Inertial Gait Sensors to Measure Mobility and Functioning in Hereditary Spastic Paraplegia: A Cross-sectional Multicenter Clinical Study.

Background And Objectives: Hereditary spastic paraplegia (HSP) causes progressive spasticity and weakness of the lower limbs. As neurologic examination and the clinical Spastic Paraplegia Rating Scale (SPRS) are subject to potential patient-dependent and clinician-dependent bias, instrumented gait analysis bears the potential to objectively quantify impaired gait. The aim of this study was to investigate gait cyclicity parameters by application of a mobile gait analysis system in a cross-sectional cohort of patients with HSP and a longitudinal fast progressing subcohort.

Functional gait measures correlate to fear of falling, and quality of life in patients with Hereditary Spastic Paraplegia: A cross-sectional study.

Objective: Gait impairment is the cardinal motor symptom in hereditary spastic paraplegias (HSPs) possibly linked to increased fear of falling and reduced quality of life (QoL). Disease specific symptoms in HSP are rated using the Spastic Paraplegia Rating Scale (SPRS). However, limited studies evaluated more objectively easy-to-apply gait measures by comparing these standardized assessments with patients' self-perceived impairment and clinically established scores.

CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data.

Machine learning is a promising approach to evaluate human movement based on wearable sensor data. A representative dataset for training data-driven models is crucial to ensure that the model generalizes well to unseen data. However, the acquisition of sufficient data is time-consuming and often infeasible.

Indoor Trajectory Reconstruction of Walking, Jogging, and Running Activities Based on a Foot-Mounted Inertial Pedestrian Dead-Reckoning System.

The evaluation of trajectory reconstruction of the human body obtained by foot-mounted Inertial Pedestrian Dead-Reckoning (IPDR) methods has usually been carried out in controlled environments, with very few participants and limited to walking. In this study, a pipeline for trajectory reconstruction using a foot-mounted IPDR system is proposed and evaluated in two large datasets containing activities that involve walking, jogging, and running, as well as movements such as side and backward strides, sitting, and standing. First, stride segmentation is addressed using a multi-subsequence Dynamic Time Warping method.

A Mobile Solution for Rhythmic Auditory Stimulation Gait Training.

Recent studies showed that Parkinson's disease (PD) patients improved their gait parameters while walking with rhythmic auditory stimulation (RAS). They achieved a longer stride length, a reduced stride time variability and a higher walking speed. Combining RAS with mobile gait analysis would allow continuous monitoring of RAS effects and gait in natural environments.

Technical Validation of an Automated Mobile Gait Analysis System for Hereditary Spastic Paraplegia Patients.

Hereditary spastic paraplegias (HSP) represents a group of orphan neurodegenerative diseases with gait disturbance as the predominant clinical feature. Due to its rarity, research within this field is still limited. Aside from clinical analysis using established scales, gait analysis has been employed to enhance the understanding of the mechanisms behind the disease.

Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables.

Activity monitoring using wearables is becoming ubiquitous, although accurate cycle level analysis, such as step-counting and gait analysis, are limited by a lack of realistic and labeled datasets. The effort required to obtain and annotate such datasets is massive, therefore we propose a smart annotation pipeline which reduces the number of events needing manual adjustment to 14%. For scenarios dominated by walking, this annotation effort is as low as 8%.

Mobile Gait Analysis using Personalised Hidden Markov Models for Hereditary Spastic Paraplegia Patients.

Gait analysis provides a quantitative method to assess disease progression or intervention effect on gait disorders. While mobile gait analysis enables continuous monitoring in free living conditions, state of the art gait analysis for diseases such as hereditary spastic paraplegia (HSP) is currently limited to motion capture systems which are large and expensive. The challenge with HSP is its heterogeneous nature and rarity, leading to a wide range of ages, severity and gait patterns as well as small patient numbers.

Browsing through sealed historical manuscripts by using 3-D computed tomography with low-brilliance X-ray sources.

Severely damaged historical documents are extremely fragile. In many cases, their secrets remain concealed beneath their cover. Recently, non-invasive digitization approaches based on 3-D scanning have demonstrated the ability to recover single pages or letters without the need to open the manuscripts.

Segmentation of Gait Sequences in Sensor-Based Movement Analysis: A Comparison of Methods in Parkinson's Disease.

Robust gait segmentation is the basis for mobile gait analysis. A range of methods have been applied and evaluated for gait segmentation of healthy and pathological gait bouts. However, a unified evaluation of gait segmentation methods in Parkinson's disease (PD) is missing.

Segmentation of gait sequences using inertial sensor data in hereditary spastic paraplegia.

Gait analysis is an important tool for diagnosis, monitoring and treatment of neurological diseases. Among these are hereditary spastic paraplegias (HSPs) whose main characteristic is heterogeneous gait disturbance. So far HSP gait has been analysed in a limited number of studies, and within a laboratory set up only.

Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models.

Cyclic signals are an intrinsic part of daily life, such as human motion and heart activity. The detailed analysis of them is important for clinical applications such as pathological gait analysis and for sports applications such as performance analysis. Labeled training data for algorithms that analyze these cyclic data come at a high annotation cost due to only limited annotations available under laboratory conditions or requiring manual segmentation of the data under less restricted conditions.

Simultaneous Ultrasound Therapy and Monitoring of Microbubble-Seeded Acoustic Cavitation Using a Single-Element Transducer.

Ultrasound-driven microbubble (MB) activity is used in therapeutic applications such as blood clot dissolution and targeted drug delivery. The safety and performance of these technologies are linked to the type and distribution of MB activities produced within the targeted area, but controlling and monitoring these activities in vivo and in real time has proven to be difficult. As therapeutic pulses are often milliseconds long, MB monitoring currently requires a separate transducer used in a passive reception mode.

Blood glucose level prediction based on support vector regression using mobile platforms.

The correct treatment of diabetes is vital to a patient's health: Staying within defined blood glucose levels prevents dangerous short- and long-term effects on the body. Mobile devices informing patients about their future blood glucose levels could enable them to take counter-measures to prevent hypo or hyper periods. Previous work addressed this challenge by predicting the blood glucose levels using regression models.

Rad51 inhibition is an effective means of targeting DNA repair in glioma models and CD133+ tumor-derived cells.

High grade gliomas (HGGs) are characterized by resistance to radiotherapy and chemotherapy. Targeting Rad51-dependent homologous recombination repair may be an effective target for chemo- and radiosensitization. In this study we assessed the role of Rad51-dependent repair on sensitivity to radiation and temozolomide (TMZ) as single agents or in combination.

Cytotoxic effects of temozolomide and radiation are additive- and schedule-dependent.

Purpose: Despite aggressive therapy comprising radical radiation and temozolomide (TMZ) chemotherapy, the prognosis for patients with glioblastoma multiforme (GBM) remains poor, particularly if tumors express O(6)-methylguanine-DNA-methyltransferase (MGMT). The interactions between radiation and TMZ remain unclear and have important implications for scheduling and for developing strategies to improve outcomes.

Methods And Materials: Factors determining the effects of combination therapy on clonogenic survival, cell-cycle checkpoint signaling and DNA repair were investigated in four human glioma cell lines (T98G, U373-MG, UVW, U87-MG).

DNA repair after irradiation in glioma cells and normal human astrocytes.

We examined DNA damage responses and repair in four human glioma cell lines (A7, U87, T98G, and U373) and normal human astrocytes (NHAs) after clinically relevant radiation doses to establish whether we could identify differences among them that might suggest new approaches to selective radiosensitization. We used phosphorylation of histone H2AX visualized by immunocytochemistry to assess DNA double-strand break (DSB) formation and resolution. Fluorescence immunocytochemistry was used to visualize and quantify repair foci.

Proliferation rates in human malignant melanoma: relationship to clinicopathological features and outcome.

This prospective study investigated the clinical significance of cell kinetics, measured using bromodeoxyuridine injection and flow cytometry, in primary and metastatic cutaneous malignant melanoma. The findings illustrate that melanoma is a relatively slowly proliferating tumour, with a median potential doubling time (T(pot)) of 8.6 days.