Journal of Kerbala University

The aim of this study is to understand the importance of mycorrhizal and black tea waste on yield parameters and active compounds for sunflowers instead of chemical fertilizers, which are considered harmful to human health.The experiment was conducted in the Hindiya area / Karbala / Iraq during the 2024 agricultural season at a latitude of 34.56 degrees, a longitude of 44.45 degrees, and an elevation of 24 meters above sea level. The experiment was designed according to the complete randomized block design as a factorial experiment (2 × 7 × 3). Sunflower seeds were planted in the spring season on 4/3/2024 in rows. After 20 days of emergence, the plants were thinned to one plant per hole, and black tea waste was mixed with the soil before planting.
.The results indicated a significant effect among the studied treatments, as treatment M3 excelled in the number of seeds in the disk (seed disk-1), weight of 100 seeds (g), biological yield (Mgha-1), oleic acid %, linoleic acid %, and thymol % by injecting the soil with mycorrhizal, yielding the highest average of 1783 seed disk-1, 64.06 g, 78.07 Mgha-1, 7.83%, 32.03%, and 8.79%, respectively. While black tea extract treatment T3 excelled in the number of seeds in the disk (seed disk-1), weight of 100 seeds (g), biological yield (Mgha-1), oleic acid %, linoleic acid %, and thymol %, with the highest averages of 1745 seed disk-1, 62.40 seed disk-1, 77.90 Mgha-1, 7.36%, 31.79%, and 7.88%, respectively.

The order to lessen the effects of the antibiotic levofloxacin on male white rats weighing 150–200 g, the study sought to ascertain how well secondary chemicals in the aqueous ginger extract worked. A total of a Thirty dult rats include six equal groups, G 1 as e control. G 2, 3, 4, 5, 6 was dosed with (levofloxacin 16 mg/kg , ginger extract 100 mg/kg + 16 mg/kg , extract ginger 300 mg/kg + 16 mg/kg, extract ginger 100 mg/kg , extract ginger 300 mg/kg) respectively, statistical of results explained a significant p≤ 0.05 with liver weight when comparing the antibiotictreated rats with the control group and other experimental groups. Not significant was showed between groups treated with ginger at concentrations of 100 and 300 mg/kg when compared with others.
Biochemical parameters represented by liver enzymes ALT, AST, and GGT a significant p≤0.05 with treated with levofloxacin, while groups treated with ginger extract did not show any significant difference. As for the histopathological changes, microscopic examination revealed several histopathological changes livers of male rats given levofloxacin treatment compared with control and other groups. However, the histological sections livers treated with the antibiotic + ginger extract at concentrations of 100 and 300 mg/kg did not show histopathological changes, thereby, conclude that aqueous ginger extract at a concentration of 300 mg/kg contributes significantly to minimizing liver damage and preserving the integrity of liver enzymes and cell membranes, whereas 100 mg/kg of ginger extract resulted in very slight cellular alterations.

Software-Defined Networking (SDN) has evolved as a revolutionary framework in contemporary network infrastructures, providing centralized control, programmability, scalability, and dynamic configuration. Nevertheless, its logically centralized architecture also presents vulnerabilities that adversaries may exploit, resulting in significant security dangers. Traditional security techniques frequently inadequately meet the intricate and dynamic threat landscape of SDN systems.
This paper offers an extensive analysis of machine learning (ML) techniques for threat identification and mitigation in Software-Defined Networking (SDN). It connects theoretical advancements with practical applications, emphasizing how machine learning may function as a versatile and intelligent instrument to enhance software-defined networking security. The review commences by classifying principal attack vectors aimed at SDN components, encompassing the control plane, data plane, and communication channels. It subsequently analyzes supervised, unsupervised, and deep learning techniques utilized to identify and alleviate threats including Distributed Denial of Service (DDoS), spoofing, poisoning, and rule manipulation. The discussion also encompasses benchmark datasets and evaluation measures frequently employed in the literature.
Results indicate that machine learning substantially improves detection precision, flexibility, and scalability. Supervised learning is efficacious when labeled data are accessible, whereas unsupervised learning is beneficial for detecting novel or zero-day risks.Deep learning, specifically, attains exceptional efficacy in intricate assault situations. Nonetheless, significant hurdles persist, such as the scarcity of high-quality information, substantial computational demands, and the necessity for real-time adaptation. Future research must concentrate on hybrid models, collaborative detection, and the creation of realistic SDN-specific datasets to facilitate effective, scalable, and resilient security solutions.

To investigate the effects of spraying complex nano potassium fertilizer and multi-walled carbon nanotubes, as well as their interactions, on the content of proline, abscisic acid, and chlorophyll content in the leaves of Vigna radiata plants growing under water stress conditions in sandy loam soil during the winter of 2023–2024, a pot experiment was conducted at the College of Agriculture, University of Kerbala. experiment consisted of three components: involved applying complex nano potassium fertilizer at concentrations of 0, 2, and 4 g per liter-1; the second involved applying of multi-walled carbon nanotubes at concentrations of 0 and 0.1 g L-1 ,the third was irrigation at two levels 50% and 100% of soil's field capacity .The experiment followed a completely randomized design (C.R.D.) with four replications, the factorial experiment includes (48) experimental units. The results of the study showed a significant decrease in the activities of proline and abscisic acid and a significant increase in the chlorophyll content of the leaves when spraying with complex nano-potassium fertilizer and multi-walled carbon nanotubes and their interactions when irrigating at a level of 100% of the field capacity value of the soil, While the opposite was shown when irrigating at 50% from the field capacity value of the soil. The results showed an increase in the concentration of
proline and abscisic acid, while the chlorophyll content decreased at the level of 50% of the field capacity value of the soil at (S 1 ).

Background: Psoriasis is a chronic immune-mediated inflammatory skin disorder marked by excessive keratinocyte proliferation and T-cell infiltration of the dermis and epidermis. Interleukin-32 (IL-32), a proinflammatory cytokine, has been implicated in psoriasis pathogenesis.
Materials and Methods: A case-control study was conducted on 96 psoriasis patients (41 men, 55 women) and 50 healthy controls (26 men, 24 women) recruited from Imam Hassan Al-Mujtaba Hospital, Karbala. Data on age, gender, height, weight, psoriasis type, and family history were collected via questionnaire. Serum IL-32 levels were measured using ELISA. Statistical analysis used SPSS version 26 with t-tests for mean comparisons and Chi-square tests for categorical variables.
Results: Patients were significantly older, with 36.5% aged 42–54 years and 33.3% aged 55–67 years, compared with a control peak of 36% in the 29–41-year group. Obesity was more prevalent among patients (78.1%). Plaque psoriasis was the dominant clinical type (69%), followed by guttate psoriasis, while nail involvement was rare. Absence of a family history was significantly more common than presence (p = 0.000). Serum IL-32 levels were markedly higher in psoriasis patients than in controls.
Conclusion: Psoriasis is significantly associated with older age and higher body-mass index, whereas sex and family history show no meaningful effect. Elevated IL-32 supports its role in the inflammatory milieu and highlights its potential as an immune biomarker and therapeutic target. Its involvement in systemic inflammation may link skin manifestations to comorbidities such as psoriatic arthritis and cardiovascular disease. Future studies should investigate IL-32 isoform-specific expression and evaluate IL-32 levels longitudinally before and after biologic therapy to clarify its value as a treatment-response indicator.

Acinetobacter baumannii has emerged as the best recognized and very important pathogen responsible for healthcare-associated infections as a result of its ability to survive under harsh environmental conditions. To investigate the prevalence of A.baumannii across various clinical sources and to evaluate selected virulence factors associated with this pathogen, a total of 150 clinical swabs were collected from burns, wounds, urine and sputum samples. These were cultured on blood agar and MacConkey agar, then incubated at 37°C for 24–48 hours. Bacterial isolates were identified based on colony morphology, cultural characteristics and standard biochemical tests, including catalase, oxidase and Simon’s citrate tests. Virulence factors, including hemolysis, protease production, and polysaccharide capsule formation, were also evaluated using qualitative methods. The results showed that out of 120 bacterial isolates, 30 (25%) were identified as A. baumannii, whereas the remaining 90 (75%) were belonging to other bacterial genera. The A. baumannii isolates were Gram-negative, non-motile and exhibited no hemolytic activity on blood agar. Biochemical testing confirmed that all the isolates were catalase and citrate positive. Confirmation of A. baumannii was performed using the VITEK 2 system and polymerase chain reaction (PCR) by targeting a specific gene namely blaOXA-51 (353 base pairs in size), The results confirmed that 30 (100% ) of the isolates were A. baumannii. The identified bacteria was also investigated for various virulence factors, the results revealed that all the isolates were incapable of producing hemolysin, whereas 25 (83.3%) of the tested becteria were positive for the protease qualitative test and all the isolates produced a polysaccharide capsule.

This study involved the preparation of two lanthanum (III) complexes using 1-(2-pyridylazo)-2-naphthol (PAN) as the primary ligand, combined with the secondary ligands 1,10-phenanthroline (Phen) and 8-hydroxyquinoline (8HQ). The complexes were synthesized by controlling the reaction medium with a buffer solution. The prepared complexes were characterized by spectroscopic analyses, including Fourier-transform infrared spectroscopy (FT-IR), UV-Vis spectroscopy, and proton nuclear magnetic resonance (¹H-NMR), as well as by elemental analysis (CHN) and molar conductivity measurements. In the [La(PAN)₂(NO₃)] complex, the PAN ligand coordinates as a tridentate ligand through the azo nitrogen, the pyridine-ring nitrogen, and the phenolic oxygen atom, while the nitrate group behaves as a bidentate ligand. Thus, the coordination number of the central atom is eight. In the mixed-ligand complexes, the secondary ligand Phen coordinates as a bidentate ligand through its two nitrogen donor atoms (N,N). In the [La(PAN)₂(8HQ)] complex, the 8HQ ligand coordinates as a bidentate ligand via its nitrogen and oxygen donor atoms (N,O). Based on the spectroscopic data, the primary ligand PAN behaves consistently as a tridentate ligand (N,N,O). The biological activity of these complexes was studied using the agar diffusion method. The study revealed that the mixed-ligand complexes exhibited enhanced biological activity, particularly the [La(PAN)₂(8HQ)] complex.

The present work was designed to identify Candida species associated with skin infections in patients from Karbala Governorate, Iraq, through the application of molecular approaches. Out of one hundred clinical specimens examined, seventy-seven fungal isolates were confirmed as belonging to the genus Candida. For molecular identification, the Internal Transcribed Spacer (ITS) region was targeted using three species-specific primers: CALB1 for Candida albicans, CGL1 for C. glabrata, and CTR1 for C. tropicalis. Polymerase chain reaction (PCR) analysis generated reproducible bands of 273 bp for C. albicans, 423 bp for C. glabrata, and 357 bp for C. tropicalis, confirming the efficiency of the primers in species recognition. Among the isolates, C. albicans was the most frequent species, followed by C. glabrata and C. tropicalis. The predominance of C. albicans emphasizes its epidemiological significance as a leading cause of skin mycoses in the studied population. The findings of this investigation provide useful baseline data on the distribution of Candida species in Iraq and demonstrate the value of PCR-based methods for achieving accurate and rapid diagnosis. Such information may assist in guiding appropriate antifungal therapy and improving patient management in clinical practice.

Missing information or worth in a dataset can influence the execution of a classifier, which prompts trouble in separating useful data from datasets. The principal objective of our work is managing missing qualities by utilizing ascription strategies for it, talking about their ease of use, and discussing their relevance in the case of information sets with the goal of being more reasonable for information mining investigation and the arrangement. This paper likewise talks about the "REP Tree" information mining approach that has been used for breast cancer analysis in the wake of a preprocessing step (dealing with missing values), with the ultimate goal of improving the order of information (diagnosis) or conclusion. Experimental results proved that all used algorithms are efficient for dealing with missing values in the data set and diagnosis. We conducted an analysis of the Wisconsin dataset from UCI machine learning with the aim of developing accurate breast cancer prediction models using data mining techniques. The results of the proposed system demonstrated that data distortion can be reduced while classification dataset accuracy remains high. DI imputation was shown to be a superior strategy for impute missing values with accuracy 93%, thus the system successfully achieves its requirements.