Current therapies in exotic animal oncology.

The majority of information on oncology therapies has been reported in humans, canine, and feline patients, and laboratory animals with experimentally induced tumors. A variety of treatments,including radiation therapy, chemotherapy, photodynamic therapy, and others have been used with exotic animals. There are many species of exotic pets, and anatomic differences, as well as husbandry and nutritional requirements, must be taken into account to provide optimal care. By providing a broad overview of therapies and considerations for treatment, this article is intended to provide the practitioner with an overview of approach and options when addressing oncology cases in exotic animals.     

Simulation of growth and production in sheep-model 1: A computer program to estimate energy and nitrogen utilisation,body composition and empty liveweight change,day by day for sheep of any age

A computer program based on empirical relationships is described. It predicts daily energy and nitrogen utilisation repetitively for sheep of any age, before, during and after weaning; provision is also made for pregnancy, lactation and cold stress. Input information includes: intake, protein content and digestibility of the diet; age, empty body weight, fat content and feeding activity of the sheep; ambient temperature and wind speed; times of shearing and mating.Metabolisable energy from milk and/or dry feed is estimated and energy requirements for maintenance, including the cost of feeding activities and homeostasis in the cold, are deducted to obtain energy balance. The amount of amino acid nitrogen absorbed from the small intestine is estimated, and nitrogen balance in body tissues and wool is calculated from this, allowing for body weight and net energy intake. Potential wool growth is calculated from nitrogen and energy intakes, and potential conceptus growth or milk production is estimated primarily from stage of pregnancy or lactation. The use of nitrogen and energy for these products is assessed and balances of energy and nitrogen in body tissues are then obtained by difference. If achievement of the potential rates of production in pregnant or lactating animals would cause excessive loss of energy or nitrogen from body tissues, production of wool and conceptus or milk is reduced sufficiently to avoid this problem. Gain or loss of body fat and protein, and hence change of empty live weight, are finally derived and the animal parameters are incremented before proceeding to calculation for the next day.Evidence is presented that the model is stable in predicting lifetime performance, and that predictions of growth curves, body composition and various nutritional parameters are reasonably accurate in a variety of circumstances.     

The Validation of Pesticide Leaching Models

The validation of pesticide leaching models presents particular problems where the number of model predictions is far in excess of the observed data. Normally, however, there are more frequent field observations for other parameters (notably the site hydrology) than for pesticide concentrations in either water or soil. A five-stage validation procedure which takes advantage of the most frequently available observations and which tests each of the components of the model in a cumulative way, is thus advocated: Stage 1: Parameterisation of the model using only independently measured parameters. Stage 2: Hydrological validation: the validation of the predictions of water movement and water content of the soil. Stage 3: Solute movement validation: where field data are available for solutes other than pesticide, the model should first be validated for them, especially if they are more abundant than the pesticide observations. Conserved solutes such as chloride or bromide are preferred, although nitrate may be used for short periods. Stage 4: Pesticide fate in the soil: models should use parameters of pesticide fate derived from independent studies. Stage 5: Pesticide leaching: only in the last stage are the relatively small number of pesticide observations compared with the model predictions with respect to patterns and orders of magnitude of occurrence. With this scheme, the results of each stage are carried forward to the next, and confidence in the model is built with each stage. This is illustrated using the CRACK-P model and hydrological, nitrate and pesticide data from the Brimstone Farm Experiment Oxfordshire, UK.