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Marchiafava Bignami disease is a demyelinating and necrotic disease of the central nervous system in chronic alcohol users and malnourished patients. The primary characteristic lesion of this disease is present in the corpus callosum in the form of its necrosis, but plenty of evidence suggests that it can also affect other parts of the brain. The main pathophysiology revolves around the consumption of alcohol and its ability to cause thiamine depletion in the body and hinder various metabolic pathways. There is also a hindrance in myelin synthesis, which further damages the signal transmission leading to an array of symptoms and signs. It is present in different degrees in patients in the form of different stages, namely acute, subacute, and chronic. The diagnosis of the disease becomes tough as the presenting symptoms are very generic and vague. Before the innovation of advanced imaging techniques, it was mainly a finding during an autopsy, but presently it can be diagnosed by a well-taken history and imaging techniques which can help to rule out other diseases having a similar clinical presentation. The gold standard for the diagnosis of the disease is using magnetic resonance imaging (MRI) techniques to visualize the lesions present in the corpus callosum and other areas, but other methods like computed tomography (CT) are also used. The prognosis of the disease is influenced by many factors, and it varies greatly. Some factors such as broad involvement of the cerebral cortex and severe disturbances in consciousness are indicative of a poor prognosis. The differential diagnosis consists of other alcohol use disorders like Wernicke's encephalopathy, neoplastic conditions, and multiple sclerosis, to mention a few. Each one should be carefully eliminated before finalizing the diagnosis. The treatment of the disease is not concrete, but evidence shows improvement with specific interventions.

This study presents an artificial neural network (ANN) model predicting values of sodium adsorption ratio (SAR), residual sodium carbonate, magnesium adsorption ratio, Kellys ratio and percent sodium (%Na) in the groundwater of Nanded tehsil. The 50 groundwater samples were analyzed for different physicochemical parameters such as pH, EC, TDS, Ca, Mg, Na, K, Cl, CO 3 , HCO 3 , SO 4 and NO 3 , for the pre monsoon season 2012. The ANN model is developed through R programming and compared with MS-Excel software. These parameters were used as input variables in the ANN based groundwater quality indices for irrigation suitability prediction. The best back propagation algorithm and neuron numbers were determined for optimization of the model architecture. The resilient backpropagation algorithm with weight back tracking was used for optimization of seven neurons through sensitive analysis. It showed that a network with seven neurons was highly accurate in predicting the irrigation suitability indices. The relative mean squared error, coefficient of determination (R 2) and mean absolute relative error between experimental data and model outputs were calculated. It is observed that is a good agreement between actual data and ANN outputs of groundwater for irrigation suitability indices for training and testing datasets. The spatial distribution maps of measured and predicted values of irrigation indices were prepared using ArcGIS software. Hence, the result confirms that the ANN model is an applied tool to predict the groundwater suitability for irrigation purpose in Nanded tehsil.