Parkin interacts with Ambra1 to induce mitophagy

Mutations in the gene encoding Parkin are a major cause of recessive Parkinson's disease. Recent work has shown that Parkin translocates from the cytosol to depolarized mitochondria and induces their autophagic removal (mitophagy). However, the molecular mechanisms underlying Parkin-mediated mitophagy are poorly understood. Here, we investigated whether Parkin interacts with autophagy-regulating proteins. We purified Parkin and associated proteins from HEK293 cells using tandem affinity purification and identified the Parkin interactors using mass spectrometry. We identified the autophagy-promoting protein Ambra1 (activating molecule in Beclin1-regulated autophagy) as a Parkin interactor. Ambra1 activates autophagy in the CNS by stimulating the activity of the class III phosphatidylinositol 3-kinase (PI3K) complex that is essential for the formation of new phagophores. We found Ambra1, like Parkin, to be widely expressed in adult mouse brain, including midbrain dopaminergic neurons. Endogenous Parkin and Ambra1 coimmunoprecipitated from HEK293 cells, SH-SY5Y cells, and adult mouse brain. We found no evidence for ubiquitination of Ambra1 by Parkin. The interaction of endogenous Parkin and Ambra1 strongly increased during prolonged mitochondrial depolarization. Ambra1 was not required for Parkin translocation to depolarized mitochondria but was critically important for subsequent mitochondrial clearance. In particular, Ambra1 was recruited to perinuclear clusters of depolarized mitochondria and activated class III PI3K in their immediate vicinity. These data identify interaction of Parkin with Ambra1 as a key mechanism for induction of the final clearance step of Parkin-mediated mitophagy

Multiscale fatigue modelling of additively manufactured metallic components

Additively manufactured metallic components have been used in medical and aerospace applications. In these components, surface roughness and porosity are integral features that might significantly reduce their fatigue lives, especially in the high cycle fatigue regime. Thus, to precisely estimate the fatigue life of an additively manufactured component, these defective features are incorporated into our proposed fatigue model. To capture the local plasticity caused by the defects, a nonlinear isotropic-kinematic hardening elasto-plasticity model is employed in our finite element (FE) models. Additionally, the gas-entrapped pores are modeled as circles whilst the surface topography, which was measured using stylus-based profilometer, is explicitly mo deled in the FE models. The finite element results are post-processed by our in-house software to extract the Smith-Watson-Topper (SWT) fatigue indicator parameter. This parameter is calculated at each element centroid of the FE mesh, i.e., the local indicator. Afterward, an average value of the SWT parameter over a so-called critical area whose center is located at the considered centroid is also calculated, i.e., the nonlocal indicator. The results show that the local SWT indicator is too conservative in predicting the fatigue life of the componentwhile the nonlocal SWT one can provide good results

ALVIC versus the Internet: Redesigning a Networked Virtual Environment Architecture

The explosive growth of the number of applications based on networked virtual environment technology, both games and virtual communities, shows that these types of applications have become commonplace in a short period of time. However, from a research point of view, the inherent weaknesses in their architectures are quickly exposed. The Architecture for Large-Scale Virtual Interactive Communities (ALVICs) was originally developed to serve as a generic framework to deploy networked virtual environment applications on the Internet. While it has been shown to effectively scale to the numbers originally put forward, our findings have shown that, on a real-life network, such as the Internet, several drawbacks will not be overcome in the near future. It is, therefore, that we have recently started with the development of ALVIC-NG, which, while incorporating the findings from our previous research, makes several improvements on the original version, making it suitable for deployment on the Internet as it exists today

Automated image registration of cerebral digital subtraction angiography

Purpose: Our aim is to automatically align digital subtraction angiography (DSA) series, recorded before and after endovascular thrombectomy. Such alignment may enable quantification of procedural success. Methods: Firstly, we examine the inherent limitations for image registration, caused by the projective characteristics of DSA imaging, in a representative set of image pairs from thrombectomy procedures. Secondly, we develop and assess various image registration methods (SIFT, ORB). We assess these methods using manually annotated point correspondences for thrombectomy image pairs. Results: Linear transformations that account for scale differences are effective in aligning DSA sequences. Two anatomical landmarks can be reliably identified for registration using a U-net. Point-based registration using SIFT and ORB proves to be most effective for DSA registration and are applicable to recordings for all patient sub-types. Image-based techniques are less effective and did not refine the results of the best point-based registration method. Conclusion: We developed and assessed an automated image registration approach for cerebral DSA sequences, recorded before and after endovascular thrombectomy. Accurate results were obtained for approximately 85% of our image pairs.</p

Evaluation of an injectable, photopolymerizable, and three-dimensional scaffold based on methacrylate-endcapped poly(D,L-lactide-co-ε-caprolactone) combined with autologous mesenchymal stem cells in a goat tibial unicortical defect model

An in situ crosslinkable, biodegradable, methacrylate-endcapped poly(D,L-lactide-co-epsilon-caprolactone) in which crosslinkage is achieved by photoinitiators was developed for bone tissue regeneration. Different combinations of the polymer with bone marrow-derived mesenchymal stem cells (BMSCs) and alpha-tricalcium phosphate (alpha-TCP) were tested in a unicortical tibial defect model in eight goats. The polymers were randomly applied in one of three defects (6.0 mm diameter) using a fourth unfilled defect as control. Biocompatibility and bone-healing characteristics were evaluated by serial radiographies, histology, histomorphometry, and immunohistochemistry. The results demonstrated cell survival and proliferation in the polymer-substituted bone defects. The addition of alpha-TCP was associated with less expansion and growth of the BMSCs than other polymer composites

CAVE:Cerebral artery–vein segmentation in digital subtraction angiography

Cerebral X-ray digital subtraction angiography (DSA) is a widely used imaging technique in patients with neurovascular disease, allowing for vessel and flow visualization with high spatio-temporal resolution. Automatic artery–vein segmentation in DSA plays a fundamental role in vascular analysis with quantitative biomarker extraction, facilitating a wide range of clinical applications. The widely adopted U-Net applied on static DSA frames often struggles with disentangling vessels from subtraction artifacts. Further, it falls short in effectively separating arteries and veins as it disregards the temporal perspectives inherent in DSA. To address these limitations, we propose to simultaneously leverage spatial vasculature and temporal cerebral flow characteristics to segment arteries and veins in DSA. The proposed network, coined CAVE, encodes a 2D+time DSA series using spatial modules, aggregates all the features using temporal modules, and decodes it into 2D segmentation maps. On a large multi-center clinical dataset, CAVE achieves a vessel segmentation Dice of 0.84 (±0.04) and an artery–vein segmentation Dice of 0.79 (±0.06). CAVE surpasses traditional Frangi-based k-means clustering (P &lt; 0.001) and U-Net (P &lt; 0.001) by a significant margin, demonstrating the advantages of harvesting spatio-temporal features. This study represents the first investigation into automatic artery–vein segmentation in DSA using deep learning. The code is publicly available at https://github.com/RuishengSu/CAVE_DSA.</p

Spatio-Temporal U-Net for Cerebral Artery and Vein Segmentation in Digital Subtraction Angiography

X-ray digital subtraction angiography (DSA) is widely used for vessel and/or flow visualization and interventional guidance during endovascular treatment of patients with a stroke or aneurysm. To assist in peri-operative decision making as well as post-operative prognosis, automatic DSA analysis algorithms are being developed to obtain relevant image-based information. Such analyses include detection of vascular disease, evaluation of perfusion based on time intensity curves (TIC), and quantitative biomarker extraction for automated treatment evaluation in endovascular thrombectomy. Methodologically, such vessel-based analysis tasks may be facilitated by automatic and accurate artery-vein segmentation algorithms. The present work describes to the best of our knowledge the first study that addresses automatic artery-vein segmentation in DSA using deep learning. We propose a novel spatio-temporal U-Net (ST U-Net) architecture which integrates convolutional gated recurrent units (ConvGRU) in the contracting branch of U-Net. The network encodes a 2D+t DSA series of variable length and decodes it into a 2D segmentation image. On a multi-center routinely acquired dataset, the proposed method significantly outperformed U-Net (P<0.001) and traditional Frangi-based K-means clustering (P$<$0.001). Particularly in artery-vein segmentation, ST U-Net achieved a Dice coefficient of 0.794, surpassing the existing state-of-the-art methods by a margin of 12\%-20\%. Code will be made publicly available upon acceptance

autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients

The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter- and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) sequence is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural network (CNN), which exploits spatio-temporal features. The network also incorporates sequence level label dependencies in the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is computed using the motion corrected arterial and parenchymal frames. On the MINIP image, vessel, perfusion and background pixels are segmented. Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a routinely acquired multi-center dataset, the proposed autoTICI shows good correlation with the extended TICI (eTICI) reference with an average area under the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate that autoTICI is overall comparable to eTICI.Comment: 10 pages; submitted to IEEE TM