Additional file 1: Table S1. of Instrumented gait analysis: a measure of gait improvement by a wheeled walker in hospitalized geriatric patients

Correlations analysis between gait parameter and clinical/functional assessments. (DOCX 19 kb

Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease

<div><p>Motor impairments are the prerequisite for the diagnosis in Parkinson's disease (PD). The cardinal symptoms (bradykinesia, rigor, tremor, and postural instability) are used for disease staging and assessment of progression. They serve as primary outcome measures for clinical studies aiming at symptomatic and disease modifying interventions. One major caveat of clinical scores such as the Unified Parkinson Disease Rating Scale (UPDRS) or Hoehn&Yahr (H&Y) staging is its rater and time-of-assessment dependency. Thus, we aimed to objectively and automatically classify specific stages and motor signs in PD using a mobile, biosensor based Embedded Gait Analysis using Intelligent Technology (eGaIT). eGaIT consist of accelerometers and gyroscopes attached to shoes that record motion signals during standardized gait and leg function. From sensor signals 694 features were calculated and pattern recognition algorithms were applied to classify PD, H&Y stages, and motor signs correlating to the UPDRS-III motor score in a training cohort of 50 PD patients and 42 age matched controls. Classification results were confirmed in a second independent validation cohort (42 patients, 39 controls). eGaIT was able to successfully distinguish PD patients from controls with an overall classification rate of 81%. Classification accuracy increased with higher levels of motor impairment (91% for more severely affected patients) or more advanced stages of PD (91% for H&Y III patients compared to controls), supporting the PD-specific type of analysis by eGaIT. In addition, eGaIT was able to classify different H&Y stages, or different levels of motor impairment (UPDRS-III). In conclusion, eGaIT as an unbiased, mobile, and automated assessment tool is able to identify PD patients and characterize their motor impairment. It may serve as a complementary mean for the daily clinical workup and support therapeutic decisions throughout the course of the disease.</p> </div

aSyn secondary structure alterations induced by DA.

<p>Conformational changes of aSyn in the absence (....) and in the presence of 10 µM (- - -); 100 µM (-- --) and 1000 µM (___) of dopamine were monitored by CD. The concentration of aSyn used was 70 µM and the spectra were taken at 37°C in a 0.1 mm path quartz cuvette at 25°C. The inset graphs show the percentage of random coil and beta-sheet as a function of DA concentration and was calculated after deconvolution of the CD spectra according to the description in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0006906#s2" target="_blank">material and methods</a> section (average of 5 independent experiments +/− SD).</p

Classification within patient subgroups.

<p>Subgroups of PD patients defined by either <i>H&Y I, II, III</i>, or UPDRS-III based levels of motor impairment (<i>UPDRS low</i>: 0–12, <i>mild</i>: 13–22; <i>high</i>: 23–50) were classified by two additional pattern recognition algorithms (“A_H&Y, A_UDPRS”) and cross-validated from all PD patients. Best classification results were obtained with classifier listed, resulting in the algorithms using selected features from specific tasks (A:10-meter walk, B: heel-toe tapping, C: circling).</p

Feature characteristics.

<p>Overview of individual features extracted from eGaIT based gait analysis. Feature were extracted from both shoes during defined tasks using raw sensor data from gyroscope (G) and/or accelerometer (A) using designated axes (for complete list of feature see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056956#pone.0056956.s003" target="_blank">table S3</a>).</p

aSyn adopts different conformations in primary neuronal cultures.

<p>Primary cortical neurons were transfected with Myc-aSyn-V5 and immunocytochemistry was performed as in 1. A. Intensity image. B. Representative FLIM image showing the conformation of aSyn varies throughout the processes. C. Lifetime scale. D. Analysis of the contribution of intermolecular interactions for the lifetimes registered for aSyn. Dilution of the intermolecular interactions with untagged WT aSyn shows the majority of the detected interactions result from intramolecular interactions, i.e., different conformational states of aSyn (n = 3, 30–40 cells per condition).</p

Compounds tested in the FLIM assay in primary neurons.

<p>Compounds with different activities were used and their effects on the conformation of aSyn were assessed via the FLIM assay. Only dopamine affected the conformation of aSyn (+) (*n = 3, 30–50 cells per condition, p<0.01).</p

Expression of aSyn in primary cortical neurons.

<p>A. Schematic of the Myc-aSyn-V5 construct. B. Immunocytochemistry with anti-Myc (green) and anti-V5 (red) antibodies showing expression throughout the cell (including in the nucleus).</p

Clinical characteristics of correct and false negative classified PD patients.

<p>Clinical characteristics of false negative classified PD patients at early stages (<i>H&Y I</i>) or only mild motor impairment (<i>UPDRS-low</i>; <12 UPDRS-III motor score) by the algorithm “A_PD” compared to correctly classified patients revealed age as significantly reduced in the false negative patient groups (Student T-test, *: p<0.05).</p

Characteristics of the study population.

*<p>: p<0.001 Student's T-test.</p