83 research outputs found
Spike pattern recognition by supervised classification in low dimensional embedding space
© The Author(s) 2016. This article is published with open access at Springerlink.com under the terms of the Creative Commons Attribution License 4.0, (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts’ manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min−1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.Peer reviewedFinal Published versio
Rapid generation of clinical-grade antiviral T cells: selection of suitable T-cell donors and GMP-compliant manufacturing of antiviral T cells
Curative or pre-emptive adenovirus-specific T cell transfer from matched unrelated or third party haploidentical donors after HSCT, including UCB transplantations: a successful phase I/II multicenter clinical trial
Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies
The epilepsies affect around 65 million people worldwide and have a substantial missing
heritability component. We report a genome-wide mega-analysis involving 15,212 individuals
with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11
are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at
these loci, with the majority in genetic generalized epilepsies. These genes have diverse
biological functions, including coding for ion-channel subunits, transcription factors and a
vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants
associated with epilepsy play a role in epigenetic regulation of gene expression in the brain.
The results show an enrichment for monogenic epilepsy genes as well as known targets of
antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and
overlapping genetic basis to seven different epilepsy subtypes. Together, these findings
provide leads for epilepsy therapies based on underlying pathophysiology
C4d and/or immunoglobulins deposition in peritubular capillaries in perioperative graft biopsies in ABO-incompatible renal transplantation
Incidence of C4d Stain in Protocol Biopsies from Renal Allografts: Results from a Multicenter Trial
Plasmapheresis in C4d-positive Acute Humoral Rejection Following Kidney Transplantation: A Review of 4 Cases
In Vitro Detection of C4d-Fixing HLA Alloantibodies: Associations With Capillary C4d Deposition in Kidney Allografts
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