AIMC Topic: Carbon Monoxide
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PathInHydro, a Set of Machine Learning Models to Identify Unbinding Pathways of Gas Molecules in [NiFe] Hydrogenases.
Journal of chemical information and modeling
Jan 7, 2025
Machine learning (ML) is a powerful tool for the automated data analysis of molecular dynamics (MD) simulations. Recent studies showed that ML models can be used to identify protein-ligand unbinding pathways and understand the underlying mechanism. T...
Spatiotemporal analysis of airborne pollutants and health risks in Mashhad metropolis: enhanced insights through sensitivity analysis and machine learning.
Environmental geochemistry and health
Dec 26, 2024
The study delved into an extensive assessment of outdoor air pollutant levels, focusing specifically on PM, SO, NO, and CO, across the Mashhad metropolis from 2017 to 2021. In tandem, it explored their intricate correlations with meteorological condi...
High spatiotemporal resolution estimation and analysis of global surface CO concentrations using a deep learning model.
Journal of environmental management
Nov 1, 2024
Ambient carbon monoxide (CO) is a primary air pollutant that poses significant health risks and contributes to the formation of secondary atmospheric pollutants, such as ozone (O). This study aims to elucidate global CO pollution in relation to healt...
Quantification of uncertainty in short-term tropospheric column density risks for a wide range of carbon monoxide.
Journal of environmental management
Oct 2, 2024
The short-term risks associated with atmospheric trace gases, particularly carbon monoxide (CO), are critical for ecological security and human health. Traditional statistical methods, which still dominate the assessment of these risks, limit the pot...
Use of machine learning algorithms to determine the relationship between air pollution and cognitive impairment in Taiwan.
Ecotoxicology and environmental safety
Aug 15, 2024
Air pollution has become a major global threat to human health. Urbanization and industrialization over the past few decades have increased the air pollution. Plausible connections have been made between air pollutants and dementia. This study used m...
The Lag -Effects of Air Pollutants and Meteorological Factors on COVID-19 Infection Transmission and Severity: Using Machine Learning Techniques.
Journal of research in health sciences
Jul 31, 2024
BACKGROUND: Exposure to air pollution is a major health problem worldwide. This study aimed to investigate the effect of the level of air pollutants and meteorological parameters with their related lag time on the transmission and severity of coronav...
IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon.
Environmental monitoring and assessment
Jun 15, 2024
This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is a...
Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation.
PloS one
Sep 5, 2023
The pectoralis muscle is an important indicator of respiratory muscle function and has been linked to various parenchymal biomarkers, such as airflow limitation severity and diffusing capacity for carbon monoxide, which are widely used in diagnosing ...
Heart-Brain 346-7 Score: the development and validation of a simple mortality prediction score for carbon monoxide poisoning utilizing deep learning.
Clinical toxicology (Philadelphia, Pa.)
Jul 7, 2023
INTRODUCTION: Acute mortality from carbon monoxide poisoning is 1-3%. The long-term mortality risk of survivors of carbon monoxide poisoning is doubled compared to age-matched controls. Cardiac involvement also increases mortality risk. We built a cl...
Prognostic value of deep learning-based fibrosis quantification on chest CT in idiopathic pulmonary fibrosis.
European radiology
Mar 16, 2023
OBJECTIVE: To investigate the prognostic value of deep learning (DL)-driven CT fibrosis quantification in idiopathic pulmonary fibrosis (IPF).
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