| 1. |
- Donadio, Vincenzo, et al.
(författare)
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Phosphorylated α-synuclein in skin Schwann cells : a new biomarker for multiple system atrophy
- 2023
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Ingår i: Brain. - : Oxford University Press. - 0006-8950 .- 1460-2156. ; 146:3, s. 1065-1074
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Tidskriftsartikel (refereegranskat)abstract
- Multiple system atrophy (MSA) is characterized by accumulation of phosphorylated α-synuclein (p-syn) as glial cytoplasmic inclusions in the brain and a specific biomarker for this disorder is urgently needed. We aimed at investigating if p-syn can also be detected in skin Remak non-myelinating Schwann cells (RSCs) as Schwann cell cytoplasmic inclusions (SCCi) and may represent a reliable clinical biomarker for MSA.This cross-sectional diagnostic study evaluated skin p-syn in 96 patients: 46 with probable MSA (29 with parkinsonism type MSA and 17 with cerebellar type MSA), 34 with Parkinson's disease (PD) and 16 with dementia with Lewy bodies (DLB). We also included 50 healthy control subjects. Patients were recruited from five different medical centres. P-syn aggregates in skin sections were stained by immunofluorescence, followed by analyses with confocal microscopy and immuno-electron microscopy. All analyses were performed in a blinded fashion.Overall, p-syn aggregates were found in 78% of MSA patients and 100% of patients with PD/DLB, whereas they could not be detected in controls. As for neuronal aggregates 78% of MSA patients were positive for p-syn in somatic neurons, whereas all PD/DLB patients were positive in autonomic neurons. When analysing the presence of p-syn in RSCs, 74% of MSA patients were positive, whereas no such SCCi could be observed in PD/DLB patients. Analyses by immuno-electron microscopy confirmed that SCCi were only found in cases with MSA and thus absent in those with PD/DLB.In conclusion, our findings demonstrate that (i) fibrillar p-syn in RSCs is a pathological hallmark of MSA and may be used as a specific and sensitive disease biomarker; (ii) in Lewy body synucleinopathies (PD/DLB) only neurons contain p-syn deposits; and (iii) the cell-specific deposition of p-syn in the skin thus mirrors that of the brain in many aspects and suggests that non-myelinated glial cells are also involved in the MSA pathogenesis.
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| 2. |
- Kristiansen, Ivar, et al.
(författare)
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Short-Term Cost and Health Consequences of Duodenal Levodopa Infusion in Advanced Parkinson's Disease in Sweden : An Exploratory Study
- 2009
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Ingår i: Applied Health Economics and Health Policy. - 1175-5652 .- 1179-1896. ; 7:3, s. 167-180
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Tidskriftsartikel (refereegranskat)abstract
- Levodopa is the cornerstone treatment for Parkinson's disease, but the short half-life of levodopa limits its usefulness in late stages of the disease. Duodenal levodopa infusion (DLI) allows more stable plasma levels and better motor symptom control. To explore the costs and health benefits of replacing conventional oral polypharmacy with DLI in patients with advanced Parkinson's disease, from a Swedish healthcare payer perspective. Based on a clinical, randomized, crossover study with 24 patients (DIREQT), a decision analytic model predicted 2-year drug costs and QALYs for conventional oral therapy and for DLI. Health-related quality of life (HR-QOL) was recorded using a 15-dimensional (15D) utility instrument at baseline and during the two 3-week trial periods, and then at eight follow-up visits during the subsequent 6 months. Use of medication was based on data from DIREQT and previous studies. Unit costs were based on market prices (drugs) and customary charges in Sweden. All costs were expressed in Swedish kronor (SEK), year 2004 values (&U20AC;1.00 approximately SEK9.17, $US1.00 = SEK7.47). Future costs and outcomes were discounted at 3%. One-way and probabilistic sensitivity analyses were conducted. The mean utility scores were 0.77 for DLI and 0.72 for conventional therapy (p = 0.02). A considerable variation in the scores was observed during the study. The expected per-patient 2-year cost of DLI was SEK562 000 while it was SEK172 000 for conventional therapy. The mean number of QALYs was 1.48 and 1.42, respectively, representing an incremental cost of SEK6.1 million per QALY for DLI (all values discounted at 3%). Using other assumptions in sensitivity analyses, the cost per QALY could be as low as SEK456 000. This analysis can be considered exploratory only; it is based on very limited data. Nevertheless, our findings suggest that DLI results in a significant improvement in HR-QOL. However, the cost per QALY is likely to be higher than customary cost-effectiveness thresholds. Whether these benefits justify the additional costs depends on how the health benefits are measured and how these benefits are valued by society.
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| 3. |
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| 4. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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A multiple motion sensors index for motor state quantification in Parkinson's disease
- 2020
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Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier BV. - 0169-2607 .- 1872-7565. ; 189
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Tidskriftsartikel (refereegranskat)abstract
- Aim: To construct a Treatment Response Index from Multiple Sensors (TRIMS) for quantification of motor state in patients with Parkinson's disease (PD) during a single levodopa dose. Another aim was to compare TRIMS to sensor indexes derived from individual motor tasks. Method: Nineteen PD patients performed three motor tests including leg agility, pronation-supination movement of hands, and walking in a clinic while wearing inertial measurement unit sensors on their wrists and ankles. They performed the tests repeatedly before and after taking 150% of their individual oral levodopa-carbidopa equivalent morning dose.Three neurologists blinded to treatment status, viewed patients’ videos and rated their motor symptoms, dyskinesia, overall motor state based on selected items of Unified PD Rating Scale (UPDRS) part III, Dyskinesia scale, and Treatment Response Scale (TRS). To build TRIMS, out of initially 178 extracted features from upper- and lower-limbs data, 39 features were selected by stepwise regression method and were used as input to support vector machines to be mapped to mean reference TRS scores using 10-fold cross-validation method. Test-retest reliability, responsiveness to medication, and correlation to TRS as well as other UPDRS items were evaluated for TRIMS. Results: The correlation of TRIMS with TRS was 0.93. TRIMS had good test-retest reliability (ICC = 0.83). Responsiveness of the TRIMS to medication was good compared to TRS indicating its power in capturing the treatment effects. TRIMS was highly correlated to dyskinesia (R = 0.85), bradykinesia (R = 0.84) and gait (R = 0.79) UPDRS items. Correlation of sensor index from the upper-limb to TRS was 0.89. Conclusion: Using the fusion of upper- and lower-limbs sensor data to construct TRIMS provided accurate PD motor states estimation and responsive to treatment. In addition, quantification of upper-limb sensor data during walking test provided strong results. © 2019
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| 5. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms
- 2018
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Title: Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptomsObjective: To assess the feasibility of measuring Parkinson’s disease (PD) motor symptoms with a multi-sensor data fusion method. More specifically, the aim is to assess validity, reliability and sensitivity to treatment of the methods.Background: Data from 19 advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were collected in a single center, open label, single dose clinical trial in Sweden [1].Methods: The patients performed leg agility and 2-5 meter straight walking tests while wearing motion sensors on their limbs. They performed the tests at baseline, at the time they received the morning dose, and at pre-specified time points until the medication wore off. While performing the tests the patients were video recorded. The videos were observed by three movement disorder specialists who rated the symptoms using a treatment response scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). The sensor data consisted of lower limb data during leg agility, upper limb data during walking, and lower limb data during walking. Time series analysis was performed on the raw sensor data extracted from 17 patients to derive a set of quantitative measures, which were then used during machine learning to be mapped to mean ratings of the three raters on the TRS scale. Combinations of data were tested during the machine learning procedure.Results: Using data from both tests, the Support Vector Machines (SVM) could predict the motor states of the patients on the TRS scale with a good agreement in relation to the mean ratings of the three raters (correlation coefficient = 0.92, root mean square error = 0.42, p<0.001). Additionally, there was good test-retest reliability of the SVM scores during baseline and second tests with intraclass-correlation coefficient of 0.84. Sensitivity to treatment for SVM was good (Figure 1), indicating its ability to detect changes in motor symptoms. The upper limb data during walking was more informative than lower limb data during walking since SVMs had higher correlation coefficient to mean ratings. Conclusions: The methodology demonstrates good validity, reliability, and sensitivity to treatment. This indicates that it could be useful for individualized optimization of treatments among PD patients, leading to an improvement in health-related quality of life.
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| 6. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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Motion sensor-based assessment of Parkinson's disease motor symptoms during leg agility tests : results from levodopa challenge
- 2020
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Ingår i: IEEE journal of biomedical and health informatics. - : IEEE Computer Society. - 2168-2194 .- 2168-2208. ; 24:1, s. 111-118
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Tidskriftsartikel (refereegranskat)abstract
- Parkinson's disease (PD) is a degenerative, progressive disorder of the central nervous system that mainly affects motor control. The aim of this study was to develop data-driven methods and test their clinimetric properties to detect and quantify PD motor states using motion sensor data from leg agility tests. Nineteen PD patients were recruited in a levodopa single dose challenge study. PD patients performed leg agility tasks while wearing motion sensors on their lower extremities. Clinical evaluation of video recordings was performed by three movement disorder specialists who used four items from the motor section of the Unified PD Rating Scale (UPDRS), the treatment response scale (TRS) and a dyskinesia score. Using the sensor data, spatiotemporal features were calculated and relevant features were selected by feature selection. Machine learning methods like support vector machines (SVM), decision trees and linear regression, using 10-fold cross validation were trained to predict motor states of the patients. SVM showed the best convergence validity with correlation coefficients of 0.81 to TRS, 0.83 to UPDRS #31 (body bradykinesia and hypokinesia), 0.78 to SUMUPDRS (the sum of the UPDRS items: #26-leg agility, #27-arising from chair and #29-gait), and 0.67 to dyskinesia. Additionally, the SVM-based scores had similar test-retest reliability in relation to clinical ratings. The SVM-based scores were less responsive to treatment effects than the clinical scores, particularly with regards to dyskinesia. In conclusion, the results from this study indicate that using motion sensors during leg agility tests may lead to valid and reliable objective measures of PD motor symptoms.
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| 7. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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Objective assessment of Parkinson’s disease motor symptoms during leg agility test using motion sensors
- 2018
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Konferensbidrag (refereegranskat)abstract
- Title: Objective assessment of Parkinson’s disease motor symptoms during leg agility test using motion sensorsObjective: To develop and evaluate machine learning methods for assessment of Parkinson’s disease (PD) motor symptoms using leg agility (LA) data collected with motion sensors during a single dose experiment.Background: Nineteen advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were recruited in a single center, open label, single dose clinical trial in Sweden [1].Methods: The patients performed up to 15 LA tasks while wearing motions sensors on their foot ankle. They performed tests at pre-defined time points starting from baseline, at the time they received a morning dose (150% of their levodopa equivalent morning dose), and at follow-up time points until the medication wore off. The patients were video recorded while performing the motor tasks. and three movement disorder experts rated the observed motor symptoms using 4 items from the Unified PD Rating Scale (UPDRS) motor section including UPDRS #26 (leg agility), UPDRS #27 (Arising from chair), UPDRS #29 (Gait), UPDRS #31 (Body Bradykinesia and Hypokinesia), and dyskinesia scale. In addition, they rated the overall mobility of the patients using Treatment Response Scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). Sensors data were processed and their quantitative measures were used to develop machine learning methods, which mapped them to the mean ratings of the three raters. The quality of measurements of the machine learning methods was assessed by convergence validity, test-retest reliability and sensitivity to treatment.Results: Results from the 10-fold cross validation showed good convergent validity of the machine learning methods (Support Vector Machines, SVM) with correlation coefficients of 0.81 for TRS, 0.78 for UPDRS #26, 0.69 for UPDRS #27, 0.78 for UPDRS #29, 0.83 for UPDRS #31, and 0.67 for dyskinesia scale (P<0.001). There were good correlations between scores produced by the methods during the first (baseline) and second tests with coefficients ranging from 0.58 to 0.96, indicating good test-retest reliability. The machine learning methods had lower sensitivity than mean clinical ratings (Figure. 1).Conclusions: The presented methodology was able to assess motor symptoms in PD well, comparable to movement disorder experts. The leg agility test did not reflect treatment related changes.
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| 8. |
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| 9. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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Treatment response index from a multi-modal sensor fusion platform for assessment of motor states in Parkinson's disease
- 2019
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The aim of this paper is to develop and evaluate a multi-sensor data fusion platform for quantifying Parkinson’s disease (PD) motor states. More specifically, the aim is to evaluate the clinimetric properties (validity, reliability, and responsiveness to treatment) of the method, using data from motion sensors during lower- and upper-limb tests.Methods: Nineteen PD patients and 22 healthy controls were recruited in a single center study. Subjects performed standardized motor tasks of Unified PD Rating Scale (UPDRS), including leg agility, hand rotation, and walking after wearing motion sensors on ankles and wrists. PD patients received a single levodopa dose before and at follow-up time points after the dose administration. Patients were video recorded and their motor symptoms were rated by three movement disorder experts. Experts rated each and every test occasions based on the six items of UPDRS-III (motor section), the treatment response scale (TRS) and the dyskinesia score. Spatiotemporal features were extracted from the sensor data. Features from lower limbs and upper limbs were fused. Feature selection methods of stepwise regression (SR), Lasso regression and principle component analysis (PCA) were used to select the most important features. Different machine learning methods of linear regression (LR), decision trees, and support vector machines were examined and their clinimetric properties were assessed.Results: Treatment response index from multimodal motion sensors (TRIMMS) scores obtained from the most valid method of LR when using data from all tests. Features were selected by SR, and this method resulted in r=0.95 to TRS. The test-retest reliability of TRIMMS was good with intra-class correlation coefficient of 0.82. Responsiveness of the TRIMMS to levodopa treatment was similar to the responsiveness of TRS.Conclusions: The results from this study indicate that fusing motion sensors data gathered during standardized motor tasks leads to valid, reliable and sensitive objective measurements of PD motor symptoms. These measurements could be further utilized in studies for individualized optimization of treatments in PD.
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| 10. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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Verification of a Method for Measuring Parkinson's Disease Related Temporal Irregularity in Spiral Drawings
- 2017
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Ingår i: Sensors. - Basel : MDPI AG. - 1424-8220. ; 17:10
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Tidskriftsartikel (refereegranskat)abstract
- -value = 0.02). Test-retest reliability of TIS was good with Intra-class Correlation Coefficient of 0.81. When assessing changes in relation to treatment, TIS contained some information to capture changes from Off to On and wearing off effects. However, the correlations between TIS and clinical scores (UPDRS and Dyskinesia) were weak. TIS was able to differentiate spiral drawings drawn by patients in an advanced stage from those drawn by healthy subjects, and TIS had good test-retest reliability. TIS was somewhat responsive to single-dose levodopa treatment. Since TIS is an upper limb high-frequency-based measure, it cannot be detected during clinical assessment.
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