Al-Nahrain Journal of Science

Oxidative stress refers to a state of balance involving the generation of reactive oxygen species (ROS) and the body's capacity to counteract their harmful effects. ROS are naturally occurring molecules that, if not adequately regulated, can damage cells and tissues if not properly managed. During pregnancy, ROS levels fluctuate and play a role in various developmental processes. However, an excessive accumulation of reactive oxygen species can lead to oxidative stress, which is linked to numerous complications during pregnancy.

Prediabetes is a condition between normal glucose levels and increased levels of type 2 diabetes, and it is connected with a high risk of developing diabetes. Methylglyoxal is a highly reactive α-dicarbonyl compound endogenously generated naturally as a byproduct during the glycolytic pathway and is considered the precursor of an advanced glycation end product. sRAGE acting as decoy receptors help reduce inflammation, oxidative stress, and vascular complications. This study aims to measure methylglyoxal as the precursor of an AGE and its receptor in prediabetic as a biomarker for metabolic dysfunction. This study involved 80 participants, including 40 prediabetic patients and 40 healthy controls diagnosed in Al-Imamin Alkadmeen City Hospital, Baghdad, Iraq.Basic data from clinical examination and laboratory were collected for all participants. The serum methylglyoxal was measured by high-performance liquid chromatography, and soluble receptor, while advanced glycation end-products were measured by using an ELISA technique’s kit. Additional parameters were also measured, such as fasting blood glucose, HbA1c, insulin, HOMA-IR, and lipid profile. Subsequently, in prediabetic patients, methylglyoxal and sRAGE levels are significantly elevated compared to healthycontrols. Moreover, FBS, HbA1c, insulin, and HOMA-IR are significantly higher in prediabetic patients (p < 0.001). Based on the findings of this research, methylglyoxal and sRAGE may serve as potential biomarkers for prediabetic patients

Microplastics (MPs), which are resistant to degradation in the environment, have been identified in air, dust, soil, and water samples throughout the world. This review aims to represent the assessment of microplastics in road dust and their impacts on human health. The study considered available data from journals, books, and reports published within the last twenty years on MPs in road dust. Tire wear, textiles, waste disposal, road markings, building materials, and packaging material, these arethe leading sources of microplastics in road dust.To identify the MPs in road dust, microscope, FT-IR, Raman spectroscopy, GC-MS, and SEM-EDX were used. The dominant polymers in road dust were PP, PE, PVC, and PET. This review found the MPs deposition rates varied from 22 to 3.43×106 MPs/m²/day, which significantly depends on population density and traffic intensity. However, the MPs abundance in road dust ranged from 0.068 to 20.16 particles/g. Consequently, an average of 1063 and 3223 microplastic particles are ingested per year by adults and children, respectively, which may cause numerous human health problems such as gastrointestinal disorders, immune system disruption, respiratory issues, cancer, etc. Hence, all types of plastics should be collected, and enhanced recycling could be a solution to control microplastic pollution from road dust.

The synthesis of numerous chemical compounds relieson imines as a key building ingredient. Several Schiff bases were generated via the acid-catalyzed condensation of aromatic aldehyde derivatives with aniline derivatives. FT-IR, 1HNMR, spectra allowed to identify the products. The ORCA software package was used to obtain the outcome of the Density Functional Theory (DFT) and to comprehend how para substituting impacts the electronic structure of the synthesized compounds. Finally, docking simulation was performed using the Autodock application. At the binding site, the prepared compounds were docked with the 3FYV enzyme

This paper discusses the identification of a new dispersive Liquid-liquid microextraction method for evaluating carmoisine dye using spectrophotometry. It has been established that Carmoisine dye is an anionic azo food dye used in the food industry in some countries such as Iraq. This method involves the extraction of carmoisinefrom aqueous solutions using chloroform as the extraction solvent in a DLLME procedure. Parameters influencing the DLLME efficiency, including pH, type and volume of extraction as well as dispersive solvents (chloroform and ethanol), under a centrifugation conditions, were thoroughly optimized to complete extraction conditions. Moreover, the method showed a linear calibration curve in the range of 1.0-10.0 mg/L. The low values of both the limit of quantification (0.297 mg/L) and the limit of detection (0.099 mg/L) indicate a more sensitive DLLME method. The applicability and environmental impact of the developed method were assessed using the Blue Applicability Grade Index (BAGI) and the Analytical Greenness metric (AGREE), yielding scores of 70.0 and 0.55,respectively, indicating a practical method with moderate adherence to green analytic chemistry principles. The optimized DLLME procedure was successfully applied to the determination of carmoisine dye in various food samples.

Fragmented DNA samples became serious issue in crime scene and all most result in incomplete DNA profile. Keratinous tissues such as hair and nails contain highly fragmented DNA and could be seen as biological evidence at crime scene. This study aimed to evaluation of generating DNA profile from keratinous tissues. Ten samples (five from hair and five from nails) collected from volunteers. Extractionof DNA was carried out by using PrepFilerTMforensic DNA extraction kit. The quantities of DNA were measured using real-time PCR,while the amplification of eight loci (D13S31,D7S820,D2S1338,D21S11,D16S539,D18S51,CSF1PO,FGA) and amelogenin was performed using the AmpFLSTR™ MiniFiler™ PCR Amplification Kit. The genetic Analyzer 3130 XLsystem and GeneMapper ID software v.3.2 were used to perform capillary electrophoresis and data analysis. The results indicated that the mean quantities of DNA isolated from hair (3.34 ± 1.7) and the DNA isolated from nails (4.36 ± 3.02).Analysis of MiniFiler results revealed that most of hair samples generatedpartial DNA profile while all samples of nail produced full profile and the percentage of allele’sappearance for hair samples range between (30%-90%). Whereas, nail samples alleles appearance was 100%. Conclusion, although both nail and hair tissues are subject to DNA fragmentation due to their keratinized structure and cellular composition, nail samples produces more consistent and complete DNA profiles compared to hair samples when analyzed using MiniFilerkit. This is likely attributed to the relatively lower degradation and greater stability of DNA in nails. In contrast, hair samples especially those lacking follicles exhibited a high degree of allele drop-out, particularly in loci with large amplicon size

In this work, total of 50 samples of different soil types (sandy, loomy, clayey, and river deposits) were collected randomly. The physico-chemical properties such as pH, soil temperature and electrical conductivity of the soil samples were studied and analyzed Soil characteristics all were within the limits of growth and reproduction of Actinomycetes. Of the 50 soil samples, only 35 were sourced to obtain 18 colonies, were suspected to be Actinomycetes. These isolates were purified by sub culturing more than one time on International Streptomyces project-2 agar (ISP-2),the pigmentation of the selected colonies exhibited a spectrum of colors ,including shades of white, cream, gray, green ,black to yellow. These isolates were identified by culture properties which showed that they were Gram positive varieties of aerial mycelia and substrate mycelia and spore chain formation. By the third day of incubation, the isolate had attained the stationary phase of its growth cycle.at maximum OD 0.619nm.ExtractionofExtracellular secondary metabolites from Each of the Actinomycetes by ethylacetate .According to the quantity of the product and the speed of its production, 3 isolates(NI-RS 15, AM -AG 18 and MA-CL2) were selected to complete the study. All extracts with high solubility with water, had melting point range 287ºC-300°C and were powder in natural. Chemical Characterization of secondary metabolites were as follow λ max ranged from 274nm to 320 nmFTR show several bands of absorption as functional chemical group and HPLC clearly sharp peaks that appeared at 2.1-9.9 minutes which indicated a very well separated. The cytotoxicity of extracts were assessed by MTT on normal cell line was which inhibited cells range from 9.9-13.9% in 200 μg / mL

Fungi have attracted increasing attention in biodegradation due to their ability to degrade a wide range of substances through a process known as my codegradation. This study evaluated that Aspergillus nigerhas a significant effect in biodegrading contaminated water samples. TPH concentrations in water samples before and after treatment with A. nigarranged from 1,850 to 1,023 μg/l. TPH removal efficiency reached 44.7% with A. niger and 55.05% with A. fumigatus(from1.059 to 0.476 μg/l). The remaining PAHs were analyzed by GC-MS. The 10 not substituted PAHs on the list of important contaminants (EPA 10 PAHs) were the most frequently analyzed PAHs, with concentrations varying from oil to oil. A. fumigatus showed a higher degree of degradation of PAHs compared to A. niger.Treatment unit containing Aspergillus indicates to be effective in removing heavy metals and improving most physical and chemical properties and nutrients in industrial waste waters analyzed

This research paper presents a comparative study on the effect of the geometric design of a thermionic electron gun on the quality of the electron beam. Although several studies have been conducted on the thermionic electron gun design. However, the effect of the geometry of the Wehnelt electrode on the performance of the electron gun needs to be investigated through accurate and comparative analysis. Simulations were performed to test three different shapes of Wehnelt electrodes and their effect on electron gun performance behavior of the electron gun in which the characteristics of electron beams for different designs were analyzed with the aid of computer aided simulations. The results showed that the conical shape is the most efficient, producing low emittance (ԑ) and a small crossover diameter (R) of the electron beam. Based on these results, the performance of the electron gun can be improved by selecting an appropriate shape for the Wehnelt electrode, which produces high-quality electron beams.

In this research, an aerosol-assisted chemical vapor deposition(AACVD) was successfully employed to synthesize Cd₁₋ₓCuₓS (0 ≤x ≤1) semiconductor thin films. The structural, morphological, and optical properties of the deposited films were systematically investigated as a function of copper concentration using energy-dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), and UV-Vis spectroscopy. The results demonstrated that the optical behavior of the films is strongly influenced by Cu incorporation. Specifically, an increase in CdS fraction resulted in a decrease in the Urbach energy, whereas the activation energy exhibited a progressive increase with higher CuS content

Understanding the rotation curves (RCs) of our Milky Way galaxy represents a main tool to understand its dynamical evolution and the connection between different regions in the galaxy. The present work is intended to test the applicability of three rotation curve models against two datasets, the first being the well-known Sofue data, and the second being the Baghdad University Radio Telescope (BURT) data, both for the HI 21-cm emission line. The tested models are the Time of Events (ToE) model, the Brandt model, and the Navarro-Frenk-White (NFW) model. The fitting results showed that the ToE model provided the best prediction for the experimental data compared to the other two models for both BURT and Sofue data within all regions in the galaxy. The other two models applied slightly well only in the central region, while their results diverge near the edge region. The reduced R2values that we obtained for BURT and Sofue data are equal to 0.79 and 0.94, respectively. In addition, the ToE model worked as a calibration tool for the BURT data point, which can predict the behavior of the rotation curve and fill in the gaps of the missing points in the observed BURT data.

Early and accurate detection of crop diseases is vital for safeguarding food security and improving agricultural productivity, particularly in regions where access to the prognosis and diagnostic tools as well as expert knowledge is remote. This paper presents the first targeted application of class-conditional Generative Adversarial Networks (cGANs) to passionfruit disease detection using the publicly available Makerere Passionfruit dataset, which contains 3,001 labelled images across three classes: Healthy, Brownspot, and Woodiness. We propose a domain-specific augmentation pipeline that integrates progressive growing, spectral normalization, hinge loss, and gradient penalty to generate high-quality, class-specific synthetic images for under-represented disease categories. Experimental results demonstrate that cGAN augmentation significantly enhances minority class recognition. The F1-score for Brown spot improved from 0.72 to 0.78 and for Woodiness from 0.60 to 0.71, raising the macro-average F1-score from 0.76 to 0.80. To validate robustness, the framework is evaluated across multiple architectures, including LeNet, ResNet-18, and MobileNetV2. In each case, cGAN-based augmentation consistently improved macro-F1 scores by 3–5% without reducing overall accuracy, confirming the model-agnostic nature of the approach. These findings establish a novel and practical foundation for AI-powered plant/crop disease detection in realistic field conditions. The framework is computationally efficient and suitable for deployment on mobile or edge devices, making it particularly relevant for low-resource and inaccessible agricultural environments.

In mid-2022, Iraq experienced a massive outbreak of Crimean-Congo hemorrhagic fever (CCHF), resulting in high mortality rates. The outbreak began in Thi-Qarprovince and subsequently spread to other provinces. This research analyzes data collected from Thi-Qar province to investigate the key factors influencing patient life risk. This is accomplished by collecting a real dataset (HemoIraq24) and conducting a statistical analysis, followed by developing explainable patient outcome prediction models using several machine learning algorithms. The most important factors contributing to the decision of the predicted outcome are obtained using feature importance and SHAP techniques. In addition, a web-based application has been developed based on the best ML prediction model to assist healthcare providers in clinical decision-making. The ML algorithms tested include Decision Trees, Random Forests, Logistic Regression, Gradient Boosting, and K-nearest neighbor. The highest baseline prediction model accuracy achieved is 89%. Feature importance analysis and SHAP are utilized for further feature engineering, causing an enhancement of 3% in prediction accuracy, with up to8% enhancement in F1 score. It is found that the main factor contributing to the patient outcome is the days in the hospital, which means that the healthcare given in the hospitals is strong enough and can handle the endemic. The dataset can help with future research.

This research paper proposes a novel image fusion algorithm for assisting effective diagnosis of brain cancers. The method extracts detailed features from source images using a multi-level decomposition strategy that leverages local extrema information. The detailed layers are fused using an Enhanced Weighted Pulse Adaptive Dual Channel Neural Network (EWPADCNN), with fusion weights calculated using the Fractal Dimension with Differential Box Counting (FDDBC) method. In order to preserve low-frequency information, the base layers uses a weighted average method based on pixel significance. Comprehensive tests on 100 slices from four different datasets of brain disorders demonstrates that the proposed approach outperforms current fusion methods in both qualitative and quantitative assessments. These results support the suggested method as an efficient and trustworthy technique for improving brain imaging diagnostic quality. Quantitative evaluation demonstrates average improvements of 41%, 5%, 16%, 23%, 34%, and 4% in terms of AG, H, SF, MI, QABF, and SD metrics, respectively, compared to existing fusion methods

The main aim of this paper is to introduce a new class of one-parameter statistical models namely the length-biased weighted Kpendidum distribution. This distribution is a convex combination of three component of Gamma distribution. Some statistical and reliability properties of this distribution are discussed. The maximum likelihood estimation is used to estimate the one-parameter of the presented distribution. Moreover, five real life applications are provided to investigate the effectiveness of the new model in comparison with the Kpendidum distribution and some other one-parameter length-biased weighted models. Additionally, it is shown that in some real-life applications the proposed model gives a better results than some of the two-parameter models. Finally, different statistical measures are used to guarantee the accuracy of this model.

his paper seeks to develop a procedure to identify all pairs within a finite group that are Gelfand, symmetric, and weakly symmetric. With the focus on the projective special linear groups. This leads to a unique class of "strictly weakly symmetric pairs." For the specific case of the projective special linear group PSL(2,p) where p is prime in the range 1