Planimetry investigation of the corpus callosum in temporal lobe epilepsy patients

OBJECTIVE: To evaluate the effects of temporal lobe epilepsy (TLE) on corpus callosum (CC) morphometry in patients with TLE. METHODS: This retrospective study was conducted at the Faculty of Medicine, Tekirdag Namik Kemal University, Tekirdag, Turkey between November 2010 and December 2013. The epileptic syndrome diagnosis was based on International League Against Epilepsy criteria, and this study was conducted on the MRIs of 25 epilepsy patients and 25 control subjects. We classified the patients according to their duration of epilepsy <10 and ≥10 years. The projection area length (PAL) of the CC was also estimated. Total brain volumes (TBV) were measured on CT images. RESULTS: The mean values of TBV for patients with TLE and the control group were not statistically different, but the CC PAL values were statistically different. The mean CC PAL values of under and over 25 years of age in patients with TLE were statistically different. The mean values of TBV of under and over 10 years duration of TLE were small statistically, but the CC PAL values were statistically different. CONCLUSION: The results indicate a clear influence of TLE on the structure of the CC rather than TBV

Three-Dimensional Analysis of Binding Sites for Predicting Binding Affinities in Drug Design

Understanding the interaction between drug molecules and proteins is one of the main challenges in drug design. Several tools have been developed recently to decrease the complexity of the process. Artificial intelligence and machine learning methods offer promising results in predicting the binding affinities. It becomes possible to do accurate predictions by using the known protein-ligand interactions. In this study, the electrostatic potential values extracted from 3-dimensional grid cubes of the drug-protein binding sites are used for predicting binding affinities of related complexes. A new algorithm with a dynamic feature selection method was implemented, which is derived from Compressed Images For Affinity Prediction (CIFAP) study, to predict binding affinities of Checkpoint Kinase 1 and Caspase 3 inhibitors