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Abstract Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors and the occurrence of new-onset of neoplasms have already been demonstrated in a body of research. Nevertheless, few studies have specifically dealt with the nature of relationship, significance of risk factors, and geographic variation of them, particularly in low- and middle-income communities. This study, thus, set out to (1) analyze spatiotemporal variations of the age-adjusted incidence rate (AAIR) of neoplasms in Iran throughout five time periods, (2) investigate relationships between a collection of environmental and socioeconomic indicators and the AAIR of neoplasms all over the country, and (3) evaluate geographical alterations in their relative importance. Our cross-sectional study design was based on county-level data from 2010 to 2020. AAIR of neoplasms data was acquired from the Institute for Health Metrics and Evaluation (IHME). HotSpot analyses and Anselin Local Moran's I indices were deployed to precisely identify AAIR of neoplasms high- and low-risk clusters. Multi-scale geographically weight regression (MGWR) analysis was worked out to evaluate the association between each explanatory variable and the AAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors and AAIR of neoplasms. AAIR of neoplasms displayed a significant increasing trend over the study period. According to the MGWR, the only factor that significantly varied spatially and was associated with the AAIR of neoplasms in Iran was the UV index. A good accuracy RF model was confirmed for both training and testing data with correlation coefficients R 2 greater than 0.91 and 0.92, respectively. UV index and Gini coefficient ranked the highest variables in the prediction of AAIR of neoplasms, based on the relative influence of each variable. More research using machine learning approaches taking the advantages of considering all possible determinants is required to assess health strategies outcomes and properly formulate policy planning.

The presence of arsenic in water bodies poses a significant risk to both human health and the environment. Arsenic (As) contamination in water sources is a global environmental concern caused by both natural processes and human activities. Due to its toxic and persistent nature, arsenic has detrimental effects on ecosystems and human wellbeing. This study aimed to elucidate the mechanisms behind arsenic accumulation in water bodies. In aquatic environments, arsenic concentrations in drinking water have been reported as high as 1,320 μg/L (Nicaragua), while groundwater levels exceeded 5,000 μg/L (Thailand), and wastewater contained up to 134,000 μg/L (landfill leachate in Brazil). Furthermore, bioaccumulation of arsenic (μg/g) in fish species ranges from 0.4 (catfish in the Paraná River Delta, Brazil) to 362 (Pteromylaeus bovinus, Northern Adriatic Sea). Recent research has predominantly focused on removing arsenic from aqueous solutions through adsorption methods. Notably, nanoparticle adsorbents and graphene-based adsorbents demonstrate a high capacity for arsenic removal from water bodies.

Background: Laccase enzyme is capable of oxidizing many resistant and non-biodegradable environmental pollutants, so it has been studied frequently in recent years and is widely used in biodegradation of contaminants. Despite its abundant applicability, due to its short life span, nonrecovery, thermal instability and instability in organic environments, its widespread use is very limited. The present study aimed to increase the stability of laccase by immobilizing it on silica coated iron oxide nanoparticles. Materials and Methods: Fe3O4 nanoparticles were synthesized based on the co-precipitation method and after coating with silica, their surface was modified by amine groups. The enzyme was then immobilized by covalent binding using glutaraldehyde. Specifications of synthesized nanoparticles and immobilized enzyme were investigated using X-ray diffraction (XRD), Field Emission Scanning Electron Microscopy (FESEM), and Energy-dispersive X-ray spectroscopy (EDX). Results: Results of successful laccase immobilization on nanoparticles showed that laccase immobilization significantly increased storage and thermal stability, maintaining activity in a wider range of temperature and pH than free laccase. Conclusion: The immobilization of laccase on silica-coated iron oxide nanoparticles can reduce the barriers and challenges of various enzymes by increasing its efficiency and stability.