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Clinical Significance of Antibiotic Resistance

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Methicillin-resistant Staphylococcus aureus (MRSA) presents a critical public health threat, arising from its capacity to resist β-lactam antibiotics due to acquisition of the mecA gene within the staphylococcal cassette chromosome mec (SCCmec). This gene encodes penicillin-binding protein 2a (PBP2a), which impairs binding efficacy of methicillin and other β-lactams. MRSA has evolved into distinct clonal lineages impacting humans and animals alike, reinforcing its significance within the One...

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Updated: May 9, 2026

Integrated Field Lysimetry and Porewater Sampling for Evaluation of Chemical Mobility in Soils and Established Vegetation

Integrated Field Lysimetry and Porewater Sampling for Evaluation of Chemical Mobility in Soils and Established Vegetation

Published on: July 4, 2014

Assessment of Global Antibiotic Exposure Risk for Crops: Incorporating Soil Adsorption via Machine Learning.

Han Zhu¹, Jianliang He¹, Yanmei Wu¹

¹Center for Water Resources and Environment, and Guangdong Key Laboratory of Marine Civil Engineering, School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China.

Environmental Science & Technology

|July 20, 2024

Summary

Antibiotic accumulation in soils poses a food security risk. This study models crop exposure, finding colder months and soil moisture significantly influence risk in key agricultural regions globally.

Keywords:

antibiotics exposure risk machine learning soil adsorption unsaturated soil

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Area of Science:

Environmental Science
Agricultural Science
Toxicology

Background:

Antibiotic overuse leads to soil accumulation, potentially entering the food chain via crops.
This poses a significant threat to global food security and human health.
Assessing crop exposure risk to antibiotics is critical for mitigation.

Purpose of the Study:

To assess global antibiotic exposure risk for crops.
To develop and optimize a machine learning adsorption model for this assessment.
To incorporate unsaturated soil conditions and spatiotemporal variations in soil moisture and temperature into risk assessment.

Main Methods:

Utilized a machine learning adsorption model based on 4893 datasets covering nine antibiotics.
Employed the eXtreme Gradient Boosting algorithm and a class-specific modeling strategy.
Introduced unsaturated soil conditions and considered spatiotemporal variations in soil moisture and temperature.

Main Results:

The optimized machine learning model demonstrated good performance in predicting antibiotic adsorption.
Soil moisture was identified as a significant factor influencing crop exposure risk.
High exposure risks were observed during colder months (June in Southern Hemisphere, December in Northern Hemisphere).

Conclusions:

Global antibiotic exposure risk for crops varies spatiotemporally, influenced by soil moisture and temperature.
Specific agricultural regions, including southern Canada, western Russia, and southern Australia, are identified as high-risk areas.
The study provides crucial data for addressing antibiotic contamination in agricultural systems and safeguarding food security.