This study was designed to develop predictive equations estimating carcass tissue composition in growing Blackbelly male lambs using as predictor variables for tissue composition of wholesale cuts of low economic value (i.e., neck and shoulder). For that, 40 lambs with 29.9 ± 3.18 kg of body weight were slaughtered and then the left half carcasses were weighed and divided in wholesale cuts, which were dissected to record weights of fat, muscle, and bone from leg, loin, neck, rib, and shoulder. Total weights of muscle (CM), bone (CB) and fat (CF) in carcass were recorded by adding the weights of each tissue from cuts. The CM, CF and CB positively correlated (p < 0.05; 0.36 ≤ r ≤ 0.86), from moderate to high, with most of the shoulder tissue components, but it was less evident (p ≤ 0.05; 0.32≤ r ≤0.63) with the neck tissue composition. In fact, CM did not correlate with neck fat and bone weights. Final models explained (p < 0.01) 94, 92 and 88% of the variation observed for CM, CF and CB, respectively. Overall, results showed that prediction of carcass composition from shoulder (shoulder) tissue composition is a viable option over the more accurate method of analyzing the whole carcass.

The aim of this study is to evaluate the model performance in the classification of FAMACHA© scores using Support Vector Machines (SVMs) with a focus on the estimation of the FAMACHA© scoring system used for early diagnosis and treatment management of parasitic infections. FAMACHA© scores are a color-based visual assessment system used to determine parasite load in animals, and in this study, the accuracy of the model was investigated. The model’s accuracy rate was analyzed in detail with metrics such as sensitivity, specificity, and positive/negative predictive values. The results showed that the model had high sensitivity and specificity rates for class 1 and class 3, while the performance was relatively low for class 2. These findings not only demonstrate that SVM is an effective method for classifying FAMACHA© scores but also highlight the need for improvement for class 2. In particular, the high accuracy rate (97.26%) and high kappa value (0.9588) of the model indicate that SVM is a reliable tool for FAMACHA© score estimation. In conclusion, this study demonstrates the potential of SVM technology in veterinary epidemiology and provides important information for future applications. These results may contribute to efforts to improve scientific approaches for the management of parasitic infections.

El desarrollo de la resistencia a los antimicrobianos aumenta el riesgo de mortalidad por una infección bacteriana, incluso si el enfermo recibe antibióticos estos podrían resultar inefectivos para tratar la infección. La OMS clasifica a Salmonella spp. como patógeno prioritario por su resistencia a los antibióticos, principalmente a fluoroquinolonas, esta bacteria puede contaminar distintos alimentos, sin embargo, las gallinas y el huevo son su principal reservorio. El objetivo de esta investigación fue determinar la presencia de cepas de Salmonella spp. resistente a los antibióticos en excretas de gallinas productoras de huevo en Sinaloa, México. El aislamiento de Salmonella spp. de heces se realizó en agua peptona buferizada, caldo rapapport vasiliadis, agar XLD y agar entérico Hektoen, la identificación del patógeno se realizó por pruebas bioquímicas y se determinó el perfil de resistencia a antibióticos por el método de difusión en disco de Kirby-Bauer detectando 40.0 % (28/70) de muestras positivas a Salmonella spp. y 100 % (9/9) de resistencia a Betalactámicos y Fluoroquinolonas, además, el 77.8 % (7/9) de estas cepas presentó multirresistencia, esto representa una amenaza para la salud pública y la inocuidad del huevo en Sinaloa, por lo que se recomienda la implementación de estrategias que reduzcan la contaminación del huevo.