Al-Nahrain Journal of Science

Some of organic ammonium salts, namely: 2-((4-carboxyphenyl) carbamoyl) benzoate tetraethylammonium, B1, 4-oxo-4-(pyridin-3-ylamino)-2-butenoate tetraethylammonium, B2, 2-((4-carbamoylphenyl) carbamoyl) benzoate tetraethylammonium, B3, 4-((4-carbamoylphenyl)amino)-4-oxobutanoate tetraethylammonium, B4, are successfully synthesized as ionic liquids. By spectral techniques of FTIR, 1H-NMR and 13C-NMR the structures of organic salts are confirmed. The thermal analysis by differential scanning calorimetry (DSC) has shown the thermal stability of the organic ammonium salts (B1-B4). A high conductivity characteristic as compared with that of NaCl has been detected in the aqueous solution.

Hypothyroidism and hyperthyroidism are prevalent endocrine diseases. These have significant implications for lipid synthesis, mobilization, and metabolism. Retinol-binding protein 4 is a new adipokine implicated in some physiological and pathological processes, such as metabolic and endocrine disorders. Its elevation aids in the diagnosis of thyroid problems. The present study includes 60 women with thyroid diseases; 30 of them have hypothyroidism and 30 have hyperthyroidism. This study includes 20 healthy women as a control group. For each participant anthropometric, biochemical Thyroid-stimulating hormone, triiodothyronine, Thyroxine, lipid profile, and retinol binding protein-4 are measured. The results show that hypothyroid women have significantly higher levels of total cholesterol, triglycerides, and low-density lipoprotein cholesterol as compared to the hyperthyroid or control group. The serum of total cholesterol, triglycerides, and low-density lipoprotein cholesterol are significantly lower in hyperthyroid women compared to the hypothyroid or control group. In addition, there are no significant differences in blood high-density lipoprotein cholesterol concentrations among the three groups. Furthermore, serum retinol-binding protein-4 levels were higher in the hyperthyroid group compared to the hypothyroid or control groups. According to the findings of this study, hypothyroidism causes dyslipidemia, which raises the risk of cardiovascular disease while hyperthyroidism causes abnormalities in lipid profiles. Additionally, hyperthyroidism causes an increase in serum retinol-binding protein-4 levels in the blood.

Type-2 diabetes mellitus (T2DM) is a disease accompanied by induced oxidative stress (OS) conditions. A reduction in antioxidant activity characterizes the OS conditions. Antioxidants protect cell components from the harmful effects of reactive oxygen species (ROS). The oxidation of the lipid components by ROS has the potential to generate malondialdehyde (MDA). The assessment of MDA (which is a byproduct of lipid peroxidation) may serve as a sign of oxidative damage. This can shed light on the impact of ROS on lipids. This study aims to elucidate the correlation between MDA oxidative stress biomarkers and lipids in individuals diagnosed with type-2 diabetes mellitus. The study comprises two case groups of 30 diabetic patients, 30 obese patients, and 30 healthy patients as the control group. The levels of MDA in the plasma specimens are measured. The serum glucose, serum lipid, and glycated haemoglobin (HbA1c) are evaluated for these groups. The results among patients with and without T2DM are analyzed with regard to age and BMI. The concentration of the MDA is [17.08 ± 1.989 (nmol/ml)] for diabetic patients, [16.08 ± 2.049 (nmol/ml)] for obese patients, and [12.12 ± 1.508 (nmol/ml)] for control patients. A statistically significant difference of p <0.0001 is observed in the elevated levels of MDA among T2DM patients. As compared to healthy control participants, there is no significant difference between T2DM and obese patients. It is concluded that the MDA can be utilized for observing the oxidative stress related to T2DM. The data indicate that utilising MDA could help predict the oxidative environment.

The present study is investigated the relationship between Apelin concentration and various biochemical parameters in patients with type 2 diabetic nephropathy. A total of 30 patients diagnosed with Diabetic Nephropathy at Al-Yarmouk and Al-Karama teaching hospitals were included in the study and compared to 40 healthy individuals forming the control group. The study was identified a significant increase in Apelin concentration among patients with type 2 diabetic nephropathy, compared to the control group. Additionally, various biochemical parameters were analyzed, and a strong correlation was found between their concentrations and Apelin levels. Considerable elevation of serum nitrogen compounds like urea and creatinine, as well as fasting blood glucose and glycated hemoglobin (HbA1c) were observed in the type 2 diabetic nephropathy patients. A positive correlation between Apelin and both creatinine and glycated hemoglobin levels was observed. Conversely, a negative correlation was observed between Apelin and the glomerular filtration rate (GFR), indicating potential implications for kidney function in these patients. These findings emphasize the significance of monitoring Apelin concentration along with other biochemical variables as essential indicators for the management and understanding of type 2 diabetic nephropathy. Further research in type 2 diabetic nephropathy could offer valuable insights into potential interventions and treatment strategies for improving patient outcomes.

The plant Gossypium Hirsutumgenotypes were grown in Qwshtapa district, Grdmala village, which is 30 kilometres from the centre of Erbil, Iraq. The cotton genotypes grown there included Coker 310, Lachata (Iraqi genotypes), Cafko, Dunn 1047, Montana, Stone Ville (USA genotypes), Bakhtegon, Khdorda, and Vanamin (Iranian genotypes). The anatomical investigation demonstrates distinct petioles, midribs, laminas, and margin outline shapes among the genotypes. They contain secretory tissues, cluster crystals, and tannins. While some genotypes lack the trichomes, others have; these genotypes are multicellular glandular and unicellular branching or unbranched non-glandula.

Information security is one of the most important parts of information technology and communication in the modern world. Information security has gained a lot of attention recently due to the need to transmit information through global communication channels and through the Internet. In this research, the genetic algorithm is used to develop the chaotic maps that are randomly generated by the chaotic system to find the best map by which the locations of image data are inferred to be hidden in the audio. An average image size of 64×64 pixels is used to be hidden in an audio recording of 7 seconds duration at average resolution specifications. The results show that one of the chaotic maps created at the 739 generations is the best in determining image byte hiding locations in the high-frequency band of the audio. The quality measures show the cover audio that includes the hidden image is not much different from the original input one. This confirms the validity of the proposed method and the good selection of hiding locations in accordance with information security concepts.

Many deepfake techniques in the early years are spread to create successful deepfake videos (i.e., FaceSwap, DeepFake, etc.). These methods enable anyone to manipulate faces in videos, which can negatively impact society. One way to reduce this problem is the deepfake detection. It has become such a hot topic and the most crucial task in recent years. This paper proposesa deep learning model to detect and evaluate deepfake video methods using convolutional neural networks. The model is evaluated on the FaceForensics++ video dataset that contains four different deepfake ways (deepfake, face2face, faceswap, and neuraltexture), and it achieved 0.96 accuracy on the deepfake method, 0.95 accuracy on face2face approach, 0.94 precision on faceswap method and 0.76 accuracy on neuraltexture method.

The main objective of this paper is to introduce interval two-point fuzzy boundary value problems, in which the fuzziness course when the coefficients of the governing ordinary differential equation and/or the boundary conditions include fuzzy numbers of either triangular or trapezoidal types. Such equations will be solved by introducing the concept of α –level sets, α [0,1]to treat the fuzzy ordinary differential equation into two nonfuzzy ordinary differential equations, which correspond to the lower and upper solutions of the interval fuzzy solutions. The well-known variational iteration method has been used to solve two-point fuzzy boundary value problems and linear equations have been examined.

Social media and the World Wide Web have led to a worrying rise in spreading false information, which presents a significant worldwide issue. Identifying and preventing false information is crucial in promoting an informed and knowledgeable society. The identification of false information, specifically in the Arabic dialect, presents inherent difficulties due to its diverse characteristics and linguistic intricacies. This study implements AutoKeras, a deep learning-based machine learning framework. Using advanced optimization techniques, the neural network architecture search, hyperparameter adjustments, and model selection can all be automated in AutoKeras. Therefore, it is suitable for our fake news detection task. The methodology employs proficient deep learning algorithms and natural language processing methods to acquire distinct characteristics that enable accurate differentiation between genuine and fake news. The present study uses various sources, including news websites, social media platforms, and blogs, to construct the dataset. The AutoKeras-based approach is superior to multiple state-of-the-art approaches to detecting fabricated news in Arabic, as evidenced by the experimental results. The suggested method outperforms 93.2% accuracy in identifying fake news, demonstrating its superior efficacy. This demonstrates the great promise of the deep learning-based Auto model for detecting false information.