Comparative evaluation of the use of artificial neural networks for modelling the epidemiology of schistosomiasis mansoni.

There has been a marked increase in the application of approaches based on artificial intelligence (AI) in the field of computer science and medical diagnosis, but AI is still relatively unused in epidemiological settings. In this study we report results of the application of neural networks (NN; a special category of AI) to schistosomiasis. It was possible to design an NN structure which can process and fit epidemiological data collected from 251 schoolchildren in Egypt using the first year's data to predict second and third years' infection rates. Data collected over 3 years included age, gender, exposure to canal water and agricultural activities, medical history and examination, and stool and urine parasitology. Schistosoma mansoni infection rates were 50%, 78% and 66% at the baseline and the 2 follow-up periods, respectively. NN modelling was based on the standard back-propagation algorithm, in which we built a suitable configuration of the network, using the first year's data, that optimized performance. It was implemented on an IBM compatible computer using commercially available software. The performance of the NN model in the first year compared favourably with logistic regression (NN sensitivity = 83% (95% confidence interval [CI] 78-88%) and positive predictive value (PPV) = 63% (95% CI 57-69%); logistic regression sensitivity = 66% (95% CI 60%-72%) and PPV = 59% (95% CI 53%-65%). The NN model generalized the criteria for predicting infection over time better than logistic regression and showed more stability over time, as it retained its sensitivity and specificity and had better false positive and negative profiles than logistic regression. The potential of NN-based models to analyse and predict wide-scale control programme data, which are inevitably based on unstable egg excretion rates and insensitive laboratory techniques, is promising but still untapped.

Cell calcium, cell injury and cell death.

The role of calcium in cell injury has been the subject of much recent investigation. The movement and redistribution of this cation from extra to intracellular compartments and the calcium shifts between intracellular compartments may well play a determinate role in the cell's reaction to injury. Therefore, data of such shifts and their correlation with morphological, biochemical and cytoskeletal studies will provide a better understanding of these processes. To study the effects of calcium regulation on acute lethal anoxic injury and the effects of inhibition of respiration with cyanide, three experimental systems were utilized: Ehrlich ascites tumor cells, isolated rabbit proximal tubule segments and suspended or cultured rat proximal tubule cells. Although our data showed no correlation between total cell calcium and cell death except in highly selected cell systems, they did indicate that calcium can be an important control variable. Therefore, massive increases in total cell calcium, as seen in Ca3(PO4)2 precipitation in mitochondria, must be a secondary event and represent the modern day equivalent of the classical dystrophic calcification seen by pathologists in the past. Although the involvement of extracellular calcium in cell death may well be significant in some cell types, redistribution of calcium within the intracellular compartments may play an even more important role.