OJS University of Karbala
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D Detection of Some Antibiotic Resistance of beta lactam and flouroquinolines Gene Among Coliform Bacteria isolated from Al-Hussainiya river: University of kerbala , College of science, Iraq, Corresponding author
No full text Background: Coliform bacteria are the most important bacterial contaminate the water bodies.
Objectives: the present study aimed to detect antibiotics resistance and biofilm formation activities among coliform bacteria isolated from different places on Al-Hussainiya river.
Method :- During a period of July 2023 to February 2024, 18 samples were collected from three different places ( Baron, Al-Atishi area and the white arch) on Al-Hussainiya river in summer and winter (9 samples in summer and 9 samples in winter for isolation of coliform. Most probable number (MPN) methods were used for evaluated the numbers of coliform bacteria during two seasons. The isolated coliform were further identified using Biochemical tests and Vitek 2 technique. PCR method was used to detect some genes including SHV, TEM, CTX, qur-A and Aac(6) Ib in 15 isolates of coliform during summer and winter. The current data showed that the numbers of coliform were significantly higher in summer in the white arch and Baron hotel. In contrast coliform appeared to be significantly lower in winter compared to summer in Al-Atishi point. Two way anova showed significant differences between stations and seasons (P≤0.05).
The results :- of the antibiotics resistance test showed isolates of E.coli , Klebsiella and Enterobacter spp resistant to all tested types of antibiotic during summer, where\u27s in winter season only E. coli were resistance to a few types of antibiotics.. The results of molecular identification showed that SHV, TEM, CTX-M qur-A, Aac (6)Ib, blaSHV, blaTEM and bla CTX-M genes were detected in 3 isolates of E. coli during winter only. However, these genes were not identified in Klebsiella and Enterobacter spp. In contrast, no isolates were reported to have any genes during summer season. In addition, most isolates of coliform were found to be biofilm producer bacteria.
Conclusion: coliform bacteria were ability to biofilm formation and their resist or sensitive for antibiotics
The Effect of Aqueous and Oil Extracts of Foeniculum vulgare (Sweet Fennel) on the Larval Stages of Ephestia cautella (Fig Moth): 1,2,Al-Musayyib Technical College, Al-Furat Al-Awsat University, Iraq
No full textA series of laboratory experiments were conducted in the Graduate Studies Laboratory, Department of Biological Resistance Technologies, Al-Musayyib Technical College, from 1st October 2023 to 1st December 2023. The experiments aimed to assess the effects of hot, cold aqueous, and oil extracts of Foeniculum vulgare (sweet fennel) on the various larval stages of Ephestia cautella (fig moth). The results indicated that the oil extract of fennel seeds outperformed the cold and hot aqueous extracts, with the highest mortality rate of 66.70% observed for the oil extract at a a concentration of 1% after 72 hours. In comparison, the highest mortality rates for the hot aqueous extract and cold aqueous extract at the same concentration were 40.00% and 53.30%, respectively, after 72 hours. The lowest mortality rate for the oil extract was observed at a concentration of 0.25%, registering 10.00%, while the cold aqueous extract resulted in a minimum mortality rate of 6.70% at a concentration of 0.25% after 72 hours post-treatment.
The findings also revealed that the percentage of mortality increased with higher extract concentrations and longer exposure times
MRI Image Segmentation Using Machine Learning Methods: A Survey
No full textMagnetic resonance imaging (MRI) has been utilized as a non-invasive imaging technique to detect and diagnose central nervous system disorders, as well as to monitor their treatment course. Neurologists can more accurately detect abnormalities from brain imaging because of the three-dimensional images that MRI creates. The machine learning techniques such as K-Means, naive Bayesian, logistic, Decision tree, or random forest. Furthermore, deep learning used CNN to segment images into specific regions, such as “UNet”, “ResNet”, “GoogleNet”, etc. A computer-aided method for analyzing MRI images and precisely identifying abnormalities has been made possible by advancements in machine learning and rapid processing. Image segmentation has become more popular and a focal point of research in medical image analysis. The ability to rapidly classify the disease for early treatment is made possible by the computer-aided technique for identifying brain abnormalities. The research articles on brain tumor segmentation from MRI images are reviewed in this article. The comparison of segmentation methods in accuracy is in thresholding is about 0.75, in k-means clustering is about 0.8, in a U-Net is about 0.9, and in V-Net is about 0.92, respectively
Catalyzing Organic Reactions Using Environmentally Friendly Green Catalysts: An Applied Study on Alkylation Reactions
No full textAmid the global shift towards green chemistry and the growing demand to reduce the environmental impact of chemical processes, this study investigates the effectiveness of a modified natural zeolite catalyst as a sustainable alternative to conventional, toxic alkylation catalysts such as aluminum chloride. A series of controlled laboratory experiments were conducted to compare the green and traditional catalysts in terms of reaction yield, product purity, ease of catalyst separation, environmental safety, economic feasibility, and catalyst reusability.
The experimental results revealed that the green catalyst achieved a yield of 84% ± 1.2 compared to 88% ± 1.5 for aluminum chloride. The product purity was 91% ± 0.8 with the green catalyst versus 93% ± 0.6 with the conventional catalyst. Additionally, the green catalyst demonstrated excellent ease of recovery and could be reused for up to three consecutive cycles with only a 3–4% decrease in activity. Analytical techniques including Gas Chromatography–Mass Spectrometry (GC-MS), Fourier Transform Infrared Spectroscopy (FTIR), and Proton Nuclear Magnetic Resonance (¹H-NMR) confirmed the identity and purity of the reaction product, ethylbenzene.
This research highlights the practical potential of natural zeolite-based catalysts in promoting greener chemical synthesis. The findings support the adoption of green catalytic systems as viable, eco-friendly alternatives for industrial organic transformations, particularly in alkylation reactions
Machine Learning Framework for Hate Speech Detection in Iraqi Dialect YouTube Comments
No full textSocial media platforms like Facebook, YouTube, and Twitter have witnessed remarkable growth, and the type of data and information shared on these sites has evolved dramatically. Because users of all ages can readily access these platforms, this technological advancement has also been essential in encouraging the spread of hate speech and enhancing its impact on society. Researchers have sought to develop a range of strategies and technology models to detect and mitigate this growing threat.
Even though hate speech identification in English-language literature using Natural Language Processing (NLP) approaches has advanced significantly, research on the Arabic language, especially the Iraqi dialect, is still lacking. This research aims to identify hate speech in the Iraqi dialect by creating a database of more than 150,000 comments taken from YouTube videos about Iraqi topics that have sparked public debate. The gathered remarks were prepared and processed in several steps, including human cleaning. The comments were then divided into four major semantic classes: hate speech, abusive, offensive, and normal.
The efficiency of many machine learning models in processing texts written in the Iraqi dialect was evaluated. Graph neural networks (GNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Arabic Bidirectional Encoder Representations from Transformers (AraBERT) model, Bidirectional Long Short-Term Memory networks (BiLSTM), and the FastText model were among the models. The outcomes showed that these models performed differently when it came to digesting content in the Iraqi dialect. FastText, on the other hand, recorded a performance rate of 96.1% in both the training phase and in predicting previously unseen remarks, achieving the greatest Accuracy, Precision, Recall, and F1-Score. Therefore, despite its simplicity, the FastText model offers a practical solution for classifying hate speech in different Arabic dialects
A Review: Role of Dipeptidyl Peptidase 4 (DPP4) Enzyme Levels in Severity of Diseases
No full textThis literature review article discusses the latest developments in the mechanism and role of dipeptidyl peptidase 4 (DPP4) actions in the severity of diseases, particularly in women with polycystic ovary syndrome PCOS, coronavirus disease 2019, autoimmune thyroid diseases, diabetic patients, cardiovascular disease, cancer, and clarified the functions of DPP4 inhibitors in variety of diseases.
Recent scientists have revealed experimental, preclinical, and clinical data for DPP4 inhibitors, which demonstrate the enzyme\u27s functional role in illness treatment or severity reduction. Interestingly, DPP4 actions are a complicated mechanism, with numbers of metabolic pathways engaged depending on the kind and severity of the disease. DPP4 is one of the serine proteolytic enzymes that function via multiple biochemical processes mediators in a variety of endocrinological tissues, including the effect on the regulation of the incretin hormones, play a major role in regulate insulin secretion based on blood glucose. Interestingly, current experimental and preclinical findings suggest that DPP4 inhibitors may also maintain glycemic control in PCOS disease development, have beneficial effects in cancer, and prevent cardiovascular disease in T2DM. In this present review, we summarized the impacts of DPP4 inhibitors therapies based on the latest studies, as well as the possible mechanism of action and the effects of DPP4 enzyme levels and activity with its relation to disease progression
Studying the Correlation Between Serum Hormone Levels in Infertile Women and the Results of IVF and Various Causes of Infertility
No full textBackground: Infertility is defined as the inability to achieve a clinical pregnancy following 12 months of consistent, unprotected sexual activity. Both male and female factors, or both, may contribute to infertility. Infertility is most often caused by ovulatory dysfunction, such as inadequate ovarian reserve (POR) and polycystic ovaries (PCO).
Methods: A cross sectional study includes 37 participants. The samples of blood were collected at cycle day two and detected the hormonal levels by MINI VIDAS system.
Results: The result of present study showed that follicle-stimulating hormone (FSH) and luteinizing hormone (LH) in women with female and combined factors with significantly different higher from male and unexplained factors with (p=0.000, p=0.000). While, the level of estradiol (E2), anti-Mullerian hormone (AMH) and Estradiol hormone (E2) at day of human chorionic gonadotropin (HCG) injection in women with male and unexplained factors with significantly different higher from female and combined factors (P=0.007, P=0.000, P=0.003) respectively. While, progesterone in women, there was no significant different between cause of infertility groups with p=0.467. In addition, Total oocyte number, Fertilization rate, Embryo Grade l(GI ), Embryo Grade ll (GII) and transferred embryo of women with unexplained and male factor were significantly different from female and combined factor cases with (P=0.056, P=0.037, P=0.001, p=0.059 and p=0.057) respectively. Regarding the correlation this hormone with pregnancy outcomes, there is no statistical significance.
Conclusion: Serum FSH and LH levels were significantly associated with female and combined factor cases, whereas E2 day2, AMH, and E2 HCG were significantly associated with unexplained and male factor cases. In terms of progesterone, there was no significant difference between the causes of infertility groups. Furthermore, the total number of oocytes, fertilization rate, embryo grade l (GI), embryo grade ll (GII), and transferred embryo were all significantly associated with unexplained and male factor cases. There is no statistical significance to the correlation between these hormones and pregnancy outcomes.
 
Evaluation of Cytomegalovirus Infection and Interleukin-33 Levels in Women with Recurrent Pregnancy Loss
No full textBackground
Recurrent pregnancy loss determine by the "American Society for Reproductive Medicine" and the "European Society of Human Reproduction and Embryology", is defined as two or three clinically identifiable failed pregnancies before twenty to twenty-four weeks of gestation, as confirmed by histopathologic examination or ultrasound. The cytomegalovirus is a member of the Herpesviridae family\u27s Betaherpesvirinae subfamily. It is a common virus that is known to cause congenital infections in infants and in people with weakened immune systems as pregnant. Interleukin 33 (IL-33) a cytokine, is a member of the Interleukin 1 family. bind to the special ST2 receptor.
The aim of study
To determine the unbalanced inflammatory factors such as interleukins 33 (IL-33) and cytomegalovirus infections can be an important factor in recurrent pregnancy loss.
Methods & materials
This study\u27s case-control methodology included 60 Recurent pregnancy loss as case group and 60 healthy controls with successful delivery. IL-33 measure & CMV IgM and IgG from serum blood samples using ELISA technic method.
Results
The obtained results showed that measure of IL-33 in the control was significantly more than case (p = 0.001). cytomegalovirus antibodies in the control were significantly more than case (IgM) (p = 0.01848). & (IgG) (p = 0.00001) , BMI and Age non-significant to link with RPL .
Conclusion
Generally, we showed that the BMI & Age no important role but IL- 33 level and cytomegalovirus infections play an important role in RPL
Evaluation the Risk of Atherosclerosis Among Some Iraqi Hyperlipidemic Patients Taking Atorvastatin
No full textBackground: Hyperlipidemia is a family of disorders that are characterized by abnormally high levels of lipids in the blood. While fats play a vital role in the body’s metabolic processes, high blood levels increase the risk of atherosclerosis and cardiovascular diseases, especially coronary heart disease (CHD). This research aims to provide an investigation of hyperlipidemia and will focus on the atherogenic index of plasma (AIP) and The Castelli’s risk indexes (CRI-I & CRI-II) which are strong markers to predict the risk of atherosclerosis and coronary heart disease.
Methodology: In this cross-sectional study, one hundred forty-nine Iraqi male and female patients with primary hyperlipidemia of the age of 28 to 85 years, who were treated with atorvastatin 40mg for at least 6 months were recruited. Lipid profile and liver functions were assessed, and the AIP and CRI-I & CRI-II were calculated to predict the risk of atherosclerosis and coronary heart disease.
Results: The study\u27s finding shows there are 42 patients (28.2%) with good response to the statin therapy (atorvastatin 40 mg), about 35% of studied patients with a moderate response and about 39% of patients had poor or non-response after at least 6 months of the treatment. Additionally, there are 17 patients (11.4%) with a low risk of IHD, 3.4% with a moderate risk, and 85% of studied patients had a high risk of IHD according to the results of AIP. According to CRI-I and CRI-II there are 68 and 118 patients at low risk and 81, 31 patients at high risk to IHD respectively. Significant differences were observed in the levels of TC, BMI, and AIP between the age groups of the studied patients. Moreover, there are significant differences in the levels of TC, and AST regarding to the duration of treatment groups of the studied patients.
Conclusion: The study highlights varying responses to atorvastatin, with 39% showing poor or no response. AIP results indicate that 85% of patients are still at high IHD risk, supported by CRI-I and CRI-II assessments
Vit D and Interleukin-17 Levels in Patients with Acne Vulgaris Severity in Anbar Governorate
No full textAcne vulgaris is a common inflammatory skin disorder influenced by immune responses, particularly involving interleukin-17 (IL-17) and vitamin D (Vit.D). This study aimed to evaluate the relationship between serum IL-17 and Vit.D levels with acne severity. Blood samples were collected from 96 patients with acne vulgaris and 84 healthy controls in Haditha District, Anbar Governorate. Participants were matched by age and gender. Serum IL-17 and Vit.D levels were measured using Sandwich-ELISA. Results showed significantly higher IL-17 levels and lower Vit.D levels in acne patients compared to controls (P < 0.0001). Acne severity was positively correlated with IL-17 and inversely with Vit.D levels (P < 0.001). Female patients had higher IL-17 levels and more pronounced Vit.D deficiency than males. Although no direct correlation was found between IL-17 and Vit.D levels, both markers were significantly associated with disease severity and gender. ROC analysis demonstrated their diagnostic potential. In conclusion, elevated IL-17 and Vit.D deficiency are strongly linked to acne pathogenesis and may serve as biomarkers or therapeutic targets in acne management