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.

Enzymes are highly selective and efficient biological catalysts that play a critical role in modern industrial biocatalysis. Their ability to operate under mild conditions and reduce environmental impact makes them ideal alternatives to conventional chemical catalysts. This review provides a comprehensive overview of advances in enzyme-based catalysis, focusing on enzyme classification, engineering strategies, and industrial applications. The six major enzyme classes—hydrolases, oxidoreductases, transferases, lyases, isomerases, and ligases—are discussed in the context of their catalytic roles across sectors such as pharmaceuticals, food processing, textiles, biofuels, and environmental remediation. Recent developments in protein engineering, including directed evolution, rational design, and computational modeling, have significantly enhanced enzyme performance, stability, and substrate specificity. Emerging tools such as machine learning and synthetic biology are accelerating the discovery and optimization of novel enzymes. Progress in enzyme immobilization techniques and reactor design has further improved process scalability, reusability, and operational robustness. Enzyme sourcing has expanded from traditional microbial and plant origins to extremophiles, metagenomic libraries, and recombinant systems. These advances support the integration of enzymes into green chemistry and circular economy frameworks. Despite challenges such as enzyme deactivation and cost barriers, innovative solutions continue to emerge. Enzymes are increasingly enabling cleaner, safer, and more efficient production pathways across industries, supporting the global shift toward sustainable and circular manufacturing.

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.

Nanotechnology is revolutionizing medicine by enabling highly precise diagnostics, targeted therapies, and personalized healthcare solutions. This review explores the multifaceted applications of nanotechnology across medical fields such as oncology and infectious disease control. Engineered nanoparticles (NPs), such as liposomes, polymeric carriers, and carbon-based nanomaterials, enhance drug solubility, protect therapeutic agents from degradation, and enable site-specific delivery, thereby reducing toxicity to healthy tissues. In diagnostics, nanosensors and contrast agents provide ultra-sensitive detection of biomarkers, supporting early diagnosis and real-time monitoring. Nanotechnology also contributes to regenerative medicine, antimicrobial therapies, wearable devices, and theranostics, which integrate treatment and diagnosis into unified systems. Advanced innovations such as nanobots and smart nanosystems further extend these capabilities, enabling responsive drug delivery and minimally invasive interventions. Despite its immense potential, nanomedicine faces challenges, including biocompatibility, environmental safety, manufacturing scalability, and regulatory oversight. Addressing these issues is essential for clinical translation and public acceptance. In summary, nanotechnology offers transformative tools that are reshaping medical diagnostics, therapeutics, and disease prevention. Through continued research and interdisciplinary collaboration, it holds the potential to significantly enhance treatment outcomes, reduce healthcare costs, and usher in a new era of precise and personalized medicine.

Exposure to lead (Pb) has been associated with endocrine, hematological, gastrointestinal, renal and neurological problems in humans. However, effects on the thyroid gland are controversial. The aim of the present study was to assess thyroid function in foundry workers occupationally exposed to Pb and the mechanism of oxidative-antioxidant imbalance. Thyroid function parameters and markers of oxidative stress were examined in 59 adult males who had been occupationally exposed to Pb. The results were then compared to those of 28 male subjects who had no history of Pb exposure or thyroid abnormalities and served as a control group. Mean blood lead levels (16.5±1.74 μg/dl) were significantly higher among the exposed workers compared to those of the control group (12.8±1.16 μg/dl, (p <0.001)). The exposed group had significantly increased free triiodothyronine (FT3), free thyroxine (FT4) and significantly decreased thyroid stimulating hormone (TSH) (1.77±0.44 μIU/ml), whereas the control group had a TSH level of 2.61±0.94 μIU/ml (p< 0.0001). A state of oxidative stress was indicated by the significant increase in mean levels of malondialdehyde (MDA) and significant decrease in glutathione (GSH) (p < 0.0001). There was a significant positive correlation (r=0.358, p <0.05) between blood lead levels (BLL) and duration of employment, while BLL showed a significant negative correlation with TSH (r =-0.486, p <0.001), and GSH (r =-0.336, p <0.05). Of the occupationally exposed workers, 32.76% had elevated thyroid hormones. The results showed a significant positive relationship between GSH and TSH (β coefficient=0.274, p < 0.05), MDA with FT3 (β coefficient=0.355, p < 0.05) and FT4 (β coefficient = 0.491, p < 0.0001) among exposed workers. Workers exposed to Pb dust proved to be at risk for hyperthyroidism, which was found to have a significant role in oxidative-antioxidant imbalance present among workers with increasing duration of exposure. Obtained. This study was approved by the Ethical Committee of the National Research Centre in Egypt (NRC) under the registration number 15225. The authors declare no competing financial interests.

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 (Na₂CO₃). 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 Cu²⁺, 13 ± 0.04 mg/g for Pb²⁺, and 12.2 ± 0.05 mg/g for Ni²⁺, 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 (R² = 0.9979 for Cu²⁺, 0.9972 for Pb²⁺, and 0.9973 for Ni²⁺). 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.