Variable Anisotropic Brain Electrical Conductivities in Epileptogenic Foci

Source localization models assume brain electrical conductivities are isotropic at about 0.33 S/m. These assumptions have not been confirmed ex vivo in humans. This study determined bidirectional electrical conductivities from pediatric epilepsy surgery patients. Electrical conductivities perpendicular and parallel to the pial surface of neocortex and subcortical white matter (n = 15) were measured using the 4-electrode technique and compared with clinical variables. Mean (±SD) electrical conductivities were 0.10 ± 0.01 S/m, and varied by 243% from patient to patient. Perpendicular and parallel conductivities differed by 45%, and the larger values were perpendicular to the pial surface in 47% and parallel in 40% of patients. A perpendicular principal axis was associated with normal, while isotropy and parallel principal axes were linked with epileptogenic lesions by MRI. Electrical conductivities were decreased in patients with cortical dysplasia compared with non-dysplasia etiologies. The electrical conductivity values of freshly excised human brain tissues were approximately 30% of assumed values, varied by over 200% from patient to patient, and had erratic anisotropic and isotropic shapes if the MRI showed a lesion. Understanding brain electrical conductivity and ways to non-invasively measure them are probably necessary to enhance the ability to localize EEG sources from epilepsy surgery patients

Molecular chess? Hallmarks of anti-cancer drug resistance

Background The development of resistance is a problem shared by both classical chemotherapy and targeted therapy. Patients may respond well at first, but relapse is inevitable for many cancer patients, despite many improvements in drugs and their use over the last 40 years. Review Resistance to anti-cancer drugs can be acquired by several mechanisms within neoplastic cells, defined as (1) alteration of drug targets, (2) expression of drug pumps, (3) expression of detoxification mechanisms, (4) reduced susceptibility to apoptosis, (5) increased ability to repair DNA damage, and (6) altered proliferation. It is clear, however, that changes in stroma and tumour microenvironment, and local immunity can also contribute to the development of resistance. Cancer cells can and do use several of these mechanisms at one time, and there is considerable heterogeneity between tumours, necessitating an individualised approach to cancer treatment. As tumours are heterogeneous, positive selection of a drug-resistant population could help drive resistance, although acquired resistance cannot simply be viewed as overgrowth of a resistant cancer cell population. The development of such resistance mechanisms can be predicted from pre-existing genomic and proteomic profiles, and there are increasingly sophisticated methods to measure and then tackle these mechanisms in patients. Conclusion The oncologist is now required to be at least one step ahead of the cancer, a process that can be likened to ‘molecular chess’. Thus, as well as an increasing role for predictive biomarkers to clinically stratify patients, it is becoming clear that personalised strategies are required to obtain best results