Visualization of elusive structures using intracardiac echocardiography: Insights from electrophysiology

Electrophysiological mapping and ablation techniques are increasingly used to diagnose and treat many types of supraventricular and ventricular tachycardias. These procedures require an intimate knowledge of intracardiac anatomy and their use has led to a renewed interest in visualization of specific structures. This has required collaborative efforts from imaging as well as electrophysiology experts. Classical imaging techniques may be unable to visualize structures involved in arrhythmia mechanisms and therapy. Novel methods, such as intracardiac echocardiography and three-dimensional echocardiography, have been refined and these technological improvements have opened new perspectives for more effective and accurate imaging during electrophysiology procedures. Concurrently, visualization of these structures noticeably improved our ability to identify intracardiac structures. The aim of this review is to provide electrophysiologists with an overview of recent insights into the structure of the heart obtained with intracardiac echocardiography and to indicate to the echo-specialist which structures are potentially important for the electrophysiologist

Periprosthetic fractures of the femur after total knee arthroplasty

Periprosthetic fracture following total knee arthroplasty is a potentially serious complication. This injury can involve the distal femur, proximal tibia or the patella. This review article analyzes the prevalence, risk factors, classification and treatment options for periprosthetic fractures of the femur

Development of the rhopalial nervous system in Aurelia sp.1 (Cnidaria, Scyphozoa)

We examined the development of the nervous system in the rhopalium, a medusa-specific sensory structure, in Aurelia sp.1 (Cnidaria, Scyphozoa) using confocal microscopy. The rhopalial nervous system appears primarily ectodermal and contains neurons immunoreactive to antibodies against tyrosinated tubulin, taurine, GLWamide, and FMRFamide. The rhopalial nervous system develops in an ordered manner: the presumptive gravity-sensing organ, consisting of the lithocyst and the touch plate, differentiates first; the “marginal center,” which controls swimming activity, second; and finally, the ocelli, the presumptive photoreceptors. At least seven bilaterally arranged neuronal clusters consisting of sensory and ganglion cells and their neuronal processes became evident in the rhopalium during metamorphosis to the medusa stage. Our analysis provides an anatomical framework for future gene expression and experimental studies of development and functions of scyphozoan rhopalia

Diversity, Phylogeny and Expression Patterns of Pou and Six Homeodomain Transcription Factors in Hydrozoan Jellyfish Craspedacusta sowerbyi

Formation of all metazoan bodies is controlled by a group of selector genes including homeobox genes, highly conserved across the entire animal kingdom. The homeobox genes from Pou and Six classes are key members of the regulation cascades determining development of sensory organs, nervous system, gonads and muscles. Besides using common bilaterian models, more attention has recently been targeted at the identification and characterization of these genes within the basal metazoan phyla. Cnidaria as a diploblastic sister group to bilateria with simple and yet specialized organs are suitable models for studies on the sensory organ origin and the associated role of homeobox genes. In this work, Pou and Six homeobox genes, together with a broad range of other sensory-specific transcription factors, were identified in the transcriptome of hydrozoan jellyfish Craspedacusta sowerbyi. Phylogenetic analyses of Pou and Six proteins revealed cnidarian-specific sequence motifs and contributed to the classification of individual factors. The majority of the Craspedacusta sowerbyi Pou and Six homeobox genes are predominantly expressed in statocysts, manubrium and nerve ring, the tissues with sensory and nervous activities. The described diversity and expression patterns of Pou and Six factors in hydrozoan jellyfish highlight their evolutionarily conserved functions. This study extends the knowledge of the cnidarian genome complexity and shows that the transcriptome of hydrozoan jellyfish is generally rich in homeodomain transcription factors employed in the regulation of sensory and nervous functions

Data augmentation with Mixup: Enhancing performance of a functional neuroimaging-based prognostic deep learning classifier in recent onset psychosis

Although deep learning holds great promise as a prognostic tool in psychiatry, a limitation of the method is that it requires large training sample sizes to achieve replicable accuracy. This is problematic for fMRI datasets as they are typically small due to the considerable time, cost, and resources necessary to obtain them. A recently developed self-supervised learning method called Mixup may help overcome this challenge. In Mixup, the learner combines pairs of training instances to produce a virtual third instance that is a linear combination of the two instances and their labels. This procedure is also well-suited to the coregistered images typically found in fMRI datasets. Here we compared performance of a task fMRI-based deep learner with Mixup vs without Mixup on predicting response to treatment in recent onset psychosis. Whole brain fMRI time series data were extracted from a cognitive control task in 82 patients with recent onset psychosis and used to predict "Improver" (n = 47) vs "Non-Improver" (n = 35) status, with Improver defined as showing a 20 % reduction in total Brief Psychiatric Rating Scale score after 1 year of treatment. Mixup significantly improved performance (accuracy without Mixup: 76.5 % [95 % CI: 75.9-77.1 %]; accuracy with Mixup: 80.1 % [95 % CI: 79.4-80.8 %]). Ablation showed the improvement was due to improvement in both Improvers and Non-Improvers. These results suggest that using Mixup may significantly improve performance and reduce overfitting of fMRI-based prognostic deep learners and may also help overcome the small sample size challenge inherent to many neuroimaging datasets