Zanco Journal of Pure and Applied Sciences

Lung cancer (LC) remains one of the key factors contributing to cancer-related deaths globally, necessitating precise and personalized therapeutic approaches. The clinical management of LC has been markedly advanced with the identification and incorporation of biomarkers, facilitating more accurate and individualized treatment strategies. Biomarkers are essential for directing targeted therapeutic approaches, selecting immunotherapy, and predicting prognosis. The usage of genetic-based biomarkers, including the identification of driver mutations, has incredibly generated genotype-directed therapy and constructed altering LC treatment from cytotoxic chemotherapy into targeted and individualized therapy. They have become essential tools for managing LC, enabling focused treatments, and bettering patient outcomes. Despite the considerable promise of biomarker-driven precision medicine, substantial challenges remain to be addressed. These challenges include the genetic heterogeneity of LC, the variability in the sensitivity and specificity of biomarker assays, and the complexity of incorporating biomarker data into clinical decision-making. However, many biomarkers lack the necessary sensitivity and specificity for early-stage LC detection, and the emergence of these biomarkers remains poorly understood. Thus, in this study, in order to fully exploit the capabilities of these advancements, continuous research efforts should prioritize addressing the constraints of existing methods, creating reliable techniques that involve several biomarkers, and guaranteeing the widespread availability of advanced diagnostic tools.

A simple, selective, cost-effective, and reliable kinetic spectrophotometric method has been developed for the simultaneous quantification of sulfamethoxazole (SMX) and trimethoprim (TMP) in pharmaceutical formulations. The method is based on the differential oxidation reaction rates of SMX and TMP with potassium permanganate (KMnO₄) in an alkaline medium (NaOH), resulting in the formation of green potassium manganate (K₂MnO₄), which exhibits a maximum absorbance at 610 nm. Kinetic studies of the oxidation reaction were conducted, and simultaneous determination of SMX and TMP was achieved using the H-point standard addition method. Absorbance measurements were recorded at 610 nm after 120 and 300 seconds following the addition of TMP. The method demonstrated high accuracy and precision, with percentage errors of 1.04% to -2.24% for SMX and 3.01% to -2.33% for TMP. Repeatability, assessed over five consecutive determinations of 10 µg/mL SMX and 4 µg/mL TMP, yielded relative standard deviations of 1.39% and 0.58%, respectively. The detection limits were 0.7 µg/mL for SMX and 0.3 µg/mL for TMP. The method was successfully applied to the analysis of various tablet formulations containing both active pharmaceutical ingredients.

Backgrounds: Researchers have just commenced the exploration of miRNAs as a prospective new class of biomarkers. This study investigates the potential of miR-1226 as a significant prognostic factor for BC. Methods: The miR-1226 expression was detected in 30 pairs of FFPE tissues using qRT-PCR. The clinicopathological characteristics of patients regarding miR-1226 expression, together with fold change analysis using the 2-ΔΔCT method, were further investigated. All statistical analyses were performed with GraphPad and MedCalc. Results: We found that the miR-1226 level in BC FFPE tissues is slightly increased compared to control tissues. Also, we examined the relationship associated with clinicopathological features and miR-1226 expression levels of BC patients. This investigation revealed no significant association between miR-1226 expression levels and the clinical progression. Age, lymph node involvement, and Ki-67 expression had no significant correlation with outcome status. No significant relationships were observed between tumor-related factors such as grade and size. The hormone receptor status, PR status, and HER-2 status did not exhibit a significant correlation with outcome stratification. The trend regarding PR status may necessitate additional examination for its prognostic significance. Moreover, the p-value, AUC, and Std. Error, sensitivity, and specificity are (p < 0.006, 0.684,0.0723, 83.3, and 60, respectively), which signifies a moderate capacity of the test to differentiate between tumors and controls. The results of fold change analysis employing the 2-ΔΔCT method indicated a (1)-fold elevation in miR-1226 expression in tumors relative to controls. Conclusions: We demonstrated that a slight elevation of miR-1226 expression correlates with the progression of BC, and this indicates that miR-1226 may possess an oncogenic function in BC tumorigenesis and progression.

The development of high-performance, environmentally friendly lubricants is crucial for sustainable industrial practices. This study investigates the synthesis and comprehensive characterization of binary and novel ternary deep eutectic solvents (DESs) made from choline chloride (ChCl) with various hydrogen bond donors (HBDs): urea, glycerol (Gly), ethylene glycol (EG), and oxalic acid (OA). Fourier-transform infrared FT-IR spectroscopy confirmed the formation of stable hydrogen-bonded networks in all DES formulations. Detailed analysis of their physicochemical, tribological, and electrochemical properties revealed the superior performance of ternary DESs. In particular, the ternary systems ChCl/Urea/EG (1:1:1) and ChCl/Gly/EG (1:1:1), show very high viscosity index (VI > 175), and excellent fluidity at low temperatures (pour points as low as −47°C). These are superior-performing compared to regular mineral base oils. Tribological tests demonstrated that several DESs, notably ChCl/Gly and ChCl/Urea/Gly, significantly reduced the coefficient of friction (μ≈0.170–0.186) through the formation of stable boundary films. Electrochemical analysis using Tafel plots unveiled a critical dichotomy in corrosion behavior towards iron. OA-based DESs caused active dissolution due to their acidic nature, while urea, glycerol, and EG-based DESs induced passivation. Glycerol and EG notably acted as corrosion inhibitors in the aggressive ChCl/OA system, although the ChCl/Urea/EG system exhibited pitting corrosion. This behavior was linked to mass transfer limitations imposed by DES viscosity, influencing both cathodic reactant supply and anodic passive layer formation. Overall, this study highlights that while ternary DESs offer a promising combination of physicochemical and tribological properties for lubrication, their interaction with metal surfaces is a critical design parameter. The choice of HBDs directly dictates the corrosion mechanism, underscoring the necessity for a holistic approach in designing next-generation DES-based lubricants.

Background: Pseudomonas aeruginosa is an opportunistic pathogen that contributes to treatment failure due to its strong biofilm-forming ability and multidrug resistance (MDR). This study intended to assess the antibacterial and antibiofilm properties of silver nanoparticles (AgNPs) synthesized from Glycyrrhiza glabra root extract against MDR-P. aeruginosa clinical isolates.

Methods: 128 clinical specimens were collected from patients who were receiving medical care in Erbil, Kurdistan Region, Iraq. Clinical specimens were cultured and analyzed using standard biochemical tests to identify P. aeruginosa. Antibiotic susceptibility testing was performed for identify MDR isolate. Biofilm production was evaluated with a microtiter plate technique through safranin staining. AgNPs were synthesized from an aqueous extract of G. glabra root. The antibacterial activity was assessed by a well diffusion test, and the Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) were determined using microdilution in 96-well plates. AgNPs were tested for their capacity to inhibit and eradicate biofilms.

Results: Thirty-four (26.6%) isolates were identified as P. aeruginosa, of which 26 (76.5%) exhibited MDR. Among these MDR isolates, 80.7% exhibited robust biofilm formation, 11.5% moderate, 3.8% weak, and 3.8% produced no biofilm. AgNPs demonstrated inhibition zones measuring 17 mm in 1500 µg/mL, and MIC values between 62.5 and 500 µg/mL. At a concentration of 500 µg/mL, AgNPs exhibited 97% biofilm inhibition and 85% eradication.

Conclusion: AgNPs derived from G. glabra root extract shown significant antibacterial and antibiofilm properties against MDR-P. aeruginosa isolates. These findings demonstrate their potential as alternative therapeutic agents for the management of MDR-P. aeruginosa infections.

This study investigates a modified fractional Stretch-Twist-Fold (STF) model with a Caputo fractional-order derivative. The local stability and Hopf bifurcation of equilibrium points are analyzed by using an Adams-type predictor-corrector method implemented in MATLAB software. We study the influence of variation of parameters on the system's behavior and verify chaotic dynamics by computing maximal Lyapunov exponents. In addition to supporting analytical findings, numerical simulations are used to reveal chaotic characteristics such as bifurcations, phase portraits, limit cycles, and attractive chaotic sets, highlighting the crucial role of the fractional-order derivative in the system's dynamic behavior.

In this study, aging effect on gut microbiota, production performance, and product quality in two commercial laying strains Lohmann Brown and Lohmann White under intensive farming system in the Kurdistan Region were evaluated. At 90 weeks of age, a population of 504,000 hens was examined with respect to changes in composition of microbiota, egg production, carcass traits, and reproductive physiology. Inter-strain substantially disparity was demonstrated by the statistical analyses. White Lohmann chickens showed improved feed conversion efficiency and egg production due to a significantly higher abundance of beneficial lactic acid bacteria (LAB) in their cecal contents. On the other hand, the Brown Lohmann had a heavier egg, better body weight and carcass yield with better thigh, back muscle rate. Their reduced rate of mortality suggests better tolerance at late production stages. Nevertheless, their cecal microbiota showed an elevated proportion of Enterobacteriaceae, reflecting an unfavorable gut ecology. Reproductive organ assessment showed that White hens displayed more developed ovaries, whereas Brown chickens had larger oviducts, which likely contributed to the larger size of their eggs. Results indicated that viable plate counts and product quality in late life are more spatially associated with gut colonization than with the gut colonization levels determined by strain resistance, overall levels of host health, or the effects of age on the host morphology. These findings justify the use of customized dietary and microbiological strategies in order to ensure longevity of productive performance in aged layer flocks.

Excessive water use is one of the world's largest problems today. Using an appreciating irrigation system rather than a conventional irrigation system to irrigate agriculture is one way to address this problem. The research was carried out in the Grdarasha Field, College of Agricultural Engineering Sciences, Salahaddin University, Erbil, Kurdistan Region. The location was divided into lines for three sweet corn hybrids (Talar F1 (Biotek) H1, MESSENGER (Semins) H2 and SENTINEL (Talar type) EliSem (CLAUSE) H3, with seven irrigation systems. The experiment was applied with (3×7) Factorial randomized complete block design with three replicates. Implement Responsive Drip Irrigation (RDI) systems with axial pipes were installed at two depths (8 cm and 16 cm) and at varying distances (0 cm, 7.5 cm, and 15 cm) from the axial pipe. The irrigation systems assigned as follows: I1 Standard Drip Irrigation (SDI), I2: (RDI. 16 cm, 0 cm), I3: (RDI. 16 cm, 7.5 cm), I4: (RDI. 16 cm, 15 cm), I5: (RDI. 8 cm, 0 cm), I6: (RDI. 8 cm, 7.5 cm) and I7: (RDI. 8 cm, 15 cm). At eight Weeks After Emergence the tallest plant was recorded for H1 with I1 and I2. According the irrigation systems, RDI (I2, I3, I4 and I5) increased significantly (P<0.05) the wet kernal weight per cob compared with SDI. Kernal yield and harvest index increased significantly (P<0.05) for all RDI systems compared with SDI; the highest Kernal yield recorded for I2 (10.83 ton/ha) compared with SDI (6.66 ton/ha). Also, all RDI treatments reduced water use compared to SDI. Irrigation Water Use Efficiency (IWUE) increased significantly (P<0.05) in all RDI system compared to SDI system. This investigation found that the treatments using responsive drip irrigation (RDI) show substantial promise in reducing water use while maintaining crop productivity.

In this study, forty Platanus orientalis trees located in duhok-Kurdistan Region of Iraq were selected to measure tree height using two different methods: UAV-Terra and Terra software, and Total station (TS), then the results were compared with that measured by tape-mounted drone when wind speed is less than 5km/hour. Results showed the tree heights measured using the TS closely aligned with those obtained from the tape-mounted drone and Terra software method demonstrating high accuracy (R2 was 98.51%). Among the thirty-nine observations, a strong correlation was also found between tape-mounted drone and UAV-Terra with an R2 value of 90.89%. The results also depicted that the best model that can describe the relationship Between Tape- mounted drone and the UAV-Terra values was a Double-squared one: (Tape-mounted drone = sqrt (18.6982 + 0.946374*UAV-Terra^2)), and the model that fits the relationship Between Tape- mounted drone and the TS values was linear: (Tape- mounted drone = 0.1997 + 0.98302*TS).

The main objective of this research is to identify the role of agricultural professional leadership in the diffusion of agricultural innovation. In addition, it seeks to rank the main fields of this leadership role in innovation diffusion and to analyze its relationship with a set of independent variables. A questionnaire was developed to collect the research data, comprising two main sections: the independent variables and the role of agricultural professional leadership in the diffusion of agricultural innovation. The latter section was organized into two fields: the first field included eleven items addressing the preparation of agricultural innovation, while the second field encompassed nineteen items related to the diffusion of agricultural innovation. The research population comprised all 94 agricultural professional leaders in the extension centers of Sulaimani Governorate, distributed across its districts and sub-districts. The research findings indicated that the role of professional leadership in diffusing agricultural innovation in Sulaimani Governorate was moderate, with the diffusion dimension ranking higher than innovation preparation. Additionally, this role was significantly correlated with age, educational attainment, and attitudes toward innovation, while no significant relationships were found with gender, academic specialization, years of extension service, participation in training programs, or leadership style. Therefore, the researchers recommend involving professional leaders in the development and adaptation of agricultural innovations, not just in their diffusion, and consider experience, educational attainment, and positive attitudes toward innovation as key criteria for their selection and promotion.

This research evaluates how organic amendments, particularly water hyacinth compost (WHC) and chicken manure (CM), can rehabilitate the degraded sandy loam soil at the J.K. College Premises (CP), Purulia, West Bengal, India. Soil quality and plant growth had been severely impacted due to construction activities. Spinach was used as an indicator crop because of its rapid foliar growth, making it a sensitive marker for amendments’ effects. Treatment with WHC and CM significantly improved several growth parameters, including root length, root diameter, shoot height, and leaf area. The application of WHC and CM (organic compost and manure, referred to as OCM or amended soil) increased soil fertility, evidenced by reductions in bulk density from 1.194 g/cm³ to 0.825 g/cm³ and an increase in porosity from 45.28% to 68.87%. Total nitrogen rose from 357.8 to 1120 mg/kg, with notable gains in phosphorus and potassium as well. Statistical analysis using Two-Way ANOVA confirmed significant treatment effects. Results were visualized through matrix-based heatmaps in R 4.3.2, showing clear biometric trends. Microbial diversity, assessed via 16S rRNA and ITS amplicon sequencing, was markedly higher in OCM (2,962 bacterial and 937 fungal species) compared to CP (427 and 899 species, respectively). This elevated diversity in OCM soil reflects the effect of nutrient-rich inputs like WHC and CM, which support beneficial taxa such as Actinomadura keratinolytica. In contrast, the lower diversity in CP soil suggests nutrient limitations that may weaken ecological resilience and hinder crop productivity, with dominated by Bacillus mannanilyticus. Fungal community shifts were less pronounced. Visualization tools, including stacked bar charts at various taxonomic levels and Sankey diagrams for dominant taxa, effectively illustrated microbial community changes. Overall, this study shows that WHC and CM amendments significantly restore soil health, boost microbial diversity, and improve crop productivity in degraded semi-urban soils.

Chronic diseases such as diabetes, cardiovascular conditions, and respiratory ailments are increasing globally, necessitating continuous and efficient health monitoring. This research develops a multi-sensor IoT-based system for remote, real-time monitoring of vital health parameters including non-invasive blood glucose, heart rate, oxygen saturation (SpO2), and body temperature. The system integrates the MAX30100 sensor for blood glucose, heart rate, and SpO2 measurement, and the DS18B20 sensor for body temperature, all interfaced with a Raspberry Pi 4B microcontroller. Additionally, a SIM7600E GSM/GNSS module provides patient location tracking to enhance emergency response. Data are securely transmitted and stored on a cloud platform and accessed via a cross-platform mobile application, facilitating timely clinical interventions and personalized care. Validation against clinical hospital tests showed approximately 90% accuracy for non-invasive glucose monitoring and over 96% accuracy for other vital signs, demonstrating reliable performance. This low-cost, portable, and pain-free monitoring solution addresses the limitations of traditional invasive methods, improving chronic disease management, reducing hospital visits, and supporting proactive healthcare delivery, particularly in underserved regions. The proposed system was evaluated on 80 participants (male and female, aged 1–80 years) and its performance was compared with standard medical devices. Following calibration using a regression model, glucose readings achieved an overall accuracy of approximately 90%, while the mean errors for SpO₂, heart rate, and body temperature were 2%, 4%, and 3%, respectively. These findings demonstrate that the system provides reliable performance for most physiological parameters. Future work will focus on incorporating advanced machine learning algorithms to enhance glucose prediction, extending the system to monitor additional parameters such as ECG and blood pressure, and conducting large-scale trials with diverse patient populations to confirm its reliability and clinical applicability.

Modern AI models use deep architectures that obscure how predictions are made. Without understanding how models reach their predictions, it becomes difficult to verify reasoning, identify biases, or trust their reliability in high-stakes domains like healthcare. Many COVID-19 chest X-ray (CXR) studies report high accuracy and present qualitative gradient-weighted class activation mapping (Grad-CAM) heatmaps, providing no quantitative evidence of alignment with lung anatomy and relying on manual, subjective inspection. We introduce an automated quantitative pipeline that converts interpretability into objective, anatomy grounded metrics between Grad-CAM heatmaps and lung masks. We evaluate six convolutional neural networks (CNNs): VGG16, VGG19, ResNet-101, NASNet-Mobile, NASNet-Large, and Xception, for both classification performance and anatomical interpretability in COVID-19 CXR detection. Classification accuracies ranged from 90% to 96%, with Xception achieving the highest accuracy (95.90%) and a balanced precision, recall, and F1-score of 95.92%. NASNet-Large and VGG19 followed at 94.87%, with VGG19 reaching the highest precision (98.89%). To assess model transparency, we automated interpretability analysis by thresholding the Grad-CAM outputs and comparing them to radiologist-annotated lung masks using Intersection-over-Union (IoU) and Dice score metrics.

Accurate short-term electricity demand forecasting is essential for ensuring reliable power supply, optimizing grid operations, and supporting sustainable energy planning. In Tamil Nadu, seasonal variability, economic growth, and climatic anomalies make peak demand prediction particularly challenging. This study aims to develop a robust forecasting framework that addresses these challenges by integrating statistical time-series modeling with machine learning techniques. Historical monthly peak demand data from April 2006 to March 2023, sourced from the Tamil Nadu Transmission Corporation, were analyzed alongside climate and socio-economic variables. Preprocessing involved missing value imputation, stationarity checks, and feature engineering, followed by model development using Autoregressive Integrated Moving Average (ARIMA), Vector Autoregression (VAR), and a hybrid VAR–machine learning ensemble incorporating Lasso, Ridge, and XGBoost regressors. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Results show that the ARIMA model achieved the lowest MAPE (1.26%), outperforming VAR and hybrid approaches, particularly in capturing seasonal trends. However, error margins increased during anomalous months influenced by extreme weather events, highlighting the need for incorporating additional real-time predictors. This research demonstrates that a well-calibrated ARIMA model offers a reliable and practical solution for Tamil Nadu’s short-term peak demand forecasting, providing actionable insights for policymakers, utility planners, and grid operators.

Construction projects are major contributors to environmental degradation, as they generate substantial quantities of waste, intensively consume natural resources, and accelerate resource depletion. This study examines the key factors influencing construction waste generation from a professional perspective. Specifically, it identifies the types and sources of waste produced during the construction of residential complexes in the Erbil Governorate and evaluates the prevailing waste disposal practices. A questionnaire survey was administered to 150 professionals, covering 54 factors categorized into seven major groups, distributed among relevant stakeholders. The analysis revealed that management-related factors represent the most significant contributors to waste generation, followed sequentially by material-handling practices, worker-related issues, and design deficiencies. Ten factors were classified between the categories of “most important” and “very important.” The most influential waste-generating factors included inadequate planning, inappropriate material storage methods, poor site management, absence of waste management plans, insufficient control and supervision, construction errors, inefficient unloading practices, design changes, and material damage caused by workers. Findings further demonstrated that no formal waste management strategies are currently implemented in residential complex projects. Instead, construction waste is often disposed of illegally in remote areas without consideration of environmental impacts. The study emphasizes the urgent need for the enforcement of regulatory frameworks by Erbil city authorities to ensure the sustainable management of construction waste, thereby mitigating environmental pollution and promoting resource efficiency.

The implementation of a solar chimney in buildings can enhance ventilation and create a comfortable indoor environment using renewable energy. A research study conducted in Erbil City, Iraq, utilized numerical modelling with MATLAB to investigate the impact of design parameters on the ventilation performance of rooftop solar chimneys throughout the year. The study focused on the relationship between solar energy availability and its conversion within the chimney, particularly considering inclination angles. Results demonstrated that the highest solar radiation occurred in June, while the lowest was observed in December. The angle at which the solar incident struck the chimney had a significant impact on the amount of solar energy captured at different times of day and in different seasons. During the summer, a 30-degree inclination angle maximized solar energy capture, whereas a 60-degree angle was best for the spring and fall. Interestingly, with a chimney angled vertically, wintertime energy conversion efficiency is at its maximum. The conversion of solar energy to thermal energy within the chimney was also impacted by the angle of inclination. A 60-degree inclination angle was found to be ideal for a 2 m2 solar chimney arrangement in Erbil City. The amount of solar energy captured is greatly influenced by the angle at which sunlight strikes the chimney, which varies depending on the season and time of day. In the summer, a 30-degree tilt is ideal for capturing the most energy; in the spring and fall, a 60-degree slant works best. It's interesting to note that in the winter, a vertical chimney achieves the best energy conversion efficiency. The process by which solar energy is converted into thermal energy inside the chimney is also greatly influenced by this inclination angle. For example, a 60-degree angle was found to be the most efficient in a 2 m² solar chimney arrangement in Erbil City, producing a ventilation rate exceeding 3.75 air changes per hour (ACH) for a 27 m³ space. The study finds that these design factors can greatly improve the ventilation effectiveness of solar chimneys.