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Johansson, D., Thomas, I., Ericsson, A., Johansson, A., Medvedev, A., Memedi, M., . . . Bergquist, F. (2019). Evaluation of a sensor algorithm for motor state rating in Parkinson's disease. Parkinsonism & Related Disorders, 64, 112-117
Open this publication in new window or tab >>Evaluation of a sensor algorithm for motor state rating in Parkinson's disease
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2019 (English)In: Parkinsonism & Related Disorders, ISSN 1353-8020, E-ISSN 1873-5126, Vol. 64, p. 112-117Article in journal (Refereed) Published
Abstract [en]

INTRODUCTION: A treatment response objective index (TRIS) was previously developed based on sensor data from pronation-supination tests. This study aimed to examine the performance of TRIS for medication effects in a new population sample with Parkinson's disease (PD) and its usefulness for constructing individual dose-response models.

METHODS: Twenty-five patients with PD performed a series of tasks throughout a levodopa challenge while wearing sensors. TRIS was used to determine motor changes in pronation-supination tests following a single levodopa dose, and was compared to clinical ratings including the Treatment Response Scale (TRS) and six sub-items of the UPDRS part III.

RESULTS: As expected, correlations between TRIS and clinical ratings were lower in the new population than in the initial study. TRIS was still significantly correlated to TRS (rs = 0.23, P < 0.001) with a root mean square error (RMSE) of 1.33. For the patients (n = 17) with a good levodopa response and clear motor fluctuations, a stronger correlation was found (rs = 0.38, RMSE = 1.29, P < 0.001). The mean TRIS increased significantly when patients went from the practically defined off to their best on state (P = 0.024). Individual dose-response models could be fitted for more participants when TRIS was used for modelling than when TRS ratings were used.

CONCLUSION: The objective sensor index shows promise for constructing individual dose-response models, but further evaluations and retraining of the TRIS algorithm are desirable to improve its performance and to ensure its clinical effectiveness.

Keywords
Independent evaluation, Levodopa challenge test, Machine learning algorithms, Parkinson's disease, Wearable sensors
National Category
Other Medical Engineering
Research subject
Complex Systems – Microdata Analysis; Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-29857 (URN)10.1016/j.parkreldis.2019.03.022 (DOI)000487567800016 ()30935826 (PubMedID)2-s2.0-85063430752 (Scopus ID)
Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2019-10-18Bibliographically approved
Fleyeh, H. & Westin, J. (2019). Extracting Body Landmarks from Videos for Parkinson Gait Analysis. In: Proceedings - IEEE Symposium on Computer-Based Medical Systems: . Paper presented at 32nd IEEE CBMS International Symposium on Computer-Based Medical Systems, Instituto Maimónides de Investigación Biomédica de Córdoba, Córdoba, Spain, 5-7 June 2019 (pp. 379-384). , 2019-June, Article ID 8787477.
Open this publication in new window or tab >>Extracting Body Landmarks from Videos for Parkinson Gait Analysis
2019 (English)In: Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2019, Vol. 2019-June, p. 379-384, article id 8787477Conference paper, Published paper (Refereed)
Abstract [en]

Patients with Parkinson disease (PD) exhibit a gait disorder called festinating gait which is caused by deficiency of dopamine in the basal ganglia. To analyze gait of patients with PD, different spatiotemporal parameters such as stride length, cadence, and walking speed should be calculated. This paper aims to present a method to extract useful information represented by the positions of certain landmarks on the human body that can be used for analysis of PD patients’ gait. This method is tested using 132 videos collected from 7 PD patients and 7 healthy controls. The positions of 4 body landmarks, namely body’s center of gravity (COG), the position of the head, and the position of the feet, was computed using a total of more than 41000 of video frames. Results of object’s movement plots show high level of accuracy in the calculation of the body landmarks.

Series
Proceedings - IEEE Symposium on Computer-Based Medical Systems, ISSN 10637125
National Category
Medical Image Processing
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30583 (URN)10.1109/CBMS.2019.00082 (DOI)2-s2.0-85070980917 (Scopus ID)9781728122861 (ISBN)
Conference
32nd IEEE CBMS International Symposium on Computer-Based Medical Systems, Instituto Maimónides de Investigación Biomédica de Córdoba, Córdoba, Spain, 5-7 June 2019
Available from: 2019-07-29 Created: 2019-07-29 Last updated: 2019-09-17Bibliographically approved
Saqlain, M., Alam, M., Rönnegård, L. & Westin, J. (2019). Investigating Stochastic Differential Equations Modelling for Levodopa Infusion in Patients with Parkinson's Disease. European journal of drug metabolism and pharmacokinetics
Open this publication in new window or tab >>Investigating Stochastic Differential Equations Modelling for Levodopa Infusion in Patients with Parkinson's Disease
2019 (English)In: European journal of drug metabolism and pharmacokinetics, ISSN 0378-7966, E-ISSN 2107-0180Article in journal (Refereed) Epub ahead of print
Abstract [en]

BACKGROUND AND OBJECTIVES: Levodopa concentration in patients with Parkinson's disease is frequently modelled with ordinary differential equations (ODEs). Here, we investigate a pharmacokinetic model of plasma levodopa concentration in patients with Parkinson's disease by introducing stochasticity to separate the intra-individual variability into measurement and system noise, and to account for auto-correlated errors. We also investigate whether the induced stochasticity provides a better fit than the ODE approach.

METHODS: In this study, a system noise variable is added to the pharmacokinetic model for duodenal levodopa/carbidopa gel (LCIG) infusion described by three ODEs through a standard Wiener process, leading to a stochastic differential equations (SDE) model. The R package population stochastic modelling (PSM) was used for model fitting with data from previous studies for modelling plasma levodopa concentration and parameter estimation. First, the diffusion scale parameter (σw), measurement noise variance, and bioavailability are estimated with the SDE model. Second, σw is fixed to certain values from 0 to 1 and bioavailability is estimated. Cross-validation was performed to compare the average root mean square errors (RMSE) of predicted plasma levodopa concentration.

RESULTS: Both the ODE and the SDE models estimated bioavailability to be approximately 75%. The SDE model converged at different values of σw that were significantly different from zero. The average RMSE for the ODE model was 0.313, and the lowest average RMSE for the SDE model was 0.297 when σw was fixed to 0.9, and these two values are significantly different.

CONCLUSIONS: The SDE model provided a better fit for LCIG plasma levodopa concentration by approximately 5.5% in terms of mean percentage change of RMSE.

National Category
Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30962 (URN)10.1007/s13318-019-00580-w (DOI)31595429 (PubMedID)2-s2.0-85074493836 (Scopus ID)
Available from: 2019-10-16 Created: 2019-10-16 Last updated: 2019-12-06
Thomas, I., Alam, M., Bergquist, F., Johansson, D., Memedi, M., Nyholm, D. & Westin, J. (2019). Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson's disease: a first experience. Journal of Neurology, 266(3), 651-658
Open this publication in new window or tab >>Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson's disease: a first experience
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2019 (English)In: Journal of Neurology, ISSN 0340-5354, E-ISSN 1432-1459, Vol. 266, no 3, p. 651-658Article in journal (Refereed) Published
Abstract [en]

OBJECTIVE: Dosing schedules for oral levodopa in advanced stages of Parkinson's disease (PD) require careful tailoring to fit the needs of each patient. This study proposes a dosing algorithm for oral administration of levodopa and evaluates its integration into a sensor-based dosing system (SBDS).

MATERIALS AND METHODS: In collaboration with two movement disorder experts a knowledge-driven, simulation based algorithm was designed and integrated into a SBDS. The SBDS uses data from wearable sensors to fit individual patient models, which are then used as input to the dosing algorithm. To access the feasibility of using the SBDS in clinical practice its performance was evaluated during a clinical experiment where dosing optimization of oral levodopa was explored. The supervising neurologist made dosing adjustments based on data from the Parkinson's KinetiGraph™ (PKG) that the patients wore for a week in a free living setting. The dosing suggestions of the SBDS were compared with the PKG-guided adjustments.

RESULTS: The SBDS maintenance and morning dosing suggestions had a Pearson's correlation of 0.80 and 0.95 (with mean relative errors of 21% and 12.5%), to the PKG-guided dosing adjustments. Paired t test indicated no statistical differences between the algorithmic suggestions and the clinician's adjustments.

CONCLUSION: This study shows that it is possible to use algorithmic sensor-based dosing adjustments to optimize treatment with oral medication for PD patients.

Keywords
Algorithmic suggestions, Levodopa, Oral medication, Parkinson’s disease, Sensor data
National Category
Probability Theory and Statistics Other Medical Sciences
Research subject
Complex Systems – Microdata Analysis; Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-29314 (URN)10.1007/s00415-019-09183-6 (DOI)000459203400013 ()30659356 (PubMedID)2-s2.0-85060256040 (Scopus ID)
Available from: 2019-01-21 Created: 2019-01-21 Last updated: 2019-03-14Bibliographically approved
Thomas, I., Memedi, M., Westin, J. & Nyholm, D. (2019). The effect of continuous levodopa treatment during the afternoon hours. Acta Neurologica Scandinavica, 139(1), 70-75
Open this publication in new window or tab >>The effect of continuous levodopa treatment during the afternoon hours
2019 (English)In: Acta Neurologica Scandinavica, ISSN 0001-6314, E-ISSN 1600-0404, Vol. 139, no 1, p. 70-75Article in journal (Refereed) Published
Abstract [en]

OBJECTIVE: The aim of this retrospective study was to investigate if patients with PD, who are treated with levodopa-carbidopa intestinal gel (LCIG), clinically worsen during the afternoon hours and if so, to evaluate whether this occurs in all LCIG-treated patients or in a sub-group of patients.

METHODS: Three published studies were identified and included in the analysis. All studies provided individual response data assessed on the treatment response scale (TRS) and patients were treated with continuous LCIG. Ninety-eight patients from the three studies fulfilled the criteria. T-tests were performed to find differences on the TRS values between the morning and the afternoon hours, linear mixed effect models were fitted on the afternoon hours' evaluations to find trends of wearing-off, and patients were classified into three TRS categories (meaningful increase in TRS, meaningful decrease in TRS, non -meaningful increase or decrease).

RESULTS: In all three studies significant statistical differences were found between the morning TRS values and the afternoon TRS values (p-value <= 0.001 in all studies). The linear mixed effect models had significant negative coefficients for time in two studies, and 48 out of 98 patients (49%) showed a meaningful decrease of TRS during the afternoon hours.

CONCLUSION: The results from all studies were consistent, both in the proportion of patients in the three groups and the value of TRS decrease in the afternoon hours. Based on these findings there seems to be a group of patients with predictable "off" behavior in the later parts of the day. This article is protected by copyright. All rights reserved.

Keywords
diurnal motor fluctuation; infusion pumps; levodopa; Parkinson disease
National Category
Medical Engineering Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-28478 (URN)10.1111/ane.13020 (DOI)000452067700007 ()30180267 (PubMedID)2-s2.0-85053714059 (Scopus ID)
Available from: 2018-09-11 Created: 2018-09-11 Last updated: 2019-02-06Bibliographically approved
Thomas, I., Westin, J., Alam, M., Bergquist, F., Nyholm, D., Senek, M. & Memedi, M. (2018). A treatment–response index from wearable sensors for quantifying Parkinson's disease motor states. IEEE journal of biomedical and health informatics, 22(5), 1341-1349
Open this publication in new window or tab >>A treatment–response index from wearable sensors for quantifying Parkinson's disease motor states
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2018 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 22, no 5, p. 1341-1349Article in journal (Refereed) Published
Abstract [en]

The goal of this study was to develop an algorithm that automatically quantifies motor states (off,on,dyskinesia) in Parkinson's disease (PD), based on accelerometry during a hand pronation-supination test. Clinician's ratings using the Treatment Response Scale (TRS), ranging from -3 (very Off) to 0 (On) to +3 (very dyskinetic), was used as target. For that purpose, 19 participants with advanced PD and 22 healthy persons were recruited in a single center open label clinical trial in Uppsala, Sweden. The trial consisted of single levodopa dose experiments for the people with PD (PwP), where participants were asked to perform standardized wrist rotation tests, using each hand, before and at pre-specified time points after the dose. The participants used wrist sensors containing a 3D accelerometer and gyroscope. Features to quantify the level, variation and asymmetry of the sensor signals, three-level Discrete Wavelet Transform features and approximate entropy measures were extracted from the sensors data. At the time of the tests, the PwP were video recorded. Three movement disorder specialists rated the participants’ state on the TRS scale. A Treatment Response Index from Sensors (TRIS) was constructed to quantify the motor states based on the wrist rotation tests. Different machine learning algorithms were evaluated to map the features derived from the sensor data to the ratings provided by the three specialists. Results from cross validation, both in 10-fold and a leave-one-individual out setting, showed good predictive power of a support vector machine model and high correlation to the TRS scale. Values at the end tails of the TRS scale were under and over predicted due to the lack of observations at those values but the model managed to accurately capture the dose - effect profiles of the patients. In addition, the TRIS had good test-retest reliability on the baseline levels of the PD participants (Intraclass correlation coefficient of 0.83) and reasonable sensitivity to levodopa treatment (0.33 for the TRIS). For a series of test occasions the proposed algorithms provided dose - effect time profiles for participants with PD, which could be useful during therapy individualization of people suffering from advanced PD

Keywords
Accelerometers, Accelerometry, Diseases, Feature extraction, Levodopa response, Machine learning, Parkinson's disease, Pattern recognition, Sensor phenomena and characterization, Signal processing, Wearable sensors, Wrist
National Category
Computer and Information Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-26745 (URN)10.1109/JBHI.2017.2777926 (DOI)000441795800002 ()29989996 (PubMedID)2-s2.0-85035809095 (Scopus ID)
Available from: 2017-12-11 Created: 2017-12-11 Last updated: 2019-02-06Bibliographically approved
Thomas, I., Alam, M., Nyholm, D., Senek, M. & Westin, J. (2018). Individual dose-response models for levodopa infusion dose optimization. International Journal of Medical Informatics, 112, 137-142
Open this publication in new window or tab >>Individual dose-response models for levodopa infusion dose optimization
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2018 (English)In: International Journal of Medical Informatics, ISSN 1386-5056, E-ISSN 1872-8243, Vol. 112, p. 137-142Article in journal (Refereed) Published
Abstract [en]

Background and Objective

To achieve optimal effect with continuous infusion treatment in Parkinson’s disease (PD), the individual doses (morning dose and continuous infusion rate) are titrated by trained medical personnel. This study describes an algorithmic method to derive optimized dosing suggestions for infusion treatment of PD, by fitting individual dose-response models. The feasibility of the proposed method was investigated using patient chart data.

Methods

Patient records were collected at Uppsala University hospital which provided dosing information and dose-response evaluations. Mathematical optimization was used to fit individual patient models using the records’ information, by minimizing an objective function. The individual models were passed to a dose optimization algorithm, which derived an optimized dosing suggestion for each patient model.

Results

Using data from a single day’s admission the algorithm showed great ability to fit appropriate individual patient models and derive optimized doses. The infusion rate dosing suggestions had 0.88 correlation and 10% absolute mean relative error compared to the optimal doses as determined by the hospital’s treating team. The morning dose suggestions were consistency lower that the optimal morning doses, which could be attributed to different dosing strategies and/or lack of on-off evaluations in the morning.

Conclusion

The proposed method showed promise and could be applied in clinical practice, to provide the hospital personnel with additional information when making dose adjustment decisions.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Levodopa infusion; Algorithmic dosing suggestions; Patient-specific models; Parkinson’s disease
National Category
Computer and Information Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-27065 (URN)10.1016/j.ijmedinf.2018.01.018 (DOI)000426130900018 ()29500011 (PubMedID)
Available from: 2018-02-02 Created: 2018-02-02 Last updated: 2019-02-06Bibliographically approved
Johansson, D., Ericsson, A., Johansson, A., Medvedev, A., Nyholm, D., Ohlsson, F., . . . Westin, J. (2018). Individualization of levodopa treatment using a microtablet dispenser and ambulatory accelerometry. CNS Neuroscience & Therapeutics, 24(5), 439-447
Open this publication in new window or tab >>Individualization of levodopa treatment using a microtablet dispenser and ambulatory accelerometry
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2018 (English)In: CNS Neuroscience & Therapeutics, ISSN 1755-5930, E-ISSN 1755-5949, Vol. 24, no 5, p. 439-447Article in journal (Refereed) Published
Abstract [en]

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, < 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.

Keywords
Parkinson's disease; accelerometry; dose titration; microtablets; observational study
National Category
Medical Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-27206 (URN)10.1111/cns.12807 (DOI)000430058800008 ()29652438 (PubMedID)2-s2.0-85041030284 (Scopus ID)
Available from: 2018-02-14 Created: 2018-02-14 Last updated: 2018-05-03Bibliographically approved
Schiavella, M., Pelagatti, M., Westin, J., Lepore, G. & Cherubini, P. (2018). Profiling online poker players: Are executive functions correlated with poker ability and problem gambling?. Journal of Gambling Studies, 34(3), 823-851
Open this publication in new window or tab >>Profiling online poker players: Are executive functions correlated with poker ability and problem gambling?
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2018 (English)In: Journal of Gambling Studies, ISSN 1050-5350, E-ISSN 1573-3602, Vol. 34, no 3, p. 823-851Article in journal (Refereed) Published
Abstract [en]

Poker playing and responsible gambling both entail the use of the executive functions (EF), which are higher-level cognitive abilities. This study investigated if online poker players of different ability showed different performances in their EF and if so, which functions were the most discriminating for their playing ability. Furthermore, it assessed if the EF performance was correlated to the quality of gambling, according to self-reported questionnaires (PGSI, SOGS, GRCS). Three poker experts evaluated anonymized poker hand history files and, then, a trained professional administered an extensive neuropsychological test battery. Data analysis determined which variables of the tests correlated with poker ability and gambling quality scores. The highest correlations between EF test results and poker ability and between EF test results and gambling quality assessment showed that mostly different clusters of executive functions characterize the profile of the strong(er) poker player and those ones of the problem gamblers (PGSI and SOGS) and the one of the cognitions related to gambling (GRCS). Taking into consideration only the variables overlapping between PGSI and SOGS, we found some key predictive factors for a more risky and harmful online poker playing: a lower performance in the emotional intelligence competences (Emotional Quotient inventory Short) and, in particular, those grouped in the Intrapersonal scale (emotional self-awareness, assertiveness, self-regard, independence and self-actualization).

Keywords
Executive functions, GRCS, Online poker, PGSI, Problem gambling, SOGS
National Category
Other Computer and Information Science
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-26938 (URN)10.1007/s10899-017-9741-z (DOI)000441533700013 ()29330827 (PubMedID)2-s2.0-85054893588 (Scopus ID)
Available from: 2018-01-15 Created: 2018-01-15 Last updated: 2018-10-29Bibliographically approved
Saqlain, M., Alam, M., Brandt, D., Rönnegård, L. & Westin, J. (2018). Stochastic differential equations modelling of levodopa concentration in patients with Parkinson's disease. In: : . Paper presented at The 40th Conference on Stochastic Processes and their Applications – SPA 2018, June 11-15 2018, Gothenburg.
Open this publication in new window or tab >>Stochastic differential equations modelling of levodopa concentration in patients with Parkinson's disease
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2018 (English)Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

The purpose of this study is to investigate a pharmacokinetic model of levodopa concentration in patients with Parkinson's disease by introducing stochasticity so that inter-individual variability may be separated into measurement and system noise. It also aims to investigate whether the stochastic differential equations (SDE) model provide better fits than its ordinary differential equations (ODE) counterpart, by using a real data set. Westin et al. developed a pharmacokinetic-pharmacodynamic model for duodenal levodopa infusion described by four ODEs, the first three of which define the pharmacokinetic model. In this study, system noise variables are added to the aforementioned first three equations through a standard Wiener process, also known as Brownian motion. The R package PSM for mixed-effects models is used on data from previous studies for modelling levodopa concentration and parameter estimation. First, the diffusion scale parameter, σ, and bioavailability are estimated with the SDE model. Second, σ is fixed to integer values between 1 and 5, and bioavailability is estimated. Cross-validation is performed to determine whether the SDE based model explains the observed data better or not by comparingthe average root mean squared errors (RMSE) of predicted levodopa concentration. Both ODE and SDE models estimated bioavailability to be about 88%. The SDE model converged at different values of σ that were signicantly different from zero while estimating bioavailability to be about 88%. The average RMSE for the ODE model wasfound to be 0.2980, and the lowest average RMSE for the SDE model was 0.2748 when σ was xed to 4. Both models estimated similar values for bioavailability, and the non-zero σ estimate implies that the inter-individual variability may be separated. However, the improvement in the predictive performance of the SDE model turned out to be rather small, compared to the ODE model.

Keywords
levodopa, parkinson's disease, pharmacokinetic model, stochastic modelling, PSM.
National Category
Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis, General Microdata Analysis - methods
Identifiers
urn:nbn:se:du-28268 (URN)
Conference
The 40th Conference on Stochastic Processes and their Applications – SPA 2018, June 11-15 2018, Gothenburg
Available from: 2018-08-08 Created: 2018-08-08 Last updated: 2018-12-17Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0403-338X

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