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. The rats were divided into four groups: control group (CG), low concentration group (LCG), medium concentration group (MCG), and high concentration group (HCG). In a 6-month exposure period, we used two boxes connected to ultrasonic nebulizers for herbicide spraying. After this period, the rats were euthanized for the collection and study of lung tissue. For each image, counts of injuries and blisters were performed automatically using a methodology based on digital image processing techniques. For analysis of the results, an electronic database (Excel®) was created. We used the Pearson method for correlation analysis; values of p <0.05 were considered significant. In the evaluation of healthy alveoli, we recorded positive and significant correlations between analysis from a pathologist and computational analysis. In the evaluation of injured alveoli, we recorded a positive but non-significant correlation between analysis from a pathologist and computational analysis. These results show the effectiveness of digital image processing when evaluating alveolar integrity.

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. In the acute phase, 88 Swiss mice were used, distributed in five groups and exposed to nebulizations at different time intervals (24, 48, 72 and 192 hr). In the chronic phase, 88 Wistar rats were used, distributed in two groups (inhaled and oral) and exposed for six months. Femoral bone marrow cells were collected for a micronucleus test and comet assay. Data were evaluated by pattern recognition algorithms. In acute exposure, medium and high concentrations induced DNA damage in the comet assay, but these concentrations did not increase micronucleated cells. In the chronic exposure, there was an increase in micronuclei and DNA damage in the comet assay in all exposed groups regardless of the exposure route. The data showed a robust pattern of distinction between exposed and nonexposed groups to 2,4-D. Our data showed that both acute inhalation exposure and chronic oral and inhalation exposure to 2,4-D can cause genotoxic effects regardless of concentration. Machine learning showed a clear distinction between the control groups and those exposed to 2,4-D, and the effects of exposure are not concentration-dependent.

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-1 kg bw-1day (D50) of difenoconazole for 30 days, by gavage. Classical sperm parameters and surface-enhanced Raman scattering (SERS) were performed. Progressive motility, acrosomal integrity, and percentage of morphologically normal spermatozoa were reduced in the D10 and D50 groups in comparison with the control group. Sperm viability was reduced only in the D50 group. Sperm number in the testis and caput/corpus epididymis and daily sperm production were reduced in the three exposed groups. SERS measurements showed changes in the spectra of spermatozoa from D50 group, suggesting DNA damage. In addition, machine learning (ML) methods were used to evaluate the performance of three classification algorithms (artificial neural network-ANN, K-nearest neighbors-K-NN, and support vector machine-SVM) in the identification task of the groups exposed to difenoconazole. The results obtained by ML algorithms were very promising with accuracy ≥ 90% and validated the hypothesis of the exposure to difenoconazole reduces sperm quality. In conclusion, exposure of rats to different doses of the fungicide difenoconazole may impair sperm quality, with a recognizable classification pattern of exposure groups.

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). Meatoscopy, pure tone audiometry, logoaudiometry, high-frequency thresholds, and immittance testing were performed. Data were evaluated by artificial neural network (ANN), K-nearest neighbors (K-NN), and support vector machine (SVM). There was symmetry between the right and left ears, an increase in the incidence of hearing loss at high frequency and of downward sloping audiometric curve configuration, and alteration of stapedial reflex in the three exposed groups. The machine-learning classifiers achieved good classification performance (control and exposed). The best classification results occur in high type (I and II) datasets (about 90% accuracy) in k-NN test. It is concluded that both xenobiotic substances have ototoxic potential; however, their combined use does not present additive or potentiating effects recognizable by the algorithms.

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-1 bw day-1 of gasohol (EC), for 30 consecutive days. Sperm quality of the groups exposed to two higher doses was reduced in comparison to the control group. The sperm parameters decreased were: daily sperm production, sperm number in the caput/corpus epididymis, progressive motility, mitochondrial activity, and acrosomal membrane integrity. Sperm transit time in the epididymis cauda and sperm isolated head were increased in EB and EC. Sertoli cells number was decreased in these groups, but their support capacity was maintained. ML methods were used to identify patterns between samples of control and exposure groups. The results obtained by ML methods were very promising, obtaining about 90% of accuracy. It was concluded that the exposure of rats to different doses of gasohol impair spermatogenesis and sperm quality, with a recognizable classification pattern of exposure groups at ML.

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). Their oral mucosa cells were stained with Giemsa for cytogenetic analysis. The total numbers of nuclear abnormalities (CG = 27.16 ± 14.32, SG = 118.23 ± 74.78, PG = 184.23 ± 52.31, and SPG = 191.53 ± 66.94) and micronuclei (CG = 1.46 ± 1.40, SG = 12.20 ± 10.79, PG = 21.60 ± 8.24, and SPG = 20.26 ± 12.76) were higher (p < 0.05) in the three exposed groups compared to the GC. In this study, we considered several different classification algorithms (the artificial neural network, K-nearest neighbors, support vector machine, and optimum path forest). All of the algorithms displayed good classification (accuracy > 80%) when using dataset2 (without the redundant exposure type SPG). It is clear that the data form a robust pattern and that classifiers could be successfully trained on small datasets from the exposure groups. In conclusion, exposing agricultural workers to pesticides and/or tobacco had genotoxic potential, but concomitant exposure to xenobiotics did not lead to additive or potentiating effects.