Ultrapure alginate gel decreased the incidence of postoperative adhesion formation in this rat model.

Back propagation is most widely used in neural network projects because it is easy to train and for its accuracy. Back propagation learning algorithm consists of two facets, the first one generate the input pattern of the network and the another one to adjust the output through altering the weights of the network. The back propagation algorithm can be for predicting rainfall. This paper materialize training, testing of data set and detecting the hidden neuron in the network. In this research, rainfall prediction in the region of DELHI (India) has been analyzed using neural network back propagation algorithm. Three layer model has been used for training and studying different attributes of the hidden neurons in the network.

Ultrapure alginate gel reduces adhesion severity but not abscesses. The gel seemed to be safe, not aggravating intra-peritoneal infection in this abdominal infection model.

Ultrapure alginate gel reduces adhesion reformation following adhesiolysis.

Ultrapure alginate gel does not interfere with repair of ileal anastomoses constructed under conditions in which chances of anastomotic dehiscence are high. The alginate gel performs better than the HA/CMC film.