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.

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.