Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson's Disease.

Parkinson's disease (PD) is a common neurodegenerative disorder resulting in a range of mobility deficits affecting gait, balance and turning. In this paper, we present: (i) the development and validation of an algorithm to detect turns during gait; (ii) a method to extract turn characteristics; and (iii) the classification of PD using turn characteristics. Thirty-seven people with PD and 56 controls performed 180-degree turns during an intermittent walking task. Inertial measurement units were attached to the head, neck, lower back and ankles. A turning detection algorithm was developed and validated by two raters using video data. Spatiotemporal and signal-based characteristics were extracted and used for PD classification. There was excellent absolute agreement between the rater and the algorithm for identifying turn start and end (ICC ≥ 0.99). Classification modeling (partial least square discriminant analysis (PLS-DA)) gave the best accuracy of 97.85% when trained on upper body and ankle data. Balanced sensitivity (97%) and specificity (96.43%) were achieved using turning characteristics from the neck, lower back and ankles. Turning characteristics, in particular angular velocity, duration, number of steps, jerk and root mean square distinguished mild-moderate PD from controls accurately and warrant future examination as a marker of mobility impairment and fall risk in PD

Classification of Neurological Patients to Identify Fallers Based on Spatial-Temporal Gait Characteristics Measured by a Wearable Device

Neurological patients can have severe gait impairments that contribute to fall risks. Predicting falls from gait abnormalities could aid clinicians and patients mitigate fall risk. The aim of this study was to predict fall status from spatial-temporal gait characteristics measured by a wearable device in a heterogeneous population of neurological patients. Participants (n = 384, age 49-80 s) were recruited from a neurology ward of a University hospital. They walked 20 m at a comfortable speed (single task: ST) and while performing a dual task with a motor component (DT1) and a dual task with a cognitive component (DT2). Twenty-seven spatial-temporal gait variables were measured with wearable sensors placed at the lower back and both ankles. Partial least square discriminant analysis (PLS-DA) was then applied to classify fallers and non-fallers. The PLS-DA classification model performed well for all three gait tasks (ST, DT1, and DT2) with an evaluation of classification performance Area under the receiver operating characteristic Curve (AUC) of 0.7, 0.6 and 0.7, respectively. Fallers differed from non-fallers in their specific gait patterns. Results from this study improve our understanding of how falls risk-related gait impairments in neurological patients could aid the design of tailored fall-prevention interventions

The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control

Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions—including Parkinson’s disease, ataxia, and dementia— we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel ‘big data’ approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction

Celector®: An Innovative Technology for Quality Control of Living Cells

Among the in vitro and ex vivo models used to study human cancer biology, cancer cell lines are widely utilized. The standardization of a correct tumor model including the stage of in vitro testing would allow for the development of new high-efficiency drug systems. The poor correlation between preclinical in vitro and in vivo data and clinical trials is still an open issue, hence the need for new systems for the quality control (QC) of these cell products. In this work, we present a new technology, Celector®, capable of the label-free analysis and separation of cells based on their physical characteristics with full preservation of their native properties. Two types of cancer cell lines were used: HL60 as cells growing in suspension and SW620 as adherent cells. Cell lines in general show a growth variability depending on the passage and method of culture. Celector® highlights physical differences that can be correlated to cell viability. This work demonstrates the use of Celector® as an analytical platform for the QC of cells used for drug screening, with fundamental improvement of preclinical tests. Cells with a stable doubling time under analysis can be collected and used as standardized systems for high-quality drug monitoring

Quality Control Platform for the Standardization of a Regenerative Medicine Product

Adipose tissue is an attractive source of stem cells due to its wide availability. They contribute to the stromal vascular fraction (SVF), which is composed of pre-adipocytes, tissue-progenitors, and pericytes, among others. Because its direct use in medical applications is increasing worldwide, new quality control systems are required. We investigated the ability of the Non-Equilibrium Earth Gravity Assisted Dynamic Fractionation (NEEGA-DF) method to analyze and separate cells based solely on their physical characteristics, resulting in a fingerprint of the biological sample. Adipose tissue was enzymatically digested, and the SVF was analyzed by NEEGA-DF. Based on the fractogram (the UV signal of eluting cells versus time of analysis) the collection time was set to sort alive cells. The collected cells (F-SVF) were analyzed for their phenotype, immunomodulation ability, and differentiation potential. The SVF profile showed reproducibility, and the alive cells were collected. The F-SVF showed intact adhesion phenotype, proliferation, and differentiation potential. The methodology allowed enrichment of the mesenchymal component with a higher expression of mesenchymal markers and depletion of debris, RBCs, and an extracellular matrix still present in the digestive product. Moreover, cells eluting in the last minutes showed higher circularity and lower area, proving the principles of enrichment of a more homogenous cell population with better characteristics. We proved the NEEGA-DF method is a "gentle" cell sorter that purifies primary cells obtained by enzymatic digestion and does not alter any stem cell function

Effective Label-Free Sorting of Multipotent Mesenchymal Stem Cells from Clinical Bone Marrow Samples

Mesenchymal stem cells (MSC) make up less than 1% of the bone marrow (BM). Several methods are used for their isolation such as gradient separation or centrifugation, but these methodologies are not direct and, thus, plastic adherence outgrowth or magnetic/fluorescent-activated sorting is required. To overcome this limitation, we investigated the use of a new separative technology to isolate MSCs from BM; it label-free separates cells based solely on their physical characteristics, preserving their native physical properties, and allows real-time visualization of cells. BM obtained from patients operated for osteochondral defects was directly concentrated in the operatory room and then analyzed using the new technology. Based on cell live-imaging and the sample profile, it was possible to highlight three fractions (F1, F2, F3), and the collected cells were evaluated in terms of their morphology, phenotype, CFU-F, and differentiation potential. Multipotent MSCs were found in F1: higher CFU-F activity and differentiation potential towards mesenchymal lineages compared to the other fractions. In addition, the technology depletes dead cells, removing unwanted red blood cells and non-progenitor stromal cells from the biological sample. This new technology provides an effective method to separate MSCs from fresh BM, maintaining their native characteristics and avoiding cell manipulation. This allows selective cell identification with a potential impact on regenerative medicine approaches in the orthopedic field and clinical applications

Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers:A Step toward Better Management of Neurological Disorders

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43-99% sensitivity and 48-98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making

Accelerometry-Based Digital Gait Characteristics for Classification of Parkinson's Disease: What Counts?

Objective: Gait may be a useful biomarker that can be objectively measured with wearable technology to classify Parkinson's disease (PD). This study aims to: (i) comprehensively quantify a battery of commonly utilized gait digital characteristics (spatiotemporal and signal-based), and (ii) identify the best discriminative characteristics for the optimal classification of PD. Methods: Six partial least square discriminant analysis (PLS-DA) models were trained on subsets of 210 characteristics measured in 142 subjects (81 people with PD, 61 controls (CL)). Results: Models accuracy ranged between 70.42-88.73% (AUC: 78.4-94.5%) with a sensitivity of 72.84-90.12% and a specificity of 60.3-86.89%. Signal-based digital gait characteristics independently gave 87.32% accuracy. The most influential characteristics in the classification models were related to root mean square values, power spectral density, step velocity and length, gait regularity and age. Conclusions: This study highlights the importance of signal-based gait characteristics in the development of tools to help classify PD in the early stages of the disease

Unravelling Heterogeneity of Amplified Human Amniotic Fluid Stem Cells Sub-Populations

Human amniotic fluid stem cells (hAFSCs) are broadly multipotent immature progenitor cells with high self-renewal and no tumorigenic properties. These cells, even amplified, present very variable morphology, density, intracellular composition and stemness potential, and this heterogeneity can hinder their characterization and potential use in regenerative medicine. Celector\uae (Stem Sel ltd.) is a new technology that exploits the Non-Equilibrium Earth Gravity Assisted Field Flow Fractionation principles to characterize and label-free sort stem cells based on their solely physical characteristics without any manipulation. Viable cells are collected and used for further studies or direct applications. In order to understand the intrapopulation heterogeneity, various fractions of hAFSCs were isolated using the Celector\uae profile and live imaging feature. The gene expression profile of each fraction was analysed using whole-transcriptome sequencing (RNAseq). Gene Set Enrichment Analysis identified significant differential expression in pathways related to Stemness, DNA repair, E2F targets, G2M checkpoint, hypoxia, EM transition, mTORC1 signalling, Unfold Protein Response and p53 signalling. These differences were validated by RT-PCR, immunofluorescence and differentiation assays. Interestingly, the different fractions showed distinct and unique stemness properties. These results suggest the existence of deep intra-population differences that can influence the stemness profile of hAFSCs. This study represents a proof-of-concept of the importance of selecting certain cellular fractions with the highest potential to use in regenerative medicine

Evaluating the clinical and cost-effectiveness of permissive hypotension in critically ill patients aged 65 years or over with vasodilatory hypotension: Statistical and health economic analysis plan for the 65 trial in article.

The 65 trial is a pragmatic, multicentre, parallel-group, open-label, randomised clinical trial of permissive hypotension (targeting a mean arterial pressure target of 60-65 mmHg during vasopressor therapy) versus usual care in critically ill patients aged 65 years or over with vasodilatory hypotension. The trial will recruit 2600 patients from 65 United Kingdom adult general critical care units. The primary outcome is all-cause mortality at 90 days. An economic evaluation is embedded. This paper describes the proposed statistical and health economic analysis for the 65 trial