JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences
JOURNAL of UNIVERSITY of BABYLON for Pure and Applied Sciences, (JUBPAS) is an official journal of the University of Babylon for Pure and Applied Sciences in Iraq established in 1995. JUBPAS is a scientific peer-reviewed open access journal publishing high-quality research and review papers at the forefront of modern research describing significant developments in Pure and Applied Sciences. Online ISSN: 2312-8135 Print ISSN: 1992-0652
A New Subclass of Univalent Functions Using Chebyshev Polynomials
Background: Geometric Function Theory (GFT) is a vital area in complex analysis, offering powerful techniques for solving physical and engineering problems. Particularly in two-dimensional problems, isomorphic mappings play a crucial role in simplifying complex regions, enabling more tractable analysis. Materials and Methods: This study investigates key topics within GFT, focusing on a -generalized subclass of univalent functions . Chebyshev polynomials of the second kind are employed to estimate bounds for the coefficients of functions in this subclass. Theoretical tools from mathematical analysis and quantum calculus are integrated into the study to support this exploration. Results: We derive estimate coefficient bounds for functions in the class and provide estimations for two important functionals: the Fekete–Szegö functional and the second-order Hankel determinant. These findings offer deeper insight into the geometric properties of the considered function class. Conclusion: The work strengthens the connection between geometric function theory and quantum calculus by introducing and analyzing a new subclass of univalent functions. The methods and estimations developed here enhance the understanding of analytic function behavior and open pathways for future work in applied mathematics and engineering models.
Second Hankel Determinant and Coefficient Bounds
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Predicting Student Performance with Secure Data Handling and Deep Learning-Based Classification Models
Predicting student academic performance is a critical task for educational institutions, as it enables early identification of at-risk students and supports informed academic decision-making. This study proposes a binary classification framework for predicting student performance using deep learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models. The proposed approach formulates the prediction task as a classification problem, where students are assigned to predefined performance categories rather than predicting exact numerical scores. Prior to model training, data preprocessing and feature standardization are applied to enhance learning efficiency. To ensure data confidentiality and integrity, the Advanced Encryption Standard (AES) is employed to encrypt the student dataset before storage. The performance of the proposed models is evaluated using standard classification metrics, including accuracy, precision, sensitivity, and F1-score. Experimental results demonstrate that the RNN model achieves superior performance with an accuracy of 96%, outperforming the LSTM model, which attains an accuracy of 75%. These findings highlight the effectiveness of deep learning models for student performance classification and emphasize the importance of secure data handling in educational prediction systems.
KNN-Based Behavioral Intrusion Detection Using Mouse Dynamics and Session Timing
This research proposes a behavioral intrusion detection system that uses mouse movement, session duration, and K-Nearest Neighbors (KNN) for classifying sessions. This system attempts to classify users into legit and non-legit users based on session time, number of mouse clicks, and mouse movement speed. The system was built using one of the class sessions, and the other class session was set aside for testing. The result was impressive, as every single metric of classification evaluation, such as accuracy, precision, recall, and F1-score, scored 1. The more advanced metrics also display an MCC of 1.00, a FRR of 0.00, and a FAR of 0.00, indicating that there were no misclassifications whatsoever. This indicates that the behavioral characteristics the system relied on captured user patterns of identification effectively and distinctly. The system has proven to be effective in the application of behavioral biometrics in intrusion detection systems.
A Hybrid Transfer Learning Framework for Efficient Facial Keypoint Detection
Background: Face keypoint detection is one of the basic supporting tasks for applications ranging from emotion and facial expression human-computer interaction. Typical deep learning models struggle to make a trade-off among accuracy, generalization, and computational efficiency. Materials and Methods: A hybrid deep learning model is proposed in this paper by stacking three pre-trained CNNs: VGG16, ResNet50, and MobileNetV2 to improve both accuracy and speed of facial landmark localization. This contains several steps of data preprocessing-normalization, converting into grayscale, resizing images to 150 × 150 pixels; using MTCNN for face detection and ROI extraction. In the extraction phase, fully connected layers predict the 2D coordinates of 15 key facial landmarks based on concatenated output features from three parallel CNN branches. The network is trained with the optimizer [learning rate = 0.001], Mean Squared Error(MSE) as the loss function over 50 epochs. Results: The hybrid model showed good convergence and stability with final error values of MAE = 3.3, MAPE = 7.6, and RMSE = 4.39. From both quantitative and qualitative analysis results, the predicted keypoints nearly match ground-truth values for different facial images clearly proving the model to be robust as well as having high localization accuracy. Conclusion: The proposed hybrid architecture effectively combines several CNN models to optimize and balance representational depth, feature diversity, and computational efficiency. It attains high performance and generalizability in facial keypoint detection, thus making it a potential solution for state-of-the-art applications in emotion recognition, facial analytics, and human-computer interaction
Aloe vera (L.) Burm: A Review of its Pharmacological and Therapeutic Potential
Aloe vera (Aloe (L.) Burm.) is considered a miracle plant. Its thick leaves have been widely recognized for their medicinal and therapeutic uses, both ancient and modern. This review aims to critically evaluate the vitamins, minerals, enzymes, and bioactive compounds in the gel, which exhibits antioxidant, antibacterial, and anti-inflammatory effects. It treats most digestive problems and combats various infections. It also helps stabilize blood sugar levels and improves overall health and appearance. Aloe vera is a staple plant with numerous health benefits. This study, conducted using the databases PubMed, Scopus, Web of Science, and Google Scholar, focused on peer-reviewed publications from 2000 to 2025, examining both traditional and contemporary literature on the botanical description of aloe vera, its phytochemicals, pharmacological activity, and medicinal uses. By discussing the mechanisms of action of its bioactive components and their medical applications, this review connects empirical knowledge with scientific research. Ultimately, understanding the broad pharmacological spectrum of aloe vera and its bioactive components contributes to the development of evidence-based herbal medicines. Realizing the full medicinal potential of this plant in modern medicine requires standardization, clinical validation, and the development of innovative pharmaceutical formulations. Conclusion: Its various pharmacological activities make aloe vera a popular therapeutic plant with many health advantages. Its well-known anti-inflammatory, antimicrobial, immunomodulatory, antidiabetic, anticancer, and wound-healing qualities make it beneficial in traditional and contemporary medicine. Due to advances in analytical chemistry, we now know more about Aloe vera's chemical composition, allowing for its safe and effective use in food, cosmetics, and alternative medicine. Despite extensive Aloe vera research, certain areas need further study. Aloe-derived products should be standardized and quality-controlled in future research to achieve consistent therapeutic results. Metabolomics, transcriptomics, and proteomics will be required to understand the bioactive chemicals' modes of action. To prove its pharmacological claims and establish long-term safety, bigger and more diversified clinical studies are required. Researchers should create new formulations and medication delivery technologies to improve Aloe-based treatments' bioavailability and stability. Therefore, combining traditional wisdom with contemporary science may reveal novel bioactive compounds and improve Aloe vera's medicinal potential. Future herbal medicines will be safer, more effective, and scientifically verified by integrating empirical traditional medicine with evidence-based contemporary pharmacology.
Synthesis of Manganese Dioxide (MnO₂) Nanostructures Using a Modified Chemical Method as a Fluid Loss Control Additive for Water-Based Drilling Fluids (WBDF)
Background: Water-based drilling fluids (WBDFs) remain the most widely used drilling fluids because they are less expensive and more environmentally acceptable than oil-based and synthetic-based fluids. Their performance depends heavily on rheological and filtration characteristics, which are highly sensitive to the chemical additives incorporated into the mud. This study explores the influence of manganese dioxide (MnO₂) nanoparticles on the rheological behavior and filtration performance of WBDF. Materials and Methods: MnO₂ nanostructures were synthesized using a modified chemical route and characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM) to confirm their crystalline phase, bonding structure, and morphology. WBDF samples were then prepared with and without MnO₂ nanoparticles. Key parameters, including plastic viscosity, yield point, density, filtrate volume, and mud cake thickness, were measured to assess the effect of nanoparticle incorporation. Results: XRD analysis confirmed the formation of crystalline MnO₂, while FTIR spectra revealed strong Mn–O vibrations characteristic of MnO₂ structures. SEM imaging showed well-defined nanostructures suitable for fluid modification. Compared with the base WBDF, MnO₂ enhanced fluids demonstrated improved rheological stability, higher yield point and viscosity consistency, and a notable reduction in fluid loss. Additionally, mud cakes formed in the presence of MnO₂ exhibited reduced thickness and signs of structural modification, indicating improved sealing efficiency. Conclusion: The addition of MnO₂ nanoparticles significantly enhances the rheological and filtration properties of WBDF. These improvements suggest that MnO₂-modified WBDF can effectively minimize filtrate invasion into formations and provide more stable drilling performance.
A New Neutrosophic Sets in Neutrosophic Topology
The neutrosophic sets approach was proposed by Smarandache. Uncertain data is handled using neutrosophic sets. Within thiss articles, we introduce a novel forms of somewhat open set within neutrosophic topological space also we investigate several their basic characteristics and examples within neutrosophic topological space. Further neutrosophic somewhat interior, neutrosophic somewhat closure, neutrosophic somewhat derived set, neutrosophic somewhat frontier operators are discussed. Also, the notion of neutrosophic somewhat functions and somewhat irresolute functions were introduced in neutrosophic topological spaces.
NeutrosophicS_w-closure and Neutrosophic S_w-frontier.
Numerical Solution of the 2D Poisson Equation Using Finite Difference Method and Iterative Solvers
Background: The two-dimensional Poisson equation is a fundamental equation to model many physical phenomena including electrostatics, heat conduction and fluid flow. The finite difference method (FDM) is a reliable and popular numerical technique, particularly on structured domains as in the unit square, although analytical solutions are confined to idealized geometries. Materials and Methods: Implemented standard five-point FDM discretization of 2D Poisson equation with Dirichlet boundary conditions, and systematically compared four iterative solvers (Jacobi, Gauss–Seidel, Successive Over-Relaxation (SOR), Preconditioned Conjugate Gradient (PCG) with Jacobi and Incomplete Cholesky (IC(0)) preconditioners) at three grid resolutions (n = 64, 128, 256). We assess performance in terms of iteration count, runtime, convergence rate, scalability, and statistical significance (ANOVA + Tukey HSD, p < 10⁻¹⁵). Results: PCG with IC(0) preconditioning required as few as 99% fewer iterations than Jacobi and hence yielded O(n) scaling very close to optimal. When tuned well (ω ≈ 1.92), SOR achieved equally good results but SOR was more sensitive to ω. All solvers achieved discretization-limited accuracy (∼10⁻⁵), and parallel (OpenMP) tests demonstrated strong scaling for Jacobi and the PCG method, but limited parallel efficiency for the Gauss–Seidel solver due to data dependencies. Conclusion: In the case of 2D Poisson problems on regular grids that are of general relevance, IC-preconditioned PCG achieves by far the best balance of speed, robustness and ease of implementation. While SOR excels with brass knobs for ADI when considered in a two-dimensional, rectangular lens, SOR remains viable for educational or reduced discrete unit use if selectively tuned. Scope of Statistical Validation & Open Source ReproducibilityNumerical benchmarks are strengthened by statistical validation and open source replicability.
Relationship of Complete Blood Count Indices with Severity of COVID-19
Background: The coronavirus disease (COVID-19) is a worldwide pandemic. One of the important things that characterizes the disease is its unpredictable clinical course in which it can rapidly deteriorate from a moderate to severe or even lethal sequence. It can affect all body systems in addition to some psychological problems like suspicion, anxiety, and fear. The role of hematological parameters and their relation with the disease severity, despite extensive studies, is not clear till now. The study aimed to illustrate the relation between alterations of some hematological parameters and the severity of COVID-19 infection. Method: This cross-sectional study was performed in Merjan Medical City, Hilla, Iraq. The period of the study continued from September 2021 to April 2023, it included 243 COVID-19 patients (122 males and 121 females). Several blood parameters were measured that involved hemoglobin level, red blood cell count, neutrophil count, lymphocyte count, platelet count, and neutrophil/lymphocyte ratio. Results: The study included 243 patients with COVID-19, their mean age was 52.25±17.14. years. Regarding the severity, the most predominant cases were those with the severe level (144, 59.2%). Regarding the hemoglobin (Hb) level between groups, the study revealed a significant difference between critical and severe groups (p=0.02). Regarding white blood cell (WBC) count, there were significant differences between the critical group and all other groups (p value=0.03, 0.007, 0.02 respectively). In respect of platelet count, there were significant differences in platelet count between the critical patient group and the mild patient group (p value=0.04) and moderate patient groups (p values =0.03). Regarding comparison of neutrophil/lymphocyte ratio (NLR) between critical patients and patients with mild, moderate, and severe groups, the study illustrated significant differences between all groups (p=0.00, 0.00 and 0.005 respectively). Conclusions: This study revealed that Covid-19 had significant effects on platelet and white blood cell counts with no or slight effects on red blood cells and hemoglobin, and these effects were significantly associated with the severity of the disease.
Comparative Evaluation of Penetration Depth of Nano-Silver Reinforced Pit and Fissure Sealant: Scanning Electron Microscope Study
Background: Recently developed nano-silver reinforced pit and fissure sealants have been introduced to increase their antimicrobial properties. However, the addition of silver nanoparticles may influence the viscosity and flow of the material. Therefore, evaluating penetration performance of nano-silver reinforced pit and fissure sealant is essential to determine clinical effectiveness. Objective: This study aims at comparing and evaluating the penetration depth of Nano-silver reinforced pit and fissure sealant to other pit and fissure sealants. Materials and methods: A total of 60 extracted premolars for orthodontic purposes were divided into four groups (n = 15 per group). Group I Nano-silver reinforce sealant, Group II Unfilled sealant, Group III Filled sealant Group IV Nano-filled flowable composite sealant. Standard occlusal fissure preparations were conducted, and each fissure sealant was applied according to the manufacturer’s instructions. Teeth were then sectioned buccolingually. The sections were prepared for scanning electron microscope (SEM) evaluation and the penetration was measured using ImageJ software. Data were analyzed using ANOVA followed by post-hoc testing to compare differences among groups. Results: Among all four groups, unfilled resin-based sealant showed the highest penetration depth. followed by the filled resin-based sealant. Flowable composite exhibited moderate penetration, while the nano-silver reinforced sealant demonstrated the lowest penetration depth. Conclusion: The nano-silver reinforced sealant recorded the least penetration depth.
A Generalized Nonlinear Grey Differential Framework with Power-Law Transformation for Engineering Systems under Limited Data
Background: This research focuses on developing a generalized nonlinear grey differential framework by extending the differential structure of the conventional GM(1,1) model and incorporating a versatile nonlinear transformation from the power-law family. Grey system theory addresses the problem of insufficient data. The suggested framework tries to understand how nonlinear engineering systems behave over time. Materials and Methods: The suggested model differs from typical nonlinear grey models in that it does not impose a specific type of nonlinearity. Instead, it lets the best nonlinear transformation be chosen based on error indicators, making it more adaptable to the data being analyzed. We used the Airfoil Self-Noise dataset from the UCI Machine Learning Repository to test how well the suggested framework worked in a real-world engineering setting with nonlinear dynamic interactions. Results: The results showed that the best base selection for the nonlinear transformation yielded better performance on some error indicators than the traditional GM(1,1) model. This confirms that the main strengths of the proposed framework are its structural generality and adaptive flexibility, rather than its ability to impose a fixed nonlinearity. Conclusion: The theoretical study of the suggested model's differential equation looks at whether solutions exist and are unique, as well as if equilibrium points are stable under general conditions on the nonlinear transformation function. This analysis provides the proposed framework with a solid mathematical foundation and makes it more reliable for modeling nonlinear systems. This study is a unique methodological contribution to the theory of grey systems, combining mathematical analysis with practical flexibility. It also lays the groundwork for creating more advanced nonlinear grey models that can solve differential equations in engineering systems with minimal data.
Nonlinear ordinary differential equations
Optical and Structural Modification of Polyvinyl Alcohol Induced by MoS₂ and WS₂ Nanomaterial Incorporation
As a semi-crystalline polymer, polyvinyl alcohol (PVA) has been extensively used in optical/opto-electronic applications owing to its excellent transparency, chemical stability, and processability.1–3 However, the large optical band gap of PVA limits eﬃcient light absorption in the visible region. The optical, structural, and morphological properties of PVA thin films have been systematically tuned by embedding semiconductor MoS2/WS2 and its hybrid forms via solution casting to obtain uniform ≈ 150 nm-thick films. X-ray diffraction studies indicated that the hexagonal crystalline nature of MoS2 and WS2 was retained in the PVA matrix and that the hybrid nanocomposite exhibited improved ordering. Field-emission scanning microscopy revealed that well-dispersed GNPs and MWCNTs are induced by strong interactions with the nanomaterials, as evidenced by rough surfaces and larger grain sizes on the PVA film with increasing amounts of nanomaterial, especially dual-filler. Optical studies using UV-Vis spectroscopy showed a substantial increase in absorption and the absorption coefficient, and a decrease in transmittance of the nanocomposite films. The optical band gap as calculated from Tauc plots corresponding to direct transitions went down monotonically from 4.1 eV for neat PVA to 3.8 eV for PVA/MoS2, 3.68 eV for PVA/WS2, and finally 3.5 eV for the PVA/MoS2/WS2 hybrid, reflecting the emergence of localized electronic states and synergy between the two nano-materials.
A Hybrid Framework for Arabic Extractive Document Summarization Using Pre-Trained Language Models and Topic Modeling
One of the hardest things to perform in Natural Language Processing (NLP) is automatically summarizing text. This is especially true for Arabic, which has complicated morphology, a lot of semantic information, and syntactic ambiguity. The goal of this study is to suggest a hybrid method for creating Arabic extractive summaries that combines the strengths of pre-conditioned language models (PLMs) with topic modeling techniques to create summaries that are very accurate, cover a lot of ground, and make sense semantically. The suggested model uses an Arabic pre-trained language method, like AraBERT, to get deep contextual sentence representations and figure out how they fit with the document. You can use BERTopic or Latent Dirichlet Allocation (LDA) to find out what the text is really about. This will make sure that the summary has all the important points. The system chooses the most representative sentences by combining the semantic and topical parts without making the text less clear. We check how well the suggested strategy works by using both standard automatic metrics like ROUGE-N and ROUGE-L and human evaluations of the quality and coherence of the content. The results show that the hybrid system is much better at summarizing Arabic text than just using standard math or deep learning methods. This makes it easier to find information and helps Arabic NLP applications move forward. The proposed hybrid approach achieves superior ROUGE-1, ROUGE-2, and ROUGE-L scores in an Arabic language news dataset compared to the baseline extractive analysis methods. This indicates that it makes the content more coherent and adds more of it.
Introducing P ̂-Open Sets and Related Topological Operators
This paper introduces a new class of open sets, referred to as -open sets, within the framework of general topology. It is shown that the family of all -open sets generates a topology that lies precisely between the classes of -open sets and ordinary open sets. Based on this concept, several associated topological operators are defined and investigated, namely the -interior, the -closure, and the -derived set. The study highlights the fundamental properties of these operators, the interrelationships among them, and their significance in extending the theory of generalized open sets and the corresponding topological structures.
A Theoretical Study On Adjoint Drazin Inverse Hyponormal Operators Based On Fuzzy Soft Set Theory
Background: The aim of this article is to introduce novel traits of a new version of the fuzzy soft hyponormal operator which namely the fuzzy soft adjoint Drazin inverse hyponormal operator symbolized by (FS-(D, )- hyponormal operator), in a fuzzy soft Hilbert space Materials and Methods: this study in the realm of Operator Theory related to Fuzzy Soft Theory (FST). Some analytical properties have been discovered via theorems related in this concept, moreover submitted the conditions required to prove the direct sum and tensor produced. Results: In this paper, a new class of fuzzy soft hyponormal operators, which is closely related to the Drazin inverse is introduced, namely the fuzzy soft adjoint Drazin inverse hyponormal operator. The necessary conditions for a new operator are investigated. The direct sum and tensor product are also examined Conclusion: In this work, we obtain significant results based on the newly introduced definition fuzzy soft (D, )-hyponormal operator, such as a theorem equivalent to the definition under certain conditions as well as the sum and product of two operators.
Predicting Stroke Risk Using Deep Learning Analyzing Key Health Indicators
Background: Stroke represents a major public health concern due to its high rates of mortality and long-term disability. When a stroke occurs, timely medical intervention can greatly enhance the outcome of the individual affected by the stroke. The prediction of a person's risk of a stroke can be improved with the help of recent developments in artificial intelligence (AI), especially through the use of deep learning techniques to analyze/interpret the large and varied amount of medical data available for use in predictive healthcare. Materials and Methods: The proposed research presents a hybrid multimodal deep learning framework that allows for binary stroke risk prediction with model architectures composed of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Dense Neural Networks (DNN). The model uses a wide variety of heterogeneous data sources to make a binary prediction with respect to strokes; these sources include the multitude of clinical, behavioral, and physiological measurements available in datasets such as Kaggle Stroke Prediction dataset. Data preprocessing involved missing-value imputation, Z-score normalization, and class imbalance correction using the Synthetic Minority Oversampling Technique (SMOTE). The model consists of three separate but parallel specialized subnetworks to process different types of input; one processes tabular data, one sequential and one spatial, and their respective outputs were integrated (fused) together through an attention-based feature integration layer. For training, the Adam optimizer was used with dropout and batch normalization, and 10-fold cross-validation was employed for evaluation with standard metrics such as accuracy, precision, recall, F1-score, and AUC-ROC done after each fold evaluation. Results: The framework obtained 99.95% for training accuracy and 99.50% for validation accuracy. The evaluation reported precision, recall, and F1-score values ranging from 0.98 to 0.99, which means that overall classification accuracy was roughly equal to 99%. The ROC analysis resulted in AUC values near 1.0, which illustrate high levels of discrimination ability. Conclusion: Overall, these results illustrate that hybrid multimodal deep learning models can use multiple forms of heterogeneous medical data for accurate prediction of stroke risk. In the future, we will validate this framework by testing it with larger and more diverse clinical data sets designed to evaluate how robustly and generally applicable this framework would perform in real-world clinical settings.
Molecular Description of Gene Associated with Biofilms in Oral Klebsiella Isolated from Obese People.
Background: Obesity is a chronic health condition resulting from excessive accumulation of body fat. It is related to an increased risk of chronic diseases such as diabetes, heart disease, and high blood pressure, and is often caused by an imbalance between calories consumed and expended. Materials and Methods: This study aims to find relation between the presence of Klebsiella bacteria in the oral microbiota and obesity and the possibility of its association with other chronic diseases; it was conducted between November and December 2025, targeting obese individuals. Oral swab samples were collected to analyze the relationship between obesity and oral bacterial composition, with a focus on the presence of Klebsiella bacteria and genotypes associated with biofilm formation. Results: The study included 65 participants, divided according to their body mass index (BMI): 2 participants had normal weight (3%), 17 were overweight (26%), 26 were obese (40%), and 20 were morbidly obese (31%). Diagnostic results showed that 10 of the 65 samples (13.38%) were positive for Klebsiella spp., indicating a relatively moderate prevalence of these bacteria in the mouths of obese individuals. To understand the mechanisms of pathogenesis, the presence of three key genes responsible for biofilm formation in these bacteria was analyzed: Only four isolates were positive for the rmpA gene, while only three isolates had the mrkD gene, and only two isolates had the mrkA gene. Conclusion: The results of this study suggested a potential association between obesity and the presence of K. pneumoniae in the oral cavity, as well as differences in the presence of genes associated with biofilm formation, which may reflect variation in the pathogenicity of these bacteria.
Evaluation the Role of MIRNA-21 AND MIRNA-205 in Patients with Breast Cancer
Background: Breast cancer is one of the most serious health problems and challenges facing women, as it is one of the most widespread tumors among women worldwide, and understanding molecular aspect is essential for improving diagnosis and treatment. Objective: The aim of this research to investigate the impact of microRNAs (miR-205, miR-21) in breast cancer patients . and Comparing gene expression results between the breast cancer patients group and the control group and explains the influence of increased or decreased gene expression of these parameters on tumor progression. Materials and Methods: A total of 80 individuals, 40 from the healthy group and 40 from the control group, all females aged 30 to 50 and older, were sampled. Two milliliters of blood were collected in an EDTA tube for RNA extraction and genomic study. Results: Achieved results indicated that the gene expression of miR-205 was decreased significantly (P<0.01) in patients group which was (0.508±0.17 fold) in comparison to control group which was (1±0 fold) with the area under the curve (AUC) was found to be 0.826. while Achieved results indicated that the gene expression of miR-21 was increased significantly (P<0.05) in patients group which was( 1.72±0.43 fold) in comparison to control group which was (1±0fold )with thr area under the curve (AUC) was found (0.478). Conclusion: The gene expression of miRNA-205 is decreased significantly in patients with breast cancer compared to the control group, ROC for miRNA-205 showed a good prognostic indicator for breast cancer patients with good sensitivity. while the gene expression of miR-21 is significantly higher in patients group compared to the healthy group. ROC for miRNA-21 demonstrated a good prognostic indicator for breast cancer patients with weak sensitivity.
The Role of the Human Microbiome from Symbiosis to Dysbiosis
Background: The Human microbiome is a complex symbiotic ecosystem, which is crucial to human health, and which plays vital roles in metabolism and immunity. Its disturbance, which is called dysbiosis, is progressively involved in a variety of chronic diseases. Objective: This review synthesizes evidence on the microbiome's dual role in health and disease, examining the mechanisms of homeostasis maintenance by a balanced microbiota and the contribution of dysbiosis to several disorders. Methods: A scientific literature narrative review was carried out, which incorporated results of human cohort studies, animal models, and mechanistic in vitro research to explain the most important microbial functions, host-microbe interactions, and inter-organ communication pathways. Results: Commensal microbes play an important role in the production of metabolites, training of the immune system, and colonization resistance. Diversity loss, pathobiont growth, and metabolite shift are caused by dysbiosis as a result of antibiotics and diet. This disturbance is associated with disorders such as IBD, diabetes, and neurodegenerative diseases. Microbial metabolites and components are two sided swords, mediating their systemic effects via gut-brain, gut-lung and gut-skin axes through immune, neural and metabolic pathways. Conclusion: Microbial harmony is essential in health, and its imbalance is the cause of chronic illness, so the microbiome is an ideal candidate of new diagnostics, therapeutics, and personal approaches to its recovery.
Systemic Lupus Erythematosus: Historical Perspective, Global Epidemiology, Immunological Dysregulation, and Multifactorial Etiology
Background: Systemic lupus erythematosus (SLE) is a multisystemic autoimmune inflammatory disease, that is chronic and complex. Its pathogenesis is a complex of intrinsic factors and environmental stimuli, hormonal effects, and epigenetic processes, and it causes a severe deregulation of the immune system. The main symptoms of the disease is an abnormal immune response, where autoregulated T cells trigger B cells to produce auto pathogenic autoantibodies. The clinical presentation of SLE is so diverse, covering mild and self-limiting manifestations, such as fatigue, skin rash, and joint pain, to severe and life-threatening organ involvement, which may include lupus nephritis, neurological problems and heart diseases. The disease can either attack one organ or manifest itself as a systemic disorder with more than one organ at a time. This broad range of clinical and serological heterogeneity is one of the distinguishing features of SLE and creates a great difficulty in the diagnosis of the disease, its monitoring, and treatment .It is crucial to gain a complete picture of the immunopathological processes and heterogeneity of SLE to enhance the accuracy of the diagnostic process, at the same time optimizing the personalized approach to treatment, and minimizing morbidity and mortality in the course of long-term follow-up of the disease.
Generalization of Certain Characteristics of Multiplication Modules to Locally Multiplication Modules
Background: Consider a commutative ring with a non-zero identity and an module . A multiplication module is an module that satisfies the multiplication property. That is, every submodule is presented as the product of an ideal of the ring and the module itself. Materials and Methods: An module is a locally multiplication module if or is a multiplication module for each prime ideal of . In this paper, certain properties of multiplication modules are extended to locally multiplication modules by providing conditions under which this generalization is possible. Results: A locally multiplication module is Noetherian whenever the ring is both semi-local and satisfies the conditions of an ascending chain on ideals with prime/semiprime property. As well as, if is a faithful locally multiplication module and is a prime ideal with , then a bijection exists between ideals and submodules of and . Also, if is a locally multiplication module and as the submodule of is both prime and primary whenever the set of contained in , along with several other results. Conclusion: Various conditions are presented in terms of faithfulness, cyclic, prime, semi-prime, primary, and others, to illustrate the generalization of certain properties of multiplication modules to locally multiplication modules.
A Directional Interpretation of Shadowing and Semicontinuity of Omega-Limit Sets
In this paper, we study the relationship between shadowing properties and the semi-continuity of ω-limit sets in dynamical systems. Building on known equivalence results, we provide a directional and topological interpretation of the asymmetry between upper and lower semi-continuity. Our approach clarifies how forward shadowing naturally supports upper semi-continuity, while the absence of backward control obstructs lower semi-continuity in non-invertible and semi-continuous systems. Several illustrative discussions and examples are provided to support this viewpoint.
Comparison among Different Methods in Treatment of Anal Fissures
Background: Anal fissures represent one of the common diseases that affect the anorectal area. Patients present with painful tears in this area that finally result in chronic discomfort and pain. Multiple surgical procedures were applied in the treatment of chronic anal fissures, but the optimal technique remains unclear. Aims of the study: This study aimed to perform an assessment of three surgical techniques used in the treatment of chronic anal fissures regarding fissure healing, pain remission, and related complications. Patients and methods: The study was performed in the period from November 2023 to April 2025, it consists of 180 patients with chronic anal fissures. Random distribution of patients into three treatment groups: Group A (Topical Nifedipine), Group B (Laser Therapy), and Group C (Lateral Internal Sphincterotomy). These procedures were evaluated using three methods of assessment: pain remission, fissure healing, and complications at period intervals of one, two, four, and six weeks post-treatment in addition to long-term follow- up through six and twelve months intervals. Results: Laser therapy represented the most effective treatment procedure. In this procedure, the pain remission ratio was 96.5% then lateral internal sphincterotomy 90.7% and lastly topical Nifedipine 75%. The highest rate of fissure healing after six weeks was performed in Laser therapy (the ratio was 94.8% while surgery was 82.9% and Nifedipine 67.3%. The fewest complications were found in laser method with a low recurrence rate of 3.6%. Conclusion: The study revealed that laser therapy technique represents the most effective and safest method of treatment for chronic anal fissures because it had rapid pain relief, rapid healing and few complications.
An Approach to Generalized High-Order Fredholm Integro-Differential Equations via Fractional Cubic Spline
This paper presents an efficient numerical technique for the approximate solution of multi-term integro-differential equations using a new form of fractional cubic spline (FCS). The proposed method is derived from a fractional boundary condition and a fractional continuity condition on a fractional spline, expressed in matrix form. A detailed convergence analysis is provided, and sufficient conditions for stability and error bounds of the method are established. Moreover, several numerical examples are solved, and the results are compared with exact solutions and methods from previous papers, presented in tables and figures. These comparisons demonstrate the efficacy and suitability of the suggested fractional spline scheme for solving integro-differential problems, achieving higher accuracy with fewer grid points. All computational results were obtained using Python 3.12.4.
A Comparative Study of Serum Melatonin and TNF-α Levels in Patients with Thyroid Dysfunction
Thyroid dysfunction, including hyperthyroidism and hypothyroidism, involve metabolic dysregulation, endocrine imbalance, and immune alterations. Cytokine signaling molecules such as TNF-α and regulatory hormones like melatonin may modulate pathogenic processes. Although autoimmune thyroiditis has been linked to elevated inflammatory markers, their diagnostic accuracy across broader thyroid dysfunction remains equivocal. This study aimed to assess serum melatonin and TNF-α levels among patients with thyroid dysfunction, and controls, and to assess their prospective utility as diagnostic indicators. The study involved 90 individuals, distributed proportionally into three groups including 30 hyperthyroidism, 30 hypothyroidism, and 30 controls group. Serum melatonin and TNF-α concentrations were quantified using enzyme-linked immunosorbent assay (ELISA). The results of serum melatonin concentrations were significantly elevated in both hyperthyroid (5.71 ± 0.84 ng/mL) and hypothyroid (5.57 ± 1.05 ng/mL) cohorts relative to controls (3.21 ± 0.31 ng/mL; P < 0.001), with no significant difference observed between the two patient groups. Receiver operating characteristic (ROC) analysis demonstrated acceptable diagnostic performance for distinguishing thyroid dysfunction from normal: hyperthyroidism, AUC = 0.761, sensitivity = 76.7%, specificity = 80.0%; hypothyroidism, AUC = 0.718, sensitivity = 73.3%, specificity = 70.0%. Mean serum TNF-α concentrations were elevated in hyperthyroid (23.56 ± 11.77 pg/mL) and hypothyroid (19.72 ± 12.05 pg/mL) patients compared with controls (15.15 ± 9.18 pg/mL), but the differences did not reach statistical significance (P = 0.101). Melatonin was significantly higher in both hyper- and hypothyroid patients, suggesting an adjunct biomarker for thyroid dysfunction. TNF-α showed a non-significant trend toward elevation, indicating potential inflammatory involvement that needs validation in larger studies.
The Japanese Encephalitis Virus Evolution and Epidemiology
The virus that causes Japanese encephalitis (JEV) is the cause of Japanese encephalitis (JE), a potentially fatal brain infection contracted via mosquito bites. The Japanese encephalitis virus, which is endemic in 24 countries and poses a threat to nearly 3 billion people, is the primary Asia's viral encephalitis cause. An estimated one hundred thousand clinical cases and 25,000 fatalities occur worldwide each year. Travelers to endemic areas must be vaccinated, even though Arab nations are still outside the transmission zones. Prevention: Inactivated (IXIARO), live attenuated (IMOJEV, CD. JEVAX), and Indian (JENVAC) vaccine formulations are among the WHO-prequalified vaccinations. Dosage, target populations, and contraindications vary for each. Although the effectiveness of current vaccines may be threatened by developing genotype V, sustained immunizations has reduced endemic countries by 73–100%. To rule out flavivirus cross-reactivity, primary diagnosis is made by IgM capture ELISA in serum/CSF (WHO-recommended), followed by confirmatory PRNT at reference laboratories. Because of temporary viraemia, RT-PCR is restricted in its ability to diagnose humans, although it is beneficial for surveillance. Socio-environmental Factors, historically recorded during the Korean War (402 US cases, 1950), social instability (war, displacement, and health system collapse) increases burden by disrupting immunization, weakening surveillance, and increasing vector exposure.