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After a liver tumor intervention the medical doctor has to compare both pre and postoperative CT acquisitions to ensure that all carcinogenic cells are destroyed. A correct assessment of the intervention is of vital importance, since it will reduce the probability of tumor recurrence. Some methods have been proposed to support the medical doctors during the assessment process, however, all of them focus on secondary tumors. In this paper a tool is presented that enables the outcome validation for both primary and secondary tumors. Therefore, a multiphase registration (preoperative arterial and portal phases) followed by a registration between the pre and postoperative CT images is carried out. The first registration is in charge of the primary tumors that are only visible in the arterial phase. The secondary tumors will be incorporated in the second registration step. Finally, the part of the tumor that was not covered by the necrosis is quantified and visualized. The method has been tested in 9 patients, with an average registration error of 1.41 mm.
Rezension zu: K. Zimmermann, Technische Mechanik – multimedial. Fachbuch Verlag Leipzig (2000)
(2002)
Enzyme und Biosensorik
(2018)
Enzymbasierte Biosensoren finden seit mehr als fünf Jahrzehnten einen prosperierenden Wachstumsmarkt und werden zunehmend auch in biotechnologischen Prozessen eingesetzt. In diesem Kapitel werden, ausgehend vom Sensorbegriff und typischen Kenngrößen für Biosensoren (Abschn. 18.1), elektrochemische Enzym-Biosensoren vorgestellt und deren typischen Einsatzgebiete diskutiert (Abschn. 18.2). Ein Blick über den „Tellerrand“ hinaus zeigt alternative Transduktorprinzipien (Abschn. 18.3) und führt abschließend in aktuelle Forschungstrends ein (Abschn. 18.4).
BACKGROUND
Immunosuppression is often considered as an indication for antibiotic prophylaxis to prevent surgical site infections (SSI) while performing skin surgery. However, the data on the risk of developing SSI after dermatologic surgery in immunosuppressed patients are limited.
PATIENTS AND METHODS
All patients of the Department of Dermatology and Allergology at the University Hospital of RWTH Aachen in Aachen, Germany, who underwent hospitalization for a dermatologic surgery between June 2016 and January 2017 (6 months), were followed up after surgery until completion of the wound healing process. The follow-up addressed the occurrence of SSI and the need for systemic antibiotics after the operative procedure. Immunocompromised patients were compared with immunocompetent patients. The investigation was conducted as a retrospective analysis of patient records.
RESULTS
The authors performed 284 dermatologic surgeries in 177 patients. Nineteen percent (54/284) of the skin surgery was performed on immunocompromised patients. The most common indications for surgical treatment were nonmelanoma skin cancer and malignant melanomas. Surgical site infections occurred in 6.7% (19/284) of the cases. In 95% (18/19), systemic antibiotic treatment was needed. Twenty-one percent of all SSI (4/19) were seen in immunosuppressed patients.
CONCLUSION
According to the authors' data, immunosuppression does not represent a significant risk factor for SSI after dermatologic surgery. However, larger prospective studies are needed to make specific recommendations on the use of antibiotic prophylaxis while performing skin surgery in these patients.
The available data on complications after dermatologic surgery have improved over the past years. Particularly, additional risk factors have been identified for surgical site infections (SSI). Purulent surgical sites, older age, involvement of head, neck, and acral regions, and also the involvement of less experienced surgeons have been reported to increase the risk of the SSI after dermatologic surgeries.1 In general, the incidence of SSI after skin surgery is considered to be low.1,2 However, antibiotics in dermatologic surgeries, especially in the perioperative setting, seem to be overused,3,4 particularly regarding developing antibiotic resistances and side effects.
Immunosuppression has been recommended to be taken into consideration as an additional indication for antibiotic prophylaxis to prevent SSI after skin surgery in special cases.5,6 However, these recommendations do not specify the exact dermatologic surgeries, and were not specifically developed for dermatologic surgery patients and treatments, but adopted from other surgical fields.6 According to the survey conducted on American College of Mohs Surgery members in 2012, 13% to 29% of the surgeons administered antibiotic prophylaxis to immunocompromised patients to prevent SSI while performing dermatologic surgery on noninfected skin,3 although this was not recommended by Journal of the American Academy of Dermatology Advisory Statement. Indeed, the data on the risk of developing SSI after dermatologic surgery in immunosuppressed patients are limited. However, it is possible that due to the insufficient evidence on the risk of SSI occurrence in this patient group, dermatologic surgeons tend to overuse perioperative antibiotic prophylaxis.
To make specific recommendations on the use of antibiotic prophylaxis in immunosuppressed patients in the field of skin surgery, more information about the incidence of SSI after dermatologic surgery in these patients is needed. The aim of this study was to fill this data gap by investigating whether there is an increased risk of SSI after skin surgery in immunocompromised patients compared with immunocompetent patients.
Postural and metabolic benefits of using a forearm support walker in older adults with impairments
(2019)
Background
Impairment of neurovascular coupling (NVC) was recently reported in the context of subarachnoid hemorrhage and may correlate with disease severity and outcome. However, previous techniques to evaluate NVC required invasive procedures. Retinal vessels may represent an alternative option for non-invasive assessment of NVC.
Methods
A prototype of an adapted retinal vessel analyzer was used to assess retinal vessel diameter in mice. Dynamic vessel analysis (DVA) included an application of monochromatic flicker light impulses in predefined frequencies for evaluating NVC. All retinae were harvested after DVA and electroretinograms were performed.
Results
A total of 104 retinal scans were conducted in 21 male mice (90 scans). Quantitative arterial recordings were feasible only in a minority of animals, showing an emphasized reaction to flicker light impulses (8 mice; 14 scans). A characteristic venous response to flicker light, however, could observed in the majority of animals. Repeated measurements resulted in a significant decrease of baseline venous diameter (7 mice; 7 scans, p < 0.05). Ex-vivo electroretinograms, performed after in-vivo DVA, demonstrated a significant reduction of transretinal signaling in animals with repeated DVA (n = 6, p < 0.001).
Conclusions
To the best of our knowledge, this is the first non-invasive study assessing murine retinal vessel response to flicker light with characteristic changes in NVC. The imaging system can be used for basic research and enables the investigation of retinal vessel dimension and function in control mice and genetically modified animals.
The light-addressable potentiometric sensor (LAPS) and scanning photo-induced impedance microscopy (SPIM) are two closely related methods to visualise the distributions of chemical species and impedance, respectively, at the interface between the sensing surface and the sample solution. They both have the same field-effect structure based on a semiconductor, which allows spatially resolved and label-free measurement of chemical species and impedance in the form of a photocurrent signal generated by a scanning light beam. In this article, the principles and various operation modes of LAPS and SPIM, functionalisation of the sensing surface for measuring various species, LAPS-based chemical imaging and high-resolution sensors based on silicon-on-sapphire substrates are described and discussed, focusing on their technical details and prospective applications.
Algal polysaccharides (extracellular polysaccharides) and carbon nanotubes (CNTs) were adsorbed on dioctadecyldimethylammonium bromide Langmuir monolayers to serve as a matrix for the incorporation of urease. The physicochemical properties of the supramolecular system as a monolayer at the air–water interface were investigated by surface pressure–area isotherms, surface potential–area isotherms, interfacial shear rheology, vibrational spectroscopy, and Brewster angle microscopy. The floating monolayers were transferred to hydrophilic solid supports, quartz, mica, or capacitive electrolyte–insulator–semiconductor (EIS) devices, through the Langmuir–Blodgett (LB) technique, forming mixed films, which were investigated by quartz crystal microbalance, fluorescence spectroscopy, and field emission gun scanning electron microscopy. The enzyme activity was studied with UV–vis spectroscopy, and the feasibility of the thin film as a urea sensor was essayed in an EIS sensor device. The presence of CNT in the enzyme–lipid LB film not only tuned the catalytic activity of urease but also helped to conserve its enzyme activity. Viability as a urease sensor was demonstrated with capacitance–voltage and constant capacitance measurements, exhibiting regular and distinctive output signals over all concentrations used in this work. These results are related to the synergism between the compounds on the active layer, leading to a surface morphology that allowed fast analyte diffusion owing to an adequate molecular accommodation, which also preserved the urease activity. This work demonstrates the feasibility of employing LB films composed of lipids, CNT, algal polysaccharides, and enzymes as EIS devices for biosensing applications.
Monitoring of organic acids (OA) and volatile fatty acids (VFA) is crucial for the control of anaerobic digestion. In case of unstable process conditions, an accumulation of these intermediates occurs. In the present work, two different enzyme-based biosensor arrays are combined and presented for facile electrochemical determination of several process-relevant analytes. Each biosensor utilizes a platinum sensor chip (14 × 14 mm²) with five individual working electrodes. The OA biosensor enables simultaneous measurement of ethanol, formate, d- and l-lactate, based on a bi-enzymatic detection principle. The second VFA biosensor provides an amperometric platform for quantification of acetate and propionate, mediated by oxidation of hydrogen peroxide. The cross-sensitivity of both biosensors toward potential interferents, typically present in fermentation samples, was investigated. The potential for practical application in complex media was successfully demonstrated in spiked sludge samples collected from three different biogas plants. Thereby, the results obtained by both of the biosensors were in good agreement to the applied reference measurements by photometry and gas chromatography, respectively. The proposed hybrid biosensor system was also used for long-term monitoring of a lab-scale biogas reactor (0.01 m³) for a period of 2 months. In combination with typically monitored parameters, such as gas quality, pH and FOS/TAC (volatile organic acids/total anorganic carbonate), the amperometric measurements of OA and VFA concentration could enhance the understanding of ongoing fermentation processes.
False spectra formation in the differential two-channel scheme of the laser Doppler flowmeter
(2018)
Noise in the differential two-channel scheme of a classic laser Doppler flowmetry (LDF) instrument was studied. Formation of false spectral components in the output signal due to beating of electrical signals in the differential amplifier was found out. The improved block-diagram of the flowmeter was developed allowing to reduce the noise.
The chemical imaging sensor is a semiconductor-based chemical sensor capable of visualizing pH and ion distributions. The spatial resolution depends on the lateral diffusion of photocarriers generated by illumination of the semiconductor substrate. In this study, two types of optical setups, one based on a bundle of optical fibers and the other based on a binocular tube head, were developed to project a hybrid illumination of a modulated light beam and a ring-shaped constant illumination onto the sensor plate. An improved spatial resolution was realized by the ring-shaped constant illumination, which suppressed lateral diffusion of photocarriers by enhanced recombination due to the increased carrier concentration.
Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains
(2007)
Rationale: Previous studies [Topolnik et al., Cereb Cortex 2003; 13: 883; Schindler et al., Brain 2007; 130: 65] indicate that the termination of focal onset seizures may be causally related to an increase of global neuronal correlation during the second half of the seizures. This increase was observed to occur earlier in complex partial seizures than in secondarily generalized seizures. We here address the question whether such an increase of neuronal correlation prior to seizure end is indeed a global phenomenon, involving both hemispheres or whether there are side-specific differences. Methods: We analyzed 20 focal onset seizures (10 complex partial, 10 secondarily generalized seizures) recorded in 13 patients who underwent presurgical evaluation of focal epilepsies of different origin. EEG was recorded intracranially from bilaterally implanted subdural strip and intrahippocampal depth electrodes. Utilizing a moving window approach, we investigated the evolution of the maximum cross correlation for all channel combinations during seizures. For each moving window the mean value of the maximum cross correlation (MCC) between all electrode contacts was computed separately for each hemisphere. After normalization of seizure durations, MCC values of the ipsi- and contralateral hemisphere for all seizures were determined. Results: We observed that the MCC of the contralateral hemisphere in complex partial seizures increased during the first half of the seizure, whereas, for the same time interval, the MCC of the ipsilateral hemisphere even declined below the level of the pre-seizure period. In contrast, no significant differences between both hemispheres could be observed for secondarily generalized seizures where both hemispheres showed a simultaneous increase of MCC during the second half of the seizures. The level of MCC for the contralateral hemisphere was higher for complex partial seizures than for secondarily generalized seizures during the first half of the seizure. Conclusions: Our findings indicate that there are indeed lateralized differences in the evolution of global neuronal correlation during complex partial and secondarily generalized seizures. The observed contralateral increase of neuronal correlation during complex partial seizures might indicate an emerging self-organizing mechanism for preventing the spread of seizure activity.
Epilepsy
(2010)
Objective
To investigate whether functional brain networks of epilepsy patients treated with antiepileptic medication differ from networks of healthy controls even during the seizure-free interval.
Methods
We applied different rules to construct binary and weighted networks from EEG and MEG data recorded under a resting-state eyes-open and eyes-closed condition from 21 epilepsy patients and 23 healthy controls. The average shortest path length and the clustering coefficient served as global statistical network characteristics.
Results
Independent on the behavioral condition, epileptic brains exhibited a more regular functional network structure. Similarly, the eyes-closed condition was characterized by a more regular functional network structure in both groups. The amount of network reorganization due to behavioral state changes was similar in both groups. Consistent findings could be achieved for networks derived from EEG but hardly from MEG recordings, and network construction rules had a rather strong impact on our findings.
Conclusions
Despite the locality of the investigated processes epileptic brain networks differ in their global characteristics from non-epileptic brain networks. Further methodological developments are necessary to improve the characterization of disturbed and normal functional networks.
Significance
An increased regularity and a diminished modulation capability appear characteristic of epileptic brain networks.
We consider recent reports on small-world topologies of interaction networks derived from the dynamics of spatially extended systems that are investigated in diverse scientific fields such as neurosciences, geophysics, or meteorology. With numerical simulations that mimic typical experimental situations, we have identified an important constraint when characterizing such networks: indications of a small-world topology can be expected solely due to the spatial sampling of the system along with the commonly used time series analysis based approaches to network characterization.
We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures – known for their complex spatial and temporal dynamics – we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.
The network approach towards the analysis of the dynamics of complex systems has been successfully applied in a multitude of studies in the neurosciences and has yielded fascinating insights. With this approach, a complex system is considered to be composed of different constituents which interact with each other. Interaction structures can be compactly represented in interaction networks. In this contribution, we present a brief overview about how interaction networks are derived from multivariate time series, about basic network characteristics, and about challenges associated with this analysis approach.
Zusammenfassung: In der Orthopädie zählt der therapeutische Ultraschall als Mittel zur Prävention und Therapiebegleitung. Er hat mechanische, thermische und physiko-chemische Auswirkungen auf den menschlichen Körper. Um mehr Erkenntnisse über die thermischen Auswirkungen zu erlangen, wurden Versuche an einem Hydrogel-Phantom und an Probanden durchgeführt. Dabei entstand eine signifikante Erwärmung des Gewebes, welche beim Probandenversuch an der Oberfläche und beim Hydrogelversuch in der Tiefe gemessen wurde.
Summary: In orthopaedics, therapeutic ultrasound is a tool of prevention and therapy support. It has mechanical, thermal and physico-chemical effects on the human body. Tests with a hydrogel phantom and with human probands have been performed in order to obtain more knowledge about their thermal effects. Both tests measured temperature increases in cell tissue, on the surface with the human proband test and in depth with the hydrogel phantom test.
Learning- and memory-related processes are thought to result from dynamic interactions in large-scale brain networks that include lateral and mesial structures of the temporal lobes. We investigate the impact of incidental and intentional learning of verbal episodic material on functional brain networks that we derive from scalp-EEG recorded continuously from 33 subjects during a neuropsychological test schedule. Analyzing the networks' global statistical properties we observe that intentional but not incidental learning leads to a significantly increased clustering coefficient, and the average shortest path length remains unaffected. Moreover, network modifications correlate with subsequent recall performance: the more pronounced the modifications of the clustering coefficient, the higher the recall performance. Our findings provide novel insights into the relationship between topological aspects of functional brain networks and higher cognitive functions.
Network theory provides novel concepts that promise an improved characterization of interacting dynamical systems. Within this framework, evolving networks can be considered as being composed of nodes, representing systems, and of time-varying edges, representing interactions between these systems. This approach is highly attractive to further our understanding of the physiological and pathophysiological dynamics in human brain networks. Indeed, there is growing evidence that the epileptic process can be regarded as a large-scale network phenomenon. We here review methodologies for inferring networks from empirical time series and for a characterization of these evolving networks. We summarize recent findings derived from studies that investigate human epileptic brain networks evolving on timescales ranging from few seconds to weeks. We point to possible pitfalls and open issues, and discuss future perspectives.
Data-driven prediction and prevention of extreme events in a spatially extended excitable system
(2015)
The stimulation and dominance of potentially harmful phytoplankton taxa at a given locale and time are determined by local environmental conditions as well as by transport to or from neighboring regions. The present study investigated the occurrence of common harmful algal bloom (HAB) taxa within the Southern California Bight, using cross-correlation functions to determine potential dependencies between HAB taxa and environmental factors, and potential links to algal transport via local hydrography and currents. A simulation study, in which Lagrangian particles were released, was used to assess travel times due to advection by prevailing ocean currents in the bight. Our results indicate that transport of some taxa may be an important mechanism for the expansion of their distributions into other regions, which was supported by mean travel times derived from our simulation study and other literature on ocean currents in the Southern California Bight. In other cases, however, phytoplankton dynamics were rather linked to local environmental conditions, including coastal upwelling events. Overall, our study shows that complex current patterns in the Southern California Bight may contribute significantly to the formation and expansion of HABs in addition to local environmental factors determining the spatiotemporal dynamics of phytoplankton blooms.
We investigate the suitability of selected measures of complexity based on recurrence quantification analysis and recurrence networks for an identification of pre-seizure states in multi-day, multi-channel, invasive electroencephalographic recordings from five epilepsy patients. We employ several statistical techniques to avoid spurious findings due to various influencing factors and due to multiple comparisons and observe precursory structures in three patients. Our findings indicate a high congruence among measures in identifying seizure precursors and emphasize the current notion of seizure generation in large-scale epileptic networks. A final judgment of the suitability for field studies, however, requires evaluation on a larger database.
Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.
Prosthetic textile implants of different shapes, sizes and polymers are used to correct the apical prolapse after hysterectomy (removal of the uterus). The selection of the implant before or during minimally invasive surgery depends on the patient’s anatomical defect, intended function after reconstruction and most importantly the surgeon’s preference. Weakness or damage of the supporting tissues during childbirth, menopause or previous pelvic surgeries may put females in higher risk of prolapse. Numerical simulations of reconstructed pelvic floor with weakened tissues and organ supported by textile product models: DynaMesh®-PRS soft, DynaMesh®-PRP soft and DynaMesh®-CESA from FEG Textiletechnik mbH, Germany are compared.