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| 2. |
- Bergquist, Maria, et al.
(författare)
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Altered adrenal and gonadal steroids biosynthesis in patients with burn injury
- 2016
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Ingår i: Clinical Mass Spectrometry. - : Elsevier BV. - 2213-8005 .- 2376-9998. ; 1, s. 19-26
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Burn injury inevitably leads to changes in the endogenous production of cytokines, as well as adrenal and gonadal steroids. Previous studies have reported gender-related differences in outcome following burn injury, which suggests that gonadal steroids may play a role. The aim of this study was to assess alterations in concentration of endogenous steroids in patients with burn injury.Methods: For this single-center, prospective descriptive study, high-sensitivity liquid chromatography tandem mass spectrometry (LC-MS/MS)-based steroid quantification was used to determine longitudinal profiles of the concentrations of endogenous steroids in plasma from sixteen adult male patients with burn injury (14.5-72% of total body surface area). Steroids were extracted from plasma samples and analyzed using multiple reaction monitoring acquisition, with electrospray ionization on a triple quadruple mass spectrometer. Total protein concentration was measured in the samples using spectrophotometry.Results: Steroid and total protein concentration distributions were compared to reference intervals characteristic of healthy adult men. Concentrations of the following steroids in plasma of burn injured patients were found to correlate positively to the area of the burn injury: cortisol (r = 0.84), corticosterone (r = 0.73), 11-deoxycortisol (r = 0.72), androstenedione (r = 0.72), 17OH-progesterone (r = 0.68), 17OH-pregnenolone (r = 0.64) and pregnenolone (r = 0.77). Concentrations of testosterone decreased during the acute phase and were up to ten-times lower than reference values for healthy adult men, while concentrations of estrone were elevated. By day 21 after injury, testosterone concentrations were increased in younger, but not older, patients. The highest concentrations of estrone were observed on day 3 after the injury and then declined by day 21 to concentrations comparable to those observed on the day of the injury.Conclusion: Burn injury alters endogenous steroid biosynthesis, with decreased testosterone concentrations and elevated estrone concentrations, during the first 21 days after the injury. Concentrations of glucocorticoids, progestagens and androgen precursors correlated positively with the area of burn injury. The finding of increased estrone following burn injury needs to be confirmed in a larger hypothesis driven study.
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| 3. |
- Hagell, Peter, et al.
(författare)
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Apomorphine formulation may influence subcutaneous complications from continuous subcutaneous apomorphine infusion in Parkinson's disease
- 2020
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Ingår i: Journal of Neurology. - 0340-5354 .- 1432-1459. ; 267:11, s. 3411-3417
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Tidskriftsartikel (refereegranskat)abstract
- Continuous subcutaneous (s.c.) apomorphine infusion is an effective therapy for Parkinson's disease (PD), but a limitation is the formation of troublesome s.c. nodules. Various chemically non-identical apomorphine formulations are available. Anecdotal experiences have suggested that shifting from one of these (Apo-Go PumpFill®; apoGPF) to another (Apomorphine PharmSwed®; apoPS) may influence the occurrence and severity of s.c. nodules. We, therefore, followed 15 people with advanced PD (median PD-duration, 15 years; median "off"-phase Hoehn and Yahr, IV) on apoGPF and with troublesome s.c. nodules who were switched to apoPS. Data were collected at baseline, at the time of switching, and at a median of 1, 2.5, and 7.3 months post-switch. Total nodule numbers (P < 0.001), size (P < 0.001), consistency (P < 0.001), skin changes (P = 0.058), and pain (P ≤ 0.032) improved over the observation period. PD severity and dyskinesias tended to improve and increase, respectively. Apomorphine doses were stable, but levodopa doses increased by 100 mg/day. Patient-reported apomorphine efficacy tended to increase and all participants remained on apoPS throughout the observation period; with the main patient-reported reason being improved nodules. These observations suggest that patients with s.c. nodules caused by apoGPF may benefit from switching to apoPS in terms of s.c. nodule occurrence and severity. Alternatively, observed benefits may have been due to the switch itself. As nodule formation is a limiting factor in apomorphine treatment, a controlled prospective study comparing local tolerance with different formulations is warranted.
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| 4. |
- Johansson, Dongni, 1988, et al.
(författare)
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Individualization of levodopa treatment using a microtablet dispenser and ambulatory accelerometry
- 2018
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Ingår i: CNS Neuroscience & Therapeutics. - : Wiley. - 1755-5930 .- 1755-5949. ; 24:5, s. 439-447
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Tidskriftsartikel (refereegranskat)abstract
- Aim: This 4-week open-label observational study describes the effect of introducing a microtablet dose dispenser and adjusting doses based on objective free-living motor symptom monitoring in individuals with Parkinson's disease (PD). Methods: Twenty-eight outpatients with PD on stable levodopa treatment with dose intervals of ≤4 hour had their daytime doses of levodopa replaced with levodopa/carbidopa microtablets, 5/1.25 mg (LC-5) delivered from a dose dispenser device with programmable reminders. After 2 weeks, doses were adjusted based on ambulatory accelerometry and clinical monitoring. Results: Twenty-four participants completed the study per protocol. The daily levodopa dose was increased by 15% (112 mg, P < 0.001) from period 1 to 2, and the dose interval was reduced by 12% (22 minutes, P = 0.003). The treatment adherence to LC-5 was high in both periods. The MDS-UPDRS parts II and III, disease-specific quality of life (PDQ-8), wearing-off symptoms (WOQ-19), and nonmotor symptoms (NMS Quest) improved after dose titration, but the generic quality-of-life measure EQ-5D-5L did not. Blinded expert evaluation of accelerometry results demonstrated improvement in 60% of subjects and worsening in 25%. Conclusions: The introduction of a levodopa microtablet dispenser and accelerometry aided dose adjustments improve PD symptoms and quality of life in the short term.
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| 5. |
- 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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| 6. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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A smartphone-based system to quantify dexterity in Parkinson's disease patients
- 2017
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Ingår i: Informatics in Medicine Unlocked. - : Elsevier BV. - 2352-9148. ; 9, s. 11-17
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Tidskriftsartikel (refereegranskat)abstract
- Objectives The aim of this paper is to investigate whether a smartphone-based system can be used to quantify dexterity in Parkinson's disease (PD). More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD. Methods Nineteen advanced PD patients and 22 healthy controls participated in a clinical trial in Uppsala, Sweden. The subjects were asked to perform tapping and spiral drawing tests using a smartphone. Patients performed the tests before, and at pre-specified time points after they received 150% of their usual levodopa morning dose. Patients were video recorded and their motor symptoms were assessed by three movement disorder specialists using three Unified PD Rating Scale (UPDRS) motor items from part III, the dyskinesia scoring and the treatment response scale (TRS). The raw tapping and spiral data were processed and analyzed with time series analysis techniques to extract 37 spatiotemporal features. For each of the five scales, separate machine learning models were built and tested by using principal components of the features as predictors and mean ratings of the three specialists as target variables. Results There were weak to moderate correlations between smartphone-based scores and mean ratings of UPDRS item #23 (0.52; finger tapping), UPDRS #25 (0.47; rapid alternating movements of hands), UPDRS #31 (0.57; body bradykinesia and hypokinesia), sum of the three UPDRS items (0.46), dyskinesia (0.64), and TRS (0.59). When assessing the test-retest reliability of the scores it was found that, in general, the clinical scores had better test-retest reliability than the smartphone-based scores. Only the smartphone-based predicted scores on the TRS and dyskinesia scales had good repeatability with intra-class correlation coefficients of 0.51 and 0.84, respectively. Clinician-based scores had higher effect sizes than smartphone-based scores indicating a better responsiveness in detecting changes in relation to treatment interventions. However, the first principal component of the 37 features was able to capture changes throughout the levodopa cycle and had trends similar to the clinical TRS and dyskinesia scales. Smartphone-based scores differed significantly between patients and healthy controls. Conclusions Quantifying PD motor symptoms via instrumented, dexterity tests employed in a smartphone is feasible and data from such tests can also be used for measuring treatment-related changes in patients. © 2017
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| 7. |
- 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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| 8. |
- 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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| 9. |
- Aghanavesi, S., 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. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 24:1, s. 111-119
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Tidskriftsartikel (refereegranskat)abstract
- Parkinsons 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 ten-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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| 10. |
- 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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