MOR-based uncertainty quantification in transcranial magnetic stimulation

Field computation for Transcranial Magnetic Stimulation requires the knowledge of the electrical conductivity profiles in the human head. Unfortunately, the conductivities of the different tissue types are not exactly known and vary from person to person. Consequently, the computation of the electric field in the human brain should incorporate the uncertainty in the conductivity values. In this paper, we compare a non-intrusive polynomial chaos expansion and a new intrusive parametric Model Order Reduction approach for the sensitivity analysis in Transcranial Magnetic Stimulation computations. Our results show that compared to the non-intrusive method, the new intrusive method provides similar results but shows two orders of magnitude reduced computation time. We find monotonically decreasing errors for increasing state-space dimensions, indicating convergence of the new method. For the sensitivity analysis, both Sobol coefficients and sensitivity coefficients indicate that the uncertainty of the white matter conductivity has the largest influence on the uncertainty in the field computation, followed by gray matter and cerebrospinal fluid. Consequently, individual white matter conductivity values should be used in Transcranial Magnetic Stimulation field computations

Tissue Impedance Spectroscopy to Guide Resection of Brain Tumours

Even expert neurosurgeons are unable to visually differentiate lower grade glioma tissue from normal brain tissue. Therefore, during tumour removal neurosurgeons can only rely on image guidance. It has been proven that higher rates of tumour resection prolong long-term survival of patients. We aim to implement impedance spectroscopy as a potential supportive tool to improve radical resection. During this pilot study, we evaluated the possibility to differentiate ex vivo tissue samples (biopsy samples during tumour surgeries) with the help of impedance spectroscopy. Tissues were collected from two patients and impedance spectra differences were found between low-grade glioma, high-grade glioma and healthy brain tissue

Complexes of Magnetic Nanoparticles with Cellulose Nanocrystals as Regenerable, Highly Efficient, and Selective Platform for Protein Separation

We present an efficient approach to develop cellulose nanocrystal (CNC) hybrids with magnetically responsive Fe₃O₄ nanoparticles that were synthesized using the (Fe³⁺/Fe²⁺) coprecipitation. After 2,2,6,6-tetramethylpiperidine-1-oxyl radical (TEMPO)-catalyzed oxidation of CNC, carbodiimide (EDC/NHS) was used for coupling amine-containing iron oxide nanoparticles that were achieved by dopamine ligand exchange (NH₂–Fe₃O₄ NPs). The as-prepared hybrids (Fe₃O₄@CNC) were further complexed with Cu­(II) ions to produce specific protein binding sites. The performance of magnetically responsive Cu–Fe₃O₄@CNC hybrids was assessed by selectively separating lysozyme from aqueous media. The hybrid system displayed a remarkable binding capacity with lysozyme of 860.6 ± 14.6 mg/g while near full protein recovery (∼98%) was achieved by simple elution. Moreover, the regeneration of Fe₃O₄@CNC hybrids and efficient reutilization for protein separation was demonstrated. Finally, lysozyme separation from matrices containing egg white was achieved, thus revealing the specificity and potential of the presented method