Explore the vast range of titles available.

This study was conducted to find out the potential efficiency of Ozone treatment in detoxification of mycotoxins Nivalenol (NIV) and Fumonisine B2 (FB2) and Deoxynivalenol (DON) associated with Triticum aestivum, grains, as well as their effect on wheat seeds quality including, essential amino acids (EAA), free fatty acids (FFA), and the protein (Gluten). For this purpose, ozone gas was applied at a concentration of 2000 mg / hour at 40 ° C at a period of 24 hours exposure. The results showed that ozone gas was effective in reducing NIV and DON. The reduction percentages were 28.8 and 60.7%, respectively, while the reduction rate of FB2 was 100%. Ozone treatment had a significant effect on chemical components of wheat grains unevenly; the results showed EAA was divided into two groups, the first group revealed an increase in the levels of EAA including: Aspartic, Glycine, Cystine and Methionine, whereas the second group showed a decrease EAA such as Glutamic, Serine, Histidine, Threonine, Arginine, Alanine, Tyrosine, Valine, Phenylalanine, Isolieucine, and Lysine. However, Leucine was found undetected after the treatment. Fatty acids profile showed similar pattern of results; two distinct groups were examined; the first one with an increased level of FFA including Oleic and Linolenic; another FFA group included a decreased level of Palmitic, Palmitoleic and Linoleic. Finally, there is no significant difference in Gluten protein levels in the treated wheat grains with Ozone compared to the control treatment, with average of 2.1 and 2.17 g, respectively

A brain tumour is one of the major reasons for death in humans, and it is the tenth most common type of tumour that affects people of all ages. However, if detected early, it is one of the most treatable types of tumours. Brain tumours are classified using biopsy, which is not usually performed before definitive brain surgery. An image classification technique for tumour diseases is important for accelerating the treatment process and avoiding surgery and errors from manual diagnosis by radiologists. The advancement of technology and machine learning (ML) can assist radiologists in tumour diagnostics using magnetic resonance imaging (MRI) images without invasive procedures. This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images. The method suggested in this paper uses hybrid deep learning classification based on CNN with two methods. The first method combines a pre-trained Google-Net model of the CNN algorithm for feature extraction with SVM for pattern classification. The second method integrates a finely tuned Google-Net with a soft-max classifier. The proposed approach was evaluated using MRI brain images that contain a total of 1426 glioma images, 708 meningioma images, 930 pituitary tumour images, and 396 normal brain images. The reported results showed that an accuracy of 93.1% was achieved from the finely tuned Google-Net model. However, the synergy of Google-Net as a feature extractor with an SVM classifier improved recognition accuracy to 98.1%.

Flying Ad Hoc Network (FANET) or drones’ technologies have gained much attraction in the last few years due to their critical applications. Therefore, various studies have been conducted on facilitating FANET applications in different fields. In fact, civil airspaces have gradually adopted FANET technology in their systems. However, FANET’s special roles made it complex to support emerging security threats, especially intrusion detection. This paper is a step forward towards the advances in FANET intrusion detection techniques. It investigates FANET intrusion detection threats by introducing a real-time data analytics framework based on deep learning. The framework consists of Recurrent Neural Networks (RNN) as a base. It also involves collecting data from the network and analyzing it using big data analytics for anomaly detection. The data collection is performed through an agent working inside each FANET. The agent is assumed to log the FANET real-time information. In addition, it involves a stream processing module that collects the drones’ communication information, including intrusion detection-related information. This information is fed into two RNN modules for data analysis, trained for this purpose. One of the RNN modules resides inside the FANET itself, and the second module resides at the base station. An extensive set of experiments were conducted based on various datasets to examine the efficiency of the proposed framework. The results showed that the proposed framework is superior to other recent approaches.

Bioethanol production offers promise in mitigating environmental impacts from ethanol consumption despite water scarcity. This study endeavors to evaluate the nuanced influence of different deheading times (45 days before harvest, 21 days before harvest, and no deheading) along with varying water regimes on select sweet sorghum cultivars (Honey, Willy, MN1500, and Atlas), focusing on yield traits, theoretical ethanol production, and water productivity. Findings underscore the substantial impact of cultivation practices on bioethanol yield. A water deficit ranging from 30% to 70% resulted in a discernible reduction in stalk yields of 17.86% to 18.54% and in sugar yields of 0.2 to 0.31 Mg ha−1, accompanied by a corresponding decline in theoretical ethanol yield of 120.9 to 180.9 L ha−1. Additionally, notable enhancements in Brix and sugar content of 16.32% to 18.42% and 16.81% to 19.03%, respectively, were observed across both seasons. Of particular significance, the Honey variety, subjected to a 30% water deficit and deheading at 21 days before harvest, demonstrated exceptional growth and yield characteristics. These empirical insights furnish valuable guidance for optimizing sweet sorghum cultivation practices, thereby augmenting sustainable bioethanol production and propelling forward the frontier of renewable energy technologies towards a more environmentally sustainable future.

The challenge of optimizing battery operating revenue while mitigating aging costs remains inadequately addressed in current literature. This paper introduces a novel cost–benefit approach for scheduling battery energy storage systems (BESS) within microgrids (MGs) that features smart grid attributes. The proposed comprehensive approach accounts for fluctuations of real-time pricing, demand charge tariffs, and battery degradation cost. Using the dynamic programming technique, a novel high-speed BESS scheduling optimization algorithm that incorporates a LiFePO4 battery degradation cost model is developed, achieving substantial monthly operational cost savings for the MG with a fine-grained sampling interval of nine minutes and execution time under one minute. The algorithm utilizes day-ahead forecasts for MG load profiles and photovoltaic output power, enabling the prediction of BESS’s optimal power profile a day in advance. The algorithm’s rapid execution enables real-time adaptability, allowing BESS scheduling to dynamically respond to grid fluctuations. The proposed approach outperforms existing methods in the literature, delivering MG operational cost savings ranging from 33.6% to 94.8% across various scenarios. Consequently, this approach enhances MG operational efficiency and provides significant cost savings.

Background and Objectives: Computer-aided diagnostic systems have achieved remarkable success in the medical field, particularly in diagnosing malignant tumors, and have done so at a rapid pace. However, the generalizability of the results remains a challenge for researchers and decreases the credibility of these models, which represents a point of criticism by physicians and specialists, especially given the sensitivity of the field. This study proposes a novel model based on deep learning to enhance lung cancer diagnosis quality, understandability, and generalizability. Methods: The proposed approach uses five computed tomography (CT) datasets to assess diversity and heterogeneity. Moreover, the mixup augmentation technique was adopted to facilitate the reliance on salient characteristics by combining features and CT scan labels from datasets to reduce their biases and subjectivity, thus improving the model’s generalization ability and enhancing its robustness. Curriculum learning was used to train the model, starting with simple sets to learn complicated ones quickly. Results: The proposed approach achieved promising results, with an accuracy of 99.38%; precision, specificity, and area under the curve (AUC) of 100%; sensitivity of 98.76%; and F1-score of 99.37%. Additionally, it scored a 00% false positive rate and only a 1.23% false negative rate. An external dataset was used to further validate the proposed method’s effectiveness. The proposed approach achieved optimal results of 100% in all metrics, with 00% false positive and false negative rates. Finally, explainable artificial intelligence (XAI) using Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to better understand the model. Conclusions: This research proposes a robust and interpretable model for lung cancer diagnostics with improved generalizability and validity. Incorporating mixup and curriculum training supported by several datasets underlines its promise for employment as a diagnostic device in the medical industry.

HER4 is a receptor tyrosine kinase that is required for the evolution of normal body systems such as cardiovascular, nervous, and endocrine systems, especially the mammary glands. It is activated through ligand binding and activates MAPKs and PI3K/AKT pathways. HER4 is commonly expressed in many human tissues, both adult and fetal. It is important to understand the role of HER4 in the treatment of many disorders. Many studies were also conducted on the role of HER4 in tumors and its tumor suppressor function. Mostly, overexpression of HER4 kinase results in cancer development. In the present article, we reviewed the structure, location, ligands, physiological functions of HER4, and its relationship to different cancer types. HER4 inhibitors reported mainly from 2016 to the present were reviewed as well.

This study investigated prospectively how frequent is difficult biliary cannulation (DBC) among patients with compensated liver cirrhosis and how it will impact the ERCP related complications. Over a period of 2-years, patients with compensated liver cirrhosis undergoing their first time ERCP with naive papilla were evaluated by history, clinical examination, investigations, imaging and ERCP and followed up for any adverse events. Out of 131 compensated cirrhotic patients; 127 were successfully cannulated and included in the final analysis and divided into easy ( n  = 72) and difficult ( n  = 55) cannulation groups. The mean age of the studied cases was 51.86 ± 11.98 years (19–78) with male predominance (51.2% versus 48.8%). The most frequent indication for ERCP was calcular obstructive jaundice. DBC was reported among 45.5% of cases. DBC was associated with 12.6% complication rate of them 8.7% had pancreatitis and 3.9% had intraoperative minor bleeding. Older age, type 2 and 3 papillae, duodenal diverticulum and precut sphincterotomy were predictors of DBC. DBC is not uncommon among compensated cirrhotic patients experiencing their first time ERCP. Because DBC is associated with ERCP complications, its predictors should be considered especially the type of papilla, the presence of duodenal diverticulum and assisted cannulation techniques.

Radiotherapy resistance remains a major challenge in cancer treatment, necessitating novel strategies to enhance therapeutic efficacy while minimizing side effects. This study aimed to develop and evaluate ferulic acid-loaded chitosan nanoparticles (FA-ChNPs) as a multimodal radiosensitizer targeting STAT3, caspase-8, and p53 pathways in Ehrlich ascites carcinoma (EAC). FA-ChNPs were synthesized using ionic gelation and thoroughly characterized for their physicochemical properties, including size, and zeta potential through TEM and DLS. The encapsulation efficiency was verified using UV–Vis spectroscopy. In vitro drug release kinetics were evaluated using the dialysis bag method, demonstrating a sustained release profile from the nanoparticles. Biological evaluation included acute toxicity testing in healthy mice to determine the LD50 value according to OECD guidelines. For antitumor assessment, EAC-bearing mice were divided into six treatment groups (n equals 6 per group), receiving either FA-ChNPs alone (115.75mg/kg via oral gavage), gamma-irradiation alone (6Gy per week), or combination therapy. Comprehensive biological parameters were measured, including tumor progression (mass and volume), metabolic profile (total cholesterol, triglycerides, HDL-C), liver and kidney function markers (ALT, AST, ALP, creatinine, urea), oxidative stress indicators (GSH, SOD, CAT, MDA), inflammatory cytokines (TNF-alpha, IL-6, VEGF), and gene expression levels (STAT3, caspase 8, P53 via qRT-PCR). Histopathological examination of liver tissue complemented these analyses. In silico studies encompassed molecular docking with key targets (STAT3, caspase 8, TP53) and ADMET property predictions. The FA-ChNPs demonstrated favorable physicochemical properties, including a spherical morphology with an average particle size of 41.63 nm and good colloidal stability indicated by a PDI of 0.2, a zeta potential of -2.45 mV. Also, FA-ChNPs exhibited sustained in vitro drug release, contrasting with the rapid diffusion of free ferulic acid, which supports their potential for prolonged therapeutic action and a high safety margin with an LD₅₀ value of 2315mg/kg. The combination therapy demonstrated superior antitumor effects, achieving 44.3% reduction in tumor mass and 67.0% reduction in tumor volume compared to EAC controls, with corresponding tumor dimensions reduced to 1.02cm (length) × 0.74cm (width), confirming potent radiosensitization and synergistic efficacy. Biological assessments revealed significant improvements across multiple parameters: metabolic profile showed 89.2% increase in HDL-C and 29.0% decrease in triglycerides; liver and kidney function markers improved with 29.0% reduction in ALT and 39.7% decrease in creatinine; oxidative stress modulation included 87.9% increase in GSH and 32.4% reduction in MDA; inflammatory markers decreased by 40% for TNF-alpha and 51.1% for IL-6. Molecular analyses showed 70% suppression of STAT3, 108.2% activation of caspase-8, and stabilization of p53. Histopathological evaluation confirmed preserved liver architecture with minimal steatosis and inflammation. Molecular docking studies revealed strong binding affinities:—6.02 kcal/mol for STAT3, -7.31kcal/mol for CASP8, and -5.15kcal/mol for TP53. FA-ChNPs represent a groundbreaking approach to enhance radiotherapy efficacy through simultaneous pathway modulation and organoprotection. These findings support further clinical translation for treatment-resistant cancers.