• Filter By:
• Specialty
• Source
• Condition
• Cancer
• Infectious Disease
• Drug
• Training
• Management
• Procedure
• Therapy
• Vaccine
• Nutrition
• Country

Questionnaire-based exposome-wide association studies (ExWAS) reveal expected and novel risk factors associated with cardiovascular outcomes in the Personalized Environment and Genes Study
In conclusion, using statistics and machine learning, these findings identify novel potential risk factors for CVD, enable hypothesis generation, provide insights into the complex relationships between risk factors and CVD, and highlight the importance of considering multiple exposures when examining CVD outcomes.PMID:35605674 | DOI:10.1016/j.envres.2022.113463
Source: Environmental Research - May 23, 2022 Category: Environmental Health Authors: Eunice Y Lee Farida Akhtari John S House Ross J Simpson Charles P Schmitt David C Fargo Shepherd H Schurman Janet E Hall Alison A Motsinger-Reif Source Type: research

Nanomaterials Versus Ambient Ultrafine Particles: An Opportunity to Exchange Toxicology Knowledge
Conclusion: There is now an opportunity to apply knowledge from NM toxicology and use it to better inform PM health risk research and vice versa. https://doi.org/10.1289/EHP424 Received: 17 December 2015 Revised: 12 August 2016 Accepted: 30 August 2016 Published: 10 October 2017 Address correspondence to V. Stone, School of Life Sciences, Heriot-Watt University, Edinburgh, UK. Telephone: +44 131 451 3460. Email: v.stone@hw.ac.uk V.S. currently receives grant funding from Byk Altana and from The European Ceramic Fibre Industry Association (ECFIA). In the past, V.S. has received funding from Unilever and GlaxoSmithKline....
Source: EHP Research - October 10, 2017 Category: Environmental Health Authors: Daniil Lyalko Tags: Review Source Type: research

IJERPH, Vol. 18, Pages 11302: Review of Deep Learning-Based Atrial Fibrillation Detection Studies
Rajendra Acharya Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming and prone to errors. To overcome these limitations, computer-aided diagnosis systems are developed using artificial intelligence techniques for automated detection of AF. Various machine learning and deep learning (DL) techniques have been developed for the automated detection of AF. In this review, we focused on the automated AF detection models developed using DL techniques. Twenty-four relevant articles published in intern...
Source: International Journal of Environmental Research and Public Health - October 28, 2021 Category: Environmental Health Authors: Fatma Murat Ferhat Sadak Ozal Yildirim Muhammed Talo Ender Murat Murat Karabatak Yakup Demir Ru-San Tan Udyavara Rajendra Acharya Tags: Review Source Type: research