Adult Human Vascular Smooth Muscle Cells on 3D Silk Fibroin Nonwovens Release Exosomes Enriched in Angiogenic and Growth-Promoting Factors

Background. Our earlier works showed the quick vascularization of mouse skin grafted Bombyx mori 3D silk fibroin nonwoven scaffolds (3D-SFnws) and the release of exosomes enriched in angiogenic/growth factors (AGFs) from in vitro 3D-SFnws-stuck human dermal fibroblasts (HDFs). Here, we explored whether coronary artery adult human smooth muscle cells (AHSMCs) also release AGFs-enriched exosomes when cultured on 3D-SFnws in vitro. Methods. Media with exosome-depleted FBS served for AHSMCs and human endothelial cells (HECs) cultures on 3D-SFnws or polystyrene. Biochemical methods and double-antibody arrays assessed cell growth, metabolism, and intracellular TGF-β and NF-κB signalling pathways activation. AGFs conveyed by CD9+/CD81+ exosomes released from AHSMCs were double-antibody array analysed and their angiogenic power evaluated on HECs in vitro. Results. AHSMCs grew and consumed D-glucose more intensely and showed a stronger phosphorylation/activation of TAK-1, SMAD-1/-2/-4/-5, ATF-2, c-JUN, ATM, CREB, and an IκBα phosphorylation/inactivation on SFnws vs. polystyrene, consistent overall with a proliferative/secretory phenotype. SFnws-stuck AHSMCs also released exosomes richer in IL-1α/-2/-4/-6/-8; bFGF; GM-CSF; and GRO-α/-β/-γ, which strongly stimulated HECs’ growth, migration, and tubes/nodes assembly in vitro. Conclusions. Altogether, the intensified AGFs exosomal release from 3D-SFnws-attached AHSMCs and HDFs could advance grafts’ colonization, vascularization, and take in vivo—noteworthy assets for prospective clinical applications

Uncertainty quantification in medical image segmentation with normalizing flows

Medical image segmentation is inherently an ambiguous task due to factors such as partial volumes and variations in anatomical definitions. While in most cases the segmentation uncertainty is around the border of structures of interest, there can also be considerable inter-rater differences. The class of conditional variational autoencoders (cVAE) offers a principled approach to inferring distributions over plausible segmentations that are conditioned on input images. Segmentation uncertainty estimated from samples of such distributions can be more informative than using pixel level probability scores. In this work, we propose a novel conditional generative model that is based on conditional Normalizing Flow (cFlow). The basic idea is to increase the expressivity of the cVAE by introducing a cFlow transformation step after the encoder. This yields improved approximations of the latent posterior distribution, allowing the model to capture richer segmentation variations. With this we show that the quality and diversity of samples obtained from our conditional generative model is enhanced. Performance of our model, which we call cFlow Net, is evaluated on two medical imaging datasets demonstrating substantial improvements in both qualitative and quantitative measures when compared to a recent cVAE based model.Comment: 12 pages. Accepted to be presented at 11th International Workshop on Machine Learning in Medical Imaging. Source code will be updated at https://github.com/raghavian/cFlo

Imaging in pleural mesothelioma: a review of the 13th International Conference of the International Mesothelioma Interest Group

Imaging plays an important role in the detection, diagnosis, staging, response assessment, and surveillance of malignant pleural mesothelioma. The etiology, biology, and growth pattern of mesothelioma present unique challenges for each modality used to capture various aspects of this disease. Clinical implementation of imaging techniques and information derived from images continue to evolve based on active research in this field worldwide. This paper summarizes the imaging-based research presented orally at the 2016 International Conference of the International Mesothelioma Interest Group (iMig) in Birmingham, United Kingdom, held May 1–4, 2016. Presented topics included intraoperative near-infrared imaging of mesothelioma to aid the assessment of resection completeness, an evaluation of tumor enhancement improvement with increased time delay between contrast injection and image acquisition in standard clinical magnetic resonance imaging (MRI) scans, the potential of early contrast enhancement analysis to provide MRI with a role in mesothelioma detection, the differentiation of short- and long-term survivors based on MRI tumor volume and histogram analysis, the response-assessment potential of hemodynamic parameters derived from dynamic contrast-enhanced computed tomography (DCE-CT) scans, the correlation of CT-based tumor volume with post-surgical tumor specimen weight, and consideration of the need to update the mesothelioma tumor response assessment paradigm

Evaluation of sixty-eight cases of fracture stabilisation by external hybrid fixation and a proposal for hybrid construct classification

Background: Hybrid external fixation (HEF) is an emerging technique for fracture stabilization in veterinary orthopedics, but its use has been reported in few papers in the veterinary literature. The linear and circular elements that form hybrid fixators can be connected in a very high number of combinations, and for this reason just referring to HEF without any classification is often misleading about the actual frame structure. The aim of this study was to retrospectively evaluate fracture stabilization by HEF in 58 client-owned dogs and 8 cats, and to extend the already existing classification for hybrid constructs to include all frame configurations used in this study and potentially applicable in clinical settings. Animal signalment, fracture classification, surgical procedure and frame configuration were recorded. Complications, radiographic, functional and cosmetic results were evaluated at the time of fixator removal.Results: Sixty-eight fractures in 58 dogs and eight cats were evaluated. Two dogs had bilateral fractures. Fifty-one percent were radio-ulna, 34% tibial, 9% humeral, 3% femoral and 3% scapular fractures. One ring combined with one or two linear elements was the most widely employed configuration in this case series. Radiographic results at the time of frame removal were excellent in 59% of the cases, good in 38% and fair in 3%, while functional and cosmetic results were excellent in 69% of the cases, good in 27% and fair in 4%.Conclusions: HEF is a useful option for fracture treatment in dogs and cats, particularly for peri and juxta-articular fractures. It can be applied with a minimally invasive approach, allows adjustments during the postoperative period and is a versatile system because of the large variety of combinations that can fit with the specific fracture features. The classification used enables to determine the number of linear and circular elements used in the frame

Preserving Differential Privacy in Convolutional Deep Belief Networks

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing epsilon-differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions

Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study

Diagnosis of pulmonary lesions from computed tomography (CT) is important but challenging for clinical decision making in lung cancer related diseases. Deep learning has achieved great success in computer aided diagnosis (CADx) area for lung cancer, whereas it suffers from label ambiguity due to the difficulty in the radiological diagnosis. Considering that invasive pathological analysis serves as the clinical golden standard of lung cancer diagnosis, in this study, we solve the label ambiguity issue via a large-scale radio-pathomics dataset containing 5,134 radiological CT images with pathologically confirmed labels, including cancers (e.g., invasive/non-invasive adenocarcinoma, squamous carcinoma) and non-cancer diseases (e.g., tuberculosis, hamartoma). This retrospective dataset, named Pulmonary-RadPath, enables development and validation of accurate deep learning systems to predict invasive pathological labels with a non-invasive procedure, i.e., radiological CT scans. A three-level hierarchical classification system for pulmonary lesions is developed, which covers most diseases in cancer-related diagnosis. We explore several techniques for hierarchical classification on this dataset, and propose a Leaky Dense Hierarchy approach with proven effectiveness in experiments. Our study significantly outperforms prior arts in terms of data scales (6x larger), disease comprehensiveness and hierarchies. The promising results suggest the potentials to facilitate precision medicine.Comment: MICCAI 2020 (Early Accepted

Case Report: Could Hennebert's Sign Be Evoked Despite Global Vestibular Impairment on Video Head Impulse Test? Considerations Upon Pathomechanisms Underlying Pressure-Induced Nystagmus due to Labyrinthine Fistula

We describe a case series of labyrinthine fistula, characterized by Hennebert's sign (HS) elicited by tragal compression despite global hypofunction of semicircular canals (SCs) on a video-head impulse test (vHIT), and review the relevant literature. All three patients presented with different amounts of cochleo-vestibular loss, consistent with labyrinthitis likely induced by labyrinthine fistula due to different temporal bone pathologies (squamous cell carcinoma involving the external auditory canal in one case and middle ear cholesteatoma in two cases). Despite global hypofunction on vHIT proving impaired function for each SC for high accelerations, all patients developed pressure-induced nystagmus, presumably through spared and/or recovered activity for low-velocity canal afferents. In particular, two patients with isolated horizontal SC fistula developed HS with ipsilesional horizontal nystagmus due to resulting excitatory ampullopetal endolymphatic flows within horizontal canals. Conversely, the last patient with bony erosion involving all SCs developed mainly torsional nystagmus directed contralaterally due to additional inhibitory ampullopetal flows within vertical canals. Moreover, despite impaired measurements on vHIT, we found simultaneous direction-changing positional nystagmus likely due to a buoyancy mechanism within the affected horizontal canal in a case and benign paroxysmal positional vertigo involving the dehiscent posterior canal in another case. Based on our findings, we might suggest a functional dissociation between high (impaired) and low (spared/recovered) accelerations for SCs. Therefore, it could be hypothesized that HS in labyrinthine fistula might be due to the activation of regular ampullary fibers encoding low-velocity inputs, as pressure-induced nystagmus is perfectly aligned with the planes of dehiscent SCs in accordance with Ewald's laws, despite global vestibular impairment on vHIT. Moreover, we showed how pressure-induced nystagmus could present in a rare case of labyrinthine fistulas involving all canals simultaneously. Nevertheless, definite conclusions on the genesis of pressure-induced nystagmus in our patients are prevented due to the lack of objective measurements of both low-acceleration canal responses and otolith function

Amyloid-\u3b225-35, an Amyloid-\u3b21-42 Surrogate, and Proinflammatory Cytokines Stimulate VEGF-A Secretion by Cultured, Early Passage, Normoxic Adult Human Cerebral Astrocytes

Cerebrovascular angiopathy affects late-onset Alzheimer's disease (LOAD) brains by possibly increasing vascular endothelial growth factor (VEGF). A expression, thereby stimulating endothelial cell proliferation and migration. Indeed, VEGF-A gene upregulation, with increased VEGF-A protein content of reactive astrocytes and microglia, occurs in LOAD brains, and neovascularization was observed one week after injecting amyloid-\u3b2 (A\u3b2)1-42 into rat hippocampus. We have now found, with cultured 'normoxic' normal adult human astrocytes (NAHAs), that fibrillar A\u3b225-35 (an active A\u3b21-42 fragment) or a cytokine mixture (the (CM)-trio (interleukin [IL]-1\u3b2+interferon [IFN]-\u3b3+tumor necrosis factor [TNF]-\u3b1), or pair (IFN-\u3b3+TNF-\u3b1) like those produced in LOAD brains) stimulates the nuclear translocation of stabilized hypoxia-inducible factor (HIF)-1\u3b1 protein and its binding to VEGF-A hypoxia-response elements; the mRNA synthesis for three VEGF-A splice variants (121, 165, 189); and the secretion of VEGF-A165. The CM-trio was the most powerful stimulus, IFN-\u3b3+TNF-\u3b1 was less potent, and other cytokine pairs or single cytokines or A\u3b235-25 were ineffective. While A\u3b225-35 did not change HIF-1\u3b2 protein levels, the CM-trio increased both HIF-1\u3b1 and HIF-1\u3b2 protein levels, thereby giving an earlier and stronger stimulus to VEGF-A secretion by NAHAs. Thus, increased VEGF-A secretion from astrocytes stimulated by A\u3b21-42 and by microglia-released cytokines might restore angiogenesis and A\u3b21-42 vascular clearance

Learning Visual Context by Comparison

Finding diseases from an X-ray image is an important yet highly challenging task. Current methods for solving this task exploit various characteristics of the chest X-ray image, but one of the most important characteristics is still missing: the necessity of comparison between related regions in an image. In this paper, we present Attend-and-Compare Module (ACM) for capturing the difference between an object of interest and its corresponding context. We show that explicit difference modeling can be very helpful in tasks that require direct comparison between locations from afar. This module can be plugged into existing deep learning models. For evaluation, we apply our module to three chest X-ray recognition tasks and COCO object detection & segmentation tasks and observe consistent improvements across tasks. The code is available at https://github.com/mk-minchul/attend-and-compare.Comment: ECCV 2020 spotlight pape