265 research outputs found
Cerebral Hemodynamic Failure Presenting as Limb-Shaking Transient Ischemic Attacks
Limb-shaking transient ischemic attacks (TIA) may occur in patients with insufficient brain perfusion due to an underlying occlusive disease. We present the case of a 64-year-old patient who suffered from repetitive TIA presenting with shaking movements of the right-sided extremities and accompanying speech arrest. Symptoms are documented in the online supplementary video (www.karger.com/doi/10.1159/000327683). These episodes were frequently triggered in orthostatic situations. The diagnosis of limb-shaking TIA was established. The diagnostic workup revealed pseudo-occlusion of the left internal carotid artery, a poor intracranial collateral status and, as a consequence, an exhausted vasomotor reserve capacity. At ultrasound examination, symptoms were provoked by a change of the patient's position from supine to sitting. During evolvement of symptoms, a dramatic decrease of flow velocities in the left middle cerebral artery was observed. This case thus documents the magnitude and dynamics of perfusion failure in a rare manifestation of cerebral ischemic disease
Genome-Scale Oscillations in DNA Methylation during Exit from Pluripotency
Pluripotency is accompanied by the erasure of parental epigenetic memory, with naive pluripotent cells exhibiting global DNA hypomethylation both in vitro and in vivo. Exit from pluripotency and priming for differentiation into somatic lineages is associated with genome-wide de novo DNA methylation. We show that during this phase, co-expression of enzymes required for DNA methylation turnover, DNMT3s and TETs, promotes cell-to-cell variability in this epigenetic mark. Using a combination of single- cell sequencing and quantitative biophysical modeling, we show that this variability is associated with coherent, genome-scale oscillations in DNA methylation with an amplitude dependent on CpG density. Analysis of parallel single-cell transcriptional and epigenetic profiling provides evidence for oscillatory dynamics both in vitro and in vivo. These observations provide insights into the emergence of epigenetic heterogeneity during early embryo development, indicating that dynamic changes in DNA methylation might influence early cell fate decisions
Diagnostic Potential of Imaging Flow Cytometry
Imaging flow cytometry (IFC) captures multichannel images of hundreds of thousands of single cells within minutes. IFC is seeing a paradigm shift from low- to high-information-content analysis, driven partly by deep learning algorithms. We predict a wealth of applications with potential translation into clinical practice
Deep Learning for Predicting Refractive Error From Retinal Fundus Images
PURPOSE. We evaluate how deep learning can be applied to extract novel information such as
refractive error from retinal fundus imaging.
METHODS. Retinal fundus images used in this study were 45- and 30-degree field of view images
from the UK Biobank and Age-Related Eye Disease Study (AREDS) clinical trials, respectively.
Refractive error was measured by autorefraction in UK Biobank and subjective refraction in
AREDS. We trained a deep learning algorithm to predict refractive error from a total of
226,870 images and validated it on 24,007 UK Biobank and 15,750 AREDS images. Our model
used the ‘‘attention’’ method to identify features that are correlated with refractive error.
RESULTS. The resulting algorithm had a mean absolute error (MAE) of 0.56 diopters (95%
confidence interval [CI]: 0.55–0.56) for estimating spherical equivalent on the UK Biobank
data set and 0.91 diopters (95% CI: 0.89–0.93) for the AREDS data set. The baseline expected
MAE (obtained by simply predicting the mean of this population) was 1.81 diopters (95% CI:
1.79–1.84) for UK Biobank and 1.63 (95% CI: 1.60–1.67) for AREDS. Attention maps
suggested that the foveal region was one of the most important areas used by the algorithm to
make this prediction, though other regions also contribute to the prediction.
CONCLUSIONS. To our knowledge, the ability to estimate refractive error with high accuracy
from retinal fundus photos has not been previously known and demonstrates that deep
learning can be applied to make novel predictions from medical images
Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires
The adaptive immune system recognizes antigens via an immense array of
antigen-binding antibodies and T-cell receptors, the immune repertoire. The
interrogation of immune repertoires is of high relevance for understanding the
adaptive immune response in disease and infection (e.g., autoimmunity, cancer,
HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the
quantitative and molecular-level profiling of immune repertoires thereby
revealing the high-dimensional complexity of the immune receptor sequence
landscape. Several methods for the computational and statistical analysis of
large-scale AIRR-seq data have been developed to resolve immune repertoire
complexity in order to understand the dynamics of adaptive immunity. Here, we
review the current research on (i) diversity, (ii) clustering and network,
(iii) phylogenetic and (iv) machine learning methods applied to dissect,
quantify and compare the architecture, evolution, and specificity of immune
repertoires. We summarize outstanding questions in computational immunology and
propose future directions for systems immunology towards coupling AIRR-seq with
the computational discovery of immunotherapeutics, vaccines, and
immunodiagnostics.Comment: 27 pages, 2 figure
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