Histopathological, Cytological and Radiological Correlations in Allergy and Public Health Concerns: A Comprehensive Review.

Allergies represent a significant and growing public health concern, affecting millions worldwide and burdening healthcare systems substantially. Accurate diagnosis and understanding of allergy is crucial for effective management and treatment. This review aims to explore the historical evolution, current advances, and prospects of histopathological and cytological techniques in allergy diagnosis, highlighting their crucial role in modern medicine.

Breast density awareness and cancer risk in the UAE: Enhancing Women's engagement in early detection.

Introduction: Increased breast density (BD) is significantly correlated to higher rates of breast cancer (BC), yet awareness among women remains low. This study assesses women's understanding of BD, its implications for cancer risk, and their engagement in screening practices.

Methods: A cross-sectional survey of 212 women aged 40 to 74 was conducted using an online questionnaire developed within Google Forms, including open and closed-ended questions.

Advanced AI and ML frameworks for transforming drug discovery and optimization: With innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine.

Artificial Intelligence (AI) and Machine Learning (ML) are transforming drug discovery by overcoming traditional challenges like high costs, time-consuming, and frequent failures. AI-driven approaches streamline key phases, including target identification, lead optimization, de novo drug design, and drug repurposing. Frameworks such as deep neural networks (DNNs), convolutional neural networks (CNNs), and deep reinforcement learning (DRL) models have shown promise in identifying drug targets, optimizing delivery systems, and accelerating drug repurposing.

Highly selective and reusable nanoadsorbent based on FeO-embedded sodium alginate-based hydrogel for cationic dye adsorption: Adsorption interpretation using multiscale modeling.

This study aims to develop a stable and efficient magnetic nanocomposite hydrogel (MNCH) for selective removal of methylene blue (MB) and crystal violet (CV). MNCHs with different FeO contents (0-9 wt%) were synthesized following graft co-polymerization method using sodium alginate, acrylamide, itaconic acid, ammonium persulfate and N,N-methylene bisacrylamide. Among them, MNCH, with 5 wt% FeO, showed highest removal efficiency (>95 %).

Enhanced Fault Diagnosis in Milling Machines Using CWT Image Augmentation and Ant Colony Optimized AlexNet.

A method is proposed for fault classification in milling machines using advanced image processing and machine learning. First, raw data are obtained from real-world industries, representing various fault types (tool, bearing, and gear faults) and normal conditions. These data are converted into two-dimensional continuous wavelet transform (CWT) images for superior time-frequency localization.