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Considered heavy metals, such as As(III), Bi(II), Cd(II), Cr(VI), Mn(II), Mo(II), Ni(II), Pb(II), Sb(III), Se(-II), Zn(II), and contaminating chemical compounds (monocyclic aromatic hydrocarbons such as phenolic or polycyclic derivatives) in wastewater (petrochemical industries: oil and gas production plants) are currently a major concern in environmental toxicology due to their toxic effects on aquatic and terrestrial life. In order to maintain biodiversity, hydrosphere ecosystems, and people, it is crucial to remove these heavy metals and polluting chemical compounds from the watery environment. In this study, different Nanoparticles (α-Fe 2 O 3 , CuO, and ZnO) were synthesized by green synthesis method using Portulaca oleracea leaf extract and characterized by UV–Vis spectrophotometers, FTIR spectroscopy, X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS) techniques in order to investigate morphology, composition, and crystalline structure of NPs, these were then used as adsorbent for the removal of As(III), Bi(II), Cd(II), Cr(VI), Mn(II), Mo(II), Ni(II), Pb(II), Sb(III), Se(-II), and Zn(II) from wastewater, and removal efficiencies of were obtained 100% under optimal conditions.

The novel synthesis of MgO from Laurus nobilis L. leaves was prepared using the green synthesis method. It is using direct blending process to decorate MgO/PEG nanocomposite to enhance the photodegradation properties and examine its physical properties using diverse characterization techniques, including XRD, FTIR, SEM, EDX, and UV–Vis. X-ray diffraction reveals a cubic phase of MgO with a 37-nm grain size. SEM images confirm spherical nanoparticles with a diameter size of 22.9 nm. The optical energy gap of MgO NPs was 4.4 eV, and the MgO/PEG nanocomposite was 4.1 eV, which made it an efficient catalyst under sunlight. The photocatalytic activity of Rose Bengal (RB) and Toluidine Blue (TB) dyes at 5 × 10 −5  mol/l dye concentration indicates excellent degradation efficiencies of 98% and 95% in 120 min, respectively, under sunlight irradiation. MgO/PEG is an excellent candidate nanocomposite for applications of photodegradation and could be used for its potential capability to develop conventionally used techniques.

Breast cancer, a prevalent and life-threatening disease, necessitates early detection for the effective intervention and the improved patient health outcomes. This paper focuses on the critical problem of identifying breast cancer using a model called Attention U-Net. The model is utilized on the Breast Ultrasound Image Dataset (BUSI), comprising 780 breast images. The images are categorized into three distinct groups: 437 cases classified as benign, 210 cases classified as malignant, and 133 cases classified as normal. The proposed model leverages the attention-driven U-Net’s encoder blocks to capture hierarchical features effectively. The model comprises four decoder blocks which is a pivotal component in the U-Net architecture, responsible for expanding the encoded feature representation obtained from the encoder block and for reconstructing spatial information. Four attention gates are incorporated strategically to enhance feature localization during decoding, showcasing a sophisticated design that facilitates accurate segmentation of breast tumors in ultrasound images. It displays its efficacy in accurately delineating and segregating tumor borders. The experimental findings demonstrate outstanding performance, achieving an overall accuracy of 0.98, precision of 0.97, recall of 0.90, and a dice score of 0.92. It demonstrates its effectiveness in precisely defining and separating tumor boundaries. This research aims to make automated breast cancer segmentation algorithms by emphasizing the importance of early detection in boosting diagnostic capabilities and enabling prompt and targeted medical interventions.

Bacteria and phages have co-evolved, developing bacterial immune systems that defend against phage infections. This ongoing evolutionary arms race has led to exploring phages as potential therapeutic alternatives. The sustainability of phage therapy depends on a comprehensive understanding of phage pharmacokinetics and bacterial defense mechanisms. While several studies show that phages generally exhibit good distribution across most organs, some experiments involving phage administration to humans or animals have not always succeeded. This failure can be attributed to the role of innate and adaptive immunity in neutralizing phages, despite phage therapy's limited clinical application. The immune adaptations bacteria develop in response to phage attacks provide time for them to evolve stronger defenses, raising concerns about the long-term efficacy of phage therapy. This review highlights the importance of understanding the interplay between phage pharmacokinetics and bacterial defenses to ensure the future success of phage therapy.

Electrical load forecasting provides knowledge about future consumption and generation of electricity. There is a high level of fluctuation behavior between energy generation and consumption. Sometimes, the energy demand of the consumer becomes higher than the energy already generated, and vice versa. Electricity load forecasting provides a monitoring framework for future energy generation, consumption, and making a balance between them. In this paper, we propose a framework, in which deep learning and supervised machine learning techniques are implemented for electricity-load forecasting. A three-step model is proposed, which includes: feature selection, extraction, and classification. The hybrid of Random Forest (RF) and Extreme Gradient Boosting (XGB) is used to calculate features’ importance. The average feature importance of hybrid techniques selects the most relevant and high importance features in the feature selection method. The Recursive Feature Elimination (RFE) method is used to eliminate the irrelevant features in the feature extraction method. The load forecasting is performed with Support Vector Machines (SVM) and a hybrid of Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN). The meta-heuristic algorithms, i.e., Grey Wolf Optimization (GWO) and Earth Worm Optimization (EWO) are applied to tune the hyper-parameters of SVM and CNN-GRU, respectively. The accuracy of our enhanced techniques CNN-GRU-EWO and SVM-GWO is 96.33% and 90.67%, respectively. Our proposed techniques CNN-GRU-EWO and SVM-GWO perform 7% and 3% better than the State-Of-The-Art (SOTA). In the end, a comparison with SOTA techniques is performed to show the improvement of the proposed techniques. This comparison showed that the proposed technique performs well and results in the lowest performance error rates and highest accuracy rates as compared to other techniques.

Software vulnerabilities pose a significant threat to system security, necessitating effective automatic detection methods. Current techniques face challenges such as dependency issues, language bias, and coarse detection granularity. This study presents a novel deep learning-based vulnerability detection system for Java code. Leveraging hybrid feature extraction through graph and sequence-based techniques enhances semantic and syntactic understanding. The system utilizes control flow graphs (CFG), abstract syntax trees (AST), program dependencies (PD), and greedy longest-match first vectorization for graph representation. A hybrid neural network (GCN-RFEMLP) and the pre-trained CodeBERT model extract features, feeding them into a quantum convolutional neural network with self-attentive pooling. The system addresses issues like long-term information dependency and coarse detection granularity, employing intermediate code representation and inter-procedural slice code. To mitigate language bias, a benchmark software assurance reference dataset is employed. Evaluations demonstrate the system's superiority, achieving 99.2% accuracy in detecting vulnerabilities, outperforming benchmark methods. The proposed approach comprehensively addresses vulnerabilities, including improper input validation, missing authorizations, buffer overflow, cross-site scripting, and SQL injection attacks listed by common weakness enumeration (CWE).

Background Acute kidney injury (AKI) is prevalent in critical care, often due to nephrotoxic drug exposure, which accounts for significant morbidity and mortality. Current biomarkers, like serum creatinine, lack sensitivity for early detection of nephrotoxicity. Aim This study evaluates proteinuria and serum cystatin C as early indicators of nephrotoxicity in acutely poisoned patients at Sohag University Hospitals. Methods This prospective study involved 100 acutely poisoned patients with nephrotoxic effects admitted to Sohag University Hospitals from April to August 2021. Inclusion criteria required symptomatic patients who provided at least four blood or urine samples, including one within 24 h post-ingestion. AKI was classified using the Acute Kidney Injury Network (AKIN) criteria, with baseline serum creatinine estimated from the lowest value during hospitalization. Biomarkers, including serum creatinine and cystatin C, were measured using standard assays for analysis. Results The study included 100 patients aged 2 to 58 years, predominantly male (72%). Most participants were from rural areas (82%). Serum creatinine levels significantly increased from day 1 (mean ± SD: 1.67 ± 0.6 mg/dL) to day 2 (mean ± SD: 2.98 ± 1.35 mg/dL). Significant predictors of acute renal toxicity included serum creatinine on both days ( P  < 0.001), proteinuria ACR ( P  = 0.023), and cystatin C ( P  < 0.001). Cystatin C had the highest predictive value (AUC = 0.993), while proteinuria ACR and day 2 serum creatinine showed significant predictive capabilities (AUCs of 0.805 and 0.873, respectively). Conclusion In conclusion, proteinuria and cystatin C are reliable predictors for early nephrotoxicity detection in acutely poisoned patients at Sohag University Hospitals. These biomarkers effectively indicate and assess the severity of kidney injury caused by toxicity.

Background Cervical cancer, though one of the most common cancers affecting women globally, holds immense potential for prevention through screening. Therefore, we conducted this meta-analysis to assess the rate of cervical cancer screening in Arab countries and identify barriers among those who did not participate. Methods A comprehensive search was conducted from January 1st to June 1st,2024, including all observational studies that reported cervical cancer screening uptake in any Arab country. A meta-analysis was performed using a random-effects model to estimate the pooled prevalence, and sensitivity analyses were conducted to test the robustness of the findings. The study followed PRISMA guidelines. Results This meta-analysis, covering 55 studies and 204,940 Arab women, found an overall cervical cancer screening uptake rate of 18.2% (95% CI: 13.9–23.6), with sensitivity analysis confirming the reliability of this estimate. Country disparities were evident, with Bahrain having the highest uptake at 44.1%, while Somalia had the lowest at 8.9%. Among women who underwent screening, the majority were ever-married (94.7%) and held positive attitudes towards screening (91.0%). Barriers to screening were common among women who did not participate, with the most frequent reasons being a lack of information (25.1%), the misconception of feeling healthy (24.5%), fear of the procedure (19.3%), and feelings of embarrassment (13.2%). Additionally, women who were screened had lower perceived barrier scores (SMD = -0.466) and higher perceived benefits scores (SMD = 0.379) than those who were not ( p  < 0.05). Conclusion This meta-analysis reveals a low overall cervical cancer screening uptake (18.2%) among Arab women. Key barriers such as lack of information, fear, the misconception of feeling healthy, and embarrassment hinder uptake. This alarmingly low rate underscores the urgent need for targeted interventions to address these barriers and promote awareness of early detection’s life-saving potential.

Objective: To compare the effectiveness of different oral antibiotics for prevention of dry socket and infection in adults following the surgical extraction of teeth under LA. Methods: This randomized controlled study was conducted from 10 September 2020 until 10 May 2021. Forty-six patients were randomly allocated to three groups. Sixteen patients were in the postoperative co-amoxiclav (625 mg) group, fifteen in the preoperative co-amoxiclav (625 mg) plus postoperative metronidazole (500 mg) group and fifteen in the preoperative co-amoxiclav (625 mg) plus postoperative amoxicillin (500 mg) group. Evaluation of the postoperative signs of alveolar osteitis and infection was made by a dental surgeon five days postoperatively. Evaluation of the post-surgical extraction pain was made by patients immediately and five days postoperatively on standard 100 mm visual analogue scales (VAS). Furthermore, difficulty of surgery was recorded for all patients immediately postoperatively using (VAS). Results: all antibiotics used in this study were effective. Only 15% of patients had painful alveolar osteitis and 2% had oral infections. There was no significant decrease in the number of patients with severe alveolar osteitis or infection for co-amoxiclav plus metronidazole and co-amoxiclav plus amoxicillin groups compared to co-amoxiclav group at 5 days post-operation (p-values: 0.715, 0.819 & 0.309). Clinically, metronidazole was more effective in protecting the extracted tooth socket from alveolar osteitis compared to co-amoxiclav and amoxicillin. Moreover, there were significant decreases in mean pain scores at 5 days post-operation compared with the levels of pain immediately after surgery (p-value: 0.001). Conclusions: Administration of a single preoperative dose of co-amoxiclav with a full postoperative dose of amoxicillin or metronidazole was more effective than conventional treatment with postoperative co-amoxilcalv in reducing the incidence of both alveolar osteitis and infection after surgical extractions. However, these differences were not statistically significant. Interestingly, patients in metronidazole group had the lowest incidence of dry socket.