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Healthcare systems worldwide face growing challenges, including rising costs, workforce shortages, and disparities in access and quality, particularly in low- and middle-income countries. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these issues by enhancing diagnostics, treatment planning, patient monitoring, and healthcare efficiency. AI’s role in modern medicine spans disease detection, personalized care, drug discovery, predictive analytics, telemedicine, and wearable health technologies. Leveraging machine learning and deep learning, AI can analyze complex data sets, including electronic health records, medical imaging, and genomic profiles, to identify patterns, predict disease progression, and recommend optimized treatment strategies. AI also has the potential to promote equity by enabling cost-effective, resource-efficient solutions in low-resource and remote settings, such as mobile diagnostics, wearable biosensors, and lightweight algorithms. Successful deployment requires addressing critical challenges, including data privacy, algorithmic bias, model interpretability, regulatory oversight, and maintaining human clinical oversight. Emphasizing scalable, ethical, and evidence-driven implementation, key strategies include clinician training in AI literacy, adoption of resource efficient tools, global collaboration, and robust regulatory frameworks to ensure transparency, safety, and accountability. By complementing rather than replacing healthcare professionals, AI can reduce errors, optimize resources, improve patient outcomes, and expand access to quality care. This review emphasizes the responsible integration of AI as a powerful catalyst for innovation, sustainability, and equity in healthcare delivery worldwide.

Magnetic nanoparticles (MNPs) have emerged as powerful tools in biomedicine due to their distinct physicochemical characteristics, including a high surface-area-to-volume ratio, adjustable size, magnetic sensitivity, and compatibility with biological systems. These properties enable precise control through external magnetic fields, making MNPs highly effective in targeted therapeutic and diagnostic applications. Although not inherently intelligent, they can exhibit programmable and responsive behavior under external influence, enhancing their utility in drug delivery and hyperthermia-based treatments. In the medical field, MNPs have been extensively explored for their role in magnetic resonance imaging (MRI) enhancement, selective drug transport, hyperthermia cancer therapy, and biomolecular separation. Within oncology, they facilitate the direct delivery of therapeutic compounds to tumors, reducing systemic side effects and increasing treatment specificity. Additionally, their capacity to produce localized heat when exposed to alternating magnetic fields makes them instrumental in hyperthermia therapy, where malignant cells are selectively eradicated. A key advantage of MNPs is their adaptable surface chemistry, which allows for functionalization with biocompatible polymers, ligands, and other stabilizing agents. These modifications enhance their stability, minimize immune responses, and optimize their performance in physiological environments. Functionalized MNPs have contributed significantly to improving MRI contrast, refining drug delivery mechanisms, and increasing the effectiveness of hyperthermia treatments. This review examines recent breakthroughs in MNP-based medical technologies, with an emphasis on tumor targeting, drug delivery across the blood–brain barrier, and hyperthermia applications. Highlights MNPs can be controlled with magnetic fields for targeted treatments and imaging. MNPs improve cancer therapy by delivering drugs directly to tumors with minimal side effects. Surface functionalization enhances MNPs' stability, imaging, and drug delivery efficiency in biomedical use.

Skin crack defects can develop in sandwich honeycomb composite structures during service life due to static and impact loads. In this study, the fracture behavior of sandwich honeycomb composite (SHC) beams containing crack at the skin was investigated experimentally and numerically under four-point loading. Three different arrangements of unidirectional (UD) carbon fiber composite and the triaxially woven (TW) fabric were considered for the skins. The presence of a 10 mm crack at mid-span of the top skin, mid-span of the bottom skin, and mid-way between load and support of the top skin, respectively, were considered. Failure load equations of the load initiating the skin crack extension were analytically derived and then numerically developed using the J-integral approach. The crack extension failure mode dominated all cracked specimens except those with low-stiffness skin which were controlled by the compressive skin debonding and core shear failures.

Nociplastic pain is the third classification of pain as described by the International Association for the Study of Pain (IASP), in addition to the neuropathic and nociceptive pain classes. The main pathophysiological mechanism for developing nociplastic pain is central sensitization (CS) in which pain amplification and hypersensitivity occur. Fibromyalgia is the prototypical nociplastic pain disorder, characterized by allodynia and hyperalgesia. Much scientific data suggest that classical activation of microglia in the spinal cord mediates neuroinflammation which plays an essential role in developing CS. In this review article, we discuss the impact of microglia activation and M1/M2 polarization on developing neuroinflammation and nociplastic pain, besides the molecular mechanisms engaged in this process. In addition, we mention the impact of microglial modulators on M1/M2 microglial polarization that offers a novel therapeutic alternative for the management of nociplastic pain disorders. Graphical abstract Illustrating the mechanisms underlying microglia activation in central sensitization and nociplastic pain. LPS lipopolysaccharide, TNF-α tumor necrosis factor-α, INF-γ Interferon gamma, ATP adenosine triphosphate, 49 P2Y12/13R purinergic P2Y 12/13 receptor, P2X4/7R purinergic P2X 4/7 receptor, SP Substance P, NK-1R Neurokinin 1 receptor, CCL2 CC motif ligand 2, CCR2 CC motif ligand 2 receptor, CSF-1 colony-stimulating factor 1, CSF-1R colony-stimulating factor 1 receptor, CX3CL1 CX3C motif ligand 1, CX3XR1 CX3C motif ligand 1 receptor, TLR toll-like receptor, MAPK mitogen-activated protein kinases, JNK jun N-terminal kinase, ERK extracellular signal-regulated kinase, iNOS Inducible nitric oxide synthase, IL-1β interleukin-1β, IL-6 interleukin-6, BDNF brain-derived neurotrophic factor, GABA γ-Aminobutyric acid, GABAR γ-Aminobutyric acid receptor, NMDAR N-methyl-D-aspartate receptor, AMPAR α-amino-3-hydroxy-5-methyl-4-isoxazolepropi-onic acid receptor, IL-4 interleukin-4, IL-13 interleukin-13, IL-10 interleukin-10, Arg-1 Arginase 1, FGF fibroblast growth factor, GDNF glial cell-derived neurotrophic factor, IGF-1 insulin-like growth factor-1, NGF nerve growth factor, CD Cluster of differentiation.

The degradation of the condition of wastewater is becoming more and more serious due to the endless development. One of the main reasons is heavy metal contamination, which causes significant harm to the climate and humanity, such as bad health consequences, environmental degradation, and air pollution. Adsorption, which uses proven adsorbents such as activated carbon, is one of the most common methods for heavy metal removal in wastewater. However, since activated carbon is very expensive to build and repair due to complex production, most people choose another material to overcome this problem. Researchers have recently focused on finding low-cost adsorbents, which are typically industrial, agricultural and food wastes that can generate in large quantities. However, Alkali-Activated Materials (AAMs) have been recognized as a novel possible adsorbent because they are cheap, made from solid aluminosilicate and extremely alkaline activator solution, making them appropriate for usage in the civil engineering specialty. Moreover, they have become an option for various applications due to their unique geopolymer structure, which is highly mechanically, chemically and thermally stable. Hydroxyapatite (HAP) can be extremely useful in this application, as it is a promising biomaterial that has great potential for a low-cost AAMs adsorbent. The purpose of this study is to analyze the present development of a potential economic alternative adsorbent, particularly based on alkali-activated materials (known as geopolymers), for the elimination of heavy metal pollutants in wastewater using adsorption techniques.

Medical imaging is considered a suitable alternative testing method for the detection of lung diseases. Many researchers have been working to develop various detection methods that have aided in the prevention of lung diseases. To better understand the condition of the lung disease infection, chest X-Ray and CT scans are utilized to check the disease’s spread throughout the lungs. This study proposes an automated system for the detection multi lung diseases in X-Ray and CT scans. A customized convolutional neural network (CNN) and two pre-trained deep learning models with a new image enhancement model are proposed for image classification. The proposed lung disease detection comprises two main steps: pre-processing, and deep learning classification. The new image enhancement algorithm is developed in the pre-processing step using k-symbol Lerch transcendent functions model which enhancement images based on image pixel probability. While, in the classification step, the customized CNN architecture and two pre-trained CNN models Alex Net, and VGG16Net are developed. The proposed approach was tested on publicly available image datasets (CT, and X-Ray image dataset), and the results showed classification accuracy, sensitivity, and specificity of 98.60%, 98.40%, and 98.50% for the X-Ray image dataset, respectively, and 98.80%, 98.50%, 98.40% for the CT scans dataset, respectively. Overall, the obtained results highlight the advantages of the image enhancement model as a first step in processing.

Anaerobic digestion (AD) is one of the most important technologies for converting organic waste into bioenergy, mostly in the form of biogas, and offers a wide range of environmental, economic, and public health benefits. This review provides a detailed description of the process of AD and its key biological phases, namely, hydrolysis, acidogenesis, acetogenesis, and methanogenesis, and emphasizes why microbial consortia are important in the breakdown of organic matter. The review analyzes the different pretreatment methods that enhance substrate digestibility and increase biogas yield, along with the key operational parameters that affect the operation of a process, including operating temperature, organic loading rate (OLR), pH, mixing efficiency, and hydraulic retention time (HRT). Moreover, there are metabolic difficulties associated with the choice of feedstock, feedstock stability, interference by product, and also the economic viability. The scalability and versatility of the technology are evidenced by several case studies used to show successful applications of AD systems in various fields, such as the treatment of olive pomace and livestock waste. Moreover, the inclusion of AD in the circles of the economic system is also deliberated as a good avenue towards the sustainable management of waste. The growing environmental use of this technology is evidenced by the growing innovations in the field, including membrane‐based AD systems to control pathogens and odors. Overall, AD is one of the sustainable solutions to the issues of waste treatment, the production of renewable energy, and the reduction of the impact of climate change on the entire world.

This study evaluates the efficiency of alkali-activated Egyptian calcium bentonite, obtained from the El Alamein region in northern Egypt, for the removal of copper (Cu 2⁺ ), lead (Pb 2⁺ ), and nickel (Ni 2⁺ ) from synthetic wastewater. The bentonite samples underwent a series of preparation steps, including crushing, ball milling, magnetic separation, acid treatment with 0.1N acetic acid, and alkali activation using 5% sodium carbonate (Na 2 CO 3 ). Various analytical techniques, such as X-ray fluorescence (XRF), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), cation exchange capacity (CEC) measurements, scanning electron microscopy (SEM), and free swelling analysis, were employed to characterize the materials. Absorption experiments were performed to examine the effects of pH, temperature, starting metal concentration, bentonite dose, and contact duration on heavy metal removal. The characterization results confirmed that montmorillonite was the predominant mineral in both the natural and activated bentonite samples. Adsorption studies indicated a significant improvement in heavy metal removal efficiency after activation. Under optimal conditions (pH 7, 1 g/L adsorbent dose, 120 min contact time, 20 mg/L initial metal concentration, and 20 °C), the maximum adsorption capacities of the activated bentonite were determined as 14 ± 0.03 mg/g for Cu 2+ , 13 ± 0.04 mg/g for Pb 2+ , and 12.2 ± 0.05 mg/g for Ni 2+ , exceeding those of the natural bentonite, which recorded capacities of 9.2 ± 0.04 mg/g, 9 ± 0.03 mg/g, and 8 ± 0.02 mg/g, respectively. Adsorption equilibrium data according to the Langmuir isotherm model, exhibiting high correlation values (R 2  = 0.9979 for Cu 2+ , 0.9972 for Pb 2+ , and 0.9973 for Ni 2+ ). Moreover, kinetic modeling demonstrated that the adsorption followed a pseudo-second-order mechanism, suggesting an intense chemisorption process. The thermodynamic analysis indicated that the adsorption process was spontaneous and endothermic, demonstrating enhanced adsorption at higher temperatures.

Cancer remains a major global health challenge. Conventional therapies are often limited by poor tumor selectivity, systemic toxicity, multidrug resistance, and suboptimal pharmacokinetics. Nanotechnology offers a transformative approach in oncology. It enables targeted drug delivery, controlled or stimuli-responsive release, and multifunctional platforms that combine therapy and diagnostics. Nanoparticles enhance drug solubility, prolong circulation, and improve intracellular uptake. These advances are achieved through passive enhanced permeability and retention (EPR) and active targeting mechanisms. Despite robust preclinical success, clinical translation remains inconsistent. Factors such as pronounced inter- and intratumoral EPR variability, long-term toxicity or biodistribution concerns, immune recognition, and stringent regulatory or manufacturing hurdles contribute to high attrition rates. Approved nanomedicines, such as liposomal doxorubicin (Doxil®) and albumin-bound paclitaxel (Abraxane®), have achieved meaningful clinical impact. Others, such as thermosensitive liposomal doxorubicin (ThermoDox®), have shown variable or limited benefits. This gap highlights persistent differences between promise and performance. This review critically examines nanoparticle synthesis strategies (bottom-up, top-down, microfluidic, and green methods); structural and physicochemical characterization techniques; tumor-targeting mechanisms; and major classes of organic (liposomes, solid lipid nanoparticles, nanostructured lipid carriers, polymeric, micelles, dendrimers), inorganic (carbon-based, metallic, silica, magnetic), and hybrid nanocarriers. Emphasis is placed on mechanistic insights, comparative performance, and translational limitations. Key issues include batch-to-batch variability, lack of standardized nanotoxicology protocols, patient-specific heterogeneity, and regulatory challenges. This review aims to guide the successful integration of nanotechnology into precision cancer therapy.

Rapid growth of the textile industry, along with the excessive use of water and dyes, has led to significant environmental concerns. This study introduces a straightforward, low-temperature aqueous reflux method for the fabrication of magnetic nickel ferrite (NiFe 2 O 4 ) nanoparticles. The synthesized nanoparticles, characterized by X-ray diffraction (XRD), Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and vibrating sample magnetometry (VSM), exhibited a cubic spinel structure, an average particle size of 23 ± 2.3 nm (range: 18–29.8 nm), and a magnetization of 56.96 ± 0.9 emu/g, enhancing their surface area and magnetic separability. These NiFe 2 O 4 nanoparticles achieved a 96.5 ± 0.4% removal efficiency of Remazol Red dye from aqueous solutions after 90 min, with an adsorption capacity (q max ) of 169.5 ± 0.8 mg/g, as tested across pH 2–12, contact times of 10–120 min, and initial dye concentrations of 20–200 mg/L. Optimal removal occurred at pH 2, with a dye concentration of 20 mg/L and a 1 g/L dose, yielding 99 ± 0.5% efficiency, while adsorption decreased at high pH due to surface charge effects (PZC = 6.7). The results indicated that dye adsorption increased with decreasing pH and higher nickel ferrite dosage. Kinetic studies over 10–120 min followed pseudo-first-order (R 2  = 0.96), Boyd, and Weber–Morris models, while isotherms across 20–200 mg/L conformed to the Freundlich model (R 2  = 0.98), reflecting multilayer adsorption. These properties high crystallinity, nanoscale size, and strong magnetic responsiveness enhance the material’s surface area, adsorption capacity, and ease of separation, contributing to its efficiency as an adsorbent. Reusability tests confirmed the stability of the nanoparticles and their consistent performance across multiple cycles. These results establish NiFe 2 O 4 as an economical, magnetically separable, and ecologically sustainable adsorbent for wastewater treatment purposes.