UHD Journal of Science and Technology

Prevalence of Hepatitis B Core Antibodies and Occult Hepatitis B Infection among Blood Donors in Erbil Governorate, Iraq

2025-05-30T18:46:12+00:00

The Hepatitis B Virus (HBV) remains a considerable risk to blood transfusion safety, especially through occult hepatitis B infection (OBI), defined by undetectable Hepatitis B surface antigen (HBsAg) yet the presence of HBV DNA in the bloodstream. Identifying and investigating the prevalence of OBI is essential as these infections can get past normal screening tests, which can lead to accidental transmission through transfusion. This study aimed to evaluate the prevalence of total hepatitis B core antibody (HBcAb) and identify OBI among blood donors in Erbil Governorate, Iraq. A total of 31,631 blood donors were tested for total HBcAb between September 2024 and January 2025, using the Liaison XL chemiluminescence immunoassay machine. Out of these 31,631 blood donors, 388 (1.23%) showed positive results for the total HBcAb. Among the positive cases, 65 samples were randomly chosen to detect OBI by viral load detection using quantitative real-time polymerase chain reaction. All samples were negative for HBsAg during routine screenings. Occult OBI was detected within 17 (26.15%) of the HBcAb-positive, HBsAg-negative blood donors. Despite the application of HBcAb screening, the absence of molecular testing may continue to provide an opportunity for HBV transmission. Incorporating HBV DNA testing for positive cases may enhance the safety of blood transfusions.

Environmental Impact Assessment of Sulaymaniyah Solid Waste Dumpsite Using Leachate and Soil Pollution Indices

2025-05-26T15:59:54+00:00

Global urban population is rising that resulting more waste production. Globally, municipal solid waste (MSW) generation considered as a serious threat on the global environment and human wellbeing. Leachate from solid waste dumps poses significant environmental and health risks, particularly due to contamination in soil and water caused by heavy metals. In this study, environmental impacts of MSW are assessed and estimated for Sulaymaniyah city, KRG, Iraq, which is located at 10 km south of the city in the Tanjaro dumpsite. Soil and leachate samples were collected and analyzed for various expected pollutant, to assess the environmental contamination through using pollution indices. For assessing the leachate pollution index (LPI), some parameters were determined, such as potential of hydrogen (pH), total dissolved solid, biochemical oxygen demand (BOD5), and chemical oxygen demand (COD), and chloride (Cl). LPI value (20.1377) is much higher than the related standards. High concentrations of metals, such as cadmium (Cd), iron (Fe), manganese (Mn), copper (Cu), chromium (Cr), nickel (Ni), and zinc (Zn), found in the soil near the site, however, the contamination level is not serious based on the checked pollution indices, such as pollution index (PI) and nemerow PI (PInemerow). PI for Cd, Fe, Mn, Cu, Cr, Ni, and Zn were 0.158, 0.024, 0.088, 0.176, 0.613, 0.786, and 0.225, respectively, whereas, PInemerow value was 0.606, which classified the soil as a non-contaminated soil. Results of this study reveals that the Tanjaro dumpsite needs an engineered landfill and decent leachate treatment right away; since present conditions far over safe limits and threaten soil and water quality.

Assessment of Nurses’ Knowledge and Competence in Managing Preeclampsia at Maternity Teaching Hospital in Sulaimani city

2025-04-11T00:35:23+00:00

Background: Preeclampsia is one of the most commonly reported complications during pregnancy, affecting approximately 2–15% of all pregnancies. It is characterized by the onset of hypertension after 20 weeks of gestation, accompanied by proteinuria, generalized edema, or evidence of organ dysfunction. This condition poses a serious threat to both maternal and fetal health, significantly increasing the risk of morbidity and mortality. According to the Centers for Disease Control and Prevention, “the number of pregnant women with high blood pressure has been increasing, with cases doubling from 1.8% in 2008 to 3.7% in 2021” reported in the United States. Risk factors for preeclampsia included race, advanced maternal age, obesity, null parity, multifetal pregnancy, and co-existing medical disorders. Managing preeclampsia is important because it can lead to complications for both the mother and baby. Aim: This study aimed to assess nurses’ knowledge and competence in managing preeclampsia at the Maternity Teaching Hospital in Sulaimani City. Materials and Methods: A cross-sectional descriptive study was conducted at Maternity Teaching Hospital in Sulaimani city, Iraq, from January 5, 2024, to June 13, 2024. The study included 25 nurses working in the emergency care unit and labor room of the Obstetrics and Gynecology Department. A questionnaire format was created according to the aim of the study and delivered by a team of five experts, consisting of two parts. Part one: The sociodemographic characteristics of the nurses, and the second part assessed their knowledge and practices regarding the management of preeclampsia data were collected through a structured face-to-face questionnaire and analyzed using the Statistical Package for the Social Sciences version 24. Results: The majority of nurses were over 35 years old, with a mean age of 36 (standard deviation = 12.86). In terms of education, most nurses held a diploma (48%), and 64% were married. Around 56% of nurses reported participating in workshops or programs related to preeclampsia in the obstetrics field. When asked to define eclampsia, 80% of the nurses answered correctly, and the same percentage correctly identified the best anticonvulsant for managing preeclampsia. Overall, the nurses demonstrated a fair level of knowledge in managing preeclampsia. No significant association was found between age and knowledge level, suggesting age did not influence knowledge or competency in managing preeclampsia. Conclusion and Recommendations: Based on the study’s findings, the nurses demonstrated a fair level of knowledge in managing preeclampsia, especially in treatment and prevention. To improve patient care and reduce maternal and fetal risks, addressing the gaps in nurses’ knowledge through regular training courses and educational programs is essential. These efforts will enhance their qualifications and ensure better care for women with preeclampsia.

The Effectiveness of Geographic Information Systems in Sustainable Urban Planning in Iraq: An Analytical Study of Experts’ Opinions

2025-06-14T04:36:15+00:00

This descriptive analytical study assesses the effectiveness of geographic information systems (GIS) in supporting sustainable urban planning in Iraq. The study aims to gather and analyze the opinions of specialists and experts in urban planning, environmental management, and GIS across various cities in Iraq. A sample of 100 experts from Baghdad, Basra, Erbil, Mosul, and Najaf was selected based on their experience and competence in projects utilizing GIS. Data was collected through a structured questionnaire consisting of closed-ended questions and a Likert scale, focusing on four main dimensions: Experts’ knowledge and use of GIS, the effectiveness of GIS in urban planning, its role in achieving sustainable development goals (SDGs), and the challenges faced in implementing these systems in Iraq. Data analysis was conducted using the Statistical Package for the Social Sciences statistical analysis software. The results showed that most experts had a good level of knowledge of GIS and considered it an effective tool in improving the accuracy of urban planning and facilitating decision-making. However, some challenges were identified, such as the lack of updated data and the technical capacity to use complex software. The study also indicated that GIS significantly contributes to achieving SDGs, especially in the areas of environmental sustainability and monitoring urban expansion. However, there is a need to improve institutional support and provide updated data and financial resources to implement GIS more effectively. Regarding statistical analysis, the results of the analysis of variance test showed significant differences in the effectiveness of GIS based on experience level, with more advanced experts showing greater effectiveness in using the system. The t-test revealed a significant difference between those who received formal GIS training and those who did not, with the trained group demonstrating higher knowledge levels. Finally, the correlation analysis results indicated a positive relationship between GIS knowledge and its effectiveness in urban planning. This study provides valuable insights into the effectiveness of GIS in enhancing sustainable urban planning in Iraq, highlighting the challenges faced in its implementation in this context.

Climatic Impacts on Drug Therapy Usage: A Comparative Study of Kurdish Populations in Sulaimani, Iraq, and Stockholm, Sweden

2025-06-14T04:40:13+00:00

The geographical area is influenced by climate impacts, which, in turn, affect the use of different drug therapies during seasonal weather fluctuations. Thus, this study investigates how geographical climate differences influence drug therapy usage by comparing two Kurdish populations residing in Sulaimani, Iraq, and Stockholm, Sweden. It also highlights significant variations in healthcare practices, demonstrating how environmental conditions shape medication patterns. Data collection was conducted through a structured online survey, covering sociodemographic factors, health behaviors, and medication practices, followed by statistical analysis using Python and SPSS. Geographic Information System (GIS) tools were applied to spatially analyze environmental variables across the two cities, enabling the validation of sampling locations and the statistical determination of optimal limitations for the sample collection dataset. In Stockholm, 73.33% of respondents reported that the cold and humid climate affected their health behavior, whereas in Sulaimani, 50.27% described the climate as moderate but highly variable. The study revealed that the key statistical values such as antibiotic usage were significantly higher in Sulaimani (38.03%) than Stockholm (14.00%, P < 0.001), indicating a more treatment-focused approach in Sulaimani versus a preventive focus in Stockholm. Similarly, painkiller usage was significantly higher in Sulaimani, correlating with climate-related seasonal illnesses. Meanwhile, multivitamin usage in Stockholm reached 44.67%, surpassing Sulaimani’s 37.77%, reflecting a stronger emphasis on preventive healthcare strategies in colder climates. These findings emphasize that climate, more than cultural differences, significantly influences drug therapy patterns. The study determines that healthcare strategies should integrate climate variability, prioritizing preventive care in colder climates and infection control in warmer regions. Finally, the study concludes with key findings and outlines directions for future research, emphasizing the need for further investigation into climate-adaptive healthcare approaches.

Enhancing Clinical Decision Support: A Deep Learning Approach for Automated Diagnosis of Eye Diseases from Fundus Images

2025-05-20T01:42:07+00:00

Background and Objective: One of the most crucial sensory organs that helps the human brain receive information about the outside world is the eye. Due to its structural features, the back surface of the eye (retina) provides valuable insights about various disorders. It is essential to protect the eyes from diseases that could lead to vision impairment. If diseases affecting the retina are not identified and treated promptly, vision loss cannot be reversed. Therefore, effective automatic detection systems are necessary, as manual diagnosis is not only time-consuming, expensive, and labor-intensive but also requires a high level of expertise. To address this issue, many deep learning (DL)-based solutions have been proposed for screening retinal conditions. This study aimed to develop an effective system for the automated classification of four major eye conditions to support clinical decision-making. Methods: In this research, various convolutional neural network (CNN) architectures were applied to the dataset, and their performance was recorded. The CNN models are the common transfer learning pre-trained models on the ImageNet dataset. Finally, we developed a hybrid DL model combining DenseNet169 and MobileNetV1 to extract deep features from fundus images and perform multiclass classification into four categories: diabetic retinopathy, cataract, glaucoma, and normal fundus. Results: This hybrid approach yielded impressive results, attaining 92.99%, 93.02%, 92.85%, 92.90%, and 98.77% for accuracy, precision, recall, F1-score, and area under the curve (AUC) on a publicly available Kaggle dataset, i.e., eye disease classification. These results indicate that the hybrid approach enhances classification accuracy compared to other individual pre-trained CNN models. Conclusion: In summary, this study evaluated a substantial number of pre-trained models and developed a framework based on the top two optimal-performing models. Given that retinal image detection and diagnosis are critical for patient eye therapy and rehabilitation, our study offers an innovative framework that can function as a diagnostic aid for eye-related diseases.

Enhanced Integer-Based Homomorphic Encryption Scheme with Windowing Mechanism for Securing Electronic Health Records

2025-05-20T01:46:33+00:00

The frequent breaches of healthcare data annually make robust encryption mechanisms crucial, especially those that preserve the usefulness of the data while ensuring privacy. This study addresses specific integer-based homomorphic encryption systems and their critical vulnerabilities. The vulnerability identified in these systems is the possibility of decryption using other values, such as factors or primes, instead of the claimed unique secret key. We propose an enhanced cryptographic formula to address this vulnerability using a double random value technique that ensures decryption depends solely on the designated secret key. We also apply a windowing technique for prime selection to enhance the key properties against pattern detection attacks. Security analysis shows that the enhanced system prevents decryption using values other than the dedicated key while maintaining additive and multiplicative homomorphism. Performance evaluations show that the improved system maintains decryption times and ciphertext expansion ratios similar to the original system, with a reasonable decryption time reduction. Statistical testing results using the National Institute of Standards and Technology tests demonstrate the robustness of the proposed approach compared to the original, with the windowing technique exhibiting superior randomness properties.

Molecular Identification and Antibiotic Resistance Profile of Some Pseudomonas aeruginosa Clinical Isolates

2025-06-29T01:45:14+00:00

Pseudomonas aeruginosa is a Gram-negative, opportunistic bacterium being increasingly recognized as the causative agent of hospital-acquired infection, especially in immunocompromised patients. The bacterium is well known for its environmental persistence and multidrug resistance (MDR). This study aimed to characterize the antibacterial persistence profiles and genetic diversity of P. aeruginosa isolates from clinical settings in Sulaymaniyah city, Iraq. Twenty-eight suspected P. aeruginosa isolates were collected from hospitals and private laboratories from October 2024 to January 2025. The collected bacteria were identified with standard microbiological procedures, the VITEK 2 system, and confirmation through 16S RNA sequencing. Ten antibiotics were tested following the guidelines of the Clinical and Laboratory Standards Institute for antibiotic susceptibility testing. 12 out of 28 collected isolates were confirmed as P. aeruginosa. The antimicrobial susceptibility testing indicated that resistance to Imipenem, Ceftazidime, and Cefepime was seen in 66.7% of the isolates (MDR isolates), while Ceftolozane/Tazobactam had the lowest resistance rate (41.7%). It is observed that 66.7% of isolates subjected to MDR show resistance to three or more antibiotic classes. There is a high prevalence of P. aeruginosa in clinical isolates that are resistant to antibiotics. These results underscore the urgent need for improved antimicrobial stewardship programs and the development of alternative treatment options to address this rising public health concern. Through media genomics and molecular methods, reliable identification has been enhanced, which signifies the importance of both studies.

Enhancing Pelican Optimization Algorithm with Differential Evolution: A Novel Hybrid Metaheuristic Approach

2025-05-14T01:10:12+00:00

In the field of solutions for composite objective functions, the problem of identifying a proper trade-off between exploitation and exploration is still urgent. Classical methods can hardly avoid early iteration convergence or be insufficient in terms of searching throughout the space of potential solutions, especially when dealing with multi-variate multi-dimensional problems. To overcome this problem, this work proposes a combination of the pelican optimization algorithm (POA) and differential evolution (DE), known as the POA-DE metaheuristic method, which comprises the explorative characteristic of POA and the exploitative feature of DE. The main issue dealt with in this work relates to the conflict of global search and local exploitation in the context of solving complex optimization tasks. In global exploration, the POA technique is applied to improve the performances of the search in the large area, and the DE method is used in the local search space for improving the solution. To this end, the proposed solution hybrid model tries to avoid the shortcomings associated with using either of the two key aspects when used independently. To support the results obtained through POA-DE, it is necessary to perform the intensive empirical examination of several benchmark functions. The results also show that the proposed method has achieved better stability, efficiency, and convergence speed than the basic POA. Therefore, extending the hybrid optimization techniques is significant in enhancing the meta-heuristic algorithms that form a powerful tool to solve the optimization problems.

Intelligent System for Screening Epileptic Seizures in the Erbil Electroencephalogram Epilepsy Dataset Images Utilizing Cascaded Histogram of Oriented Gradients-Gray Level Co-occurrence Matrix Features

2025-06-25T13:51:38+00:00

This study proposes a novel approach for epileptic seizure detection from EEG signals using a statistical feature extraction method that being derived from a cascaded Histogram of Oriented Gradients (HOG) and Gray Level Co-occurrence Matrix (GLCM) techniques for (117 normal) non-elliptical seizures and (117 abnormal) elliptical seizures diagnosed EEG signal images collected from Erbil teaching hospital. Four classification algorithms namely—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), and Discriminator (DR)— with rigorous hyperparameter optimization using Bayesian techniques were utilized to improve classification: three feature extraction approaches: cascaded HOG-GLCM, GLCM, based statistical features extraction and HOG were calculated. The proposed comprehensive simulation results revealed that the cascaded HOG-GLCM approach significantly outperforms single-feature methods. The SVM and KNN classifiers achieved exceptional performance with the cascaded features, both approximately reaching 98.57% accuracy ensuring almost no epileptic events went undetected, which represents a substantial improvement over GLCM (best: 92.86% accuracy) and HOG approaches (best: 94.29% accuracy). The synergistic effect observed between gradient-based and texture-based features demonstrates how HOG captures directional patterns characteristic of seizure activity, while GLCM extracts spatial relationships within the signal. Neither feature type alone provides sufficient discriminative power, as evidenced by the 5-8% accuracy drop in single-feature approaches.