Ein anthropomorphes Phantom zur Evaluation eines chirurgischen Assistenzsystems mit intraoperativer Bildgebung

Zahlreiche chirurgische Assistenzsysteme sind in der klinischen Praxis im Einsatz, um die Genauigkeit und Sicherheit medizinischer Eingriffe zu erhöhen. Die Verwendung von Bildgebungsverfahren durch solche Systeme und die Teilautomatisierung von Prozessen kann einen weiteren Schritt in Richtung höherer Effizienz chirurgischer Interventionen und höherer Patientensicherheit darstellen. Dies stellt jedoch große Herausforderungen an die Systementwickler, welche zur Evaluation dieser Systeme während der Konstruktion geeignete Konzepte und Testmethoden benötigen. Diese Arbeit hat zwei wesentliche Zielsetzungen: Zum einen soll vorgestellt werden, wie zur zielführenden Entwicklung eines duplexsonographisch geführten, semiautomatisch arbeitenden Assistenzsystems zur Gefäßpräparation (ASTMA-System) ein anthropomorphes, physiologisches Phantom anhand zuvor definierter, für die Entwicklung relevanter, Anforderungen konstruiert wurde. Dieses ermöglichte es, die Arbeitsprozesse des Systems und deren Eignung bereits in vitro umfangreich zu testen. Zum andern soll dargestellt werden, wie das Phantom hinsichtlich dieser Anforderungen in einer Studie validiert wurde, um zu gewährleisten, dass dieses für die Systementwicklung erforderliche Eigenschaften aufwies. Dadurch konnten wichtige Informationen über Nutzen und Limitierung der Verwendung des Phantoms und mögliche Probleme des ASTMA-Systems gewonnen werden. Hiermit soll demonstriert werden, wie ein Entwicklungs- und Validierungsansatz für ein Phantom als Testsystem zur Entwicklung und Evaluation ähnlicher komplexer medizintechnischer Systeme mit intraoperativer Bildgebung gestaltet werden kann und welchen Anforderungen solche Phantome genügen sollten. Dies kann dabei helfen, die Systementwicklung zielführend und ressourceneffizient durchzuführen, Probleme bereits während früher Entwicklungsschritte aufzudecken und zu lösen und die Eignung des Verfahrens des entwickelten Systems zu beurteilen

Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults

Falls are a major cause of morbidity and mortality in neurological disorders. Technical means of detecting falls are of high interest as they enable rapid notification of caregivers and emergency services. Such approaches must reliably differentiate between normal daily activities and fall events. A promising technique might be based on the classification of movements based on accelerometer signals by machine-learning algorithms, but the generalizability of classifiers trained on laboratory data to real-world datasets is a common issue. Here, three machine-learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were trained to detect fall events. We used a dataset containing intentional falls (SisFall) to train the classifier and validated the approach on a different dataset which included real-world accidental fall events of elderly people (FARSEEING). The results suggested that the linear SVM was the most suitable classifier in this cross-dataset validation approach and reliably distinguished a fall event from normal everyday activity at an accuracy of 93% and similarly high sensitivity and specificity. Thus, classifiers based on linear SVM might be useful for automatic fall detection in real-world applications

Evaluation of Three Machine Learning Algorithms for the Automatic Classification of EMG Patterns in Gait Disorders

Gait disorders are common in neurodegenerative diseases and distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge even for the experienced clinician. Ultimately, muscle activity underlies the generation of kinematic patterns. Therefore, one possible way to address this problem may be to differentiate gait disorders by analyzing intrinsic features of muscle activations patterns. Here, we examined whether it is possible to differentiate electromyography (EMG) gait patterns of healthy subjects and patients with different gait disorders using machine learning techniques. Nineteen healthy volunteers (9 male, 10 female, age 28.2 ± 6.2 years) and 18 patients with gait disorders (10 male, 8 female, age 66.2 ± 14.7 years) resulting from different neurological diseases walked down a hallway 10 times at a convenient pace while their muscle activity was recorded via surface EMG electrodes attached to 5 muscles of each leg (10 channels in total). Gait disorders were classified as predominantly hypokinetic (n = 12) or ataxic (n = 6) gait by two experienced raters based on video recordings. Three different classification methods (Convolutional Neural Network—CNN, Support Vector Machine—SVM, K-Nearest Neighbors—KNN) were used to automatically classify EMG patterns according to the underlying gait disorder and differentiate patients and healthy participants. Using a leave-one-out approach for training and evaluating the classifiers, the automatic classification of normal and abnormal EMG patterns during gait (2 classes: “healthy” and “patient”) was possible with a high degree of accuracy using CNN (accuracy 91.9%), but not SVM (accuracy 67.6%) or KNN (accuracy 48.7%). For classification of hypokinetic vs. ataxic vs. normal gait (3 classes) best results were again obtained for CNN (accuracy 83.8%) while SVM and KNN performed worse (accuracy SVM 51.4%, KNN 32.4%). These results suggest that machine learning methods are useful for distinguishing individuals with gait disorders from healthy controls and may help classification with respect to the underlying disorder even when classifiers are trained on comparably small cohorts. In our study, CNN achieved higher accuracy than SVM and KNN and may constitute a promising method for further investigation

Wavelength-stabilized ns-pulsed 2.2 kW diode laser bar with multiple active regions and tunnel junctions

The improvement of the performance of a distributed Bragg reflector laser bar emitting near 905 nm through the use of multiple epitaxially stacked active regions and tunnel junctions is reported. The bar consisting of 48 emitters (each having an aperture of 50 µm) emits an optical power of 2.2 kW in 8 ns long pulses at an injection current of 1.1 kA. This corresponds to an almost threefold increase of the pulse power compared to a bar with lasers having only a single active region. Due to the integrated surface Bragg grating, the bar exhibits a narrow spectral bandwidth of about 0.3 nm and a thermal tuning of only 68 pm/K

Wavelength stabilized high pulse power 48 emitter laser bars for automotive light detection and ranging application

Diode lasers generating optical pulses with high peak power and lengths in the nanosecond range are key components for light detection and ranging systems, e.g. for autonomous driving and object detection. We present here an internally wavelength stabilized distributed Bragg reflector broad area laser bar with 48 emitters. The vertical structure based on AlGaAs (confinement and cladding layers) and InGaAs (active quantum well) is specifically optimized for wavelength-stabilized pulsed operation, applying a surface Bragg grating with high reflectivity. The bar is electrically driven by a new in-house developed high-speed driver based on GaN transistors providing current pulses with amplitudes of up to 1000 A and a repetition frequency of 10 kHz. The generated 4 ns to 10 ns long optical pulses are nearly rectangular shaped and reach a pulse peak power in excess of 600 Watts at 25 °C. The optical spectrum with a centre wavelength of about 900 nm has a width of 0.15 nm (FWHM) with a side mode suppression ratio > 30 dB. © 2020 IOP Publishing Ltd

Colonization patterns of soil microbial communities in the Atacama Desert

BACKGROUND: The Atacama Desert is one of the driest deserts in the world and its soil, with extremely low moisture, organic carbon content, and oxidizing conditions, is considered to be at the dry limit for life. RESULTS: Analyses of high throughput DNA sequence data revealed that bacterial communities from six geographic locations in the hyper-arid core and along a North-South moisture gradient were structurally and phylogenetically distinct (ANOVA test for observed operating taxonomic units at 97% similarity (OTU(0.03)), P <0.001) and that communities from locations in the hyper-arid zone displayed the lowest levels of diversity. We found bacterial taxa similar to those found in other arid soil communities with an abundance of Rubrobacterales, Actinomycetales, Acidimicrobiales, and a number of families from the Thermoleophilia. The extremely low abundance of Firmicutes indicated that most bacteria in the soil were in the form of vegetative cells. Integrating molecular data with climate and soil geochemistry, we found that air relative humidity (RH) and soil conductivity significantly correlated with microbial communities’ diversity metrics (least squares linear regression for observed OTU(0.03) and air RH and soil conductivity, P <0.001; UniFrac PCoA Spearman’s correlation for air RH and soil conductivity, P <0.0001), indicating that water availability and salt content are key factors in shaping the Atacama soil microbiome. Mineralization studies showed communities actively metabolizing in all soil samples, with increased rates in soils from the southern locations. CONCLUSIONS: Our results suggest that microorganisms in the driest soils of the Atacama Desert are in a state of stasis for most of the time, but can potentially metabolize if presented with liquid water for a sufficient duration. Over geological time, rare rain events and physicochemical factors potentially played a major role in selecting micro-organisms that are most adapted to extreme desiccating conditions

Axonal Degeneration of the Vagus Nerve in Parkinson's Disease—A High-Resolution Ultrasound Study

Background: Recent histopathological studies revealed degeneration of the dorsal motor nucleus early in the course of Parkinson's disease (PD). Degeneration of the vagus nerve (VN) axons following neurodegeneration of brainstem vagal nuclei should be detectable by high-resolution ultrasound (HRUS) as a thinning of the VNs.Methods: We measured both VNs cross-sectional area (VN-CSA) of 35 patients with PD and 35 age- and sex-matched healthy controls at the level of the thyroid gland using HRUS.Results: On both sides, the VN-CSA was significantly smaller in PD patients than in controls (right: 2.1 ± 0.4 vs. 2.3 ± 0.5 mm2, left 1.5 ± 0.4 vs. 1.8 ± 0.4 mm2; both p &lt; 0.05). There was no correlation between the right or left VN-CSA and age, the Hoehn &amp; Yahr stage, disease duration, the motor part of the Unified Parkinson's Disease Rating Scale score, the Montreal Cognitive Assessment score, or the Non-motor Symptoms Questionnaire, and Scale for Parkinson's disease score including its gastrointestinal domain.Conclusions: These findings provide evidencethat atrophy of the VNs in PD patients can be detected in-vivo by HRUS

Evaluating emergency physicians’ knowledge, attitudes, and experiences of FARC ex-combatants : a pilot study of Colombia’s emergency medicine teaching hospitals

Objectives: In the 2016 Peace Accord with the Fuerzas Armadas Revolucionarias de Colombia (FARC), Colombia promised to reincorporate 14,000 ex-combatants into the healthcare system. However, FARC ex-combatants have faced significant challenges in receiving healthcare, and little is known about physicians' abilities to address this population's healthcare needs. Methods: An electronic questionnaire sent to the Colombian Emergency Medicine professional society and teaching hospitals assessed physicians' knowledge, attitudes, and experiences with the FARC ex-combatant reincorporation process. Results: Among 53 participants, most were male (60.4%), and ∼25% were affected by the FARC conflict (22.6%). Overall knowledge of FARC reincorporation was low, with nearly two-thirds of participants (61.6%) scoring in the lowest category. Attitudes around ex-combatants showed low bias. Few physicians received training about reincorporation (7.5%), but 83% indicated they would like such training. Twenty-two participants (41.5%) had identified a patient as an ex-combatant in the healthcare setting. Higher knowledge scores were significantly correlated with training about reincorporation (r = 0.354, n = 53, P = 0.015), and experience identifying patients as ex-combatants (r = 0.356, n = 47, P = 0.014). Conclusion: Findings suggested high interest in training and low knowledge of the reincorporation process. Most physicians had low bias, frequent experiences with ex-combatants, and cared for these patients when they self-identify. The emergency department (ED) serves as an entrance into healthcare for this population and a potential setting for interventions to improve care delivery, especially those related to mental healthcare. Future studies could evaluate effects of care delivery following training on ex-combatant healthcare reintegration.Revista Internacional - Indexad

Evaluation of \u3csup\u3e18\u3c/sup\u3eF-IAM6067 as a sigma-1 receptor PET tracer for neurodegeneration in vivo in rodents and in human tissue

© The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions. The sigma 1 receptor (S1R) is widely expressed in the CNS and is mainly located on the endoplasmic reticulum. The S1R is involved in the regulation of many neurotransmission systems and, indirectly, in neurodegenerative diseases. The S1R may therefore represent an interesting neuronal biomarker in neurodegenerative diseases such as Parkinson\u27s (PD) or Alzheimer\u27s diseases (AD). Here we present the characterisation of the S1R-specific 18F-labelled tracer 18F-IAM6067 in two animal models and in human brain tissue. Methods: Wistar rats were used for PET-CT imaging (60 min dynamic acquisition) and metabolite analysis (1, 2, 5, 10, 20, 60 min post-injection). To verify in vivo selectivity, haloperidol, BD1047 (S1R ligand), CM398 (S2R ligand) and SB206553 (5HT2B/C antagonist) were administrated for pre-saturation studies. Excitotoxic lesions induced by intra-striatal injection of AMPA were also imaged by 18F-IAM6067 PET-CT to test the sensitivity of the methods in a well-established model of neuronal loss. Tracer brain uptake was also verified by autoradiography in rats and in a mouse model of PD (intrastriatal 6-hydroxydopamine (6-OHDA) unilateral lesion). Finally, human cortical binding was investigated by autoradiography in three groups of subjects (control subjects with Braak ≤2, and AD patients, Braak \u3e2 & ≤4 and Braak \u3e4 stages). Results: We demonstrate that despite rapid peripheral metabolism of 18F-IAM6067, radiolabelled metabolites were hardly detected in brain samples. Brain uptake of 18F-IAM6067 showed differences in S1R anatomical distribution, namely from high to low uptake: pons-raphe, thalamus medio-dorsal, substantia nigra, hypothalamus, cerebellum, cortical areas and striatum. Pre-saturation studies showed 79-90% blockade of the binding in all areas of the brain indicated above except with the 5HT2B/C antagonist SB206553 and S2R ligand CM398 which induced no significant blockade, indicating good specificity of 18F-IAM6067 for S1Rs. No difference between ipsi- and contralateral sides of the brain in the mouse model of PD was detected. AMPA lesion induced a significant 69% decrease in 18F-IAM6067 uptake in the globus pallidus matching the neuronal loss as measured by NeuN, but only a trend to decrease (-16%) in the caudate putamen despite a significant 91% decrease in neuronal count. Moreover, no difference in the human cortical binding was shown between AD groups and controls. Conclusion: This work shows that 18F-IAM6067 is a specific and selective S1R radiotracer. The absence or small changes in S1R detected here in animal models and human tissue warrants further investigations and suggests that S1R might not be the anticipated ideal biomarker for neuronal loss in neurodegenerative diseases such as AD and PD