Al-Neelain University

Background Cutibacterium acnes (C. acnes) is a causative agent in the development of acne vulgaris, and this bacteria has been reported to show resistance against conventional antibiotics. One of the vital factors contributing to antibiotic resistance is the ability of C. acnes to form biofilms. Thus, the purpose of this review is to assess the efficacy of various recent developments and to identify acceptable methods for preventing infections associated with C. acnes biofilms. Methodology A variety of criteria considered in the selection process, such as the site of infection, the mechanism of action against biofilms, and the methodology used to evaluate antibiofilm activity, were taken into consideration when choosing the studies. Results The findings of existing research on the antibiofilm potential of conventional anibiotics, natural products and novel treatment strategies against C. acnes were compiled and compared. Clinical trials demonstrated that dalbavancin reduced biofilm formation while niosomes effectively decreased inflammation in acne lesions. Some studies have shown promising results with bacteriophages, plant-based and nanomaterial treatments, but lack further validation in the way of pre-clinical and clinical trials to accurately measure treatment effectiveness. Conclusions The review examines a range of effective agents and explores their potential applications in acne management, offering valuable insights for clinicians—especially dermatologists—seeking to optimize patient care. In addition, this review provides an understanding about the different agents and their antibiofilm properties that enable researchers to develop effective therapeutic approaches against C. acnes biofilm-related infectious diseases for the benefit of human health.

The Tagotieb gossans near Derudeb in the Red Sea Hills of northeastern Sudan occur within the Haya Terrane of the Arabian-Nubian Shield, in a geological setting prospective for volcanogenic massive sulphide (VMS) mineralisation. Two principal gossan bodies are hosted by foliated acidic metavolcanic rocks, together with associated altered granite and gabbro. Field mapping, petrographic analysis, and ore microscopy indicate that goethite, with subordinate hematite and other alteration minerals dominate the gossans, and that they preserve relict sulphides and native gold. Brecciated, botryoidal, colloform, and boxwork textures suggest the development of a mature, largely indigenous gossan formed above a polymetallic sulphide system. Landsat-9 and Sentinel-2 surface reflectance data were analysed using mineral-sensitive band ratios, spectral indices, false-colour composites, and principal component analysis to enhance iron oxides, clay minerals, barite, and silica-rich zones. Supervised Support Vector Machine (SVM), Mahalanobis Distance (MD), and Artificial Neural Network (ANN) classifiers, trained using field-validated samples, delineated gossans and host lithologies with overall accuracies of 78%, 74%, and 69%, respectively. The spatial distribution of alteration defines a NE-SW-trending corridor intersected by NW-SE-oriented structures controlled by regional tectonics. This configuration supports a three-stage genetic model involving sulphide oxidation, Fe-oxyhydroxide evolution, and subsequent mechanical reworking. These results demonstrate that multispectral indices combined with supervised machine-learning approaches provide a practical and cost-effective framework for targeting VMS-related gossans in the Red Sea Hills and comparable Neoproterozoic terranes.

CdxCo1−xFe2O4 (x = 0.25, 0.50, 0.75) spinel ferrite nanoparticles were successfully synthesized using the co-precipitation technique. X-ray diffraction (XRD) analysis confirmed the formation of cubic spinel structure with space group Fd̅3m for all compositions. The structural analysis revealed a non-monotonic dependence of the average crystallite size (D) on Cd content, varying from a minimum of 9.58 nm at x = 0.50 to a maximum of 16.36 nm at x = 0.75. A similar non-linear trend was observed in the defect-related parameters, where the dislocation density (δ), microstrain (ε), and stacking fault probability (SF) reached their highest values at x = 0.50 (δ = 10.90 × 10−3 lines/nm2, ε = 11.73 × 10−3, SF = 7.04 × 10−3). Scanning electron microscopy (SEM) images confirmed a transition from spherical nanoparticles to well-defined crystalline facets as cadmium substitution increases. Also, SEM analysis demonstrates that Cd2+ substitution significantly promotes grain growth and alters surface topography, providing a mechanism for tuning the microstructural properties of spinel ferrites. Fourier-transform infrared spectroscopy (FTIR) confirmed the formation of the spinel structure through the characteristic metal–oxygen stretching vibrations at tetrahedral (551–587 cm−1) and octahedral (416–458 cm−1) sites. The presence of residual organic species from the washing process was also identified. UV–Vis diffuse reflectance spectroscopy revealed that the optical bandgap (Eg) decreases monotonically with increasing Cd2+ concentration, from 4.89 eV (x = 0.25) to 4.83 eV (x = 0.50), and further to 4.79 eV (x = 0.75). This bandgap narrowing is attributed to lattice expansion, cation redistribution, and the possible introduction of mid-gap defect states. These findings demonstrate that controlled cadmium substitution provides an effective strategy for tailoring both the structural characteristics and optical bandgap of cobalt ferrite nanoparticles. The composition with x = 0.75, exhibiting the largest crystallite size, lowest defect density, and smallest bandgap, is identified as the most promising candidate for potential applications in photocatalysis and optoelectronics.

The simultaneous evaluation between the yield and physicochemical properties in the production of biodiesel from waste cooking oil is of great importance. In this work, an experimental study to produce fatty acid methyl ester (biodiesel) from waste cooking oil (WCO) via transesterification reaction is conducted. The study aims to optimize the maximum yield with the best physicochemical properties for the three main transesterification process parameters: methanol/WCO molar ratio, reaction time and NaOH concentration. Several fuel properties were measured to evaluate the biodiesel product according to the ASTM standards. Accordingly, the optimum values of the reaction condition are as flows: methanol/WCO molar ratio 7.5:1, reaction time 90 min and catalyst concentration as 1.0 wt.

This study presents a hybrid deep learning framework, the Vision Transformer with Residual Feature Network (VRF-Net), for recovering high-resolution system matrices in Magnetic Particle Imaging (MPI). MPI resolution often suffers from downsampling and coil sensitivity variations. VRF-Net addresses these challenges by combining transformer-based global attention with residual convolutional refinement, enabling recovery of both large-scale structures and fine details. To reflect realistic MPI conditions, the system matrix is degraded using a dual-stage downsampling strategy. Training employed paired-image super-resolution on the public Open MPI dataset and a simulated dataset incorporating variable coil sensitivity profiles. For system matrix recovery on the Open MPI dataset, VRF-Net achieved nRMSE = 0.403, pSNR = 39.08 dB, and SSIM = 0.835 at 2x scaling, and maintained strong performance even at challenging scale 8x (pSNR = 31.06 dB, SSIM = 0.717). For the simulated dataset, VRF-Net achieved nRMSE = 4.44, pSNR = 28.52 dB, and SSIM = 0.771 at 2x scaling, with stable performance at higher scales. On average, it reduced nRMSE by 88.2%, increased pSNR by 44.7%, and improved SSIM by 34.3% over interpolation and CNN-based methods. In image reconstruction of Open MPI phantoms, VRF-Net further reduced reconstruction error to nRMSE = 1.79 at 2x scaling, while preserving structural fidelity (pSNR = 41.58 dB, SSIM = 0.960), outperforming existing methods. These findings demonstrate that VRF-Net enables sharper, artifact-free system matrix recovery and robust image reconstruction across multiple scales, offering a promising direction for future in vivo applications.