Zeitschriftenaufsätze

A spinal cord injury (SCI) compresses the spinal cord, killing neurons and glia at the injury site and resulting in prolonged inflammation and scarring that prevents regeneration. Astrocytes, the main glia in the spinal cord, become reactive following SCI and contribute to adverse outcomes. The anti-inflammatory cytokine transforming growth factor beta 3 (TGFβ3) has been shown to mitigate astrocyte reactivity; however, the effects of prolonged TGFβ3 exposure on reactive astrocyte phenotype have not yet been explored. This study investigates whether magnetic core-shell electrospun fibers can be used to alter the release rate of TGFβ3 using externally applied magnetic fields, with the eventual application of tailored drug delivery based on SCI severity. Magnetic core-shell fibers are fabricated by incorporating superparamagnetic iron oxide nanoparticles (SPIONs) into the shell and TGFβ3 into the core solution for coaxial electrospinning. Magnetic field stimulation increased the release rate of TGFβ3 from the fibers by 25% over 7 days and released TGFβ3 reduced gene expression of key astrocyte reactivity markers by at least twofold. This is the first study to magnetically deliver bioactive proteins from magnetic fibers and to assess the effect of sustained release of TGFβ3 on reactive astrocyte phenotype.

Purpose: The purpose of this paper is to derive a new stress tensor for calculating the Lorentz force acting on an arbitrarily shaped nonmagnetic conductive specimen moving in the field of a permanent magnet. The stress tensor allows for a transition from a volume to a surface integral for force calculation. Design/methodology/approach: This paper derives a new stress tensor which consists of two parts: the first part corresponds to the scaled Poynting vector and the second part corresponds to the velocity term. This paper converts the triple integral over the volume of the conductor to a double integral over its surface, where the subintegral functions are continuous through the different compartments of the model. Numerical results and comparison to the standard volume discretization using the finite element method are given. Findings: This paper evaluated the performance of the new stress tensor computation on a thick and thin cuboid, a thin disk, a sphere and a thin cuboid containing a surface defect. The integrals are valid for any geometry of the specimen and the position and orientation of the magnet. The normalized root mean square errors are below 0.26% with respect to a reference finite element solution applying volume integration. Originality/value: Tensor elements are continuous throughout the model, allowing integration directly over the conductor surface.

Purpose: Age-related macular degeneration (AMD) is a complex eye disorder affecting millions worldwide. This article uses deep learning techniques to investigate the relationship between AMD, genetics and optical coherence tomography (OCT) scans. Methods: The cohort consisted of 332 patients, of which 235 were diagnosed with AMD and 97 were controls with no signs of AMD. The genome-wide association studies summary statistics utilized to establish the polygenic risk score (PRS) in relation to AMD were derived from the GERA European study. A PRS estimation based on OCT volumes for both eyes was performed using a proprietary convolutional neural network (CNN) model supported by machine learning models. The method's performance was assessed using numerical evaluation metrics, and the Grad-CAM technique was used to evaluate the results by visualizing the features learned by the model. Results: The best results were obtained with the CNN and the Extra Tree regressor (MAE = 0.55, MSE = 0.49, RMSE = 0.70, R2 = 0.34). Extending the feature vector with additional information on AMD diagnosis, age and smoking history improved the results slightly, with mainly AMD diagnosis used by the model (MAE = 0.54, MSE = 0.44, RMSE = 0.66, R2 = 0.42). Grad-CAM heatmap evaluation showed that the model decisions rely on retinal morphology factors relevant to AMD diagnosis. Conclusion: The developed method allows an efficient PRS estimation from OCT images. A new technique for analysing the association of OCT images with PRS of AMD, using a deep learning approach, may provide an opportunity to discover new associations between genotype-based AMD risk and retinal morphology.

Spectral analysis of repeatedly evoked potentials (EPs) is challenging since recordings contain a superposition of evoked signals and spontaneous activity. We developed a novel approach, N-interval Fourier Transform Analysis (N-FTA), which allows for reliable separation and simultaneous assessment of triggered and background spectral components. Median nerve stimulation data from a total of eleven volunteers recorded in two labs with different experimental settings were investigated. Consistently, short latency spectral components were mainly contained in the gamma and high gamma bands. In contrast, spontaneous activity displayed a 1/f spectral profile with distinct alpha and beta peaks. Spontaneous power spectral densities (PSDs) obtained for real and sham stimulation were highly comparable. The low frequency background PSD was more than two orders of magnitude above the spectral short latency peaks. Within the 30Hz to 90Hz band, the evoked peaks were -17dB to -4dB below the background suggesting that target band filtered short latency deflection might be extracted using less than 100 trials. SEPs following tibial nerve stimulation (3 subjects) displayed a narrower spectral band at about half the bandwidth as compared to median nerve stimulation. Evoked peaks were between 30Hz and 37Hz at PSD levels being -10dB to -4dB below the background activity. These spectral peaks were related to the short latency response of typical W-morphology. Cortical short latency responses are contained in distinct spectral target bands which are much narrower than the standard bandwidth recommendations for routine recordings. In particular, the high pass corner frequency may be selected about one order of magnitude above the current standard. This might render SEP recordings more robust since it eases the suppression of spontaneous activity and movement artifacts such as eye-blinks. Real-time zero-phase filters are required for translating these findings into improved recording systems.

Frequent complications arising from low anterior resections include urinary and fecal incontinence, as well as sexual disorders, which are commonly associated with damage to the pelvic autonomic nerves during surgery. To assist the surgeon in preserving pelvic autonomic nerves, a novel approach for intraoperative pelvic neuromonitoring was investigated that is based on impedance measurements of the innervated organs. The objective of this work was to develop an algorithm called AMINA to classify the bioimpedance signals, with the goal of facilitating signal interpretation for the surgeon. Thirty patients included in a clinical investigation underwent nerve-preserving robotic rectal surgery using intraoperative pelvic neuromonitoring. Contraction of the urinary bladder and/or rectum, triggered by direct stimulation of the innervating nerves, resulted in a change in tissue impedance signal, allowing the nerves to be identified and preserved. Impedance signal characteristics in the time domain and the time-frequency domain were calculated and classified to develop the AMINA. Stimulation-induced positive impedance changes were statistically significantly different from negative stimulation responses by the percent amplitude of impedance change Amax in the time domain. Positive impedance changes and artifacts were distinguished by classifying wavelet scales resulting from peak detection in the continuous wavelet transform scalogram, which allowed implementation of a decision tree underlying the AMINA. The sensitivity of the software-based signal evaluation by the AMINA was 96.3%, whereas its specificity was 91.2%. This approach streamlines and automates the interpretation of impedance signals during intraoperative pelvic neuromonitoring.