Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders

BACKGROUND Diagnostic classification of central vs. peripheral etiologies in acute vestibular disorders remains a challenge in the emergency setting. Novel machine-learning methods may help to support diagnostic decisions. In the current study, we tested the performance of standard and machine-learning approaches in the classification of consecutive patients with acute central or peripheral vestibular disorders. METHODS 40 Patients with vestibular stroke (19 with and 21 without acute vestibular syndrome (AVS), defined by the presence of spontaneous nystagmus) and 68 patients with peripheral AVS due to vestibular neuritis were recruited in the emergency department, in the context of the prospective EMVERT trial (EMergency VERTigo). All patients received a standardized neuro-otological examination including videooculography and posturography in the acute symptomatic stage and an MRI within 7~days after symptom onset. Diagnostic performance of state-of-the-art scores, such as HINTS (Head Impulse, gaze-evoked Nystagmus, Test of Skew) and ABCD2 (Age, Blood, Clinical features, Duration, Diabetes), for the differentiation of vestibular stroke vs. peripheral AVS was compared to various machine-learning approaches: (i) linear logistic regression (LR), (ii) non-linear random forest (RF), (iii) artificial neural network, and (iv) geometric deep learning (Single/MultiGMC). A prospective classification was simulated by ten-fold cross-validation. We analyzed whether machine-estimated feature importances correlate with clinical experience. RESULTS Machine-learning methods (e.g., MultiGMC) outperform univariate scores, such as HINTS or ABCD2, for differentiation of all vestibular strokes vs. peripheral AVS (MultiGMC area-under-the-curve (AUC): 0.96 vs. HINTS/ABCD2 AUC: 0.71/0.58). HINTS performed similarly to MultiGMC for vestibular stroke with AVS (AUC: 0.86), but more poorly for vestibular stroke without AVS (AUC: 0.54). Machine-learning models learn to put different weights on particular features, each of which is relevant from a clinical viewpoint. Established non-linear machine-learning methods like RF and linear methods like LR are less powerful classification models (AUC: 0.89 vs. 0.62). CONCLUSIONS Established clinical scores (such as HINTS) provide a valuable baseline assessment for stroke detection in acute vestibular syndromes. In addition, machine-learning methods may have the potential to increase sensitivity and selectivity in the establishment of a correct diagnosis

Reversible Random Sequential Adsorption of Dimers on a Triangular Lattice

We report on simulations of reversible random sequential adsorption of dimers on three different lattices: a one-dimensional lattice, a two-dimensional triangular lattice, and a two-dimensional triangular lattice with the nearest neighbors excluded. In addition to the adsorption of particles at a rate K+, we allow particles to leave the surface at a rate K-. The results from the one-dimensional lattice model agree with previous results for the continuous parking lot model. In particular, the long-time behavior is dominated by collective events involving two particles. We were able to directly confirm the importance of two-particle events in the simple two-dimensional triangular lattice. For the two-dimensional triangular lattice with the nearest neighbors excluded, the observed dynamics are consistent with this picture. The two-dimensional simulations were motivated by measurements of Ca++ binding to Langmuir monolayers. The two cases were chosen to model the effects of changing pH in the experimental system.Comment: 9 pages, 10 figure

Phospholipid Composition Modulates Carbon Nanodiamond-Induced Alterations in Phospholipid Domain Formation

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Langmuir, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see http://doi.org/10.1021/la504923j.The focus of this work is to elucidate how phospholipid composition can modulate lipid nanoparticle interactions in phospholipid monolayer systems. We report on alterations in lipid domain formation induced by anionically engineered carbon nanodiamonds (ECNs) as a function of lipid headgroup charge and alkyl chain saturation. Using surface pressure vs area isotherms, monolayer compressibility, and fluorescence microscopy, we found that anionic ECNs induced domain shape alterations in zwitterionic phosphatidylcholine lipids, irrespective of the lipid alkyl chain saturation, even when the surface pressure vs area isotherms did not show any significant changes. Bean-shaped structures characteristic of dipalmitoylphosphatidylcholine (DPPC) were converted to multilobed, fractal, or spiral domains as a result of exposure to ECNs, indicating that ECNs lower the line tension between domains in the case of zwitterionic lipids. For membrane systems containing anionic phospholipids, ECN-induced changes in domain packing were related to the electrostatic interactions between the anionic ECNs and the anionic lipid headgroups, even when zwitterionic lipids are present in excess. By comparing the measured size distributions with our recently developed theory derived by minimizing the free energy associated with the domain energy and mixing entropy, we found that the change in line tension induced by anionic ECNs is dominated by the charge in the condensed lipid domains. Atomic force microscopy images of the transferred anionic films confirm that the location of the anionic ECNs in the lipid monolayers is also modulated by the charge on the condensed lipid domains. Because biological membranes such as lung surfactants contain both saturated and unsaturated phospholipids with different lipid headgroup charges, our results suggest that when studying potential adverse effects of nanoparticles on biological systems the role of lipid compositions cannot be neglected

Thermosensitive hollow capsules based on thermoresponsive polyelectrolytes

Thermosensitive hollow capsules were successfully fabricated by the layer-by-layer deposition onto colloid particles of oppositely charged diblock copolymers each containing a poly (N-isoproprylacrylamide) (PNIPAM) block and by the subsequent decomposition of the core. The multilayer growth was characterized by electrophoresis and single particle light scattering. By combining confocal microscopy observation and FRAP measurements, we showed that the morphology and the permeability of the capsules change upon heating in aqueous solution. The decrease of size accompanied by a decrease of the permeability with increasing temperature was attributed to structural rearrangements in the shell. However, this process is only partially reversible upon cooling, limiting the thermoresponsive behavior of the capsules

Thermal spraying developments

Abstract not available

Angew. Chem.-Int. Edit.

Long-range structural order and alignment over different scales are of key importance for the regulation of structure and functionality in biology. However, it remains a great challenge to engineer and assemble such complex functional synthetic systems with order over different length scales from simple biologically relevant molecules, such as peptides and porphyrins. Herein we describe the successful introduction of hierarchical long-range order in dipeptide-adjusted porphyrin self-assembly by a thermodynamically driven self-orienting assembly pathway associated with multiple weak interactions. The long-range order and alignment of fiber bundles induced new properties, including anisotropic birefringence, a large Stokes shift, amplified chirality, and excellent photostability as well as sustainable photocatalytic activity. We also demonstrate that the aligned fiber bundles are able to induce the epitaxially oriented growth of Pt nanowires in a photocatalytic reaction.</p