Imported from Grants Tool 4699240

Background and Rationale: Walking is the most common and necessary form of movement for humans, as it ensures active participation in activities of daily life. In the initial stages of learning to walk, gait is rather unstable as well as variable. During this initial phase, children need to successfully perform the entire gait cycle that involves touch-down (characteristic heel-strike where the ankle is flexed), lift-off and swing phases. The characteristic heel-strike is critical to walking both effectively (stable) and efficiently (energy). Children that suffer from neuro-developmental disorders (e.g. cerebral palsy, CP) are often not able to heel-strike, they tend to keep walking with a forefoot or flatfoot pattern (i.e. toe-walking). Children that toe-walk often show poorer levels of static and dynamic stability, leading to a lower quality of life compared to typically developing children (TD). Current research suggests multifactorial adaptations in central and/or peripheral nervous as well as the musculoskeletal system contribute to and result from toe-walking. Current treatment mainly focuses on physically restoring the capability to heel-strike, however, adherence to walking with heel-strike is poor. From clinical experience, we hypothesize psychological factors (primarily fear-of-falling) as well as inadequate reflex control might contribute to toe-walking behavior. Currently, the interplay between the nervous-, musculoskeletal-, and psychological systems and their impact on resulting walking patterns are poorly understood. In order to sustain effective gait by means of effective interventions, it is therefore critical to understand the interplay among the mechanisms that underpin toe-walking adaptation. Overall Objectives & Specific Aims: The purpose of this study is to explore the interplay among nervous-, musculoskeletal-, and psychological systems and how they impact toe-walking behavior, and vice versa. Here, we will determine the effect of psychological factors (via the use of a custom-designed virtual reality environment) on static vs. dynamic stability, motor control and coordination (indirect assessment of central nervous system function), as well as reflex control (Hoffmann-reflex, H-reflex, performance of peripheral nervous system). In addition, we will also investigate the effect of restoring heel striking in toe-walkers based on the indices as mentioned above. Expected Results: It is expected that toe-walkers will show poorer stability during standing and walking, have a reduced H-reflex amplitude, reduced number of muscle synergies as well as increased fear-of-falling compared to TD. With the use of a custom-made virtual reality (VR) environment, the fear-of-falling in children will be increased. VR induced fear-of-falling will lead to poorer stability during standing and walking tasks in TD; in toe-walkers such reactions are present already without VR but worsen during VR conditions. By restoring (via the use of orthoses) heel-strike in toe-walkers stability during standing and walking tasks will be improved, number of muscle synergies will be increased, fear-of-falling will be reduced, and performance on VR induced fear-of-falling will be improved. Impact: Although development of heel-strike behavior takes place early in life, not all children demonstrate this feature during walking in daily life. Lack of heel-strike behavior is less efficient and leads to poorer quality of life. Management strategies to restore this critical feature of walking, have failed primarily due to the fact that although the capability might be restored, the adherence to walking with heel-strike is poor. By focusing on understanding the interplay between nervous-, musculoskeletal-, and psychological factors that might predispose individuals to toe-walking, we will provide solutions to design effective treatment strategies in the future.

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For a small fraction of all therapeutic drugs currently used, routine monitoring is crucial. The reason for this is that the gap between therapeutic and toxic concentration is very narrow. This, combined with the fact that there exists high inter-individual variability, has led to the need for therapeutic drug monitoring (TDM). The goal of TDM is to individualize the dosage to achieve maximum efficacy and at the same time minimize drug toxicity. TDM has obvious clinical benefits for patients and healthcare systems. However, TDM in children is particularly challenging. In addition, traditionally used venipunctures to determine drug concentrations are not well tolerated by children. The goal of this project is to address these challenges by providing a non-invasive and patient-specific solution, whereby drugs requiring TDM in children will be monitored in exhaled breath. We will use a cutting-edge analytical platform (i.e. secondary electrospray ionization-mass spectrometry) available at the University Children's Hospital Basel to detect drugs in breath with unparalleled speed, selectivity and sensitivity. Initially, we will measure simultaneously blood and breath concentrations of drugs routinely monitored in our hospital (e.g. anti-convulsants). We will then use this information as an input to develop pharmacokinetic computational models to predict blood concentrations based on the breath test read-out. During the final phase, we will validate these models in an independent group of patients to proof the clinical transferability of breath-based tests to guide drug dosage on an individual basis. This project will have a tremendous impact on current pediatric TDM clinical practice by: i) enabling a more personalized treatment, hence reducing ineffective doses and adverse effects; ii) improving patients' outcome; iii) saving hospital costs and iv) gaining new insights on pharmacokinetic aspects such as key parameters governing the diffusion of drugs in the lungs.

In collaboration with Idorsia, we propose a project for idiopathic pulmonary fibrosis segmentation. Based on an image data set (see Memo0001), where each image is assigned with a progression stage label (0-5), the relevant regions which are responsible for the specific fibrosis stage should be highlighted. Special attention should be given to collagen accumulations in non-dense tissue which might serve as a biomarker for early fibroris detection.

Exposure of children to environmental tobacco smoke (ETS) can lead to serious health consequences, impairing lung development, and increasing the risk for respiratory disease in adulthood. While there is strong evidence confirming detrimental effects of ETS from clinical observational studies, much remains unknown at the molecular level which could improve our understanding of the mechanisms by which ETS affects respiratory health. ETS has been associated with alterations in cell signaling, ultimately causing impaired cellular growth in lung tissue. The objective of this project is to identify exhaled markers altered as a result of ETS exposure, thus gaining insights on the detrimental effects of ETS. We will measure cotinine levels using standard analytical methods to objectively assess the level of exposure on an individual basis. We will seek associations between systemic cotinine concentrations and exhaled metabolite levels. Obtaining evidence of the detrimental effect of ETS exposure in the respiratory system as assessed by exhaled metabolites will provide a valuable input to public health policymakers.

Wir wissen aus mehreren Studien, einschließlich der BILD-Studie, dass die Luftverschmutzung im frühen Kindesalter Auswirkungen auf die kindliche Entwicklung der Lunge hat. Untersuchungen in Ländern mit hoher Luftverschmutzung haben gezeigt, dass die Exposition mit bestimmten Luftschadstoffen zu einer Beeinträchtigung des Lungenwachstums und der Entwicklung von Asthma führen kann. Selbst eine geringe Luftverschmutzung während der Schwangerschaft kann Auswirkungen auf die Lungenfunktion eines Säuglings kurz nach der Geburt haben. Wir wollen untersuchen ob in dieser frühen Phase der Lungenentwicklung kurz vor und nach der Geburt das Lungengewebe durch Umweltreize geschädigt oder das Immunsystem beeinflusst wird. Auch wenn noch nicht ganz klar ist, wie dies geschieht, glauben wir, dass mehrere Faktoren dazu beitragen. Wir untersuchen beispielsweise die Rolle der erblichen Veranlagung, des Geburtsprozesses, der Ernährung, früher Infektionen und der Interaktion von Genen und Umweltschadstoffen. Jeder einzelne dieser Faktoren hat eine relativ geringe Auswirkung, aber zusammen können sie potentiell bestimmen, ob ein Kind Lungenkrankheiten oder Asthma entwickeln wird. In dieser aktuellen Phase unserer Studie interessiert uns vor allem, wie diese Umweltfaktoren das Wachstum, die Alterung, aber möglicherweise auch das vorzeitige Absterben von Lungenzellen beeinflussen. Dies könnte eine wertvolle zusätzliche Information sein, um zu verstehen, wie Umwelteinflüsse Wachstum und Entwicklung der Lunge beeinflussen.

As written in the coverletter, the proposed work is an extension of the recently granted CTI project 27395.1 on brain shift correction for Neurosurgical interventions. The aim of the CTI project is to develop techniques to determine the brain shift and then to overlay the tumour and other critical structures onto the surgical microscope's image. This, however, implies that segmentation of the tumour, brain surface, vascular tree and the critical structures are available. Segmenting these often requires substantial manual input for training which is a tedious and time consuming task. With the research described herein (partially funded by a Novartis FreeNovation Project) we try to close this gap and go one step beyond. In particular we propose an approach able to learn on its own how to segment a pathology only on weakly labelled data. In other words our approach is capable to learn how to segment pathologies from a training set of images with the pathology and a second set of images without the pathology (i.e. healthy subjects). Such data sets are easy to get in contrast to the manually labelled data sets required for the state-of-the-art approaches. The proposed approaches is the first of its kind and inspired by CycleGAN (a DeepLearning domain transfer approach) [1]. Our approach can model pathologies in medical data trained only with data labelled on the image level (i.e. healthy vs. diseased). Not only can the model create pixelwise semantic segmentations of the pathologies it can also create inpaintings (i.e. heal) to render the pathological image healthy. As a side effect, we can also create new unseen pathological samples useful for example in training of medical personnel. In a proof-of-principle study we could recently show that the idea has great potential and might even be a disruptive technology in image segmentation. The significance of the proposed project is very high as it might render manual segmentation unnecessary in the near future. Training the algorithm to recognise and segment new pathologies would be simple and fast. Imaging CROs could evaluate their drug studies more cost effective and also faster speeding up the development cycle of new drugs. In this research proposal we will first give an overview on the principles and limitations of current in segmentation concepts, followed by an overview of our own research directions in this field and the detailed research plan. After the project plan and risk analysis the significance of the planned work is shown. Lastly, the budget for the planned research is detailed.

With the project "Transparent Moving Bodies on the Agora!", the researchers of the Center for medical Image Analysis & Navigation CIAN want to discuss their newest research outcome with the public by way of an interactive exhibition and workshop series at Pharmazie-Historisches Museum Basel. It will be designed and organized in cooperation with media artists and designers from the Institute of Experimental Design and Media Cultures (IXDM) of FHNW. The exhibition and workshop series is designed to trigger dialogue and discussion with two target groups: youngsters and young adults.Central topic of the exhibition is the emergence of motion as a new paradigm in medical imaging. The challenge of maximizing the precision of minimal- and non-invasive treatment is inextricably linked to the highest possible accuracy of medical imaging. Since organs are moving in the respiratory cycle, motion estimation in real time is crucial to this accuracy. The individual living body has thus to be made transparent, in order to optimize the results of medical treatment. Therefore, researchers at CIAN are investigating new tools for image registration and segmentation like CT, OCT, MRI, SPECT, ultrasound and scintigraphy data. With these tools the living and moving body can be seen and experienced in real time - Transparent Moving Bodies. The planned exhibition shows some of the most immersive researchs tools and discusses the current development with our young audiences.

Pneumonia is a severe infectious disease and a leading cause of hospitalization and death. It exacts an enormous cost in economic and human terms. Timely diagnosis is crucial for the outcome of therapy and health care associated costs. However, the high morbidity and mortality of pneumonia is partly due to the lack of efficient diagnostics. Animals with an excellent sense of smell (e.g. dogs) are capable of identifying bacterial infections by sniffing out sputum samples or even the air surrounding the infected patient. We thus hypothesize that pneumonia caused by bacterial agents can be identified by analyzing characteristic volatile metabolites produced during infection in the lung, which are eventually exhaled in breath. The goal of this project is to rapidly (within 15 min) diagnose bacterial pneumonia using a breath test. Additionally, the test should be capable to identify a subset of responsible pathogens. We will deploy an innovative mass spectrometric breath analysis technique (secondary electrospray ionization-mass spectrometry; SESI-MS) in a clinical setting. SESI-MS combines real-time response and unparalleled sensitivity (parts-per-trillion) and selectivity (~300 compounds per breath sample). We hypothesize that such a comprehensive metabolic fingerprint will provide an accurate pathogen-specific signature. We will combine cutting-edge analytical techniques, bioinformatics, mice models and access to well characterized patients in a multidisciplinary approach to elucidate and quantify exhaled metabolites that are indicators of pneumonia. This will significantly improve the current clinical and epidemiological situation by: i) enabling pathogen-based antibiotic treatment (thus reducing antibiotic resistance); ii) improving patients' outcome; iii) saving costs by supporting evidence-based hospitalization/outpatient decisions and iv) identifying altered metabolic routes, thus gaining insights on the mechanisms by which pathogens circumvent the human immune system.