Species specific anaesthetics for fish anaesthesia and euthanasia.

There is a need to ensure that the care and welfare for fish maintained in the laboratory are to the highest standards. This extends to the use of anaesthetics for both scientific study, humane killing and euthanasia at end of life. An anaesthetic should not induce negative behaviours and fish should not seek to avoid the anaesthetic. Surprisingly little information is available to facilitate a humane choice of anaesthetic agent for fish despite over 100 years of use and the millions of fish currently held in thousands of laboratories worldwide. Using a chemotaxic choice chamber we found different species specific behavioural responses among four closely related fish species commonly held in the laboratory, exposed to three widely used anaesthetic agents. As previously found for zebrafish (Danio rerio), the use of MS-222 and benzocaine also appears to induce avoidance behaviours in medaka (Oryzias latipes); but etomidate could provide an alternative choice. Carp (Cyprinus carpio), although closely related to zebrafish showed avoidance behaviours to etomidate, but not benzocaine or MS-222; and rainbow trout (Oncorhynchus mykiss) showed no avoidance to the three agents tested. We were unable to ascertain avoidance responses in fathead minnows (Pimephales promelas) and suggest different test paradigms are required for that species

Prediction of metabolisable energy of poultry feeds by estimating in vitro organic matter digestibility

The aim of this study was to develop equations to predict the in vivo apparent metabolisable energy (AME) of poultry feeds using an in vitro method for estimation of organic matter digestibility. In this study, a total of 57 samples of feedstuffs and 23 samples complete diets for poultry were used. Dry matter (DM), crude protein (CP), crude fibre (CF), crude fat (CFat) and crude ash (CA) of the diets were determined. A modified method for estimating the enzymatic digestibility of organic matter (EDOM) was used. For the determination of in vivo ME, the rooster digestibility assay was followed. Obtained laboratory results, that is in vitro and proximate analysis values were regressed against the in vivo ME values and equations for predicting the in vivo ME of feeds for poultry have been derived. Using CA, CF, CFat and in vitro EDOM as predictors, the following equation for predicting the in vivo ME in poultry feeds was derived: ME (MJ/kg DM) = 5.46 – 0.2166 x CA – 0.0946 x CF + 0.2219 x CFat + 0.1054 x EDOM (R2 = 0.844, RSD = 1.10). Using only EDOM as predictor generated the equation: ME (MJ/kg DM) = -0.41 + 0.1769 x EDOM (R2 = 0.689; RSD = 1.63). Results show that using only EDOM as a predictor was not as accurate as when the other variables were included.Key words: Metabolisable energy, prediction, poultry, feeds, organic matter digestibilit

Neutrophil extracellular traps and the dysfunctional innate immune response of cystic fibrosis lung disease:a review

Abstract Background Cystic Fibrosis (CF) is a devastating genetic disease characterised primarily by unrelenting lung inflammation and infection resulting in premature death and significant morbidity. Neutrophil Extracellular Traps (NETs) are possibly key to inflammation in the disease. This review aims to draw together existing research investigating NETs in the context of a dysfunctional innate immune system in CF. Main body NETs have a limited anti-microbial role in CF and studies have shown they are present in higher numbers in CF airways and their protein constituents correlate with lung function decline. Innate immune system cells express CFTR and myeloid-specific CFTR KO mice have greater neutrophil recruitment and higher pro-inflammatory cytokine production to both sterile and bacterial inflammatory challenges. CFTR KO neutrophils have impaired anti-microbial capacity and intrinsic abnormalities in the pH of their cytoplasm, abnormal protein trafficking, increased neutrophil elastase and myeloperoxidase function, and decreased hypochlorite concentrations in their phagolysosomes. Furthermore, neutrophils from CF patients have less intrinsic apoptosis and may be therefore more likely to make NETs. CFTR KO macrophages have high intraphagolysosomal pH and increased toll-like receptor 4 on their cell surface membranes, which inhibit their anti-microbial capacity and render them hyper-responsive to inflammatory stimuli, respectively. Pharmacological treatments for CF target these intrinsic abnormalities of immune dysfunction. Emerging evidence suggests that the absence of CFTR from neutrophils affects NETosis and the interaction of NETs with macrophages. Conclusion Current evidence suggests that NETs contribute to inflammation and lung destruction rather than working effectively in their anti-microbial capacity. Further studies focussing on the pro-inflammatory nature of NET constituents are required to identify the exact mechanistic role of NETs in CF and potential therapeutic interventions

Prediction of in vivo organic matter digestibility of ruminant feeds using in vitro techniques

Prediction equations derived from in situ and in vitro analytical techniques to determine in vivo organic matter digestibility (OMD) are useful tools to estimate the quality of livestock feed. Most derived equations are aimed at groups of feedstuffs (forages or concentrates) or feeds separately. In this study of OMD, the prediction equations of the modified two-stage Tilley & Terry in vitro technique (MT) and pepsin-acid multi-enzymatic technique (PME) are compared, validated, and improved in relation to verified in vivo results using feedstuffs and complete diets. Initial comparison with in vivo data showed that the combined dataset and that of single feedstuffs achieved acceptable R2 values for both MT and PME (0.88 and 0.92, 0.87 and 0.89, respectively). The validation with the second dataset established that the initial equations were valid with R2 values of 0.96 for MT and 0.91 for PME on the combined feeds dataset. The establishment of a prediction equation using both datasets resulted in improved R2 values over the initial equation. With combined feeds using MT it was 0.94, compared with 0.88, and using PME, it was 0.91 compared with 0.87. No significant decrease occurred in the variation of OMD between the datasets, as explained by the model when omitting on separate slope and intercept, thus confirming the same population assumption. The data sets could be combined for a new prediction equation. The R2 values were 0.94 and 0.91 for MT and PME methods for combined feeds, respectively. The new improved in vivo prediction equation in each instance was thus valid and a true improvement on the initial prediction equations. The PME method can be used for predicting OMD as it negates the use of rumen liquor and confidently replaces MT OMD determinations.Keywords: Modified two-stage in vitro, multi-enzymatic, pepsin acid, rumen liquor, validatio

Prediction of metabolizable energy content of poultry feedstuffs – response surface methodology vs. Artificial neural network approach

Metabolisable energy (ME) represents portion of energy utilized by the animal. Experiments for determination of ME require test animals, collection of samples and excreta, and determination of total energy content of used material. Therefore, ME determination can be expensive and time consuming. The aim of this study was to investigate the effect of enzymatic digestible organic matter (EDOM) and values of proximate chemical analysis on prediction of true metabolisable energy (TME) of feedstuffs for broilers. The performance of Artificial Neural Networks (ANN) was compared with the performance of second order polynomial (SOP) model, as well as with experimental data in order to develop rapid and accurate method for prediction of TME. Analysis of variance and post-hoc Tukey’s HSD test at 95% confidence limit have been calculated to show significant differences between different samples. Response Surface Method has been applied for evaluation of TME. Second order polynomial model showed high coefficients of determination (r2 = 0.927). ANN model also showed high prediction accuracy (r2 = 0.983). Principal Component Analysis was successfully used in prediction of TME