses no health risk, or malignant, also known as cancerous, which puts patients’ lives in jeopardy and has the potential to spread. The most common way to diagnose this problem is via mammograms. This kind of examination enables the detection of abnormalities in breast tissue, such as masses and microcalcifications, which are thought to be indicators of the presence of disease. This study aims to determine how histogram-based image enhancement methods affect the classification of mammograms into five groups: benign calcifications, benign masses, malignant calcifications, malignant masses, and healthy tissue, as determined by a CAD system of automatic mammography classification using convolutional neural networks. Both Contrast-limited Adaptive Histogram Equalization (CAHE) and Histogram Intensity Windowing (HIW) will be used (CLAHE). By improving the contrast between the image’s background, fibrous tissue, dense tissue, and sick tissue, which includes microcalcifications and masses, the mammography histogram is modified using these procedures. In order to help neural networks, learn, the contrast has been increased to make it easier to distinguish between various types of tissue. The proportion of correctly classified images could rise with this technique. Using Deep Convolutional Neural Networks, a model was developed that allows classifying different types of lesions. The model achieved an accuracy of 62%, based on mini-MIAS data. The final goal of the project is the creation of an update algorithm that will be incorporated into the CAD system and will enhance the automatic identification and categorization of microcalcifications and masses. As a result, it would be possible to increase the possibility of early disease identification, which is important because early discovery increases the likelihood of a cure to almost 100%

Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach

Knee osteoarthritis is a challenging problem affecting many adults around the world. There are currently no medications that cure knee osteoarthritis. The only way to control the progression of knee osteoarthritis is early detection. Currently, X-ray imaging is a central technique used for the prediction of osteoarthritis. However, the manual X-ray technique is prone to errors due to the lack of expertise of radiologists. Recent studies have described the use of automated systems based on machine learning for the effective prediction of osteoarthritis from X-ray images. However, most of these techniques still need to achieve higher predictive accuracy to detect osteoarthritis at an early stage. This paper suggests a method with higher predictive accuracy that can be employed in the real world for the early detection of knee osteoarthritis. In this paper, we suggest the use of transfer learning models based on sequential convolutional neural networks (CNNs), Visual Geometry Group 16 (VGG-16), and Residual Neural Network 50 (ResNet-50) for the early detection of osteoarthritis from knee X-ray images. In our analysis, we found that all the suggested models achieved a higher level of predictive accuracy, greater than 90%, in detecting osteoarthritis. However, the best-performing model was the pretrained VGG-16 model, which achieved a training accuracy of 99% and a testing accuracy of 92%

Image Encryption Algorithm Based on Chaotic Economic Model

In literature, chaotic economic systems have got much attention because of their complex dynamic behaviors such as bifurcation and chaos. Recently, a few researches on the usage of these systems in cryptographic algorithms have been conducted. In this paper, a new image encryption algorithm based on a chaotic economic map is proposed. An implementation of the proposed algorithm on a plain image based on the chaotic map is performed. The obtained results show that the proposed algorithm can successfully encrypt and decrypt the images with the same security keys. The security analysis is encouraging and shows that the encrypted images have good information entropy and very low correlation coefficients and the distribution of the gray values of the encrypted image has random-like behavior

Effects of Salvadora persica Extract on the Hematological and Biochemical Alterations against Immobilization-Induced Rats

A total of 24 rats were divided into 4 groups: control, stress, extract alone, and stress + extract (n=6 each), for total 21 days of treatment. The immobilization stress was induced in rats by putting them in 20 cm × 7 cm plastic tubes for 2 h/day for 21 days. Rats were postorally treated with Salvadora persica at a dose of 900 mg/kg body weight via intragastric intubations. At the end of the test period, hematological and biochemical parameters were determined in blood and serum samples with determination of vital organs weights. The vital organ weights were not significantly affected in stressed rats as compared to control rats. Compared to the control group, the stress treated group showed significances in several hematological parameters, including decreases in WBC, RBC, and PLT counts. Furthermore, in comparison to the control group, the stress group showed significantly increased blood glucose, serum total cholesterol, LDL-cholesterol, and triacylglycerols levels and decreased HDL-cholesterol level. The hematological and biochemical parameters in the stress + extract treated group were approximately similar to control group. The SP extract restored the changes observed following stress treatment

Stability of CTL immunity pathogen dynamics model with capsids and distributed delay

In this paper, a pathogen dynamics model with capsids and saturated incidence has been proposed and analyzed. Cytotoxic T Lymphocyte (CTL) immune response and two distributed time delays have been incorporated into the model. The nonnegativity and boundedness of the solutions of the proposed model have been shown. Two threshold parameters which fully determine the existence and stability of the three steady states of the model have been computed. Using the method of Lyapunov function, the global stability of the steady states of the model has been established. The theoretical results have been confirmed by numerical simulations