| 1. |
- Nicolas, Aude, et al.
(author)
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Genome-wide Analyses Identify KIF5A as a Novel ALS Gene
- 2018
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In: Neuron. - : Cell Press. - 0896-6273 .- 1097-4199. ; 97:6, s. 1268-1283.e6
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Journal article (peer-reviewed)abstract
- To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.
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| 2. |
- Mattsson, Viktor, et al.
(author)
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MAS : Standalone Microwave Resonator to Assess Muscle Quality
- 2021
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In: Sensors. - : MDPI. - 1424-8220. ; 21:16
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Journal article (peer-reviewed)abstract
- Microwave-based sensing for tissue analysis is recently gaining interest due to advantages such as non-ionizing radiation and non-invasiveness. We have developed a set of transmission sensors for microwave-based real-time sensing to quantify muscle mass and quality. In connection, we verified the sensors by 3D simulations, tested them in a laboratory on a homogeneous three-layer tissue model, and collected pilot clinical data in 20 patients and 25 healthy volunteers. This report focuses on initial sensor designs for the Muscle Analyzer System (MAS), their simulation, laboratory trials and clinical trials followed by developing three new sensors and their performance comparison. In the clinical studies, correlation studies were done to compare MAS performance with other clinical standards, specifically the skeletal muscle index, for muscle mass quantification. The results showed limited signal penetration depth for the Split Ring Resonator (SRR) sensor. New sensors were designed incorporating Substrate Integrated Waveguides (SIW) and a bandstop filter to overcome this problem. The sensors were validated through 3D simulations in which they showed increased penetration depth through tissue when compared to the SRR. The second-generation sensors offer higher penetration depth which will improve clinical data collection and validation. The bandstop filter is fabricated and studied in a group of volunteers, showing more reliable data that warrants further continuation of this development.
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| 3. |
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| 4. |
- Mattsson, Viktor, et al.
(author)
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Muscle Analyzer System : Exploring Correlation Between Novel Microwave Resonator and Ultrasound-based Tissue Information in the Thigh
- 2022
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In: 2022 16TH EUROPEAN CONFERENCE ON ANTENNAS AND PROPAGATION (EUCAP). - : Institute of Electrical and Electronics Engineers (IEEE). - 9788831299046
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Conference paper (peer-reviewed)abstract
- A microwave sensor to safely measure quality of muscle tissue for diagnosis and screening of diseases and medical conditions characterized by fat infiltration in muscle is presented. Fat infiltration in muscle may be seen by a lower dielectric constant of muscle at microwave frequencies corresponding to the large contrast between fat and muscle tissues. A planar resonator based on a bandstop filter and optimized to noninvasively interrogate muscle in the thigh on tissue quality is proposed. Currently, a study based on clinical trials is carried out, and, here, we present a preliminary correlation between skin and fat thicknesses and rectus femoris cross sectional area (CSA) measured with ultrasound and the proposed sensor's resonance frequency. CST simulations based on the ultrasound information guide the analysis. We see that although there are signs of a potential correlation between CSA and resonance, skin and fat variability is still an issue to overcome.
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| 5. |
- Mattsson, Viktor, et al.
(author)
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Machine Learning Powered Microwave Device for Local Body Composition Assessment
- 2023
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In: IEEE Sensors Journal. - 1530-437X .- 1558-1748. ; , s. 1-1
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Journal article (peer-reviewed)abstract
- In this paper a standalone microwave device is evaluated for its ability to assess local body composition with the ultimate goal to assess muscle quality. Data have been collected from volunteers who were measured on their thigh using the microwave device and ultrasound. A machine learning algorithm with three stages is designed that utilizes the stacked nature of the tissues in the thigh to predict skin and fat thickness and the cross-sectional area of the rectus femoris muscle. The input to the algorithm is the signal response from the microwave sensor and also the prediction from the previous layers. The ultrasound measurements are used as the ground truth labels for each tissue to train the machine learning models. The measurements were performed with two sensors, where usage of the combined data from both sensors produced the best results for fat and muscle, 0.57 and 0.63 in R 2 score, respectively. In the drop analysis, a step where a select proportion of the data is temporarily removed, the identified models showed increased scores with a larger amount of data available indicating the learning of the models improves with more data. Although the results are encouraging more data is ultimately needed to further study the algorithm.
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