Benign prostatic hyperplasia (BPH) is the most common canine prostatic disorder. Although most or even all intact male dogs may develop BPH by 5–8 years of age, many show no clinical signs. Taking into account the non-specific character of clinical and ultrasonographic findings, a new diagnostic approach has recently been proposed based on the augmentation of blood canine prostate-specific arginine esterase (CPSE) in hyperplasic dogs. The aim of the present study was to verify CPSE levels in negative controls and hyperplasic dogs, considering cytological findings as the reference method and taking into account the fact that controls were middle-aged intact dogs (median of 5.0 years), contrarily to previous studies carried out with very young control dogs. Significant differences of median CPSE levels were found between controls and hyperplasic dogs (29.1 versus 160.7 ng/mL, respectively); and significant positive correlations were found between median CPSE levels and age or prostatic volume (r = 0.549 and 0.448, respectively; p < 0.001). Sensitivity, specificity, positive and negative likelihood ratios put into evidence the good performance of the test. The agreement between methods was found to be very high, notably between CPSE levels and cytological results (Cohen’s kappa coefficients above 0.8). Considering the results all together, measurement of CPSE is confirmed as a useful and accurate method and should be considered as an alternative or complementary tool to conventional methods for the diagnosis of BPH in middle-aged dogs.

The aim of the present study is to define and compare the predictive power of two different Bayesian models for donkey sperm quality after the evaluation of linear and combined testicular biometry indices and their relationship with age and body weight (BW). Testicular morphometry was ultrasonographically obtained from 23 donkeys (six juveniles and 17 adults), while 40 ejaculates from eight mature donkeys were analyzed for sperm output and quality assessment. Bayesian linear regression analyses were considered to build two statistical models using gel-free volume, concentration, total sperm number, motility, total motile sperm, and morphology as dependent variables. Predictive model 1 comprised the covariate of age and the independent factors testicular measurements (length, height and width), while model 2 included the covariate of age and the factors of BW, testicular volume, and gonadosomatic ratio. Although goodness-of-fit was similar, the combination of predictors in model 1 evidenced higher likelihood to predict gel-free volume (mL), concentration (×10⁶/mL), and motility (%). Alternatively, the combination of predictors in model 2 evidenced higher predictive power for total sperm number (×10⁹), morphologically normal spermatozoa (%), and total motile sperm count (×10⁹). The application of the present models may be useful to gather relevant information that could be used hereafter for assisted reproductive technologies.