An experimental protocol for mimicking pathomechanisms of traumatic brain injury in mice

Traumatic brain injury (TBI) is a result of an outside force causing immediate mechanical disruption of brain tissue and delayed pathogenic events. In order to examine injury processes associated with TBI, a number of rodent models to induce brain trauma have been described. However, none of these models covers the entire spectrum of events that might occur in TBI. Here we provide a thorough methodological description of a straightforward closed head weight drop mouse model to assess brain injuries close to the clinical conditions of human TBI

Classification of Down Syndrome of Mice Protein Dataset on MongoDB Database

There are samples both with Down Syndrome and without in mice protein expression data set. It is important to define the reason of Down Syndrome treatment by means of mice protein for the same treatment seem human being. In the present study, mice protein expression data set from UCI repository are classified using Bayesian Network algorithm, K- Nearest Neighbor, Decision Table, Random Forest and Support Vector Machine which are some of classification methods. The classification algorithms with 10-fold cross validation and by splitting equally as test and train data are tested to classify on the mice protein data set. The classification of the data set was succeeded with 94.3519% accuracy in 0.06 seconds using Bayesian Network, with 99.2593% accuracy in 0.01 seconds using KNN, with 95.4630 % accuracy in 1.2 seconds using Decision Table, with 100% accuracy in 0.58 seconds using Random Forest and with 100% accuracy in 1.17 seconds using SVM, with 10-fold cross validation. On the other hand, the classification of the data set was succeeded with 95.3704% accuracy in 0.22 seconds using Bayesian Network, with 98.3333% accuracy in 0 seconds using KNN, with 98.3333% accuracy in 0.72 seconds using Decision Table, with 100% accuracy in 0.77 seconds using Random Forest and with 100% accuracy in 1.48 seconds using SVM, by equally train-test data partitio