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Background
Adrenalectomies are rare procedures especially in childhood. So far, no large cohort study on this topic has been published with data on to age distribution, operative procedures, hospital volume and operative outcome.
Methods
This is a retrospective analysis of anonymized nationwide hospital billing data (DRG data, 2009-2017). All adrenal surgeries (defined by OPS codes) of patients between the age 0 and 21 years in Germany were included.
Results
A total of 523 patient records were identified. The mean age was 8.6 ± 7.7 years and 262 patients were female (50.1%). The majority of patients were between 0 and 5 years old (52% overall), while 11.1% were between 6 and 11 and 38.8% older than 12 years. The most common diagnoses were malignant neoplasms of the adrenal gland (56%, mostly neuroblastoma) with the majority being younger than 5 years. Benign neoplasms in the adrenal gland (D350) account for 29% of all cases with the majority of affected patients being 12 years or older. 15% were not defined regarding tumor behavior. Overall complication rate was 27% with a clear higher complication rate in resection for malignant neoplasia of the adrenal gland. Bleeding occurrence and transfusions are the main complications, followed by the necessary of relaparotomy. There was an uneven patient distribution between hospital tertiles (low volume, medium and high volume tertile). While 164 patients received surgery in 85 different “low volume” hospitals (0.2 cases per hospital per year), 205 patients received surgery in 8 different “high volume” hospitals (2.8 cases per hospital per year; p<0.001). Patients in high volume centers were significant younger, had more extended resections and more often malignant neoplasia. In multivariable analysis younger age, extended resections and open procedures were independent predictors for occurrence of postoperative complications.
Conclusion
Overall complication rate of adrenalectomies in the pediatric population in Germany is low, demonstrating good therapeutic quality. Our analysis revealed a very uneven distribution of patient volume among hospitals.
Echolocation behavior, a navigation strategy based on acoustic signals, allows scientists to explore neural processing of behaviorally relevant stimuli. For the purpose of orientation, bats broadcast echolocation calls and extract spatial information from the echoes. Because bats control call emission and thus the availability of spatial information, the behavioral relevance of these signals is undiscussable. While most neurophysiological studies, conducted in the past, used synthesized acoustic stimuli that mimic portions of the echolocation signals, recent progress has been made to understand how naturalistic echolocation signals are encoded in the bat brain. Here, we review how does stimulus history affect neural processing, how spatial information from multiple objects and how echolocation signals embedded in a naturalistic, noisy environment are processed in the bat brain. We end our review by discussing the huge potential that state-of-the-art recording techniques provide to gain a more complete picture on the neuroethology of echolocation behavior.
The nuclear paraspeckle assembly transcript 1 (NEAT1) locus encodes two long non-coding (lnc)RNA isoforms that are upregulated in many tumours and dynamically expressed in response to stress. NEAT1 transcripts form ribonucleoprotein complexes with numerous RNA-binding proteins (RBPs) to assemble paraspeckles and modulate the localisation and activity of gene regulatory enzymes as well as a subset of messenger (m)RNA transcripts. The investigation of the dynamic composition of NEAT1-associated proteins and mRNAs is critical to understand the function of NEAT1. Interestingly, a growing number of biochemical and genetic tools to assess NEAT1 interactomes has been reported. Here, we discuss the Hybridisation Proximity (HyPro) labeling technique in the context of NEAT1. HyPro labeling is a recently developed method to detect spatially ordered interactions of RNA-containing nuclear compartments in cultured human cells. After introducing NEAT1 and paraspeckles, we describe the advantages of the HyPro technology in the context of other methods to study RNA interactomes, and review the key findings in mapping NEAT1-associated RNA transcripts and protein binding partners. We further discuss the limitations and potential improvements of HyPro labeling, and conclude by delineating its applicability in paraspeckles-related cancer research.
Ovarian cancer is the second most common gynecological malignancy in women. More than 70% of the cases are diagnosed at the advanced stage, presenting as primary peritoneal metastasis, which results in a poor 5-year survival rate of around 40%. Mechanisms of peritoneal metastasis, including adhesion, migration, and invasion, are still not completely understood and therapeutic options are extremely limited. Therefore, there is a strong requirement for a 3D model mimicking the in vivo situation. In this study, we describe the establishment of a 3D tissue model of the human peritoneum based on decellularized porcine small intestinal submucosa (SIS) scaffold. The SIS scaffold was populated with human dermal fibroblasts, with LP-9 cells on the apical side representing the peritoneal mesothelium, while HUVEC cells on the basal side of the scaffold served to mimic the endothelial cell layer. Functional analyses of the transepithelial electrical resistance (TEER) and the FITC-dextran assay indicated the high barrier integrity of our model. The histological, immunohistochemical, and ultrastructural analyses showed the main characteristics of the site of adhesion. Initial experiments using the SKOV-3 cell line as representative for ovarian carcinoma demonstrated the usefulness of our models for studying tumor cell adhesion, as well as the effect of tumor cells on endothelial cell-to-cell contacts. Taken together, our data show that the novel peritoneal 3D tissue model is a promising tool for studying the peritoneal dissemination of ovarian cancer.
Drosophila’s dorsal clock neurons (DNs) consist of four clusters (DN1as, DN1ps, DN2s, and DN3s) that largely differ in size. While the DN1as and the DN2s encompass only two neurons, the DN1ps consist of ∼15 neurons, and the DN3s comprise ∼40 neurons per brain hemisphere. In comparison to the well-characterized lateral clock neurons (LNs), the neuroanatomy and function of the DNs are still not clear. Over the past decade, numerous studies have addressed their role in the fly’s circadian system, leading to several sometimes divergent results. Nonetheless, these studies agreed that the DNs are important to fine-tune activity under light and temperature cycles and play essential roles in linking the output from the LNs to downstream neurons that control sleep and metabolism. Here, we used the Flybow system, specific split-GAL4 lines, trans-Tango, and the recently published fly connectome (called hemibrain) to describe the morphology of the DNs in greater detail, including their synaptic connections to other clock and non-clock neurons. We show that some DN groups are largely heterogenous. While certain DNs are strongly connected with the LNs, others are mainly output neurons that signal to circuits downstream of the clock. Among the latter are mushroom body neurons, central complex neurons, tubercle bulb neurons, neurosecretory cells in the pars intercerebralis, and other still unidentified partners. This heterogeneity of the DNs may explain some of the conflicting results previously found about their functionality. Most importantly, we identify two putative novel communication centers of the clock network: one fiber bundle in the superior lateral protocerebrum running toward the anterior optic tubercle and one fiber hub in the posterior lateral protocerebrum. Both are invaded by several DNs and LNs and might play an instrumental role in the clock network.
Nature is a rich source of biologically active novel compounds. Sixty years ago, the plant hormones cytokinins were first discovered. These play a major role in cell division and cell differentiation. They affect organogenesis in plant tissue cultures and contribute to many other physiological and developmental processes in plants. Consequently, the effect of cytokinins on mammalian cells has caught the attention of researchers. Many reports on the contribution and potential of cytokinins in the therapy of different human diseases and pathophysiological conditions have been published and are reviewed here. We compare cytokinin effects and pathways in plants and mammalian systems and highlight the most important biological activities. We present the strong profile of the biological actions of cytokinins and their possible therapeutic applications.
The rapid development of green and sustainable materials opens up new possibilities in the field of applied research. Such materials include nanocellulose composites that can integrate many components into composites and provide a good chassis for smart devices. In our study, we evaluate four approaches for turning a nanocellulose composite into an information storage or processing device: 1) nanocellulose can be a suitable carrier material and protect information stored in DNA. 2) Nucleotide-processing enzymes (polymerase and exonuclease) can be controlled by light after fusing them with light-gating domains; nucleotide substrate specificity can be changed by mutation or pH change (read-in and read-out of the information). 3) Semiconductors and electronic capabilities can be achieved: we show that nanocellulose is rendered electronic by iodine treatment replacing silicon including microstructures. Nanocellulose semiconductor properties are measured, and the resulting potential including single-electron transistors (SET) and their properties are modeled. Electric current can also be transported by DNA through G-quadruplex DNA molecules; these as well as classical silicon semiconductors can easily be integrated into the nanocellulose composite. 4) To elaborate upon miniaturization and integration for a smart nanocellulose chip device, we demonstrate pH-sensitive dyes in nanocellulose, nanopore creation, and kinase micropatterning on bacterial membranes as well as digital PCR micro-wells. Future application potential includes nano-3D printing and fast molecular processors (e.g., SETs) integrated with DNA storage and conventional electronics. This would also lead to environment-friendly nanocellulose chips for information processing as well as smart nanocellulose composites for biomedical applications and nano-factories.
Monarch butterflies rely on external cues for orientation during their annual long-distance migration from Northern US and Canada to Central Mexico. These external cues can be celestial cues, such as the sun or polarized light, which are processed in a brain region termed the central complex (CX). Previous research typically focused on how individual simulated celestial cues are encoded in the butterfly's CX. However, in nature, the butterflies perceive several celestial cues at the same time and need to integrate them to effectively use the compound of all cues for orientation. In addition, a recent behavioral study revealed that monarch butterflies can rely on terrestrial cues, such as the panoramic skyline, for orientation and use them in combination with the sun to maintain a directed flight course. How the CX encodes a combination of celestial and terrestrial cues and how they are weighted in the butterfly's CX is still unknown. Here, we examined how input neurons of the CX, termed TL neurons, combine celestial and terrestrial information. While recording intracellularly from the neurons, we presented a sun stimulus and polarized light to the butterflies as well as a simulated sun and a panoramic scene simultaneously. Our results show that celestial cues are integrated linearly in these cells, while the combination of the sun and a panoramic skyline did not always follow a linear integration of action potential rates. Interestingly, while the sun and polarized light were invariantly weighted between individual neurons, the sun stimulus and panoramic skyline were dynamically weighted when both stimuli were simultaneously presented. Taken together, this dynamic weighting between celestial and terrestrial cues may allow the butterflies to flexibly set their cue preference during navigation.
The conspicuous colour sexual dimorphism of guppies has made them paradigmatic study objects for sex-linked traits and sex chromosome evolution. Both the X- and Y-chromosomes of the common guppy (Poecilia reticulata) are genetically active and homomorphic, with a large homologous part and a small sex specific region. This feature is considered to emulate the initial stage of sex chromosome evolution. A similar situation has been documented in the related Endler’s and Oropuche guppies (P. wingei, P. obscura) indicating a common origin of the Y in this group. A recent molecular study in the swamp guppy (Micropoecilia. picta) reported a low SNP density on the Y, indicating Y-chromosome deterioration. We performed a series of cytological studies on M. picta to show that the Y-chromosome is quite small compared to the X and has accumulated a high content of heterochromatin. Furthermore, the Y-chromosome stands out in displaying CpG clusters around the centromeric region. These cytological findings evidently illustrate that the Y-chromosome in M. picta is indeed highly degenerated. Immunostaining for SYCP3 and MLH1 in pachytene meiocytes revealed that a substantial part of the Y remains associated with the X. A specific MLH1 hotspot site was persistently marked at the distal end of the associated XY structure. These results unveil a landmark of a recombining pseudoautosomal region on the otherwise strongly degenerated Y chromosome of M. picta. Hormone treatments of females revealed that, unexpectedly, no sexually antagonistic color gene is Y-linked in M. picta. All these differences to the Poecilia group of guppies indicate that the trajectories associated with the evolution of sex chromosomes are not in parallel.
Background
Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a measured photon distribution. These algorithms make assumptions about the symmetry of the PSF and thus, do not work well with irregular, non-linear PSFs that occur for example in confocal lifetime imaging, where a laser is scanned across the sample. An alternative method for reconstructing sparse emitter sets from noisy, diffraction-limited images is compressed sensing, but due to its high computational cost it has not yet been widely adopted. Deep neural network fitters have recently emerged as a new competitive method for localization microscopy. They can learn to fit arbitrary PSFs, but require extensive simulated training data and do not generalize well. A method to efficiently fit the irregular PSFs from confocal lifetime localization microscopy combining the advantages of deep learning and compressed sensing would greatly improve the acquisition speed and throughput of this method.
Results
Here we introduce ReCSAI, a compressed sensing neural network to reconstruct localizations for confocal dSTORM, together with a simulation tool to generate training data. We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. We found that a U-Net with a recursive structure inspired by iterative compressed sensing showed the best results on realistic simulated datasets with noise, as well as on real experimentally measured confocal lifetime scanning data. Adding a trainable wavelet denoising layer as prior step further improved the reconstruction quality.
Conclusions
Our deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisition of multiple frames. In addition, our work offers generic insights on the reconstruction of sparse measurements from noisy experimental data by combining compressed sensing and deep learning. We provide the trained networks, the code for network training and inference as well as the simulation tool as python code and Jupyter notebooks for easy reproducibility.