Scientists at the University of Bonn have unearthed the root cause for the development of temporal lobe epilepsy! At an early stage, astrocytes are uncoupled from each other, this results in the extracellular accumulation of potassium ions and neurotransmitters, which cause hyper-excitability of the neurons. Astrocytes are connected by gap junction channels composed mainly of the gap junction protein (connexin 43 and connexin 30). In this study, researchers combined patch-clamp recordings with various immunotechniques to decipher of the role of impaired gap junctions channels in the etiology of epilepsy. So, the restoration of the astrocyte dysfunction could be a novel strategy for anti-epileptogenic therapeutic intervention.
Bedner P, Dupper A, Hüttmann K, Müller J, Herde MK, Dublin P, Deshpande T, Schramm J, Häussler U, Haas CA, Henneberger C, Theis M, & Steinhäuser C (2015). Astrocyte uncoupling as a cause of human temporal lobe epilepsy. Brain : a journal of neurology, 138 (Pt 5), 1208-22 PMID: 25765328
The Microsoft researchers are working with the University of Cambridge to develop tools to assist surgeons and oncologists for treating patients with glioblastoma. These tools provides a highly effective means of computer-assisted segmentation and fully automatic, 3-D tumor delineation.
Currently, physicians will take a MRI scan of a brain and carefully draw an outline on each 2-D slice of the tumor and its constituent parts: areas of actively growing tumor, areas of tumor that have grown so fast that they have run out of nutrients and oxygen and are starting to die, and the area of brain surrounding the tumor that shows signs of swelling and inflammation. The researchers have devised an algorithm to replicate the manual annotations and to generalize on a previously unseen patient-data set.
The system can be trained to compute the segmentation accurately and efficiently. The technique used to segment the tumors into their component parts is a discriminative approach based on the use of decision forests using context-aware spatial features. Individual tissue types are classified simultaneously, and the results are computationally efficient, with low model complexity.
Zikic D, Glocker B, Konukoglu E, Criminisi A, Demiralp C, Shotton J, Thomas OM, Das T, Jena R, & Price SJ (2012). Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, 15 (Pt 3), 369-76 PMID: 23286152