Between 1979 and 1985, six of 26 patients undergoing continuous ambulatory peritoneal dialysis developed fungal peritonitis. All had received antibacterial therapy with cefamandole and/or netilmicin prior to the diagnosis. The causal organisms were Candida albicans (three), Candida glabrata (one), Cryptococcus laurentii (one) and Saccharomyces cerevisiae (one). Treatment comprised catheter removal preceded by antifungal drugs (flucytosine and/or amphotericin B) in four patients and catheter removal alone in two. All patients were transferred to haemodialysis and five of the six developed extensive intra-abdominal adhesions. The most prudent management of fungal peritonitis in children would seem to be early cannula removal.

Two groups of cattle were used to develop (model data set: 384 heifers, 228 ± 22.7 kg BW, monitored over a 225-d feeding period) and to validate (naïve data set: 384 heifers, 322 ± 34.7 kg BW, monitored over a 142-d feeding period) the use of feeding behavior pattern recognition techniques to predict morbidity in newly arrived feedlot cattle. In the model data set, cattle were defined as morbid (MO) if they were removed from their pen to be treated due to visual observation of clinical signs of bovine respiratory disease and healthy (HL) if they remained within their pen and lacked lung lesions at slaughter. Individual feeding behavior parameters collected with a GrowSafe automated feeding behavior monitoring system were reduced via principal component analysis to 5 components that captured 99% of the variability in the data set. Combinations of clustering and cluster classification strategies applied to those components, along with pattern recognition techniques over different time windows, produced a total of 105 models from which precision, negative predictive value, sensitivity, specificity, and accuracy were calculated by comparing its predictions with the actual health status of individual cattle as determined by visual assessment. When the models with the best specificity (models 79 and 87), sensitivity (models 33 and 66), and accuracy (models 3 and 14) in the model data set were used in a naïve data set, models 79 and 87 were not able to predict any MO heifers (0%), with all animals (100%) being predicted as HL. Model 33 predicted 58.3% of the HL and 66.7% of the MO heifers, with MO heifers identified 3.1 ± 1.64 d earlier than by visual observation. Model 66 predicted 50.0% of the HL and 75.0% of the MO heifers, with MO heifers predicted 3.1 ± 1.76 d earlier than by visual observation. Model 3 predicted 100% of the HL and 50.0% of the MO cattle, with MO cattle predicted 1 d earlier than by visual observation. Model 14 predicted 83.3% of the HL and 58.3% of the MO cattle, with MO cattle detected 2.4 ± 1.99 d earlier than visual observation. The application of pattern recognition algorithms to feeding behavior has potential value in identifying MO cattle in advance of overt physical signs of morbidity. Work on an integrated system that would automatically process data collected from automated feed bunk monitoring systems is still required, however, for this method to have value to the commercial feedlot industry as a practical means of identifying MO cattle in real time.

A disposable clinical thermometer, based on heat-sensitive crystals, was tested in 200 subjects and its performance compared to the available bedside instrument and an accurate British Standard thermometer. It was found to be accurate and reliable and its convenience and ease of reading made it readily acceptable to patients and staff. It has the overwhelming advantage of severance of a link in the chain of hospital cross-infection and merits widespread clinical trial.

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