University of Anbar

Background: Pseudomonas aeruginosa intrinsic and acquired resistance to specific antibiotics contributes to the increased prevalence of infections acquired in hospitals. Objective: This study aims to identify the presence of key biofilm-associated genes (algD and pslD) and carbapenemase-encoding genes (VIM and NDM) among local carbapenem-resistant P. aeruginosa (CRPA) isolates. Methodology: The existence of the genes algD, pslD, VIM, and NDM-1 was examined in 30 CRPA isolates. Chi-square analysis was used to evaluate the connection between carbapenem resistance and biofilm density. Results: Among 30 isolates tested, algD and NDM-1 genes were detected most frequently (93.3%), while pslD and VIM genes were found at lower rates (86.6% and 78.5%, respectively). “Carbapenem resistance and Pseudomonas aeruginosa ability to form biofilm were shown to be significantly correlated.” Chi-square analysis revealed a strong relationship between biofilm density and carbapenem resistance (X² = 29.007, p ≤ 0.01), Conclusion: Biofilm development in Pseudomonas aeruginosa contributes to infection persistence and. resistance mechanisms and highlighting the critical role of biofilm in enhancing resistance mechanisms and infection persistence.

Background: Ankylosing spondylitis (AS) represents a chronic inflammatory arthropathy characterized by aberrant immune regulation. Immune checkpoint molecules, including Cytotoxic T-Lymphocyte-Associated Protein 4 (CTLA-4), Programmed Death-1 (PD-1), and Programmed Death-Ligand 1 (PD-L1), serve as critical modulators of immune homeostasis. Their dysregulation may contribute to the pathogenesis of autoimmune disorders. Objectives: evaluate the diagnostic and prognostic potential of soluble immune checkpoint molecules (sPD-1, sPD-L1, and sCTLA-4) as biomarkers for disease activity and progression in patients with ankylosing spondylitis. Methodology: This case-control study enrolled 50 patients with confirmed AS and 50 age- and sex-matched healthy controls. Serum levels of sPD-1, sPD-L1, and sCTLA-4 were quantified using enzyme-linked immunosorbent assay (ELISA). Disease activity was assessed using standardized clinical indices. Statistical analyses included parametric and non-parametric tests, correlation analyses, and multivariate regression models. Result: Patients with AS demonstrated significantly elevated sCTLA-4 levels (2.82±0.41 ng/mL) compared to controls (2.12±0.28 ng/mL, p<0.001). Conversely, sPD-1 (5.43 ± 0.52 ng/mL vs. 6.31 ± 0.47 ng/mL, p<0.001) and sPD-L1 (5.01 ± 0.63 ng/mL vs. 6.24 ± 0.58 ng/mL, p<0.001) were significantly reduced in AS patients. Early-stage disease (<5 years) showed distinct checkpoint profiles compared to late-stage disease (≥5 years), with sCTLA-4 levels correlating positively with disease duration (r=0.68, p<0.001). Levels of soluble immune checkpoint molecules are altered in AS: there are increased levels of sCTLA-4 coupled with decreased levels of sPD-1/sPD-L1. Conclusion: These novel biomarkers could eventually be used for disease monitoring and prognosis; they therefore need further investigation in longitudinal cohorts.

Dengue virus (DENV) does not have any effective antiviral therapy. The Cinnamomum verum has cinnamic acid and oleic acid that could inhibit important viral proteins. To compare their inhibitory capacity with the key DENV proteins through molecular docking, molecular dynamics and in silico ADMET. Phytochemical profiling of the ethanolic extract of the bark was done by GCMS. AutoDock Vina (version 1.2.0) was used to dock cinnamic acid and oleic acid to key proteins of DENV (NS5, NS3, and envelope) in the presence of ribavirin as the reference. The best complexes were then subjected to 50 ns of molecular dynamics simulation and stability measured by RMSD, RMSF, Rg, SASA, hydrogen bonding and RDF. Validated in silico tools were used to predict the ADMET properties. Analysis of GC-MS revealed cinnamic acid (85.92%) and oleic acid (5.33%). The outcome of docking was that the cinnamic acid had the greatest affinity with NS5 (-5.970 kcal/mol) and the capsid protein (-5.755 kcal/mol), and oleic acid showed the highest affinity with the capsid (-6.150 kcal/mol) and then with NS5 (-5.209 kcal/mol). Both ligands had a relatively weak interaction with NS3. Simulation of the molecular dynamics showed the stability of the top complexes, especially the cinnamic acid-NS5 complex, that retained low RMSD (1.6-1.9 A), stable Rg and SASA profiles, and continued hydrogen bonding during the 50 ns period. The use of cinnamic acid in ADMET projections was more preferable, as it was more soluble, orally bioavailable (0.91), and drug-like (QED 0.65), but oleic acid revealed higher lipophilicity and lower drug-like properties (QED 0.29). Cinnamic acid showed specificity towards the NS5 proteins with the help of stable dynamics and good predicted pharmacokinetics, which are features that make it a promising multi-target anti-DENV scaffold. Oleic acid exhibited poor affinity and poor pharmacokinetic properties. The findings are predictive and must be validated using biochemical, cellular, and toxicological means to prove the antiviral efficacy and safety.

Zinc deficiency is a common issue in cotton cultivation that can significantly impact yield and quality, especially when the soil zinc content is unavailable due to a problem related to nutrient competition. A field experiment was conducted during the summer season of 2023, on a farmers’ field in Al-Anbar province, Iraq, to investigate the effect of zinc spraying on cotton yield and its components, as well as quality characteristics, for several cotton varieties. The experiment utilised a split-plot design that was arranged within a randomised complete block design (RCBD) with three replications. The main plots occupied by four zinc concentrations, which were 0, 75, 150 and 225 mg L-1, while the sub-plot contained four cotton varieties: Spearo888, Cocker 310, Pak-cot and Candia. The results showed that the Spearo 888 cotton variety had the highest average in terms of plant height, reaching 156.72 cm, while the Pak-Cot variety had the highest average for traits: branch per plant-1 (12.61), number of total and opened bolls per plant-1 (26.57 and 19.04), boll weight (3.97 g) and total seed cotton yield (2.95 t ha-1). The studied traits improved progressively with increasing zinc concentration, with the highest concentration yielding the best results. The integration of zinc with the varieties led to the best results. The cotton fibre characteristics were not significantly affected by zinc concentrations; rather, variety had the greatest influence, indicating that these characteristics are more influenced by gene action than by environmental conditions.

Staphylococcus aureus is increasingly resistant to β-lactam antibiotics, making non-β-lactam cell-wall-targeting drugs crucial alternatives. Growing resistance to these agents highlights the need to identify genomic factors influencing susceptibility. Machine learning can integrate genomic and phenotypic data to predict minimum inhibitory concentrations (MICs) and uncover resistance mechanisms across time and regions. We obtained 112,360 S. aureus genomes from NCBI GenBank (March 2024), applying quality filters and standardizing metadata. Resistance genes and mutations were identified using AMRFinderPlus and CARD, focusing on glycopeptide, lipopeptide, bacitracin, and fosfomycin resistance. MICs for five antibiotics were compiled, standardized, and log₂-transformed for analysis. Allelic profiles for seven housekeeping genes were assigned using PubMLST's BIGSdb and MLST CLI v2.19.0. Temporal and geographic resistance trends were modeled using logistic regression and statistical tests. Machine learning models (Random Forest, XGBoost, Elastic Net, Partial Least Squares (PLS)) predicted MICs from genomic features, with performance assessed via cross-validation. Statistical analyses and visualizations were performed in R, with all data and scripts provided for reproducibility. We analyzed 111,350 S. aureus genomes from 137 countries, with 78% from clinical sources, 10% from environmental, veterinary, or food-related origins, and some from animals. Glycopeptide MICs were low across all sources: vancomycin (0.96µg/mL) and teicoplanin (0.52µg/mL), while daptomycin showed more variability (0.44µg/mL). Fosfomycin resistance genes, particularly fosB, were detected in 65.3% of genomes overall, with significantly higher prevalence in clinical isolates (32.5%) compared to environmental (2.1%), food (4.0%), and animal sources (7.5%). Bacitracin resistance genes (bcrAB) were detected in 6.2% of clinical isolates versus 1.3% environmental and 2.8% animal sources. However, phenotypic MIC data were severely limited (fosfomycin n = 1, bacitracin n = 1), precluding validation of genotype-phenotype correlations and limiting epidemiological interpretation to genetic prevalence alone. Resistance to glycopeptides and lipopeptides remained rare (< 0.1%). Fosfomycin resistance protein B (fosB) resistance increased by 0.20% annually, especially in clinical and animal sources, while other mutations like glpT_V213I and murA_D278E declined. Geographic trends showed fosB resistance exceeded 50% in North America, Europe, and South America, with MurA_G257D most prevalent in the Middle East. Machine learning models showed moderate predictive performance for daptomycin MICs (R² = 0.49), with mprF mutations as key predictors, but demonstrated poor accuracy for glycopeptides (vancomycin R² = 0.05; teicoplanin R² = -0.13) due to extremely limited MIC variability in the dataset. Fosfomycin and bacitracin models could not be trained due to insufficient phenotypic data (n = 1 each). This study highlights the growing challenge of S. aureus resistance, especially to non-β-lactam antibiotics such as fosfomycin and bacitracin, with clinical isolates showing the highest resistance. Geographic and temporal trends indicate an increasing prevalence of fosB resistance, particularly in clinical and animal sources. Machine learning models failed to predict glycopeptide MICs (vancomycin R² = 0.05; teicoplanin R² = -0.13) due to limited phenotypic variability, achieved moderate success for daptomycin (R² = 0.49), and could not be trained for fosfomycin and bacitracin (n = 1 MIC measurement each) despite high genetic prevalence of resistance determinants. These findings emphasize the critical need for integrated genomic and phenotypic surveillance, and highlight fundamental data requirements for ML-based resistance prediction.

Background The Internet of Things (IoT) is a swiftly advancing technology with wide-ranging applications in smart cities, where substantial data exchange and real-time services necessitate high standards of security, privacy, and operational efficiency. Current IoT frameworks frequently face difficulties in delivering strong protection against emerging post-quantum threats while also preserving system performance, especially in decentralized and data-heavy smart city settings. Methods To tackle these issues, this article introduces a Post-Quantum Ring Signature and ABC-based Effective IoT (PQRAEI) framework. This model combines blockchain technology, post-quantum cryptography, and hybrid swarm optimization methods. Initially, data preprocessing is conducted using the K-Nearest Neighbour (KNN) algorithm for data imputation and min–max scaling for normalization. Secure and privacy-preserving decentralized transactions are guaranteed through a Post-Quantum Linkable Ring Signature (PQLRS) scheme, which functions through a three-phase cryptographic process. Additionally, air quality forecasting is improved using an Artificial Bee Colony (ABC) optimization algorithm enhanced with Q-learning, facilitating efficient exploration and identification of optimal environmental parameters that affect the Air Quality Index (AQI). Results The effectiveness of the proposed PQRAEI framework is assessed against existing models, specifically FHPCC, ELDSE, and PQBFR, utilizing metrics such as signing time, verification time, Peak Signal-to-Noise Ratio (PSNR), latency, and throughput. Experimental findings indicate that PQRAEI achieves significantly reduced latency and increased throughput, while ensuring strong cryptographic efficiency and data integrity in comparison to the benchmark models. Conclusions The proposed PQRAEI framework significantly improves security, privacy, and performance in IoT-based smart city environments. By utilizing post-quantum cryptography, blockchain, and intelligent swarm optimization, the model offers a scalable and future-ready solution.

This study examines the personality of the vizier Muʿāwiya ibn ʿUbayd Allāh ibn Yasār al-Ashʿarī as one of the prominent and influential figures of the Abbasid state during the second century AH. He represents a distinctive model of individuals who combined administrative and political responsibilities on the one hand with scholarly activity in historical and ḥadīth narration on the other. The research aims to shed light on the political and intellectual dimensions of his character, clarifying the nature of his administrative roles and his position within the Abbasid governing apparatus, particularly during the reigns of the caliphs Abū Jaʿfar al-Manṣūr and Muḥammad ibn ʿAbd Allāh al-Mahdī. His close relationship with the Abbasid authority enabled him to play an effective role in managing state affairs and contributing to the formulation of public policies, as well as influencing certain political decisions during a crucial phase of early Abbasid history. Moreover, the study highlights his role in transmitting and recording historical accounts related to the Abbasid caliphs, which constitute an important source for understanding the nature of governance and administrative mechanisms of the Abbasid state. The study concludes that Muʿāwiya ibn ʿUbayd Allāh al-Ashʿarī was a central figure whose contributions significantly shaped the political and intellectual life of the early Abbasid period, leaving a lasting impact on both administration and historical narration.

The research deals with the study of Sufi values in the Maqamat of Imam Jarallah Al-Zamakhshari, focusing on four central terms: example, advice, chastity, and inactivity. The research focuses on analyzing the literary texts in the Maqamat to reveal their spiritual dimensions, while making a comparison between them and the sayings of ascetics and gnostics from the Companions and Followers. It aims to highlight the influence of the early Islamic heritage in formulating these values in Al-Zamakhshari, and to shed light on his distinction in employing them in a literary and advisory style. Through this approach, the research seeks to show the overlap between Sufi literature and Islamic teachings in refining the soul and building spiritual values.

Praise be to Allah, and may blessings and peace be upon our Messenger, His family, and all His companions. The scholars of hadith paid great attention to preserving the Sunnah, protecting it from error, distortion, and alteration. They clarified the status of its narrators through criticism and verification, to distinguish the truthful ones from the liars, and the alert from the inattentive ones. The scholars used various terms for criticizing and evaluating narrators, with different meanings and varying degrees. Among the terms used for criticizing narrators are 'delusion' 'err' and 'misconception'. To study all these aspects, the authors have conducted this study entitled "Examples of Narrators Described by Ibn Hibban (died in 354 AH) as Deluded in his Book 'The Criticized Hadith Narrators - the Weak and the Abandoned': A Comparative Study." It aims to study Ibn Hibban's opinions in these terms and compare them with the opinions of the recognized imams of criticism and verification who preceded him or those who came after him. The study also tries to clarify the extent to which he agrees or disagrees with them. To achieve the purposes of the study, the authors have adopted inductive, analytical, critical, and comparative methods in this study. The study concludes that Ibn Hibban's judgment on these narrators is consistent with the opinions of the recognized imams and scholars of criticism and verification. The study presents the opinions of three narrators and ends with a conclusion outlining the most important results.