Kurdistan Journal of Applied Research (KJAR)
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High Resistance to β-Lactams but Sustained Susceptibility to Colistin and Carbapenems in Escherichia coli Isolated from Urinary Tract Infections
Get PDFMultidrug-resistant bacterial strains represent a growing public health threat, particularly in clinical environments, as they significantly impair the success of treatment and control strategies for infectious diseases. This cross-sectional study investigated the antimicrobial resistance profiles of 100 Escherichia coli (E. coli) isolates derived from urine specimens of patients with symptomatic urinary tract infections (UTIs) in Sulaymaniyah City, Iraq. The samples were obtained from individuals visiting both public and private hospitals between November 2024 and February 2025. Bacterial identification and antimicrobial susceptibility testing were conducted using the VITEK 2 automated system at hospital laboratories, and molecular confirmation of the isolates was achieved through amplification of the uidA (glucuronidase) gene specific to E. coli. A total of ten antibiotics from various antimicrobial classes were tested. Colistin demonstrated complete effectiveness, with a 100% susceptibility rate, followed by doripenem (93%), imipenem (84%), tigecycline (71%), and amoxicillin-sulbactam (65%). In contrast, amoxicillin and amoxicillin-clavulanate showed high resistance rates of 83% and 82%, respectively. Resistance to cephalosporins was also considerable, with cefixime and ceftazidime exhibiting resistance rates of 70% and 60%. The findings highlight the continued effectiveness of colistin and carbapenems but draw attention to the concerning resistance to widely used β-lactams and cephalosporins. These results underscore the necessity of sustained antimicrobial resistance surveillance and improved antibiotic stewardship. The data generated in this study are critical for guiding empirical treatment decisions and enhancing the clinical management of E. coli-associated UTIs in the region
Dealing with the Outlier Problem in Multivariate Linear Regression Analysis Using the Hampel Filter
Get PDFOutliers in multivariate linear regression models can significantly distort parameter estimates, leading to biased results and reduced predictive accuracy. These outliers may occur in the dependent variable or both independent and dependent variables, resulting in large residual values that compromise model reliability. Addressing outliers is essential for improving the accuracy and robustness of regression models. In this study, proposes a Hampel filter-modified algorithm to dynamically detect and mitigate extreme values, enhancing parameter estimation and predictive performance. The algorithm optimizes window size and threshold parameters to minimize mean square errors, making it a robust approach for handling outliers in multivariate regression analysis. To assess its effectiveness, simulations and real datasets were analyzed using a MATLAB-based implementation. The algorithm was compared with the classical Hampel approach to evaluate improvements in outlier detection and suppression. The results indicate that the proposed method effectively identifies and removes extreme values, leading to improved parameter estimation accuracy, enhanced model stability, and greater predictive performance and the performance was analyzed using the Mean Squared Error (MSE). The adaptive nature of the filter minimizes the impact of outliers, ensuring a more reliable regression model. The Hampel filter-modified algorithm provides an effective and adaptive solution for handling outliers in multivariate regression models. By dynamically identifying and mitigating extreme values, it enhances model accuracy, strengthens predictive capabilities, and ensures greater resilience against data variability. This approach offers a valuable tool for researchers and practitioners working with outlier-prone datasets, significantly improving the reliability of multivariate regression analysis
A Novel Conjugate Gradient Algorithm as a Convex Combination of Classical Conjugate Gradient Methods
Get PDFConjugate gradient (CG) algorithms are constructive for handling large-scale nonlinear optimization problems. One optimization technique intended to address unconstrained optimization issues effectively is the hybrid conjugate gradient (HCG) algorithm. The HCG algorithm aims to improve convergence properties while keeping computations simple by merging features from other conjugate gradient techniques. In this paper, a new hybrid conjugate gradient algorithm is proposed and analyzed, which is obtained as a convex combination of the Dai-Yuan (DY), Hestenes-Stiefel (HS) and Harger-Zhan (HZ) conjugate gradient methods. The primary objective is to improve convergence efficiency and computational performance. The proposed algorithm is designed to reduce the number of iterations and computational costs compared to traditional CG methods. Numerical experiments on standard unconstrained optimization criteria show that the hybrid method achieves faster convergence, often requiring much fewer iterations to reach a specified gradient norm tolerance or objective function value. Additionally, the per-iteration computational cost remains competitive, as the convex combination framework introduces minimal overhead. Theoretical analysis proves the global convergence of the algorithm under standard assumptions. The results highlight the superior performance of the hybrid method in terms of the number of iterations and the total computational cost, especially for large-scale and unconditional problems. This work advances the development of efficient and robust CG algorithms, offering a practical solution for unconstrained optimization challenges
Molecular Characterization of Biofilm-related Virulence and Resistance genes in Candida albicans Isolates from Women with Vulvovaginitis
Get PDFOne of the most prevalent reasons for gynecologic consultations is vulvovaginitis (VV), particularly vulvovaginal candidiasis (VVC). The etiology of VVC mostly associated with Candida albicans (C. albicans). The recurrence of VVs and the development of resistance to antimicrobials, along with efforts to find therapeutic alternatives are of paramount importance. Thus, this study aims to find the prevalence C. albicans virulence, resistance genes in addition to its susceptibility to antifungals. In this case control study, a total of 125 high vaginal cotton swabs attained in duplicate. from 100 wome clinically diagnosed with VVC and 25 controls (non-VVC). C. albicans was isolated with Hicrome differential agar and confirmed with species-specific primers using Polymerase chain reaction. Genes of the studied virulence determinants, Aglutinin-Like-Sequence (ALS1, ALS3), Hyphal Wall Protein1 (HWP1) as well as resistance determinants associated such as multidrug-resistance (MDR1) and Candida drug resistance (CDR1, CDR2) were also tested. The prevalence of Candida species were 70% and 32% in case and control groups, respectively. Further, the frequency of C. albicans were 88.57% (case group) and 100% (control group). The most common virulence gene was ALS3, present in 96.7% of case group and 87.5% of control group. Additionally, the results indicated that 98.39% of case group and 100% of control group exhibited MDR1 and CDR2 from confirmed isolates of C. albicans. Lastly, the result showed the highest antifungal resistance rates in case group were against voriconazole (70.97%) and fluconazole (40.32%), whereas in control the antifungal resistance was 75% for both voriconazole and fluconazole. In conclusion, high rate of virulence and resistance genes amongst women with VVC and therefore, the study suggests the importance of these genes to be targeted in new antifungal drugs
Effect of Psychological Stress on Salivary Cortisol and Trace Elements of Copper, Iron and Manganese in Patients with Periodontitis
Get PDFPeriodontitis can be described as chronic multifactorial inflammatory disease which can be modified by genetic and environment risk factors such as stress. Trace minerals may impact periodontal tissue health by influencing both locally, the hard and soft tissues, as well as systemically, the immune and inflammatory processes throughout the body. This study aimed to assess salivary levels of cortisol and trace elements between psychologically stressed and non-stressed individuals having healthy periodontium and periodontitis. In this study, eighty adult participants were included. Patients completed a stress self-assessment questionnaire by Perceived stress scale and unstimulated saliva was collected to test cortisol levels by ELISA and trace elements levels of copper (Cu), iron (Fe) and manganese (Mn) by inductively coupled plasma mass spectrometry. The study involved four groups: group 1: non stress with healthy periodontium (NSH), group 2: non stress with periodontitis (NSP), group 3: subjects with stress and healthy periodontium (SH), and group 4: subjects with stress and periodontitis (SP). The result showed that SP group had the highest salivary cortisol level (40.56±4.99), followed by SH (39.75±6.28), NSP (15.22±3.09) and NSH (13.66 ± 3.17) in nmol. Moreover, salivary concentrations of Fe (0.549±0.385mgL−1) and Cu (172±85.447μgL−1) were lowest in SP in comparison with other groups, and Mn (45.032±18.565μgL−1) was significantly reduced in SP group compared to NSP group. It was concluded that the impact of psychological stress on trace elements can result in a significant decrease in all elements in saliva and oral health is greatly influenced by dietary practices and an adequate intake of vital vitamins and minerals
Analysis of the Metabolic Profile and Biological Activity of Hawthorn Species twigs: Crataegus azarolus and Crataegus monogyna
Get PDFAll parts of the hawthorn tree (Crataegus spp.), including fruits, flowers, and leaves, have been used as a source of bioactive compounds. Thus, in this investigation, the twigs of two species of hawthorn plant of Crataegus azarolus (C. azarolus) and Crataegus monogyna (C. monogyna) were evaluated for bioactive compositions and biological activity (antioxidant and antimicrobial activities). To evaluate bioactive compositions, high-performance liquid chromatography (HPLC) was applied, and for biological activity, biochemical assays were performed. C. monogyna revealed a higher amount of total phenolic, total flavonoid, and total tannin contents compared to C. azarolus. The HPLC results indicated the highest amount of kaempferol (14.40%), catechin (17.70%), and gallic acid (25%) in twigs of C. azarolus, while the maximum quercetin (72%) compound was present in C. monogyna. C. monogyna exhibited higher antioxidant activity by 1,1-dizarophenyl-2-picrylhydrazyl (DPPH) (86.13%) and 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid (ABTS) (92.93%) compared to C. azarolus for antioxidant activity-DPPH (81.86%) and -ABTS (87.47%) assay. In the case of antimicrobial activity, the twigs of both species (especially C. azarolus) have a capacity against Bacillus subtilis, Staphylococcus aureus, and methicillin-resistant Staphylococcus aureus. The results of this study revealed that the twigs of both species contained a high amount of phenolic metabolites and antioxidant activity, while they showed low antimicrobial activity
Clinicoepidemiological Findings and Pathological Characteristics of Different Types of Cutaneous Warts
Get PDFCutaneous warts, which result from infections by human papillomaviruses, are a common skin disease worldwide. They are categorized as common, plantar, plane, genital, filiform, periungual and mosaic warts. Genital warts represent the most common sexually transmitted infections; however, no sufficient information are available in Iraqi Kurdistan region, concerning their frequency rates; therefore, this study aims to determine the epidemiological and clinical features of patients with warts in this region, with special emphasis on estimating the frequency rates of genital warts and on analyzing their histopathological characteristics. A specially designed questionnaire was designed to collect socio-demographic and clinical data, such as age, gender, occupation, education and residency, from 420 patients with wart, together with the type and anatomical location of the warts. In addition, histopathological examination was performed for 20 patients with genital warts. Out of the total number of wart patients involved in this study, 255 were males, and 165 were females. Common warts were the most common type (39.0%) followed by the plantar and genital warts (30.5% and 11.9% respectively). Students were the most common individuals affected by the warts (46.0%), followed by self-employed persons (29.5%). Among patients with genital warts, most of the wart lesions were seen in multiple locations around the genital organs, and the papular form was the most frequent type seen. Histopathological examination of the genital wart lesions showed papillomatosis, acanthosis, koilocytosis, dysplasia, parakeratosis, and one case of squamous cell carcinoma in situ
Intelligent Optimization of OSPF Path Selection Using Machine Learning Models for Adaptive Network Routing
Get PDFAt the core of enterprise networks lies routing protocols that make forwarding decisions based on a set of rules and metrics. One of the most popular and widely used routing protocols is the Open Shortest Path First (OSPF). Traditional OSPF calculates the cost of the route primarily based on interface bandwidth, without considering real-time factors such as latency, congestion, or link stability. These calculations are static and can lead to deficiencies in adapting to unstable network conditions. This study proposes the integration of multiple machine learning (ML) models and techniques to enhance OSPF routing decisions. Four important ML functions namely traffic forecast, anomaly detection, failure prediction, and dynamic cost optimization—have been used to improve OSPF performance. ML methods such as Random Forest and XGBoost are used to predict and assign costs in traffic utilization and real-time performance assessments. AutoRegressive Integrated Moving Average models and Long Short-Term Memory are applied to enable traffic predictions and route adjustments before potential congestions. Furthermore, link and node failure are common in network routing. Random Forest and logistic regression models are employed to predict these. The simulation took place in Graphical Network Simulator-3 using Cisco routers and Linux servers to allow thorough testing before and after applying the ML models. The results and findings have shown that the integration of ML models reroutes the traffic to enhance latency and throughput by approximately 30%. The findings demonstrate the upside of ML-enhanced OSPF routing as a versatile and scalable solution for high-demand networks
Balancing Speed and Accuracy in Influence Maximization: A Reinforcement Learning Solution
Get PDFInfluence maximization involves selecting an optimal subset of nodes within a graph to activate as many nodes as possible in a network. This approach is categorized as non-polynomial time, and no specific algorithm is currently available to run efficiently within a reasonable time frame, especially for large-scale networks. Numerous methods have been introduced to resolve this challenge, including greedy algorithms, structural heuristics, and metaheuristic approaches. Although greedy algorithms and their improved versions achieve high accuracy, they often suffer from poor scalability and slow execution times on large graphs. In contrast, structural methods offer faster computation but at the cost of reduced accuracy. Metaheuristic algorithms, while promising, face difficulties in balancing speed and accuracy due to the expansive search space inherent in complex social networks. This study introduces a novel method that leverages Q-learning, a reinforcement learning technique, to optimize influence maximization. The proposed method narrows down the search space by focusing on high-degree influential nodes. It dynamically updates the Q-table by assigning rewards and penalties based on the nodes’ impact during influence propagation, modeled using the Independent Cascade framework. This approach effectively balances exploration and exploitation, enabling the identification of a highly influential seed set with improved efficiency. Experiments conducted on various real-world datasets show that the Q-learning-based method significantly reduces execution time compared to genetic, particle swarm optimization, random, degree centrality, and K-shell algorithms while achieving higher influence spread in most cases. These results underscore the promise of reinforcement learning techniques in addressing complex network optimization problems such as influence maximization.
Fine-tuning SBERT for Semantic Research Title Classification in Trilingual University Repository
Get PDFRecommendation systems are essential for automatically surfacing relevant content from large datasets, reducing search time, and facilitating discovery. In academia, content-based recommendation systems are beneficial when only brief titles are available and multilingual text is standard. Universities in the Kurdistan Regional Government currently lack a centralized research repository, with records scattered across different institutions and often manually maintained. This makes it difficult for students and faculty to find related topics, potential supervisors, or cross-disciplinary connections. This paper presents a trilingual (English, Arabic, and Kurdish) recommendation system for academic research titles. Three key contributions are made: (1) the creation of the first integrated dataset of 4,257 research titles from Sulaimani Polytechnic University publicly available; (2) the development of a web-based platform for semantic search and title-level recommendations to support research discovery and student–supervisor matching; and (3) an evaluation between Sentence-BERT models—all-MiniLM-L6-v2 and paraphrase-multilingual-MiniLM-L12-v2—before and after fine-tuning with a domain-specific taxonomy and cosine embedding loss. Performance is assessed using Precision@5, Mean Reciprocal Rank, and NDCG@5 with expert-annotated relevance judgments for 20 query titles. Fine-tuning resulted in performance improvements, with paraphrase-multilingual-MiniLM-L12-v2 achieving Precision@5 of 0.94 and NDCG@5 of 0.991. The English-only model also showed improvements, Precision@5: 0.79→0.82; NDCG@5: 0.885→0.922