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This study investigated asbestos fiber concentrations and associated health risks in Shiraz over a period of one year and examined their relationship with various land use factors. Samples were analyzed using phase-contrast microscopy (PCM), and health effects were assessed using the EPA's IRIS method. We examined the relationship between asbestos fiber concentrations and road network density, population, number of bus stations, and green space. The results showed that 10% of the sampling sites in Shiraz had low asbestos fiber concentrations, 20% had medium concentrations, 60% had high concentrations, and 10% had very high concentrations. The mean ELCR for asbestos inhalation was 1.44 × 10, indicating a cancer risk for 1.44 out of every 10,000 people. The highest ELCR values were found in the west, near the Shiraz Ring expressway, and in the southeast, near high-traffic areas. Additionally, no positive correlation was found between asbestos concentrations and population, bus stations, or green space, but there was a significant positive correlation with road network density. Motor vehicle traffic is the primary source of asbestos pollution, posing a significant health risk. Traffic control measures and replacing asbestos in brake pads with alternative materials are necessary to reduce pollution in Shiraz.
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http://dx.doi.org/10.1038/s41598-025-12330-x | DOI Listing |
Int J Surg
September 2025
Department of Gynecology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China.
Background: Ovarian cancer remains the most lethal gynecological cancer, with fewer than 50% of patients surviving more than five years after diagnosis. This study aimed to analyze the global epidemiological trends of ovarian cancer from 1990 to 2021 and also project its prevalence to 2050, providing insights into these evolving patterns and helping health policymakers use healthcare resources more effectively.
Methods: This study comprehensively analyzes the original data related to ovarian cancer from the GBD 2021 database, employing a variety of methods including descriptive analysis, correlation analysis, age-period-cohort (APC) analysis, decomposition analysis, predictive analysis, frontier analysis, and health inequality analysis.
Cureus
August 2025
Acute Medicine, Southend University Hospital, Mid and South Essex NHS Foundation Trust, Southend-on-Sea, GBR.
Adenocarcinoma of the lung is the most common type of lung cancer and is classified as one of the non-small cell lung cancers. It typically arises in the peripheral regions of the lungs, affecting the dense glandular tissues. Most patients diagnosed with pulmonary adenocarcinoma are current or former smokers and present with nonspecific respiratory symptoms such as a persistent cough and shortness of breath.
View Article and Find Full Text PDFLancet Respir Med
September 2025
Asbestos and Dust Diseases Research Institute, Concord, 2139 NSW, Australia; School of Clinical Medicine, Macquarie University & University of NSW, Sydney, Australia.
J Public Health (Oxf)
September 2025
Benchmark Risk Group, 121 West Wacker Dr., Suite 2800 Chicago, IL 60601, USA.
Background: There is a demand for population level research on the potential genetic-basis of mesothelioma (e.g. BRCA1-associated protein-1 [BAP1]) independent of other risk factors, such as amphibole asbestos exposure.
View Article and Find Full Text PDFCancer Med
September 2025
Department of Quality Control, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
Background: In this study, we investigated the global burden, projection, and inequalities of cancer attributable to occupational carcinogen exposure in individuals aged over 40 years.
Methods: Using the Global Burden of Disease 2021 dataset, we examined age-standardized disability-adjusted life years (ASR-DALYs) and deaths associated with cancer attributable to occupational carcinogen exposure. Statistical analyses included: the estimated Annual Percentage Change to assess trends (1990-2021); Bayesian age-period-cohort modeling for projections to 2030 and 2050; decomposition analysis to quantify contributions of aging, population growth, and epidemiological changes; and slope and concentration indices (SII, CI) to evaluate health inequalities by sociodemographic index (SDI).