Digital image processing: a useful tool in the analysis of lung injuries caused by chronic inhalation of agricultural herbicides.

The herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) is widely used in agriculture to control various weeds. The objective of this study was to use the digital image processing method to identify alveolar lesions in the lungs of rats submitted to chronic 2,4-dichlorophenoxyacetic acid (2,4-D) inhalation exposure. We used forty adult male Wistar rats.

Evaluation of genotoxicity after acute and chronic exposure to 2,4-dichlorophenoxyacetic acid herbicide (2,4-D) in rodents using machine learning algorithms.

2,4-Dichlorophenoxyacetic acid (2,4-D) is one of the most widely used herbicides in the world, but its mutagenic and carcinogenic potential is still controversial. We simulated environmental exposure to 2,4-D, with the objective of evaluating the genotoxic effect of acute and chronic exposure to 2,4-D in rodents. We also evaluated the performance of machine learning algorithms in detecting differences in exposure groups through recognition performed from genotoxic characteristics.

Sperm quality of rats exposed to difenoconazole using classical parameters and surface-enhanced Raman scattering: classification performance by machine learning methods.

Difenoconazole is a fungicide extensively used in agriculture. The aim of this study was to evaluate the effects of difenoconazole fungicide on the sperm quality of rats. Wistar rats were divided into four groups: control and exposed to 5 (D5), 10 (D10), or 50 mg kg bwday (D50) of difenoconazole for 30 days, by gavage.

Performance of machine-learning algorithms to pattern recognition and classification of hearing impairment in Brazilian farmers exposed to pesticide and/or cigarette smoke.

The use of pesticides has been increasing in agriculture, leading to a public health problem. The aim of this study was to evaluate ototoxic effects in farmers who were exposed to cigarette smoke and/or pesticides and to identify possible classification patterns in the exposure groups. The sample included 127 participants of both sexes aged between 18 and 39, who were divided into the following four groups: control group (CG), smoking group (SG), pesticide group (PG), and smoking + pesticide group (SPG).

Experimental exposure to gasohol impairs sperm quality with recognition of the classification pattern of exposure groups by machine learning algorithms.

Contamination caused by leakage at gas stations leads to possible exposure of the general population when in contact with contaminated water and soil. The present study aimed to evaluate the reproductive effects of exposure of adult male rats to gasohol and evaluate the performance of machine learning (ML) algorithms for pattern recognition and classification of the exposure groups. Rats were orally exposed to 0 (control), 16 (EA), 160 (EB), or 800 mg kg bw day of gasohol (EC), for 30 consecutive days.

Evaluation of genotoxic effects in Brazilian agricultural workers exposed to pesticides and cigarette smoke using machine-learning algorithms.

Monitoring exposure to xenobiotics by biomarker analyses, such as a micronucleus assay, is extremely important for the precocious detection and prevention of diseases, such as oral cancer. The aim of this study was to evaluate genotoxic effects in rural workers who were exposed to cigarette smoke and/or pesticides and to identify possible classification patterns in the exposure groups. The sample included 120 participants of both sexes aged between 18 and 39, who were divided into the following four groups: control group (CG), smoking group (SG), pesticide group (PG), and smoking + pesticide group (SPG).