Farm and animal-level risk factors associated with rectal prolapse in bovine and buffalo calves in Egypt with special reference to the optimal treatment strategy.

Background: Rectal prolapse (RP) is a serious illness of the rectum and small intestine causing serious health problems in domestic animals. However, there is paucity in the estimation of the risk factors associated with this problem in calves. Aim: In the present study, we investigated the prevalence and risk factors associated with the rectal prolapse in both bovine and buffalo calves in Egypt, highlighting the most appropriate treatment strategy. Methods: Forty-two calves (23 bovine and 19 buffalo) suffering from varying degrees of rectal prolapse were used. From the owners' anamnesis, the farm- and animal-level risk factors associated with each animal were collected. Fisher's exact tests were used to determine the distribution of frequencies in the different rectal prolapse grades. Descriptive statistics were calculated in the form of mean ± standard deviation (SD) using one-way analysis of variance. Crosstabs were used to determine Spearman's correlation between variables. According to the disease severity, the appropriate treatment strategy was accomplished either by medicinal or surgical interferences. Results: The final logistic regression form demonstrated that the statistical test, Hosmer and Lemeshow's goodness of fit, indicates a significant result (χ2 = 8.91). Body score was the potential risk factor for the occurrence of RP in calves. Medicinal management along with dietary modification was sufficient to treat 70% of grade I in a successful manner, while 33.3% (grade I and grade II) were effectively treated surgically with reduction and application of purse-string sutures. Conclusion: The current study advocates the valid role of resection of rectal mucosa combined with manual reduction and retention in treating calves suffering from grade II rectal prolapse. The final multivariate logistic regression model indicates that the calf's body score is a potential risk factor for the occurrence of RP.

Phenytoin: A promising non-antibiotic drug for the topical treatment of digital dermatitis in dairy cows.

BACKGROUND AND AIM: Digital dermatitis (DD) is one of the most common causes of lameness in dairy cattle. It is seen in nearly all dairy herds across the world and has substantial welfare and economic implications. In this study, we aimed to investigate the efficacy of phenytoin sodium topical treatment on painful ulcerative stage of bovine digital dermatitis (BDD). MATERIALS AND METHODS: In total, 45 Holstein-Friesian dairy cows with DD were randomly assigned to one of the three topical treatment trials (15 each): Saline solution (first treatment, negative control), chlortetracycline spray (second treatment, positive control), or phenytoin sodium powder (third treatment, positive control) (third treatment). On day 0 (pre-treatment) and on days 7, 14, 21, and 28 post-treatment, the response of DD-affected cows to the medications used was evaluated by measuring lesion depth and size, as well as the total clinical score (lameness, pain, and discomfort). RESULTS: The cure rate in cows treated with phenytoin (86.66%) on day 28 was significantly improved compared to cows treated with either chlortetracycline (60%) or normal saline (6.66 %). CONCLUSION: Our findings highlight the superiority of phenytoin over the commonly used antibacterial agent, chlortetracycline, in the topical treatment of BDD, and subsequently suggest that phenytoin should be considered a suitable alternative treatment option for the treatment of BDD.

Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models.

OBJECTIVE: The objective of this study was to assess the veracities of most admired strategy discriminant analysis (DA), in comparison to the artificial neural network (ANN) for the anticipation and classification of milk production level in Holstein Friesian cattle using their performances. MATERIALS AND METHODS: A total of 3,460 performance records of imported and locally born Holstein Friesian cows were gathered during the period from 2000 to 2016 to compare two alternative techniques for predicting the level of production based on performance traits in dairy cattle with the use of statistical software (Statistical Package for the Social Sciences, version 20.0). RESULTS: The findings of the comparison indicated that ANN was more impressive in the expectancy of milk production level than did an imitator statistical method based on DA. The accuracy of the ANN model was high for the winter season (79.5%), whereas it was 47.3% for DA. The current findings were assured via the areas under receiver operating characteristic curves (AUROC) for DA and ANN. AUROC curves were smaller in the condition of the DA model across different calving seasons compared with the ANN model. The inaccuracies of variations were significant at a 5% significance level utilizing paired sample t-test. CONCLUSION: ANN model can be used efficiently to predict the level of production across the different calving seasons compared to the DA model.