Leo R. Quinlan

The past, present and future in scaffold-based tendon treatments

Tendon injuries represent a significant clinical burden on healthcare systems worldwide. As the human population ages and the life expectancy increases, tendon injuries will become more prevalent, especially among young individuals with long life ahead of them. Advancements in engineering, chemistry and biology have made available an array of three-dimensional scaffold-based intervention strategies, natural or synthetic in origin. Further, functionalisation strategies, based on biophysical, biochemical and biological cues, offer control over cellular functions; localisation and sustained release of therapeutics/biologics; and the ability to positively interact with the host to promote repair and regeneration. Herein, we critically discuss current therapies and emerging technologies that aim to transform tendon treatments in the years to come.

Human Centred Design Considerations for Connected Health Devices for the Older Adult

Connected health devices are generally designed for unsupervised use, by non-healthcare professionals, facilitating independent control of the individuals own healthcare. Older adults are major users of such devices and are a population significantly increasing in size. This group presents challenges due to the wide spectrum of capabilities and attitudes towards technology. The fit between capabilities of the user and demands of the device can be optimised in a process called Human Centred Design. Here we review examples of some connected health devices chosen by random selection, assess older adult known capabilities and attitudes and finally make analytical recommendations for design approaches and design specifications.

Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer

Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.

Inertial Sensor Technology for Elite Swimming Performance Analysis: A Systematic Review

Technical evaluation of swimming performance is an essential factor of elite athletic preparation. Novel methods of analysis, incorporating body worn inertial sensors (i.e., Microelectromechanical systems, or MEMS, accelerometers and gyroscopes), have received much attention recently from both research and commercial communities as an alternative to video-based approaches. This technology may allow for improved analysis of stroke mechanics, race performance and energy expenditure, as well as real-time feedback to the coach, potentially enabling more efficient, competitive and quantitative coaching. The aim of this paper is to provide a systematic review of the literature related to the use of inertial sensors for the technical analysis of swimming performance. This paper focuses on providing an evaluation of the accuracy of different feature detection algorithms described in the literature for the analysis of different phases of swimming, specifically starts, turns and free-swimming. The consequences associated with different sensor attachment locations are also considered for both single and multiple sensor configurations. Additional information such as this should help practitioners to select the most appropriate systems and methods for extracting the key performance related parameters that are important to them for analysing their swimmers’ performance and may serve to inform both applied and research practices.

Signaling Pathways in Mouse Embryo Stem Cell Self-Renewal

At the pre-implantation blastocyst stage of development, the mammalian embryo is composed of a unique collection of cells of which three major populations predominate. The outermost layer the trophectoderm (TE) gives rise to the placenta, which acts to sustain the developing fetus connecting it to the mother host. The next is a cluster of cells known as the inner cell mass (ICM) these cells are said to be pluripotent (Fig. 1). A third group of cells known as the primitive endoderm, surrounds the ICM cells at the epiblast stage. As development proceeds the ICM cells rapidly divide and eventually begin to differentiate forming the three embryonic germ layers (ectoderm, mesoderm and endoderm). Effectively these pluripotent ICM cells are the precursors of all adult tissues. As these pluripotent cells commit to a specific cellular lineage, they lose their pluripotency. Embryonic stem (ES) cells are euploid pluripotent cell lines isolated directly from cultured preimplantation embryos. The first stable ES cell lines were isolated by immunosurgery from the ICM of implantationdelayed, mouse blastocysts (Martin, 1981; Evans and Kaufman, 1981). Mouse ES cells are very closely related to early ICM cells in terms of their developmental potential (Beddington and Robertson, 1989). This chapter will focus on mouse ES cells (mES) unless otherwise stated. Three features characterize mES cells; 1. They are isolated directly from the embryo (Robertson, 1987). 2. They can colonize the germ line when introduced to the embryo. 3. They possess unrestricted proliferative potential (Suda et al., 1987). These features effectively mean that under appropriate conditions, a karyotype stable selfrenewing, pluripotent population of cells can be propagated indefinitely in vitro. mES cells have other characteristics, which prove useful when comparing embryo derived stem cells to their differentiated progenies. mES cells have a euploid (2n) chromosome complement, a feature that allows their participation in germ cell development and the formation of chimeras (Bradley et al., 1984; Evans, 1994). The functional demonstration of mES cell developmental potential through chimera formation is the definitive proof of the pluripotent nature of the cell population in question. Biomarkers are often used as indicators of the stem cell state due to the time consuming and technically more difficult nature of getting functional proof of stemness. Many of the common markers are transcription factors expressed in the ICM and mES cells and have been shown to have functional roles in selfrenewal and in the maintenance of pluripotency, in both isolated stem cells or the ICM. The

Hyaluronic acid decreases IL-6 and IL-8 secretion and permeability in an inflammatory model of interstitial cystitis.

Hyaluronic acid (HA) has received a lot of attention recently as a biomaterial with applications in wound healing, drug delivery, vascular repair and cell and/or gene delivery. Interstitial cystitis (IC) is characterised by an increase in the permeability of the bladder wall urothelium due to loss of the glycosaminoglycan (GAG) layer. The degradation of the urothelium leads to chronic pain and urinary dysfunction. The aetiology of the degradation of the GAG layer in this instance is currently unknown. At a clinical level, GAG replacement therapy using a HA solution is currently utilised as a treatment for IC. However, there is a significant lack of data on the mechanism of action of HA in IC. The current study investigates the mechanistic effect of clinically relevant HA treatment on an in vitro model of IC using urothelial cells, examining cytokine secretion, GAG secretion and trans-epithelial permeability. This study demonstrates that HA can significantly decrease induced cytokine secretion (4-5 fold increase), increase sulphated GAG production (2-fold increase) and without altering tight junction expression, decrease trans-epithelial permeability, suggesting that the HA pathway is a clinical target and potential treatment vector.

SELF-RENEWAL AND DIFFERENTIATION OF MOUSE EMBRYONIC STEM CELLS AS MEASURED BY Oct4 GENE EXPRESSION: EFFECTS OF LIF, SERUM-FREE MEDIUM, RETINOIC ACID, AND dbcAMP

SummaryIn this study we examined the interplay between serum, leukemia inhibitory factor (LIF), retinoic acid, and dibutyrl cyclic adenosine monophosphate (dbcAMP) in affecting IOUD2 embryonic stem cell self-renewal and differentiation as assessed by Oct4 expression, and cell proliferation as measured by total cell protein. Removal of LIF, reduced levels of fetal calf serum (FCS), and addition of retinoic acid all induced embryonic stem cell differentiation as measured by reduced Oct4 expression. Lower levels of retinoic acid (0.1–10 nM) promoted the formation of epithelial-like cells, whereas higher levels (100–10,000 nM) favored differentiation into fibroblastic-like cells. The effects of dbcAMP varied with the presence or absence of FCS and LIF and the concentration of dbcAMP. In FCS-containing media, a low level of dbcAMP (100 μM) increased self-renewal in the absence of LIF, but it had no effect in its presence. In contrast, at higher concentrations (1000 μM dbcAMP), regardless of LIF, differentiation was promoted. A similar effect of dbcAMP was seen in the presence of retinoic acid. In media without FCS but with serum replacement supplements, there was no effect of dbcAMP. This study shows that the Oct4 expression system of IOUD2 cells provides a novel, simple method for quantifying cellular differentiation.

A human-centered design methodology to enhance the usability, human factors, and user experience of connected health systems: a three-phase methodology

Background Design processes such as human-centered design, which involve the end user throughout the product development and testing process, can be crucial in ensuring that the product meets the needs and capabilities of the user, particularly in terms of safety and user experience. The structured and iterative nature of human-centered design can often present a challenge when design teams are faced with the necessary, rapid, product development life cycles associated with the competitive connected health industry. Objective We wanted to derive a structured methodology that followed the principles of human-centered design that would allow designers and developers to ensure that the needs of the user are taken into account throughout the design process, while maintaining a rapid pace of development. In this paper, we present the methodology and its rationale before outlining how it was applied to assess and enhance the usability, human factors, and user experience of a connected health system known as the Wireless Insole for Independent and Safe Elderly Living (WIISEL) system, a system designed to continuously assess fall risk by measuring gait and balance parameters associated with fall risk. Methods We derived a three-phase methodology. In Phase 1 we emphasized the construction of a use case document. This document can be used to detail the context of use of the system by utilizing storyboarding, paper prototypes, and mock-ups in conjunction with user interviews to gather insightful user feedback on different proposed concepts. In Phase 2 we emphasized the use of expert usability inspections such as heuristic evaluations and cognitive walkthroughs with small multidisciplinary groups to review the prototypes born out of the Phase 1 feedback. Finally, in Phase 3 we emphasized classical user testing with target end users, using various metrics to measure the user experience and improve the final prototypes. Results We report a successful implementation of the methodology for the design and development of a system for detecting and predicting falls in older adults. We describe in detail what testing and evaluation activities we carried out to effectively test the system and overcome usability and human factors problems. Conclusions We feel this methodology can be applied to a wide variety of connected health devices and systems. We consider this a methodology that can be scaled to different-sized projects accordingly.

Dopaminergic-induced dyskinesia assessment based on a single belt-worn accelerometer

BACKGROUND After several years of treatment, patients with Parkinsons disease (PD) tend to have, as a side effect of the medication, dyskinesias. Close monitoring may benefit patients by enabling doctors to tailor a personalised medication regimen. Moreover, dyskinesia monitoring can help neurologists make more informed decisions in patients care. OBJECTIVE To design and validate an algorithm able to be embedded into a system that PD patients could wear during their activities of daily living with the purpose of registering the occurrence of dyskinesia in real conditions. MATERIALS AND METHODS Data from an accelerometer positioned in the waist are collected at the patients home and are annotated by experienced clinicians. Data collection is divided into two parts: a main database gathered from 92 patients used to partially train and to evaluate the algorithms based on a leave-one-out approach and, on the other hand, a second database from 10 patients which have been used to also train a part of the detection algorithm. RESULTS Results show that, depending on the severity and location of dyskinesia, specificities and sensitivities higher than 90% are achieved using a leave-one-out methodology. Although mild dyskinesias presented on the limbs are detected with 95% specificity and 39% sensitivity, the most important types of dyskinesia (any strong dyskinesia and trunk mild dyskinesia) are assessed with 95% specificity and 93% sensitivity. CONCLUSION The presented algorithmic method and wearable device have been successfully validated in monitoring the occurrence of strong dyskinesias and mild trunk dyskinesias during activities of daily living.

Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales

Background Our group earlier developed a small monitoring device, which uses accelerometer measurements to accurately detect motor fluctuations in patients with Parkinson’s (On and Off state) based on an algorithm that characterizes gait through the frequency content of strides. To further validate the algorithm, we studied the correlation of its outputs with the motor section of the Unified Parkinson’s Disease Rating Scale part-III (UPDRS-III). Method Seventy-five patients suffering from Parkinson’s disease were asked to walk both in the Off and the On state while wearing the inertial sensor on the waist. Additionally, all patients were administered the motor section of the UPDRS in both motor phases. Tests were conducted at the patient’s home. Convergence between the algorithm and the scale was evaluated by using the Spearman’s correlation coefficient. Results Correlation with the UPDRS-III was moderate (rho −0.56; p < 0.001). Correlation between the algorithm outputs and the gait item in the UPDRS-III was good (rho −0.73; p < 0.001). The factorial analysis of the UPDRS-III has repeatedly shown that several of its items can be clustered under the so-called Factor 1: “axial function, balance, and gait.” The correlation between the algorithm outputs and this factor of the UPDRS-III was −0.67 (p < 0.01). Conclusion The correlation achieved by the algorithm with the UPDRS-III scale suggests that this algorithm might be a useful tool for monitoring patients with Parkinson’s disease and motor fluctuations.