Gene polymorphisms of TNF-α and IL-10 related to rheumatic heart disease

Background: Rheumatic fever (RF) is inherited as a single recessive gene. Several genes are Likely to predispose an individual to develop rheumatic fever and rheumatic heart disease (RHD). Polymorphisms of TNF-α gene were associated with susceptibility to develop RF.T cells from all rheumatic fever patients produce significant amounts of TNF-α in response to steptococcal peptides with the highest production attained by the chronic rheumatic heart disease patients,and IL-10 expression was characterized in heart tissue of RHD patients by immuno-histochemistry. Objectives: To test the relation of RHD and gene polymorphisms of pro-inflammatory cytokines TNF-α gene at position -308 and anti–inflammatory IL-10 gene at position -1082. Subjects and Methods: This study included 20 children with chronic rheumatic heart disease (group A) and 10 healthy children as a control group (Group B). Patients group was classified into patients with single and multiple valvular lesions, both of them were classified according to the severity by Echocardiography into: Group I: mild valvular lesion (n=7) Group II: Moderate lesion (n=4) Group III: severe lesion (n=9) Real time PCR was done for both TNF-α at-308 and IL-10 at position – 1082.Results: All cases showed significant higher frequency of TNF-α homozygous genotype G/G compared to control group (

Phishing websites detection by using optimized stacking ensemble model

Phishing attacks are security attacks that do not affect only individuals’ or organizations’ websites but may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there is still a need to enhance detection accuracy. Optimization of an ensemble classification method for phishing website (PW) detection is proposed in this study. A Genetic Algorithm (GA) was used for the proposed method optimization by tuning several ensemble Machine Learning (ML) methods parameters, including Random Forest (RF), AdaBoost (AB), XGBoost (XGB), Bagging (BA), GradientBoost (GB), and LightGBM (LGBM). These were accomplished by ranking the optimized classifiers to pick out the best classifiers as a base for the proposed method. A PW dataset that is made up of 4898 PWs and 6157 legitimate websites (LWs) was used for this study's experiments. As a result, detection accuracy was enhanced and reached 97.16 percent

Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84%

Multi-Method Diagnosis of CT Images for Rapid Detection of Intracranial Hemorrhages Based on Deep and Hybrid Learning

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84

Evaluation of the serum ceruloplasmin level before and after non-surgical periodontal therapy in patients with chronic periodontitis

Aim: This study aimed to evaluate the serum ceruloplasmin (CP) level after non-surgical periodontal therapy in chronic periodontitis patients.Methods: A prospective controlled study was conducted on 80 subjects. The study populations were divided into 2 groups: group 1 included chronic periodontitis patients (study group, n = 40), and group 2 included periodontally healthy subjects (control group, n = 40). Blood sample and periodontal clinical parameters, including periodontal pocket depth, clinical attachment level and bleeding on probing, were performed at baseline for both groups. All of the patients with chronic periodontitis (study group) received meticulous scaling and root planing twice weekly for 2 weeks. Four weeks after treatment, the second blood sample and reevaluation of clinical periodontal parameters were done.Results: Baseline serum CP level was significantly higher in chronic periodontitis patients (study group) compared to healthy subjects (control group) (P < 0.001). Concerning the chronic periodontitis group, four weeks after non-surgical periodontal therapy, the mean value of serum CP concentration was significantly decreased (P < 0.001).Conclusion: Non-surgical periodontal therapy has a reducing effect on the serum CP level in chronic periodontitis patients. Serum CP level represents a potential biomarker indicator of the chronic periodontitis disease

Optical Detection of Fat Concentration in Milk Using MXene-Based Surface Plasmon Resonance Structure

MXene (Ti3C2Tx) has emerged very recently as an interacting material for surface plasmon resonance (SPR) configuration. It was discovered that Ti3C2Tx can facilitate the adsorption of biomolecules due to its higher binding energies, stronger interaction between matter and light, and larger surface area. In this work, a two-dimensional Ti3C2Tx and silicon layer-based SPR refractometric sensor is proposed for the sensitive and fast detection of milk fat concentration due to the high significance of this issue to people all over the world. The proposed SPR structure employs BK7 (BK7 is a designation for the most common Borosilicate Crown glass used for a variety of applications in the visible range) as a coupling prism and silver as a metal layer. The layer thicknesses and the number of Ti3C2Tx sheets are optimized for the highest performance. The highest reached sensitivity is 350 deg./RIU with 50 nm silver and 4 nm silicon with a monolayer of Ti3C2Tx, which is ultra-high sensitivity compared to the latest work that utilizes SPR configuration. The proposed SPR-based sensor’s ultra-high sensitivity makes it more attractive for usage in a variety of biosensing applications

Integrative Effects of CO₂ Concentration, Illumination Intensity and Air Speed on the Growth, Gas Exchange and Light Use Efficiency of Lettuce Plants Grown under Artificial Lighting

This study investigates and quantifies the integrative effects of CO2 concentration (500, 1000 and 1500 µmol mol−1), illumination intensity (100, 200 and 300 μmol m−2 s−1) and air speed (0.25, 0.50 and 0.75 m s−1) on the growth, gas exchange and light use efficiency of lettuce plants (Lactuca sativa L.) grown under artificial lighting. The results show that lettuce growth and gas exchange are closely related to CO2 concentration and illumination intensity, while air speed enhances CO2 transport during photosynthesis. The most influential two-way interactions were observed between CO2 concentration and illumination intensity on the fresh and dry weights of lettuce shoots with effect sizes of 34% and 32%, respectively, and on the photosynthesis, transpiration and light use efficiency, with effect sizes of 52%, 47% and 41%, respectively. The most significant three-way interaction was observed for the photosynthetic rate, with an effect size of 51%. In general, the fresh and dry weights of lettuce plants increased by 36.2% and 20.1%, respectively, with an increase in CO2 concentration from 500 to 1500 µmol mol−1 and by 48.9% and 58.6%, respectively, with an increase in illumination intensity from 100 to 300 μmol m−2 s−1. The photosynthetic rate was found to be positively correlated with CO2 concentration, illumination intensity and air speed. The transpiration rate and stomatal conductance increased by 34.9% and 42.1%, respectively, when the illumination intensity increased from 100 to 300 μmol m−2 s−1. However, as CO2 concentration increased from 500 to 1500 μmol mol−1 and air speed increased from 0.25 to 0.75 m s−1, the transpiration rate decreased by 17.5% and 12.8%, respectively. With the quantified data obtained, we were able to adequately determine how CO2 concentration, illumination intensity and air speed interact with their combined effects on the growth of lettuce plants grown in indoor cultivation systems with artificial lighting

Integrative Effects of CO2 Concentration, Illumination Intensity and Air Speed on the Growth, Gas Exchange and Light Use Efficiency of Lettuce Plants Grown under Artificial Lighting

This study investigates and quantifies the integrative effects of CO2 concentration (500, 1000 and 1500 µmol mol−1), illumination intensity (100, 200 and 300 μmol m−2 s−1) and air speed (0.25, 0.50 and 0.75 m s−1) on the growth, gas exchange and light use efficiency of lettuce plants (Lactuca sativa L.) grown under artificial lighting. The results show that lettuce growth and gas exchange are closely related to CO2 concentration and illumination intensity, while air speed enhances CO2 transport during photosynthesis. The most influential two-way interactions were observed between CO2 concentration and illumination intensity on the fresh and dry weights of lettuce shoots with effect sizes of 34% and 32%, respectively, and on the photosynthesis, transpiration and light use efficiency, with effect sizes of 52%, 47% and 41%, respectively. The most significant three-way interaction was observed for the photosynthetic rate, with an effect size of 51%. In general, the fresh and dry weights of lettuce plants increased by 36.2% and 20.1%, respectively, with an increase in CO2 concentration from 500 to 1500 µmol mol−1 and by 48.9% and 58.6%, respectively, with an increase in illumination intensity from 100 to 300 μmol m−2 s−1. The photosynthetic rate was found to be positively correlated with CO2 concentration, illumination intensity and air speed. The transpiration rate and stomatal conductance increased by 34.9% and 42.1%, respectively, when the illumination intensity increased from 100 to 300 μmol m−2 s−1. However, as CO2 concentration increased from 500 to 1500 μmol mol−1 and air speed increased from 0.25 to 0.75 m s−1, the transpiration rate decreased by 17.5% and 12.8%, respectively. With the quantified data obtained, we were able to adequately determine how CO2 concentration, illumination intensity and air speed interact with their combined effects on the growth of lettuce plants grown in indoor cultivation systems with artificial lighting

One-Dimensional Phononic Crystals: A Simplified Platform for Effective Detection of Heavy Metals in Water with High Sensitivity

Recently, the pollution of fresh water with heavy metals due to technological and industrial breakthroughs has reached record levels. Therefore, monitoring these metals in fresh water has become essentially urgent. Meanwhile, the conventional periodic one-dimensional phononic crystals can provide a novel platform for detecting the pollution of heavy metals in fresh water with high sensitivity. A simplified design of a defective, one-dimensional phononic crystals (1D-PnC) structure is introduced in this paper. The sensor is designed from a lead-epoxy multilayer with a central defect layer filled with an aqueous solution from cadmium bromide (CdBr2). The formation of a resonant peak through the transmittance spectrum is highly expected. This study primarily aims to monitor and detect the concentration of cadmium bromide in pure water based on shifting the position of this resonant peak. Notably, any change in cadmium bromide concentration can affect the acoustic properties of cadmium bromide directly. The transfer matrix method has been used to calculate the transmission spectra of the incident acoustic wave. The numerical findings are mainly based on the optimization of the cadmium bromide layer thickness, lead layer thickness, epoxy layer thickness, and the number of periods to investigate the most optimum sensor performance. The introduced sensor in this study has provided a remarkably high sensitivity (S = 1904.25 Hz) within a concentration range of (0–10,000 ppm). The proposed sensor provides a quality factor (QF), a resolution, and a figure of merit of 1398.51752, 48,875,750 Hz, and 4.12088 × 10−5 (/ppm), respectively. Accordingly, this sensor can be a potentially robust base for a promising platform to detect small concentrations of heavy metal ions in fresh water

Polyvinylidene fluoride (PVDF)-α-zirconium phosphate (α-ZrP) nanoparticles based mixed matrix membranes for removal of heavy metal ions

The removal of heavy metal ions from industrial wastewater is essential as they pose serious threats to human health and the environment. In this study, novel poly(vinylidene fluoride) (PVDF)-alpha-zirconium phosphate (PVDF-α-ZrP) mixed matrix membranes (MMM) were prepared via the phase inversion method. Membranes with different α-ZrP nanoparticles (NPs) loadings (0.25, 0.50, 0.75, or 1.00 wt%) were fabricated. The impacts of α-ZrP NP loading on the membrane's morphology, functionality, surface charge, and hydrophilicity were evaluated. Fourier-transform infrared and the energy-dispersive X-ray spectroscopy were performed to verify the presence of α-ZrP NPs in the fabricated membranes. The PVDF membranes became more hydrophilic after incorporating the α-ZrP NPs. The thermal and mechanical stability and porosity of the PVDF-α-ZrP MMM were higher than those of the pristine PVDF membrane. The increased hydrophilicity, pore size and porosity and reduced surface roughness of the PVDF-α-ZrP membrane led to significant flux increase and reduced fouling propensity. The PVDF-α-ZrP membrane containing 1.00 wt% α-ZrP was capable of removing 42.8% (Cd2+), 93.1% (Cu2+), 44.4% (Ni2+), 91.2% (Pb2+), and 44.2% (Zn2+) from an aqueous solution at neutral pH during filtration