The current work aims to enhance the structural, optical, photocatalytic, and cytotoxic properties of CuO NPs at varied rGO concentrations of 5% and 10%. In the present work, a one-step hydrothermal method was successfully applied to prepare rGO/CuO NCs at different concentrations of RGO. The novelty of this work was to enhance the structural, optical, photocatalytic, and cytotoxic properties of CuO using the addition of rGO sheets. XRD, TEM, SEM-EDX, XPS, FTIR, UV-vis, PL, and DLS techniques were used to characterize the prepared samples. XRD data confirmed the formation of the monoclinic phase of CuO with a decrease in crystallite size, from 21.14 nm for CuO to 16.94 nm for the 10% rGO/CuO NCs nanocomposite. SEM and TEM images verified the uniform anchoring and excellent dispersion of CuO nanoparticles on the rGO sheets, and the EDX spectra showed the presence of Cu, O, and C elements in the obtained rGO/CuO NCs. DLS measurements showed that the hydrodynamic radius dropped from 69.98 ± 17.81 nm for CuO to 51.72 ± 10.48 nm for 10% rGO/CuO NCs. The zeta potential values remained negative for all samples, ranging from −20.50 ± 8.69 mV for CuO to −25.60 ± 9.08 mV for 10% rGO/CuO NCs, suggesting enhanced colloidal stability with rGO incorporation. Furthermore, FTIR and XPS analyses confirmed that Cu–O–C bonding formed between CuO and rGO. UV-Vis analysis revealed a redshift in the absorption edges as rGO content increased, reducing the band gap from 3.65 eV to 3.60 eV. Additionally, PL spectra showed a marked reduction in emission intensity due to a decrease in the recombination rate between electron (e⁻)–holes (h⁺) pairs. The CuO/(10%)rGO NCs showed the best photocatalytic performance with a 93.56% degradation of methylene blue (MB) after 120 min under UV irradiation, and followed pseudo-first-order kinetics with k = 0.0203 min⁻¹. Cytotoxicity studies on HT1080 cells showed a dose-dependent decrease in viability. 10% rGO/CuO NCs exhibited the highest cytotoxicity effect, resulting in 58% and 50% viability at 1.4 mg/mL, respectively. The presented results showed that the presence of rGO in CuO NPs played a role in enhancing the structural stability, charge mobility, and biological reactivity of Cu NPs. This study highlighted that the rGO/CuO NCs are a promising multi-functional material for environmental and biomedical applications.

Background: The steady rise of prostate cancer in Saudi Arabia signals a critical public health shift that requires immediate investment in early detection and prevention to mitigate a future clinical crisis. Accurate diagnosis using multiparametric MRI and PI-RADS scoring remains challenging, as interpretations are highly experience-dependent and subspecialized radiologists are limited. Methods: To address this gap, this study introduces a novel Dual-Stream Attention Network designed to automate the classification of low-risk (PIRADS 2-3) versus high-risk (PIRADS 4-5) lesions from T2-weighted MRI. Leveraging a ResNet50 backbone, the architecture employs parallel streams for Local and Global Feature Processing, each enhanced by a Channel-Spatial Attention module to highlight diagnostically relevant regions. These features are integrated through a Cross-Stream Fusion mechanism and a gate-controlled Adaptive Feature Fusion module to optimize multi-scale information. The model was developed and validated on a regional dataset of 3850 images from Jazan Specialist Hospital and Prince Mohammed bin Naser Hospital. This research provides a standardized, high-precision diagnostic path tailored to the Saudi Arabian population, conducted under institutional review board approval (No. 25138). Results: The proposed dual-stream attention network achieved an accuracy of 97.8% on the validation set and 96.4% on the test set, demonstrating high performance and generalization capabilities in classifying prostate lesions from Saudi patient populations. Conclusions: The proposed dual-stream architecture with novel attention and fusion mechanisms demonstrates high effectiveness for prostate cancer classification from T2-weighted MRI in Saudi clinical settings. This represents the first deep learning model specifically trained and validated on Saudi Arabian prostate MRI data, with the potential to address the shortage of specialized expertise and improve diagnostic efficiency in the Kingdom.