| 1. |
- Bäck, Henry, 1947, et al.
(författare)
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Metropolitan and Political Change in Sweden
- 2013
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Ingår i: The political ecology of the metropolis / edited by Jefferey M. Sellers, Daniel Kübler, Melanie Walter-Rogg, and R. Alan Walks. - Colchester : ECPR Press. - 9781907301445 ; , s. 299-323
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 2. |
- Kübler, André, et al.
(författare)
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Mycobacterium tuberculosis dysregulates MMP/TIMP balance to drive rapid cavitation and unrestrained bacterial proliferation.
- 2015
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Ingår i: Journal of Pathology. - : Wiley. - 0022-3417 .- 1096-9896. ; 235:3, s. 431-444
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Tidskriftsartikel (refereegranskat)abstract
- Active tuberculosis (TB) often presents with advanced pulmonary disease, including irreversible lung damage and cavities. Cavitary pathology contributes to antibiotic failure, transmission, morbidity and mortality. Matrix metalloproteinases (MMPs), in particular MMP-1 are implicated in TB pathogenesis. We explored the mechanisms relating MMP/TIMP imbalance to cavity formation in a modified rabbit model of cavitary TB. Our model results in consistent progression of consolidation to human-like cavities (100% by day 28) with resultant bacillary burdens (>10(7) CFU/g) far greater than those found in matched granulomatous tissue (10(5) CFU/g). Using a novel, breath-hold computerized tomography scanning and image analysis protocol. We show that cavities develop rapidly from areas of densely consolidated tissue. Radiological change correlated with a decrease in functional lung tissue as estimated by changes in lung density during controlled pulmonary expansion (R(2) =0.6356, p < 0.0001). We demonstrated that the expression of interstitial collagenase (MMP-1) is specifically greater in cavitary compared to granulomatous lesions (p < 0.01), and that TIMP-3 significantly decreases at the cavity surface. Our findings demonstrate that an MMP-1/TIMP imbalance, is associated with the progression of consolidated regions to cavities containing very high bacterial burdens. Our model provided mechanistic insight, correlating with human disease at the pathological, microbiological and molecular levels,. It also provides a strategy to investigate therapeutics in the context of complex TB pathology. We used these findings to predict a MMP/TIMP balance in active TB; and confirmed this in human plasma, revealing the potential of MMP/TIMP levels as key components of a diagnostic matrix aimed at distinguishing active from latent TB (PPV=92.9%; 95%CI 66.1-99.8%, NPV=85.6%; 95%CI 77.0-91.9%).
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| 3. |
- Luna, Brian, et al.
(författare)
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In Vivo Prediction of Tuberculosis-Associated Cavity Formation in Rabbits
- 2015
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Ingår i: The Journal of infectious diseases. - : Oxford University Press (OUP). - 1537-6613 .- 0022-1899. ; 211:3, s. 481-485
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Tidskriftsartikel (refereegranskat)abstract
- The presence of cavitary lesions in patients with tuberculosis poses a significant clinical concern due to the risk of infectivity and the risk of antibiotic treatment failure. We describe 2 algorithms that use noninvasive positron emission tomography (PET) and computed tomography (CT) to predict the development of cavitary lesions in rabbits. Analysis of the PET region of interest predicted cavitary disease with 100% sensitivity and 76% specificity, and analysis of the CT region of interest predicted cavitary disease with 83.3% sensitivity and 76.9% specificity. Our results show that restricting our analysis to regions with high [(18)F]-fluorodeoxyglucose uptake provided the best combination of sensitivity and specificity.
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| 4. |
- Schürholz, Daniel, et al.
(författare)
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MyAQI : Context-aware Outdoor Air Pollution Monitoring System
- 2019
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Ingår i: IoT 2019. - New York, NY, USA : Association for Computing Machinery (ACM).
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Konferensbidrag (refereegranskat)abstract
- Air pollution is a growing global concern that affects the health and livelihood of millions of people worldwide. The advent of the Internet of Things (IoT) has made available a plethora of data sources that provide near real-time information on air pollution. Many studies and systems have taken advantage of data stemming from the IoT and have been dedicated to enhancing the monitoring and prediction of air quality, from a fairly analytical angle, often disregarding the user's perspective in processing and presenting this data. In this paper, we research and present a novel context-aware air quality monitoring and prediction system called My Air Quality Index (MyAQI). MyAQI takes into consideration user's context (e.g. health conditions, individual sensitivities and preferences) to tailor the visualisation and notifications. We propose a context model that is used to combine user's context with air pollution data to provide context-aware recommendations to the specific user. MyAQI also incorporates a prediction algorithm based on Long Short-Term Memory Neural Network (LSTM) to predict future air quality. MyAQI is implemented as a web-based application and has the capability to consume data from a wide range of data sources including IoT devices and open data sources (via Application Programming Interfaces (API)). We demonstrate the context-aware visualisation techniques implemented in MyAQI, which adapt to changing user's context, and validate the performance of the air quality prediction algorithm.
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