Xenobiotics, radiation, and other environmental health risk factors leave their mark on human organs. This can be demonstrated through the use of pathology museum specimens. Upon completing two semesters of postgraduate studies in environmental health, a tour of the Museum of Pathology is offered to postgraduate students at Athens Medical School who are being trained in environmental health. A structured questionnaire is employed to assess the specimens' impact on several aspects: improving students' observational skills, reinforcing the taught material, acquiring new relevant knowledge, and cultivate the social-cognitive ability of empathy. Additionally, students are asked to evaluate the necessity of preserving metadata associated mainly with the social context of the specimens. This research-educational initiative, a component of an ongoing larger project, underscores the significant educational and research value of museum specimens pertaining to environmental health. Furthermore, effectively utilizing such exhibits can enrich the museum experience for visitors and increase public awareness of environmental health issues.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10634264 | PMC |
http://dx.doi.org/10.1177/11786302231211085 | DOI Listing |
Sci Rep
December 2024
KAUST Center of Excellence for Smart Health (KCSH), King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia.
Analyzing microbial samples remains computationally challenging due to their diversity and complexity. The lack of robust de novo protein function prediction methods exacerbates the difficulty in deriving functional insights from these samples. Traditional prediction methods, dependent on homology and sequence similarity, often fail to predict functions for novel proteins and proteins without known homologs.
View Article and Find Full Text PDFSci Rep
December 2024
Division of Pulmonary and Critical Care, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095-1690, USA.
Electronic cigarettes (e-cigs) fundamentally differ from tobacco cigarettes in their generation of liquid-based aerosols. Investigating how e-cig aerosols behave when inhaled into the dynamic environment of the lung is important for understanding vaping-related exposure and toxicity. A ventilated artificial lung model was developed to replicate the ventilatory and environmental features of the human lung and study their impact on the characteristics of inhaled e-cig aerosols from simulated vaping scenarios.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
View Article and Find Full Text PDFNat Commun
December 2024
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China.
Polychlorinated naphthalenes (PCNs) are persistent organic compounds that are regulated by the Stockholm Convention. Here, we estimate historical emissions from PCN production and use (1912-1987) and unintentional emissions from 20 categories (2000-2020). A random forest regression model projects emissions for 2020-2050.
View Article and Find Full Text PDFNat Commun
December 2024
School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!