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Modelling blood flow an shear induced coagulation in membraene oxygenators (MO) is challenging. The relevant geometry of oxygenator fibers (OF) and chaining threads is complex and spans several length scales. In relevant scales and regimes blood shows several significant non-Newtonian effects. Existing models are only capable of accounting for some, but not all relevant effects. Additionally, coagulation processes are influencing fluid properties and geometry significantly. Due to the enormous size of the discretised geometries highly detailed viscosity and coagulation properties of blodd flow in MOs. First step is to find a gemoetry dependent viscosity representation on basis of parametric micro channel experiments with anti-coagulated blood. Next step is a statistic coagulation model, based on micro channel experiments with human (re-calcified citrated) whole blood an evaluation of clinically used osygenators. Since shear rate dependent (i.e. viscosity dependet) coagulation in return influences the viscosity, a combined model with suitable implementation in a RANS framework is necessary. Towards this end, micro channel experiments with new and used single OFs triggering coagulation are performed. Structures of multimeric von Willebrand fibers (vWF), as indicator for shear induced coagulation, are compared to computed and measured flow conditions, using immunofluorescence microscopy, RANS-computations and µPIV, respectively. Preliminary examinations in clinically used MOs show good agreement between occurring structures of vWF, cell depositions and computed flow patterns (geometry form µCT-Scans). However, computed shear rates might be to low to actually trigger activation of vWF. The complex geometry of MOs results in huge meshes, which makes RANS with statistical modelling of viscosity and coagulation a reasonable approach. Towards this end, experimental data on micro channel level with evaluation on real application level is crucial. Especially regarding clotting processes, micro fluidic experiments are powerful research tool.
Introduction
Aim of this study is to validate some constitutive models by assessing their capabilities in describing and predicting uniaxial and biaxial behavior of porcine aortic tissue.
Methods
14 samples from porcine aortas were used to perform 2 uniaxial and 5 biaxial tensile tests. Transversal strains were furthermore stored for uniaxial data. The experimental data were fitted by four constitutive models: Holzapfel-Gasser-Ogden model (HGO), model based on generalized structure tensor (GST), Four-Fiber-Family model (FFF) and Microfiber model. Fitting was performed to uniaxial and biaxial data sets separately and descriptive capabilities of the models were compared. Their predictive capabilities were assessed in two ways. Firstly each model was fitted to biaxial data and its accuracy (in term of R2 and NRMSE) in prediction of both uniaxial responses was evaluated. Then this procedure was performed conversely: each model was fitted to both uniaxial tests and its accuracy in prediction of 5 biaxial responses was observed.
Results
Descriptive capabilities of all models were excellent. In predicting uniaxial response from biaxial data, microfiber model was the most accurate while the other models showed also reasonable accuracy. Microfiber and FFF models were capable to reasonably predict biaxial responses from uniaxial data while HGO and GST models failed completely in this task.
Conclusions
HGO and GST models are not capable to predict biaxial arterial wall behavior while FFF model is the most robust of the investigated constitutive models. Knowledge of transversal strains in uniaxial tests improves robustness of constitutive models.
Epigenetic modifiers of the histone deacetylase (HDAC) family contribute to autoimmunity, cancer, HIV infection, inflammation, and neurodegeneration. Hence, histone deacetylase inhibitors (HDACi), which alter protein acetylation, gene expression patterns, and cell fate decisions, represent promising new drugs for the therapy of these diseases. Whereas pan-HDACi inhibit all 11 Zn2+-dependent histone deacetylases (HDACs) and cause a broad spectrum of side effects, specific inhibitors of histone deacetylase 6 (HDAC6i) are supposed to have less side effects. We present the synthesis and biological evaluation of Marbostats, novel HDAC6i that contain the hydroxamic acid moiety linked to tetrahydro-β-carboline derivatives. Our lead compound Marbostat-100 is a more potent and more selective HDAC6i than previously established well-characterized compounds in vitro as well as in cells. Moreover, Marbostat-100 is well tolerated by mice and effective against collagen type II induced arthritis. Thus, Marbostat-100 represents a most selective known HDAC6i and the possibility for clinical evaluation of a HDAC isoform-specific drug.
Over the past decade, veno-venous extracorporeal membrane oxygenation (vvECMO) has been increasingly utilized in respiratory failure in patients. This study presents our institution´s experience focusing on the life span of ECMO systems reflecting the performance of a particular system. A retrospective review of our ECMO database identified 461 adult patients undergoing vvECMO (2010-2017). Patients that required more than one system and survived the first exchange >24 hours (n = 139) were included. Life span until the first exchange and exchange criteria were analyzed for all systems (PLS, Cardiohelp HLS-set, both Maquet Cardiopulmonary, Rastatt, Germany; Deltastream/Hilite7000LT, iLA-activve, Xenios/NovaLung, Heilbronn, Germany; ECC.O5, LivaNova, Mirandola, Italy). At our ECMO center, the frequency of a system exchange was 30%. The median (IQR) life span was 9 (6-12) days. There was no difference regarding the different systems (p = 0.145 and p = 0.108, respectively). However, the Deltastream systems were exchanged more frequently due to elective technical complications (e. g. worsened gas transfer, development of coagulation disorder, increased bleedings complications) compared to the other exchanged systems (p = 0.013). In summary, the used ECMO systems are safe and effective for acute respiratory failure. There is no evidence for the usage of a specific system. Only the increased predictability of an imminent exchange preferred the usage of a Deltastream system. However, the decision to use a particular system should not depend solely on the possible criteria for an exchange.
Background
Iliac crest bone harvesting is a frequently performed surgical procedure widely used to treat bone defects. The objective of this study is to assess the biomechanical quantities related to risk for pelvic fracture after harvesting an autologous bone graft at the anterior iliac crest.
Methods
Finite element models with a simulated harvest site (sized 15 × 20 mm, 15 × 35 mm, 30 × 20 mm and 30 × 35 mm) in the iliac wing are created. The relevant loading case is when the ipsilateral leg is lifted off the ground. Musculoskeletal analysis is utilized to compute the muscle and joint forces involved in this motion. These forces are used as boundary conditions for the finite element analyses. Bone tissue stress is analyzed.
Results
Critical stress peaks are located between the anterior superior iliac spine (ASIS) and the anterior edge of the harvest site. Irrespective of the graft size, the iliac wing does not show any significant stress peaks with the harvest site being 20 to 25 mm posterior to the ASIS. The harvest area itself inhibits the distribution of the forces applied on the ASIS to extend to the posterior iliac wing. This leads to a lack of stress posterior to the harvest site. A balanced stress distribution with no stress peaks appears when the bone graft is taken below the iliac crest.
Conclusion
A harvest site located at least 20 to 25 mm posterior to the ASIS should be preferred to minimize the risk of iliac fatigue fracture.
Backround
Scaphoidectomy and midcarpal fusion can be performed using traditional fixation methods like K-wires, staples, screws or different dorsal (non)locking arthrodesis systems. The aim of this study is to test the Aptus four corner locking plate and to compare the clinical findings to the data revealed by CT scans and semi-automated segmentation.
Methods:
This is a retrospective review of eleven patients suffering from scapholunate advanced collapse (SLAC) or scaphoid non-union advanced collapse (SNAC) wrist, who received a four corner fusion between August 2011 and July 2014. The clinical evaluation consisted of measuring the range of motion (ROM), strength and pain on a visual analogue scale (VAS). Additionally, the Disabilities of the Arm, Shoulder and Hand (QuickDASH) and the Mayo Wrist Score were assessed. A computerized tomography (CT) of the wrist was obtained six weeks postoperatively. After semi-automated segmentation of the CT scans, the models were post processed and surveyed.
Results
During the six-month follow-up mean range of motion (ROM) of the operated wrist was 60°, consisting of 30° extension and 30° flexion. While pain levels decreased significantly, 54% of grip strength and 89% of pinch strength were preserved compared to the contralateral healthy wrist. Union could be detected in all CT scans of the wrist. While X-ray pictures obtained postoperatively revealed no pathology, two user related technical complications were found through the 3D analysis, which correlated to the clinical outcome.
Conclusion
Due to semi-automated segmentation and 3D analysis it has been proved that the plate design can keep up to the manufacturers’ promises. Over all, this case series confirmed that the plate can compete with the coexisting techniques concerning clinical outcome, union and complication rate.
In order to fix a fracture using minimally invasive surgery approaches, surgeons are drilling complex and tiny bones with a 2 dimensional X-ray as single imaging modality in the operating room. Our novel haptic force-feedback and visual assisted training system will potentially help hand surgeons to learn the drilling procedure in a realistic visual environment. Within the simulation, the collision detection as well as the interaction between virtual drill, bone voxels and surfaces are important. In this work, the chai3d collision detection and force calculation algorithms are combined with a physics engine to simulate the bone drilling process. The chosen Bullet-Physics-Engine provides a stable simulation of rigid bodies, if the collision model of the drill and the tool holder is generated as a compound shape. Three haptic points are added to the K-wire tip for removing single voxels from the bone. For the drilling process three modes are proposed to emulate the different phases of drilling in restricting the movement of a haptic device.
This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods.
Purpose
Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these require in-depth and time-consuming analysis of fundus images. Herein, we present an automated computer-based classification algorithm.
Design Algorithm development for AMD classification based on a large collection of color fundus images. Validation is performed on a cross-sectional, population-based study.
Participants.
We included 120 656 manually graded color fundus images from 3654 Age-Related Eye Disease Study (AREDS) participants. AREDS participants were >55 years of age, and non-AMD sight-threatening diseases were excluded at recruitment. In addition, performance of our algorithm was evaluated in 5555 fundus images from the population-based Kooperative Gesundheitsforschung in der Region Augsburg (KORA; Cooperative Health Research in the Region of Augsburg) study.
Methods.
We defined 13 classes (9 AREDS steps, 3 late AMD stages, and 1 for ungradable images) and trained several convolution deep learning architectures. An ensemble of network architectures improved prediction accuracy. An independent dataset was used to evaluate the performance of our algorithm in a population-based study.
Main Outcome Measures.
κ Statistics and accuracy to evaluate the concordance between predicted and expert human grader classification.
Results.
A network ensemble of 6 different neural net architectures predicted the 13 classes in the AREDS test set with a quadratic weighted κ of 92% (95% confidence interval, 89%–92%) and an overall accuracy of 63.3%. In the independent KORA dataset, images wrongly classified as AMD were mainly the result of a macular reflex observed in young individuals. By restricting the KORA analysis to individuals >55 years of age and prior exclusion of other retinopathies, the weighted and unweighted κ increased to 50% and 63%, respectively. Importantly, the algorithm detected 84.2% of all fundus images with definite signs of early or late AMD. Overall, 94.3% of healthy fundus images were classified correctly.
Conclusions
Our deep learning algoritm revealed a weighted κ outperforming human graders in the AREDS study and is suitable to classify AMD fundus images in other datasets using individuals >55 years of age.
Im Rahmen der Entwicklung eines haptisch-visuellen Trainingssystems für das Fräsen am Felsenbein werden ein Haptikarm und ein autostereoskopischer 3D-Monitor genutzt, um Chirurgen die virtuelle Manipulation von knöchernen Strukturen im Kontext eines sog. Serious Game zu ermöglichen. Unter anderem sollen Assistenzärzte im Rahmen ihrer Ausbildung das Fräsen am Felsenbein für das chirurgische Einsetzen eines Cochlea-Implantats üben können. Die Visualisierung des virtuellen Fräsens muss dafür in Echtzeit und möglichst realistisch modelliert, implementiert und evaluiert werden. Wir verwenden verschiedene Raycasting Methoden mit linearer und Nearest Neighbor Interpolation und vergleichen die visuelle Qualität und die Bildwiederholfrequenzen der Methoden. Alle verglichenen Verfahren sind sind echtzeitfähig, unterscheiden sich aber in ihrer visuellen Qualität.