AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Cadmium

Showing 11 to 20 of 56 articles

Clear Filters

Predicting Cd accumulation in crops and identifying nonlinear effects of multiple environmental factors based on machine learning models.

The Science of the total environment
The traditional prediction of the Cd content in grains (Cd) of crops primarily relies on the multiple linear regression models based on soil Cd content (Cd) and pH, neglecting inter-factorial interactions and nonlinear causal links between external e...

Machine learning and structural equation modeling for revealing the influence factors and pathways of different water management regimes acting on brown rice cadmium.

The Science of the total environment
Excessive cadmium (Cd) in brown rice has detrimental effects on rice growth and human health. Water management is a cost-effective, eco-friendly measure to suppress Cd accumulation in rice. However, there is no acknowledged water management regime th...

Machine learning phenotyping and GWAS reveal genetic basis of Cd tolerance and absorption in jute.

Environmental pollution (Barking, Essex : 1987)
Cadmium (Cd) is a dangerous environmental contaminant. Jute (Corchorus sp.) is an important natural fiber crop with strong absorption and excellent adaptability to metal-stressed environments, used in the phytoextraction of heavy metals. Understandin...

Reducing cadmium and arsenic accumulation in rice grains: The coupled effect of sulfur's biomass dilution and soil immobilization analyzed using meta-analysis and machine learning.

The Science of the total environment
The biogeochemical cycling of sulfur (S) in paddy soil influences cadmium (Cd) and arsenic (As) migration. However, the impact of S application on Cd and As within the soil-rice system has not been fully explored. This study aimed to examine the effe...

Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning.

Ecotoxicology and environmental safety
There is limited evidence that heavy metals exposure contributes to osteoporosis. Multi-parameter scoring machine learning (ML) techniques were developed using National Health and Nutrition Examination Survey data to predict osteoporosis based on hea...

Machine learning-based identification of critical factors for cadmium accumulation in rice grains.

Environmental geochemistry and health
The aggregation of Cadmium (Cd) in rice grains is a significant threat to human healthy. The complexity of the soil-rice system, with its numerous influencing parameters, highlights the need to identify the crucial factors responsible for Cd aggregat...

Machine learning model for age-related macular degeneration based on heavy metals: The National Health and Nutrition Examination Survey 2005 to 2008.

Scientific reports
Age-related macular degeneration (AMD) is the leading cause of blindness in older people in developed countries. It has been suggested that heavy metal exposure may be associated with the development of AMD, but most studies have focused on the effec...

Detection of mussels contaminated with cadmium by near-infrared reflectance spectroscopy based on RELS-TSVM.

Journal of food science
Eating mussels contaminated with cadmium (Cd) can seriously harm health. In this study, a non-destructive and rapid detection method for Cd-contaminated mussels based on near-infrared reflectance spectroscopy was studied. The spectral data of Cd-cont...

Effects of the co-exposure of microplastic/nanoplastic and heavy metal on plants: Using CiteSpace, meta-analysis, and machine learning.

Ecotoxicology and environmental safety
Micro/nanoplastics (MNPs) and heavy metals (HMs) coexist worldwide. Existing studies have reported different or even contradictory toxic effects of co-exposure to MNPs and HMs on plants, which may be related to various influencing factors. In this st...

Leveraging machine learning for sustainable cultivation of Zn-enriched crops in Cd-contaminated karst regions.

The Science of the total environment
Karst soils often exhibit elevated zinc (Zn) levels, providing an opportunity to cultivate Zn-enriched crops. (meanwhile) However, these soils also frequently contain high background levels of toxic metals, particularly cadmium (Cd), posing potential...