Iraqi Journal for Applied Science

Empagliflozin is a selective sodium-glucose cotransporter-2 (SGLT2) inhibitor widely used in the management of type 2 diabetes (T2DM). In addition to its glucose-lowering activity, empagliflozin possess anti-inflammatory and metabolic regulatory properties. Diabetes is a chronic metabolic disorder characterized by persistent hyperglycemia and is frequently associated with structural and functional alterations in multiple organs, including the liver. This study evaluated the histopathological effects of empagliflozin on hepatic tissue in an alloxan-induced diabetic rat model. Fifteen adult male rats were randomly assigned into three groups (n = 5 per group): a healthy control group, an untreated diabetic group, and a diabetic group treated with empagliflozin (5 mg/kg/day) for 30 days. Diabetes was induced using alloxan monohydrate, and liver tissue was subsequently collected for histopathological examination using hematoxylin and eosin staining.
Microscopic examination revealed severe hepatic alterations in untreated diabetic rats, including hepatocellular necrosis, central vein congestion, sinusoidal dilatation, hemorrhage, nuclear degeneration, and disruption of normal hepatic architecture. In the empagliflozin-treated group, partial preservation of liver structure was observed; however, several pathological features persisted, including inflammatory cell infiltration, sinusoidal hemorrhage, hepatocellular hypertrophy, nuclear pyknosis, and early hepatic steatosis. Although empagliflozin treatment appeared to attenuate some histopathological abnormalities, complete restoration of normal liver structure was not achieved within the experimental period. The findings suggest that the observed hepatic injury was primarily associated with alloxan-induced toxicity and diabetes-related pathologic processes. Under the conditions of this study, empagliflozin exhibited limited hepatoprotective activity despite modest preservation of hepatic architecture. Further studies involving larger sample sizes, extended treatment durations, and molecular investigations are required to clarify the

This study investigated the efficacy of different concentrations of date vinegar in
inhibiting the growth of the phytopathogenic fungus Fusarium oxysporum. The results
demonstrated that date vinegar suppressed fungal growth, with the 20% concentration
achieving complete inhibition (100%) compared with the control treatment, which
exhibited no inhibition (0%). The findings also revealed that the germination percentage
of cucumber seeds after 10 days increased with increasing concentrations of date vinegar
compared with the treatment inoculated with the pathogen alone. The highest
germination percentage (95.14%) was recorded in the 15% and 20% date vinegar
treatments. Furthermore, the seedling mortality rate was reduced to 0% in all treatments
containing date vinegar, whereas the treatment inoculated solely with the pathogenic
fungus exhibited a seedling mortality rate of 100% after 14 days. The greenhouse
experiment further demonstrated the beneficial effects of date vinegar application on the
vegetative growth characteristics of cucumber plants cultivated in plastic pots.
Significant improvements were observed in plant height, fresh and dry biomass, and leaf
area compared with the pathogen-inoculated treatment. In addition, date vinegar
treatments reduced disease severity caused by the pathogenic fungus, resulting in
improved plant growth and vigor. These findings indicate that date vinegar has
considerable potential as an ecofriendly and sustainable alternative for the management
of fungal diseases in cucumber plants. The results of the present study represent a
promising step toward sustainable agricultural development and environmental
protection by reducing dependence on synthetic chemical pesticides that may adversely
affect ecosystems and human health

This study employed a novel, simple, and cost-effective biosynthesis approach that
combined co-precipitation, ultrasonic treatment, and green chemistry techniques to
synthesize Fe₁.₈₃Mn₀.₅Zn₀.₄₁O₄ nanoparticles using milk thistle (Silybum marianum) seed
extract. The synthesized nanoparticles were characterized using Fourier Transform
Infrared Spectroscopy (FT-IR), X-ray Diffraction (XRD), Energy-Dispersive X-ray
Spectroscopy (EDX), Zeta Potential analysis, and Scanning Electron Microscopy (SEM).
XRD analysis revealed an average crystallite size of 18.66 nm, whereas SEM analysis
indicated an average particle size of 114.38 nm, suggesting particle aggregation. The
anticancer activity of the synthesized Fe₁.₈₃Mn₀.₅Zn₀.₄₁O₄ nanoparticles was evaluated
against human leukemia HL-60 cell lines. The results demonstrated significant cytotoxic
activity at higher nanoparticle concentrations, with a pronounced reduction in cell
viability observed at concentrations of 500 and 1000 μg/mL. The percentages of viable
cells at nanoparticle concentrations of 10, 25, 50, 100, 250, 500, and 1000 μg/mL were
85.98%, 83.56%, 84.54%, 98.41%, 97.87%, 29.04%, and 15.81%, respectively. The
calculated IC₅₀ value was 470.8 μg/mL. Statistical analysis using one-way ANOVA
revealed highly significant differences among the treatment groups (P < 0.0001). The
coefficient of determination (R² = 0.9856) indicated excellent model fitting and confirmed
the strong relationship between nanoparticle concentration and biological response. The
biocompatibility of the synthesized nanoparticles was further evaluated using a hemolysis
assay with red blood cells at concentrations of 64, 128, and 256 μg/mL. The results
showed no significant hemolytic activity or cytotoxic effects on erythrocytes, indicating
that the synthesized nanoparticles were hemocompatible and safe within the tested
concentration range

Peptic Ulcer Disease (PUD) is a localized gastrointestinal inflammatory disorder
characterized by mucosal damage and is increasingly recognized as being influenced by
both physiological and psychological stressors. Interleukin-6 (IL-6), a pleiotropic proinflammatory cytokine, serves as an important mediator of inflammatory responses and
may contribute to the interaction between gastrointestinal and psychological disorders.
This study aimed to investigate the relationship between PUD and depression by
evaluating the role of IL-6 as a potential underlying inflammatory mechanism. A casecontrol study was conducted involving 50 patients diagnosed with peptic ulcer disease by
endoscopy and 50 healthy control subjects. Depression was diagnosed by a clinical
psychologist, and its severity was assessed using the Patient Health Questionnaire-9
(PHQ-9). Serum IL-6 levels were measured using enzyme-linked immunosorbent assay
(ELISA). Statistical analyses were performed using Student’s t-test and one-way analysis
of variance (ANOVA). The results showed that serum IL-6 levels were significantly
higher in PUD patients than in healthy controls, increasing from 96.22 ± 16.16 pg/mL in
the control group to 306.85 ± 39.28 pg/mL in the PUD group (p < 0.001). These findings
indicate a strong association between PUD and systemic inflammation. Elevated serum
IL-6 levels support its potential role as a key mediator in the gut–brain axis, linking
gastrointestinal inflammation with psychological distress. Therefore, IL-6 may serve as a
promising biomarker for understanding the inflammatory mechanisms underlying the
association between peptic ulcer disease and depression.

Portable computational imaging has emerged as a promising approach for infectious
disease screening. However, many medical image classification studies rely on imagelevel random data splits and evaluate model performance using a single training run, often
leading to overly optimistic performance estimates. In this study, we implemented PostTraining Quantization (PTQ) for Mpox skin lesion classification using a patient-level
evaluation protocol based on a stringent pre-registered experimental design. The publicly
available MSLD v2.0 dataset was preprocessed at the pixel level, resulting in 755 unique
original images from six diagnostic classes representing 524 patients. A stratified patientlevel split (80% training, 10% validation, and 10% testing) was applied to prevent data
leakage. Each experiment was repeated across three independent data splits and three
training seeds to assess performance variability and robustness. A two-stage transfer
learning framework based on MobileNetV2 was trained and evaluated using FP32,
Float16 PTQ, and full INT8 quantization implemented through TensorFlow Lite (TFLite).
The FP32 baseline achieved an accuracy of 69%, a macro F1-score of 69%, and a macroAUC of 94.7%, with a model size of 4.27 MB. Float16 quantization maintained
comparable predictive performance and model size, whereas full INT8 quantization
reduced the model size to 2.59 MB, representing a 39% reduction compared with the FP32
baseline. Top-2 accuracy remained stable at approximately 89% across all precision
regimes, supporting the feasibility of deployment in triage-oriented clinical settings.
Variability analyses revealed that performance fluctuations were driven primarily by
patient partitioning rather than reduced numerical precision, demonstrating that INT8
quantization preserves clinically relevant performance while substantially reducing
computational and storage requirements for deployment in resource-constrained edge
environments

The rapid emergence of antibiotic-resistant Escherichia coli poses a major global health challenge, particularly in clinical settings where infections are common and increasingly difficult to treat. Among the principal mechanisms contributing to antimicrobial resistance are efflux pumps, which reduce intracellular antibiotic concentrations, and biofilm formation, a structured survival strategy that promotes bacterial persistence and decreases susceptibility to antimicrobial agents. This study aimed to phenotypically detect efflux pump activity and biofilm formation in E. coli isolates obtained from various clinical sources in Baghdad, Iraq, and to evaluate their contribution to multidrug resistance and their association with antimicrobial susceptibility patterns. A total of 305 clinical specimens were collected from patients with urinary tract infections, diarrhea, bloodstream infections, wounds, burns, and respiratory tract infections. Fifty E. coli isolates were identified using conventional microbiological methods and the VITEK 2 automated system. Antimicrobial susceptibility testing was performed against 13 commonly used antibiotics using the disk diffusion method, and the minimum inhibitory concentration (MIC) of colistin was determined. Based on their resistance profiles, 24 multidrug-resistant isolates were selected for further phenotypic analysis. High resistance rates were observed for ampicillin (94%) and cefoxitin (84%), whereas fosfomycin demonstrated complete effectiveness against urinary isolates. Among the multidrug-resistant isolates, 79.1% exhibited biofilm-forming ability, classified as strong (20.8%), moderate (20.8%), and weak (37.5%). Furthermore, 95.8% of the isolates showed efflux pump activity. Both biofilm formation and efflux pump activity were strongly associated with resistance to β-lactams, tetracyclines, quinolones, and cephalosporins. These findings highlight the significant role of efflux pumps and biofilm formation in the development and persistence of multidrug resistance in E. coli. Routine detection of these resistance mechanisms may improve diagnostic accuracy, support appropriate antimicrobial therapy, and strengthen infection prevention and control strategies.

Patients with systemic Lupus erythematosus (SLE) are at great risk of bacterial infection because of immune system dysregulation and immunosuppressive treatment. Urinary tract and bloodstream infections are some of the most frequent complications, with high morbidity. The objective of this study was to determine the etiological defined agents associated with urine and blood samples of patients with SLE and characterize them. A total of 110 urine and 8 blood samples were taken from patients with SLE who appeared at the hospital between March and August 2024. Cultures were handled according to standard microbiological methods, and bacterial strains were identified using Gram staining, biochemistry techniques, and Vitek $4 automated equipment. Blood samples: Gram-positive had a marginal predominance (50%), and Escherichia coli 3 (37.5%) was the commonest isolate, followed by Staphylococcus aureus 2 (25%), K pneumoniae 1 (12.5%), P aeruginosa 1 (12.5%), and Enterococcus faecalis 1 (12.5%). Conclusion: The epidemiology of both UTIs and BSIs, whether about Gram status or species, is mixed, with Escherichia coli and Staphylococcus aureus as the most common overall isolates among SLE patients. Prompt identification and optimal antibiotic treatment are important for minimizing morbidity and preventing major complications in this immunocompromised population.

Precise classification of traffic signs is a basic and intrinsic element in the development of autonomous driving and intelligent transportation systems. This is due to its outstanding role in reducing traffic accidents and improving road safety. The problem revolves around the difference and variation in visual similarity between the categories. As well as in the photographic conditions. This makes the task more difficult for traditional models. In this paper, we present a deep learning approach that combines pretrained deep models in high-precision feature extraction with a unified multilayer classifier (MLP). Able to use these features very efficiently. Regarding the dataset, the proposed framework relied on the GTSRB dataset, which includes more than 50 thousand images and 43 categories. Subsequently, a standardized preprocessing operation is performed, including image normalization and resizing according to ImageNet values. This research uses several diverse deep learning models, including DenseNet121, GoogleNet, ResNet18, ResNet50, MobileNetV2, VGG19, and EfficientNet-B1, to extract high-precision features. Performance is evaluated using the following metrics: accuracy, recall, precision, and F1-score. The study concluded that using DensNet121 features with the proposed classifier we used in our research paper achieved an accuracy of 99.41%, surpassing many previous studies. The results demonstrate that the proposed model achieves outstanding performance and effectively balances high accuracy and computational efficiency, making the model practically applicable in autonomous driving environments.

Biochar is a porous, carbon-rich substance created when biomass is thermally broken down in an oxygen-limited environment. The removal of chromium ions from polluted water has been extensively investigated because of its large surface area and surface functional groups. This study observes the production of biochar from a variety of biomass sources, such as Pomegranate husks (Po- Biochar) and animal waste (An- Biochar), and assesses how well these methods remove Cr(VI) from aqueous solutions. FTIR, FE-SEM, and BET were used to describe the biochar samples. The effects of pH, contact duration, Cr(VI) concentration, and biochar dose were evaluated using adsorption studies. According to the data, An-Biochar exhibited the maximum adsorption capacity. The adsorption process also followed pseudo-second-order kinetics, and the Freundlich isotherm was found to be the best one that can describe the adsorption of chromium ions Cr(VI) over Po-Biochar and An-Biochar samples, compared with the Langmuir isotherm, all of which were noted from the correlation coefficients values R2.