Scrotal calciphylaxis in a fifty-one-year-old man with end-stage renal disease and prior bacteremia.

Isolated, ischemic scrotal lesions are an uncommon manifestation of systemic calciphylaxis. This highly morbid disease seen in patients with end-stage renal disease and other risk factors for small vessel calcification results in tissue necrosis and ulceration. Scrotal calciphylaxis is uncommon and difficult to diagnose in patients with comorbid conditions that cause isolated genital skin lesions, including prior systemic infections. We describe a 51-year-old male with end-stage renal disease on hemodialysis and recent history of bacteremia who developed isolated penile and scrotal ulceration due to calciphylaxis. The patient died two months after presentation despite multidisciplinary care.

Lessons learnt and future directions in managing dialysis access during the COVID 19 pandemic: Patient and provider experience in the United States.

BACKGROUND: The COVID 19 pandemic adversely impacted delivery of preventive, routine, urgent, and essential care worldwide. Dialysis access care was particularly affected due to the lack of specific guidelines regarding procedures for its creation and maintenance. Early guidance by Centers for Medicare and Medicaid was inadvertently interpreted as guidance to stop dialysis access procedures. Prompt action by professional societies was needed to furnish detailed guidance to establish essential nature of these procedures. METHODS: The American Society of Diagnostic and Interventional Nephrology (ASDIN) issued a joint statement with Vascular Access Society of the Americas (VASA) - "Maintaining Lifelines for ESKD Patients" to clearly establish the role of vascular access as a lifeline for ESKD (End Stage Kidney Disease) patients and the importance and urgency of its timely management. ASDIN also conducted a survey in mid-2020, that was administered to the ASDIN database as well as shared with the general public via the organization's social media platforms. The respondents reported their experiences in the care of dialysis access, practice patterns and the utility of the ASDIN-VASA statement during the COVID 19 pandemic. RESULTS: Of the 2030 individual surveys sent, 581 were opened and 53 (9.1%) responses were received from different parts of the country and from different practice settings. ASDIN COVID 19 triage document was frequently utilized and 83% of respondents found the document valuable. The survey also revealed multiple obstacles, including logistical and financial issues that led to significant disruption of services. CONCLUSIONS: The care of dialysis access was significantly affected in the United States during the COVID 19 pandemic due to multiple reasons. ASDIN actions provided valuable specific guidance regarding and explored barriers to dialysis access care. We describe those results and discuss strategies to prevent COVID 19 transmission with innovative strategies of providing access care. Individualized decision making is of essence when considering dialysis access procedures.

Global challenges with providing vascular access care during COVID era.

BACKGROUND: The COVID-19 pandemic has adversely affected health care systems and dialysis access care in the US and across the globe. Beyond the initial challenges posed by the pandemic and despite the actions taken by health care leaders/organizations/professional societies such as the "Maintaining Lifelines for ESKD Patients" joint statement, there continues to be delays in providing timely care and performing elective and emergent dialysis access procedures worldwide. The aim of this study was to assess the global challenges associated with providing dialysis vascular access care across the international vascular access community during the pandemic. METHODS: The American Society of Diagnostic and Interventional Nephrology (ASDIN) conducted an online survey in 2021, that was administered to an expert panel of dialysis vascular access specialists and global leaders spanning across the international community. The respondents who are members of ASDIN, Association of Vascular Access and InTerventionAl Renal physicians (AVATAR), Asia Pacific Society of Dialysis Access (APSDA), Peruvian Vascular Access Society (APDAV), and Australia/New Zealand Society of Interventional Nephrology (ANZSIN) reported their experiences in the care of dialysis vascular access, practice patterns, and challenges faced during the COVID pandemic. RESULTS: Of the 53 individual surveys sent, 16 were opened and 11 (69%) responses were received from across the world and from different practice settings. The survey revealed the continued challenges facing the international community, the stark disparities in care delivery, supply chain disruption and logistical, regulatory, and financial issues that the global community continues to face in the ongoing pandemic. CONCLUSIONS: The COVID19 pandemic is far from over, and the challenges and barriers to providing dialysis access care seen on the initial ASDIN survey in the US seem to extend across the globe. We describe those results and discuss options, opportunities, and innovative tools to provide dialysis and access care during these trying times.

Towards a Comprehensive and Robust Micromanipulation System with Force-Sensing and VR Capabilities.

In this modern world, with the increase of complexity of many technologies, especially in the micro and nanoscale, the field of robotic manipulation has tremendously grown. Microrobots and other complex microscale systems are often to laborious to fabricate using standard microfabrication techniques, therefore there is a trend towards fabricating them in parts then assembling them together, mainly using micromanipulation tools. Here, a comprehensive and robust micromanipulation platform is presented, in which four micromanipulators can be used simultaneously to perform complex tasks, providing the user with an intuitive environment. The system utilizes a vision-based force sensor to aid with manipulation tasks and it provides a safe environment for biomanipulation. Lastly, virtual reality (VR) was incorporated into the system, allowing the user to control the probes from a more intuitive standpoint and providing an immersive platform for the future of micromanipulation.

Considerations in Access Cannulation: Traditional and Evolving Approaches.

Needle cannulation of hemodialysis access is the soft underbelly of hemodialysis access care that has remained unchanged for a long time. Cannulation error results in complications such as infiltration, hematoma, subsequent revision procedures, and potential loss of hard-earned access. The "best" cannulation method is contingent upon access type and characteristics along with local expertise. The rope ladder technique of cannulation, characterized by successive rotation of puncture sites with each hemodialysis session, permits sufficient time for healing of prior cannulation sites, and reduction in complications such as bleeding, infection, and aneurysm development. A steeper needle angle, higher blood flow rates, and deep needle tip can lead to wall stress on the posterior wall and up to 10 cm from the needle cannulation site. Plastic cannulas provide a viable alternative to metallic needles; they have lower complications and a favorable cost-benefit ratio. There is lack of evidence to support an optimal arterial needle direction configuration. Needle injury may promote intimal thickening, but its effect on access outcomes is currently unknown. Percutaneous creation of arteriovenous fistula presents new challenges in dialysis access cannulation. Point-of-care ultrasound-guided cannulation will likely lead to a paradigm shift in access cannulation. Novel care delivery using cannulation stations is a promising development.

Gauss's law for networks directly reveals community boundaries.

The study of network topology provides insight into the function and behavior of physical, social, and biological systems. A natural step towards discovering the organizing principles of these complex topologies is to identify a reduced network representation using cohesive subgroups or communities. This procedure often uncovers the underlying mechanisms governing the functional assembly of complex networks. A community is usually defined as a subgraph or a set of nodes that has more edges than would be expected from a simple, null distribution of edges over the graph. This view drives objective such as modularity. Another perspective, corresponding to objectives like conductance or density, is that communities are groups of nodes that have extremal properties with respect to the number of internal edges and cut edges. Here we show that identifying community boundaries rather than communities results in a more accurate decomposition of the network into informative components. We derive a network analog of Gauss's law that relates a measure of flux through a subgraph's boundary to the connectivity among the subgraph's nodes. Our Gauss's law for networks naturally characterizes a community as a subgraph with high flux through its boundary. Aggregating flux over these boundaries gives rise to a Laplacian and forms the basis of our "Laplacian modularity" quality function for community detection that is applicable to general network types. This technique allows us to determine communities that are both overlapping and hierarchically organized.

Visual Analytics Tools for Sustainable Lifecycle Design: Current Status, Challenges, and Future Opportunities.

The rapid rise in technologies for data collection has created an unmatched opportunity to advance the use of data-rich tools for lifecycle decision-making. However, the usefulness of these technologies is limited by the ability to translate lifecycle data into actionable insights for human decision-makers. This is especially true in the case of sustainable lifecycle design (SLD), as the assessment of environmental impacts, and the feasibility of making corresponding design changes, often relies on human expertise and intuition. Supporting human sense-making in SLD requires the use of both data-driven and user-driven methods while exploring lifecycle data. A promising approach for combining the two is through the use of visual analytics (VA) tools. Such tools can leverage the ability of computer-based tools to gather, process, and summarize data along with the ability of human-experts to guide analyses through domain knowledge or data-driven insight. In this paper, we review previous research that has created VA tools in SLD. We also highlight existing challenges and future opportunities for such tools in different lifecycle stages-design, manufacturing, distribution & supply chain, use-phase, end-of-life, as well as life cycle assessment. Our review shows that while the number of VA tools in SLD is relatively small, researchers are increasingly focusing on the subject matter. Our review also suggests that VA tools can address existing challenges in SLD and that significant future opportunities exist.

MotionFlow: Visual Abstraction and Aggregation of Sequential Patterns in Human Motion Tracking Data.

Pattern analysis of human motions, which is useful in many research areas, requires understanding and comparison of different styles of motion patterns. However, working with human motion tracking data to support such analysis poses great challenges. In this paper, we propose MotionFlow, a visual analytics system that provides an effective overview of various motion patterns based on an interactive flow visualization. This visualization formulates a motion sequence as transitions between static poses, and aggregates these sequences into a tree diagram to construct a set of motion patterns. The system also allows the users to directly reflect the context of data and their perception of pose similarities in generating representative pose states. We provide local and global controls over the partition-based clustering process. To support the users in organizing unstructured motion data into pattern groups, we designed a set of interactions that enables searching for similar motion sequences from the data, detailed exploration of data subsets, and creating and modifying the group of motion patterns. To evaluate the usability of MotionFlow, we conducted a user study with six researchers with expertise in gesture-based interaction design. They used MotionFlow to explore and organize unstructured motion tracking data. Results show that the researchers were able to easily learn how to use MotionFlow, and the system effectively supported their pattern analysis activities, including leveraging their perception and domain knowledge.

Heat-passing framework for robust interpretation of data in networks.

Researchers are regularly interested in interpreting the multipartite structure of data entities according to their functional relationships. Data is often heterogeneous with intricately hidden inner structure. With limited prior knowledge, researchers are likely to confront the problem of transforming this data into knowledge. We develop a new framework, called heat-passing, which exploits intrinsic similarity relationships within noisy and incomplete raw data, and constructs a meaningful map of the data. The proposed framework is able to rank, cluster, and visualize the data all at once. The novelty of this framework is derived from an analogy between the process of data interpretation and that of heat transfer, in which all data points contribute simultaneously and globally to reveal intrinsic similarities between regions of data, meaningful coordinates for embedding the data, and exemplar data points that lie at optimal positions for heat transfer. We demonstrate the effectiveness of the heat-passing framework for robustly partitioning the complex networks, analyzing the globin family of proteins and determining conformational states of macromolecules in the presence of high levels of noise. The results indicate that the methodology is able to reveal functionally consistent relationships in a robust fashion with no reference to prior knowledge. The heat-passing framework is very general and has the potential for applications to a broad range of research fields, for example, biological networks, social networks and semantic analysis of documents.

Graftula: a composite access consisting of a graft used to repair a dysfunctional hemodialysis fistula.

Nonmaturing or dysfunctional hemodialysis fistulas are often repaired with interposition grafts placed either within the fistula (fistula-to-fistula configuration), or connected to another vein (fistula-to-vein configuration). The goal of this study was to compare the survival and usefulness of the composite accesses thus created, which we call "graftulas," with upperarm grafts. This was a retrospective study wherein we determined the survival and thrombosis rates of graftulas (n=24) and upper arm grafts (n=31) placed 1/1/07 through 12/31/09 and followed through 11/30/10. Graftulas resembled grafts as most (96%) were successfully cannulated in 65 ± 43 days. Survival of graftulas was also similar to grafts (58%, 47%, and 32% vs. 56%, 47%, and 39% at 1, 2, and 3 years respectively, p=0.60). However, graftulas had a lower thrombosis rate than grafts (0.5 vs. 1.2 per patient year, p=0.04), and in the fistula-to-fistula configuration, a 2-year thrombosis-free survival of 78%. Total survival of the access site (fistula+graftula) was 92%, 73%, and 42% at 1, 3, and 5 years, respectively. Graftulas possess certain beneficial properties of fistulas and grafts that allows for continued use of the original access site.

Back to the future: how biology and technology could change the role of PTFE grafts in vascular access management.

Although the arteriovenous fistula (AVF) is the preferred mode of dialysis vascular access, AVF maturation failure remains a huge clinical problem, often resulting in a prolonged duration of use of tunneled dialysis catheters. In contrast, polytetrafluoroethylene (PTFE) grafts do not suffer from early failure, but have significant problems with later stenosis and thrombosis. This review will initially summarize the pathology and pathogenesis of PTFE graft dysfunction and will then use this as a basis for describing some novel therapies, which may have the potential to reduce PTFE graft dysfunction. Finally, we will emphasize that the introduction of such therapies could be an important first step toward individualizing overall vascular access care.

3DMolNavi: a web-based retrieval and navigation tool for flexible molecular shape comparison.

BACKGROUND: Many molecules of interest are flexible and undergo significant shape deformation as part of their function, but most existing methods of molecular shape comparison treat them as rigid shapes, which may lead to incorrect measure of the shape similarity of flexible molecules. Currently, there still is a limited effort in retrieval and navigation for flexible molecular shape comparison, which would improve data retrieval by helping users locate the desirable molecule in a convenient way. RESULTS: To address this issue, we develop a web-based retrieval and navigation tool, named 3DMolNavi, for flexible molecular shape comparison. This tool is based on the histogram of Inner Distance Shape Signature (IDSS) for fast retrieving molecules that are similar to a query molecule, and uses dimensionality reduction to navigate the retrieved results in 2D and 3D spaces. We tested 3DMolNavi in the Database of Macromolecular Movements (MolMovDB) and CATH. Compared to other shape descriptors, it achieves good performance and retrieval results for different classes of flexible molecules. CONCLUSIONS: The advantages of 3DMolNavi, over other existing softwares, are to integrate retrieval for flexible molecular shape comparison and enhance navigation for user's interaction. 3DMolNavi can be accessed via https://engineering.purdue.edu/PRECISE/3dmolnavi/index.html.

Global geometric affinity for revealing high fidelity protein interaction network.

Protein-protein interaction (PPI) network analysis presents an essential role in understanding the functional relationship among proteins in a living biological system. Despite the success of current approaches for understanding the PPI network, the large fraction of missing and spurious PPIs and a low coverage of complete PPI network are the sources of major concern. In this paper, based on the diffusion process, we propose a new concept of global geometric affinity and an accompanying computational scheme to filter the uncertain PPIs, namely, reduce the spurious PPIs and recover the missing PPIs in the network. The main concept defines a diffusion process in which all proteins simultaneously participate to define a similarity metric (global geometric affinity (GGA)) to robustly reflect the internal connectivity among proteins. The robustness of the GGA is attributed to propagating the local connectivity to a global representation of similarity among proteins in a diffusion process. The propagation process is extremely fast as only simple matrix products are required in this computation process and thus our method is geared toward applications in high-throughput PPI networks. Furthermore, we proposed two new approaches that determine the optimal geometric scale of the PPI network and the optimal threshold for assigning the PPI from the GGA matrix. Our approach is tested with three protein-protein interaction networks and performs well with significant random noises of deletions and insertions in true PPIs. Our approach has the potential to benefit biological experiments, to better characterize network data sets, and to drive new discoveries.

Using diffusion distances for flexible molecular shape comparison.

BACKGROUND: Many molecules are flexible and undergo significant shape deformation as part of their function, and yet most existing molecular shape comparison (MSC) methods treat them as rigid bodies, which may lead to incorrect shape recognition. RESULTS: In this paper, we present a new shape descriptor, named Diffusion Distance Shape Descriptor (DDSD), for comparing 3D shapes of flexible molecules. The diffusion distance in our work is considered as an average length of paths connecting two landmark points on the molecular shape in a sense of inner distances. The diffusion distance is robust to flexible shape deformation, in particular to topological changes, and it reflects well the molecular structure and deformation without explicit decomposition. Our DDSD is stored as a histogram which is a probability distribution of diffusion distances between all sample point pairs on the molecular surface. Finally, the problem of flexible MSC is reduced to comparison of DDSD histograms. CONCLUSIONS: We illustrate that DDSD is insensitive to shape deformation of flexible molecules and more effective at capturing molecular structures than traditional shape descriptors. The presented algorithm is robust and does not require any prior knowledge of the flexible regions.

Estimating view parameters from random projections for Tomography using spherical MDS.

BACKGROUND: During the past decade, the computed tomography has been successfully applied to various fields especially in medicine. The estimation of view angles for projections is necessary in some special applications of tomography, for example, the structuring of viruses using electron microscopy and the compensation of the patient's motion over long scanning period. METHODS: This work introduces a novel approach, based on the spherical multidimensional scaling (sMDS), which transforms the problem of the angle estimation to a sphere constrained embedding problem. The proposed approach views each projection as a high dimensional vector with dimensionality equal to the number of sampling points on the projection. By using SMDS, then each projection vector is embedded onto a 1D sphere which parameterizes the projection with respect to view angles in a globally consistent manner. The parameterized projections are used for the final reconstruction of the image through the inverse radon transform. The entire reconstruction process is non-iterative and computationally efficient. RESULTS: The effectiveness of the sMDS is verified with various experiments, including the evaluation of the reconstruction quality from different number of projections and resistance to different noise levels. The experimental results demonstrate the efficiency of the proposed method. CONCLUSION: Our study provides an effective technique for the solution of 2D tomography with unknown acquisition view angles. The proposed method will be extended to three dimensional reconstructions in our future work. All materials, including source code and demos, are available on https://engineering.purdue.edu/PRECISE/SMDS.

Three dimensional shape comparison of flexible proteins using the local-diameter descriptor.

BACKGROUND: Techniques for inferring the functions of the protein by comparing their shape similarity have been receiving a lot of attention. Proteins are functional units and their shape flexibility occupies an essential role in various biological processes. Several shape descriptors have demonstrated the capability of protein shape comparison by treating them as rigid bodies. But this may give rise to an incorrect comparison of flexible protein shapes. RESULTS: We introduce an efficient approach for comparing flexible protein shapes by adapting a local diameter (LD) descriptor. The LD descriptor, developed recently to handle skeleton based shape deformations 1, is adapted in this work to capture the invariant properties of shape deformations caused by the motion of the protein backbone. Every sampled point on the protein surface is assigned a value measuring the diameter of the 3D shape in the neighborhood of that point. The LD descriptor is built in the form of a one dimensional histogram from the distribution of the diameter values. The histogram based shape representation reduces the shape comparison problem of the flexible protein to a simple distance calculation between 1D feature vectors. Experimental results indicate how the LD descriptor accurately treats the protein shape deformation. In addition, we use the LD descriptor for protein shape retrieval and compare it to the effectiveness of conventional shape descriptors. A sensitivity-specificity plot shows that the LD descriptor performs much better than the conventional shape descriptors in terms of consistency over a family of proteins and discernibility across families of different proteins. CONCLUSION: Our study provides an effective technique for comparing the shape of flexible proteins. The experimental results demonstrate the insensitivity of the LD descriptor to protein shape deformation. The proposed method will be potentially useful for molecule retrieval with similar shapes and rapid structure retrieval for proteins. The demos and supplemental materials are available on https://engineering.purdue.edu/PRECISE/LDD.

IDSS: deformation invariant signatures for molecular shape comparison.

BACKGROUND: Many molecules of interest are flexible and undergo significant shape deformation as part of their function, but most existing methods of molecular shape comparison (MSC) treat them as rigid bodies, which may lead to incorrect measure of the shape similarity of flexible molecules. RESULTS: To address the issue we introduce a new shape descriptor, called Inner Distance Shape Signature (IDSS), for describing the 3D shapes of flexible molecules. The inner distance is defined as the length of the shortest path between landmark points within the molecular shape, and it reflects well the molecular structure and deformation without explicit decomposition. Our IDSS is stored as a histogram which is a probability distribution of inner distances between all sample point pairs on the molecular surface. We show that IDSS is insensitive to shape deformation of flexible molecules and more effective at capturing molecular structures than traditional shape descriptors. Our approach reduces the 3D shape comparison problem of flexible molecules to the comparison of IDSS histograms. CONCLUSION: The proposed algorithm is robust and does not require any prior knowledge of the flexible regions. We demonstrate the effectiveness of IDSS within a molecular search engine application for a benchmark containing abundant conformational changes of molecules. Such comparisons in several thousands per second can be carried out. The presented IDSS method can be considered as an alternative and complementary tool for the existing methods for rigid MSC. The binary executable program for Windows platform and database are available from https://engineering.purdue.edu/PRECISE/IDSS.

Using least median of squares for structural superposition of flexible proteins.

BACKGROUND: The conventional superposition methods use an ordinary least squares (LS) fit for structural comparison of two different conformations of the same protein. The main problem of the LS fit that it is sensitive to outliers, i.e. large displacements of the original structures superimposed. RESULTS: To overcome this problem, we present a new algorithm to overlap two protein conformations by their atomic coordinates using a robust statistics technique: least median of squares (LMS). In order to effectively approximate the LMS optimization, the forward search technique is utilized. Our algorithm can automatically detect and superimpose the rigid core regions of two conformations with small or large displacements. In contrast, most existing superposition techniques strongly depend on the initial LS estimating for the entire atom sets of proteins. They may fail on structural superposition of two conformations with large displacements. The presented LMS fit can be considered as an alternative and complementary tool for structural superposition. CONCLUSION: The proposed algorithm is robust and does not require any prior knowledge of the flexible regions. Furthermore, we show that the LMS fit can be extended to multiple level superposition between two conformations with several rigid domains. Our fit tool has produced successful superpositions when applied to proteins for which two conformations are known. The binary executable program for Windows platform, tested examples, and database are available from https://engineering.purdue.edu/PRECISE/LMSfit.

Robust principal axes determination for point-based shapes using least median of squares.

A robust technique for determining the principal axes of a 3D shape represented by a point set, possibly with noise, is presented. We use techniques from robust statistics to guide the classical principal component analysis (PCA) computation. Our algorithm is based on a robust statistics method: least median of squares (LMS), for outlier detection. Using this method, an outlier-free major region of the shape is extracted, which ignores the effect on other minor regions regarded as the outliers of the shape.In order to effectively approximate the LMS optimization, the forward search technique is utilized. We start from a small outlier-free subset robustly chosen as the major region, where an octree is used for accelerating computation. Then the region is iteratively increased by adding samples at a time. Finally, by treating the points on minor regions as outliers, we are able to define the principal axes of the shape as one of the major region. One of the advantages of our algorithm is that it automatically disregards outliers and distinguishes the shape as the major and minor regions during the principal axes determination without any extra segmentation procedure. The presented algorithm is simple and effective and gives good results for point-based shapes. The application on shape alignment is considered for demonstration purpose.

Fast protein tertiary structure retrieval based on global surface shape similarity.

Characterization and identification of similar tertiary structure of proteins provides rich information for investigating function and evolution. The importance of structure similarity searches is increasing as structure databases continue to expand, partly due to the structural genomics projects. A crucial drawback of conventional protein structure comparison methods, which compare structures by their main-chain orientation or the spatial arrangement of secondary structure, is that a database search is too slow to be done in real-time. Here we introduce a global surface shape representation by three-dimensional (3D) Zernike descriptors, which represent a protein structure compactly as a series expansion of 3D functions. With this simplified representation, the search speed against a few thousand structures takes less than a minute. To investigate the agreement between surface representation defined by 3D Zernike descriptor and conventional main-chain based representation, a benchmark was performed against a protein classification generated by the combinatorial extension algorithm. Despite the different representation, 3D Zernike descriptor retrieved proteins of the same conformation defined by combinatorial extension in 89.6% of the cases within the top five closest structures. The real-time protein structure search by 3D Zernike descriptor will open up new possibility of large-scale global and local protein surface shape comparison.