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.

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.

Background: Text neck syndrome and early cervical spondylosis are increasingly prevalent among young adults due to prolonged electronic device usage and inadequate postural practices. This study examined the burden of text neck syndrome and its association with digital device use, posture, and neurological symptoms among young Sudanese people. Methods: A descriptive cross-sectional hospital-based study was conducted at the Neurology Clinic of Prince Digna Referral Hospital, Sudan, between November 2024 and January 2025. A total of 174 participants aged 17–63 years were recruited using convenience sampling. Data were collected using a structured interviewer-administered questionnaire and clinical assessment. Statistical analysis was performed using SPSS version 26. Results: The median age of participants was 34 years (IQR: 28-42 years), and 50.6% were females. Prolonged screen exposure exceeding 6 hours/day was reported by 69.5%, while 96.6% adopted forward head posture during device use. Neck pain and neck stiffness were reported by 99.4% and 97.1% of participants, respectively. Neurological symptoms included upper-limb numbness (43.7%) and weakness (24.1%). Daily screen time demonstrated a weak positive correlation with neck pain severity (rs=0.23, p=0.002). Prolonged screen exposure independently predicted severe cervical symptoms (AOR=4.21, 95% CI: 2.01-8.83, p<0.001). Receiver operating characteristic analysis demonstrated good predictive performance for screen exposure duration (AUC=0.81). Conclusions: Text neck syndrome symptoms and manifestations suggestive of early cervical spondylosis represent a substantial clinical burden among Sudanese adults and are associated with prolonged screen exposure, forward head posture, and poor ergonomic awareness.