bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of Gracilariaceae (Rhodophyta) of central Portugal by histological and genetic methods
Authors: Freitas Marta V. , Simões Marco , Valdez Christian , Afonso Clélia , Trindade Beatriz G. , Correia Ana , Baptista Teresa , Mouga Teresa 
Published: December 2021

Abstract
This study aims to identify different populations of Gracilariaceae collected from the central coast of Portugal through light microscopy, anatomical observations and genetic tools, essential approaches to correctly assign species identity. Samples were obtained from Ria de Aveiro (AV), Figueira da Foz (FFBC, FFMD), and Lagoa de Óbidos (LOBR, LOEV, LOBS). Although histological observations offered a visual representation of the characteristic pseudoparenchymatous organization, they did not allow a clear distinction among the species. The amplification of a ∼700 bp fragment of the mitochondrial cytochrome oxidase I gene, and its sequencing enabled us to assign the populations FFBC and LOBS to Gracilaria gracilis, and the populations AV, FFMD, LOBR, and LOEV to Agarophyton vermiculophyllum. This contribution will help phycologists to correctly identify the Portuguese populations of Gracilaria sensu lato at the species level, which will be crucial in ensuring that future studies and industrial exploration accurately target the correct species.
Estimation of accuracy loss by training a deep-learning-based cell organelle recognition software using full dataset and a reduced dataset containing only subviral particle distribution information
Authors: Busch Nils , Rausch Andreas , Schanze Thomas 
Published: December 2021

Abstract
In collaboration with the Institute of Virology, Philipps University, Marburg, a deep-learning-based method that recognizes and classifies cell organelles based on the distribution of subviral particles in fluorescence microscopy images of virus-infected cells has been further developed. In this work a method to recognize cell organelles by means of partial image information is extended. The focus is on investigating loss of accuracy by only providing information about subviral particles and not all cell organelles to an adopted Mask-R convolutional neural network. Our results show that the subviral particle distribution holds information about the cell morphology, thus making it possible to use it for cell organelle-labelling.
Laplace-Beltrami Refined Shape Regression Applied to Neck Reconstruction for Craniosynostosis Patients
Authors: Schaufelberger Matthias , Kühle Reinald , Weichel Frederic , Wachter Andreas , Hagen Niclas , Ringwald Friedemann , Eisenmann Urs , Freudlsperger Christian , Nahm Werner 
Published: December 2021

Abstract
This contribution is part of a project concerning the creation of an artificial dataset comprising 3D head scans of craniosynostosis patients for a deep-learning-based classification. To conform to real data, both head and neck are required in the 3D scans. However, during patient recording, the neck is often covered by medical staff. Simply pasting an arbitrary neck leaves large gaps in the 3D mesh. We therefore use a publicly available statistical shape model (SSM) for neck reconstruction. However, most SSMs of the head are constructed using healthy subjects, so the full head reconstruction loses the craniosynostosis-specific head shape. We propose a method to recover the neck while keeping the pathological head shape intact. We propose a Laplace- Beltrami-based refinement step to deform the posterior mean shape of the full head model towards the pathological head. The artificial neck is created using the publicly available Liverpool-York-Model. We apply our method to construct artificial necks for head scans of 50 scaphocephaly patients. Our method reduces mean vertex correspondence error by approximately 1.3 mm compared to the ordinary posterior mean shape, preserves the pathological head shape, and creates a continuous transition between neck and head. The presented method showed good results for reconstructing a plausible neck to craniosynostosis patients. Easily generalized it might also be applicable to other pathological shapes.
Towards Quantitative ICG Angiography: Fluorescence Monte Carlo Multi Cylinder
Authors: Hoffmann Simon , Naber Ady , Nahm Werner 
Published: December 2021

Abstract
Intraoperative blood flow measurement is an effective way to assess the quality of bypass surgery. Flow quantification from indocyanine green (ICG) angiography promises to be an easy, contact-free method. It shows deviations compared to a reference. These are given as factor k, which depends on the vessel diameter d. The radiation transport within the vessel while recording the ICG passage might cause this. It is analyzed in silico to disclose its impact on k (d). A Fluorescence Monte Carlo Multi Cylinder (FMCMC) model was developed as a static model, assuming homogeneous concentration of ICG. In contrast to published approaches utilizing a Monte Carlo Multi Layer (MCML) model assuming the deepest penetration location within a photon packet’s path to be the fluorescence location, the events are modeled. Fluorescence event modeling, Multi Cylinder geometry and a homogeneous illumination as well as combinations of these were implemented in separate aspect models. Resulting k (d) were compared to k (d) from MCML. Deviations in k (d) derived from FMCMC and MCML in each aspect model were present. The Root Mean Square Error ranges from 6,8% to 36 %, k (d) also varied comparing the aspect models to each other. The model geometry, the modeled fluorescence location and illumination mode show a clear impact on simulated k (d). Therefore, our study shows that simplifications of previous studies are invalid. The developed FMCMC model considers the named aspects, allowing the analysis of radiation transport in ICG angiography. The FMCMC model assumes a homogeneous concentration of ICG which is not true in clinical cases. Obtaining the heterogeneous distribution of ICG is possible via fluid flow models. Coupling the fluid flow model and the developed radiation transport model as well as including a detailed camera optic is the task for future work.
Sensor-based measurement for advanced monitoring and early detection of PE wear in total knee arthroplasties
Authors: Schaufler Anna , Fritzsche Holger , Bertrand Jessica , Lohmann Christoph , Boese Axel , Friebe Michael 
Published: December 2021

Abstract
Polyethylene (PE) inlays of knee endoprostheses are exposed to constant mechanical stress causing particle abrasion and volumetric wear. With the current inlay surveillance strategy, significant change often can be only detected when the implant has already failed. To reduce medical complications arising from implant malposition or failure, early and accurate assessment of the implant condition is of great value. We present a novel concept to monitor PE inlays based on an implanted sensor. Requirements of sensor integration into endoprostheses were discussed and specified for an inlay monitoring concept. A planar eddy current sensor for measuring the gradual descent of the femoral component was proposed where inductive and capacitive sensor elements form a resonant circuit. The potential sensitivity of the proposed measuring method was tested in an experimental setup using an RFID tag as a sensor substitute. A measurement concept, including the sensor and an extracorporal readout coil, was described and the validity was verified using finite element method (FEM) simulation of a simplified knee model. The experiments showed that a significant resonant frequency shift occurs in the sensor with decreasing distance to the femoral component. FEM simulation demonstrated that the sensor could be powered and readout extracorporeally through inductive coupling with an external readout coil. The proposed concept is a promising solution for feasible and accurate reading of the implant status designed to meet medical requirements. It can enable autonomous and routine monitoring as well as early detection of critical inlay deformation with a home-use device.
Dry-fog disinfection as an alternative method for room decontamination
Authors: Buhl Sebastian , Stich Alexander , Bulitta Clemens 
Published: December 2021

Abstract
Due to the fact that a relatively large area can be disinfected in a short time, dry-fog disinfection could have a great potential to improve hygienic measures. However, there are few studies to date on the effectiveness and combinability of this technology with other devices. In this work, the disinfecting potential of the TBT dry fogging technology (TBT Desinfektion GmbH & Co. KG, Germany) and the corresponding disinfectant Defeat AR (Biofluid GmbH, Germany) was examined with regard to microbiological decontamination and potential damaging effects on different devices. The airburden measurement did show a reduction of approx. 66% of the microbiological room contamination. Similarly, decontamination of test surfaces was shown to be effective at all measuring points we selected in the experimental setup. Depending on the accessibility of the surfaces this effect was in the range of several log levels. Nevertheless, a first test run showed that some technical equipment was negatively affected by the process.
A remote and immersive setup for pandemic-safe surgical education
Authors: Rosenthal Jean-Claude , Schneider Armin , Wisotzky Eric L. , Meij Senna , Dobbelsteen John van den , Arens Philipp , Uecker Florian C. 
Published: December 2021

Abstract
Existing challenges in surgical education (See one, do one, teach one) as well as the Covid-19 pandemic make it necessary to develop new ways for surgical training. This is also crucial for the dissemination of new technological developments. As today’s live transmissions of surgeries to remote locations always come with high information loss, e.g. stereoscopic depth perception, and limited communication channels. This work describes the implementation of a scalable remote solution for surgical training, called TeleSTAR (Telepresence for Surgical Assistance and Training using Augmented Reality), using immersive, interactive and augmented reality elements with a bi-lateral audio pipeline to foster direct communication. The system uses a full digital surgical microscope with a modular software-based AR interface, which consists of an interactive annotation mode to mark anatomical landmarks using an integrated touch panel as well as an intraoperative image-based stereo-spectral algorithm unit to measure anatomical details and highlight tissue characteristics.We broadcasted three cochlea implant surgeries in the context of otorhinolaryngology. The intervention scaled to five different remote locations in Germany and the Netherlands with lowlatency. In total, more than 150 persons could be reached and included an evaluation of a participant’s questionnaire indicating that annotated AR-based 3D live transmissions add an extra level of surgical transparency and improve the learning outcome.
Image processing to delineate the boundaries of peripheral arterial walls
Authors: Shekar Janani , Sukumaran Saritha , Bhave Ashish , Möller Knut 
Published: December 2021

Abstract
The analysis of the arterial wall properties is vital in the prediction of stroke events and arterial hypertension in humans. Numerous researchers have experimented with several approaches to model arterial vessels and to analyse their biomechanical behaviour for many years now. Our study is focussed on image processing of peripheral arterial cross sections to detect and isolate the distinct layers. These boundaries will enable the creation of FEM models for further analysis of arterial wall properties. In a clinical setting, it facilitates doctors to identify the optimum pressure that can be applied to the artery for the treatment of stenosis without damaging the morphology of the blood vessels. This paper aims at distinguishing the various layers of arterial walls from images by minimizing human intervention. Cross section images of arteries from various sources were collected[10][11]. The boundaries from the image were obtained using image processing techniques of MATLAB(R2021a). The approach identified was to convert the input RGB images to grayscale, thresholding and applying morphological operators to delineate the Intima, Media, and Adventitia. These regions of interests (ROI) were then superimposed to generate an image with differentiated boundaries and void of unnecessary noise and inhomogeneity. This approach gave us an insight of the differences in various methods of boundary detection and to infer the optimum approach for accurate demarcation of boundaries of the three layers of arterial walls. It paves a pathway for forward modelling and to perform detailed FEM analysis in in-vitro diagnostics. In a nutshell, it was observed that the edge detection procedure implemented could be used for healthy and stenotic arteries. Further studies must be conducted to test the efficiency across a wide range of images and hence generalise its usage. Upon satisfactory boundary detection, forward modelling could be performed using the identified geometric forms.
TAVI-Mimic for testing of heart valve leaflet materials in physiological loading situations
Authors: Finck Joschka , Oldenburg Jan , Kuske Thomas , Grabow Niels , Schmitz Klaus-Peter , Siewert Stefan , Oschatz Stefan , Stiehm Michael , Kaule Sebastian 
Published: December 2021

Abstract
The loading situation of the aortic valve is complex, complicating the identification of innovative approaches for heart valve leaflet materials, e.g. for transcatheter aortic valve implantation (TAVI). Materials engineering experiments allow for screening of materials but especially for durability testing, the consideration of physiological loads is vital/critical for the suitability-assessment of innovative leaflet materials. For this reason, a framework structure for the testing of leaflet materials in physiological loading (TAVI-Mimic) was developed. The exemplary use case for the TAVI-Mimic was a test for calcification propensity of pericardium during durability testing. The TAVI-Mimic was designed as a fourparted frame, based on previous work of our group. The leaflet material can be attached between inner and outer shells without sewing. In a second step, the TAVI-Mimic was optimized regarding radial load-deformation in comparison to a commercial TAVI by means of finite element analysis (FEA) and hydrodynamic characterization in a pulse duplicator system. Mechanical properties dependent on water uptake of different materials for 3D-printing of the TAVI-Mimic were investigated. After optimization, TAVI-Mimics were equipped with glutaraldehyde-fixated pericardial tissue and prototypes were calcified by using a heart valve durability tester and a metastable calcification-liquid, developed in earlier studies. The development of the TAVI-Mimic using FEA and experiments was successful, leading to a radial load dependent deformation of 0.6 mm which correlates with commercial TAVI. Two methacrylic photopolymers were identified for 3D-printing of the TAVI-Mimic and prototypes attached with pericardial tissue were manufactured. Pericardium TAVI-Mimics were calcified in vitro for one week and an average calciumphosphate precipitate of 0.34- 0.54 mg/cm² was measured. The optimization of the TAVR-Mimic led to an improved load-dependent behaviour compared to a commercial prosthesis while testing. The calcification method, combining the TAVI-Mimic, the metastable calcification solution and the durability tester enabled a successfully calcification of pericardial tissue, approaching the in vivo situation.
A two-stage finite element based method for the study of stiffness and failure load of osteoporotic spines
Authors: Martin Heiner , Schröder Guido , Schubert Julia , Ringel Anne , Paulsen Jorid , Pfensig Sylvia , Götz Andreas , Schober Hans-Christof , Grabow Niels 
Published: December 2021

Abstract
A two-step method for investigating the stiffnesses and failure loads of vertebral bodies is presented. It is based on the representation of the spine in a global finite element model of structural elements. A detailed model of two interconnected vertebrae and an intervertebral disc was implemented. Calculations on this model are shown which illustrate the dependencies of structural stiffnesses on vertebral body properties. These structural stiffnesses can then be used to represent the spine in the global model.
Investigations on physico-chemical properties of TSPCU nonwoven for application as prosthetic venous valve
Authors: Schubert Julia , Arbeiter Daniela , Götz Andreas , Lebahn Kerstin , Schmidt Wolfram , Grabow Niels , Illner Sabine 
Published: December 2021

Abstract
Electrospinning is used for producing nonwovens for medical polymer-based implants, such as prosthetic valves or covered scaffolds. In this study, nonwovens for prosthetic venous valves are investigated regarding their morphology and mechanics in physiological medium. Spinning molds were developed based on previous venous valve leaflet designs, 3D printed in different sizes and covered with electrospun nonwovens. Samples were stored in a physiological 0.9% saline at 37°C to investigate the influence of fiber rearrangement and swelling in medium for several weeks. Two different nonwovens of thermoplastic silicone-based polycarbonaturethane (TSPCU) were compared. Tensile test results show that storage in medium has a relevant influence on the mechanical properties. SEM images of TSPCU show substantially increased fiber diameters after 8 days stored in medium. After detaching the valve leaflet nonwovens from the molds, shrinkage of the material of approximately 12% was detected. A suitable valve size could be identified for joining with the stent structure into an interventional prosthetic venous valve. The results demonstrate the influence of storage conditions on the morphological and mechanical properties of electrospun TSPCU nonwovens. For development and dimensioning of venous valve leaflets, this change in mechanical behavior and possible shrinkage of the material has to be considered.
Systematic morphological characterization of sub-visible particles generated from cardiovascular devices using dynamic image analysis
Authors: Kurzhals Anja , Brandt-Wunderlich Christoph , Schmitz Klaus-Peter , Grabow Niels , Schmidt Wolfram 
Published: December 2021

Abstract
The assessment of particulate matter generated during a simulated use is requested by international standards for approval of cardiovascular devices. The particle suspension was generated during a simulated use test procedure of a commercially available coronary stent delivery system and analyzed by Dynamic image analysis (DIA) using FlowCAM 8000. Four parameters were analyzed: the diameter, the aspect ratio value, the circularity and the intensity. The results revealed that 53% of the particles were larger than 25 μm. The particle shapes were inhomogeneous with a wide ranging aspect ratio of 0.03 to 0.95. Around 83% had an intensity value of more than 140 grey scales and appeared translucent.
Contributions towards Data driven Deep Learning methods to predict Steady State Fluid Flow in mechanical Heart Valves
Authors: Oldenburg Jan , Renkewitz Julian , Stiehm Michael , Schmitz Klaus-Peter 
Published: December 2021

Abstract
It is commonly accepted that hemodynamic situation is related with cardiovascular diseases as well as clinical post-procedural outcome. In particular, aortic valve stenosis and insufficiency are associated with high shear flow and increased pressure loss. Furthermore, regurgitation, high shear stress and regions of stagnant blood flow are presumed to have an impact on clinical result. Therefore, flow field assessment to characterize the hemodynamic situation is necessary for device evaluation and further design optimization. In-vitro as well as in-silico fluid mechanics methods can be used to investigate the flow through prostheses. In-silico solutions are based on mathematical equitation’s which need to be solved numerically (Computational Fluid Dynamics - CFD). Fundamentally, the flow is physically described by Navier-Stokes. CFD often requires high computational cost resulting in long computation time. Techniques based on deep-learning are under research to overcome this problem. In this study, we applied a deep-learning strategy to estimate fluid flows during peak systolic steady-state blood flows through mechanical aortic valves with varying opening angles in randomly generated aortic root geometries. We used a data driven approach by running 3,500 two dimensional simulations (CFD). The simulation data serves as training data in a supervised deep learning framework based on convolutional neural networks analogous to the U-net architecture. We were able to successfully train the neural network using the supervised data driven approach. The results showing that it is feasible to use a neural network to estimate physiological flow fields in the vicinity of prosthetic heart valves (Validation error below 0.06), by only giving geometry data (Image) into the Network. The neural network generates flow field prediction in real time, which is more than 2500 times faster compared to CFD simulation. Accordingly, there is tremendous potential in the use of AIbased approaches predicting blood flows through heart valves on the basis of geometry data, especially in applications where fast fluid mechanic predictions are desired.
Particle Image Velocimetry for in vitro characterization of Paravalvular Leakage of TAVR-sealing concepts
Authors: Höing Samuel , Borowski Finja , Oldenburg Jan , Illner Sabine , Öner Alper , Stiehm Michael , Schmitz Klaus-Peter , Kaule Sebastian 
Published: December 2021

Abstract
Paravalvular leakage (PVL), defined as the leakage between the aortic annulus and a transcatheter aortic valve replacement (TAVR), is verifiably associated with short- and long-term clinical outcome, especially with increased mortality. Therefore, with the ambition to reduce or even prevent PVL of next generation TAVR, it is necessary to extend the hemodynamic understanding of PVL.
This study presents an in vitro flow measurement method to localize PVL during hydrodynamic characterization of TAVR and furthermore presents different design features, socalled outer skirt, to reduce PVL. Particle image velocimetry (PIV) measurements were performed for flow field assessment during hydrodynamic characterization of TAVR. Additionally, two different sealing concepts were developed to reduce PVL. The skirts were manufactured from polymeric-nonwoven and sued to pericardium-based TAVR-prototype. The prepared TAVR-prototypes were then deployed in a pathophysiological model of the aortic root with a calcification nodule of 2 mm according to ISO 5840:2021. To assess PVL, the flow field and the regurgitation volume was measured.
The PIV measurements showed a clearly visible leakage jet between the TAVR-prototypes without skirt and the pathophysiological aortic annulus model. Jet velocities of up to 0.5 m/s were measured depending on presence or configuration of a PVL-preventing skirt. When implanted in the physiological annulus model without calcification nodule, PVL was hardly recognizable. The regurgitation volume of a TAVR-prototype without skirt at 5 l/min was 36.26±1.89 ml (n = 10). The developed and manufactured polymeric-nonwoven skirts reduced PVL from 37.67±1.17 ml to 18.36±1.8 ml (n = 10, TAVR-skirt-design1) and from 46.97±1.07 ml to 17.85±1.29 ml (n = 10, TAVR-skirt-design2) at 5 l/min. The localization of PVL during hydrodynamic characterization by means of PIV was successful. The sealing concepts developed in this work were very effective and led to a PVL-reduction of the tested TAVR prototypes of about 50% to 70%.