The Modular Socket System as Rural Solution in Indonesia

INTRODUCTION: The majority of the people in low-income countries, who need assistive technology do not have access to prosthetic devices [1]. Instead of these people having to make a long journey to one of the few prosthetic workshops, solutions like the Modular Socket System (MSS, Össur®) may be useful, because potentially they could be delivered and manufactured on site, at the location of the person [2]. This could make it suitable for application in a Community Based Rehabilitation (CBR) setting.The aim of this study was to evaluate the technical feasibility of the MSS for implementation in a CBR setting in terms of required tools, skills and required production time. METHODS: The study was performed at the Department of Prosthetics & Orthotics of the Jakarta I Polytechnic School of Health Science (JSPO). Four JSPO students received a three days training in manufacturing of the MSS. Lower limb amputees were recruited to participate in this study from the region of Jakarta (n = 5) and Bali (n = 10). A set of standardized instruments including the two minutes’ walking test (2MWT) and Prosthesis Evaluation Questionnaire (PEQ) were used to measure performance and satisfaction with the prosthesis. Production and maintenance logbooks were filled out by the involved prosthetists to evaluate the technical feasibility of the MSS. RESULTS AND DISCUSSION: Performance (2MWT) and satisfaction (PEQ) scores were comparable to that of similar studies with other lower leg prostheses [3,4]. Both measures did not decrease significantly over time (Figure 1). This suggest that the JSPO students were able to reach sufficient quality.It took the prosthetists 3.5 to 10.5 hours to fit an amputee with a MSS prosthesis. Mean socket production time was 2.0±0.6 hours and mean prosthesis assembly and fitting time was 4.1±2.6 hours. The only non-portable machine needed for the production of the prosthesis was a grinding machine (router). Smaller portable machines used were a cast cutter/jigsaw, Icecast® Compact and resin injection tool. If in the future the grinding machine will be replaced by a handheld tool, production of the MSS could be performed on site, making it suitable for use in a rural setting. Figure 1: The results of the 2MWT at the moment of fitting (t0), at 1-3 months post fitting (t1), and at the end evaluation at 4-6 months post fitting (t2). CONCLUSIONS: Patients who normally have to travel long distances to access prosthetic services were only required to make one visit to the health facility in order to receive a prosthesis. From a technical and quality perspective the method seems feasible, although, high costs remain an issue.ACKNOWLEDGEMENTSMaterials and training for the production of all prostheses were sponsored by Össur®. REFERENCES: 1.Borg J, et al. Assistive Technology for Children with Disabilities: Creating Opportunities for Education, Inclusion and Participation - a discussion paper. 20152.Normann E, et al., Prosthetics and orthotics international. 35(1):76-80, 20113.Boonstra AM, et al. Prosthetics and orthotics international. 17(2):78-82, 19934.Zidarov D, et al. Archives of Physical Medicine and Rehabilitation. 90(4):634-645, 200

Advancing Genetic Selection and Behavioral Genomics of Working Dogs Through Collaborative Science

The ancient partnership between people and dogs is struggling to meet modern day needs, with demand exceeding our capacity to safely breed high-performing and healthy dogs. New statistical genetic approaches and genomic technology have the potential to revolutionize dog breeding, by transitioning from problematic phenotypic selection to methods that can preserve genetic diversity while increasing the proportion of successful dogs. To fully utilize this technology will require ultra large datasets, with hundreds of thousands of dogs. Today, dog breeders struggle to apply even the tools available now, stymied by the need for sophisticated data storage infrastructure and expertise in statistical genetics. Here, we review recent advances in animal breeding, and how a new approach to dog breeding would address the needs of working dog breeders today while also providing them with a path to realizing the next generation of technology. We provide a step-by-step guide for dog breeders to start implementing estimated breeding value selection in their programs now, and we describe how genotyping and DNA sequencing data, as it becomes more widely available, can be integrated into this approach. Finally, we call for data sharing among dog breeding programs as a path to achieving a future that can benefit all dogs, and their human partners too

Academic Detailing as a Health Information Technology Implementation Method: Supporting the Design and Implementation of an Emergency Department-Based Clinical Decision Support Tool to Prevent Future Falls

BACKGROUND: Clinical decision support (CDS) tools that incorporate machine learning-derived content have the potential to transform clinical care by augmenting clinicians\u27 expertise. To realize this potential, such tools must be designed to fit the dynamic work systems of the clinicians who use them. We propose the use of academic detailing-personal visits to clinicians by an expert in a specific health IT tool-as a method for both ensuring the correct understanding of that tool and its evidence base and identifying factors influencing the tool\u27s implementation. OBJECTIVE: This study aimed to assess academic detailing as a method for simultaneously ensuring the correct understanding of an emergency department-based CDS tool to prevent future falls and identifying factors impacting clinicians\u27 use of the tool through an analysis of the resultant qualitative data. METHODS: Previously, our team designed a CDS tool to identify patients aged 65 years and older who are at the highest risk of future falls and prompt an interruptive alert to clinicians, suggesting the patient be referred to a mobility and falls clinic for an evidence-based preventative intervention. We conducted 10-minute academic detailing interviews (n=16) with resident emergency medicine physicians and advanced practice providers who had encountered our CDS tool in practice. We conducted an inductive, team-based content analysis to identify factors that influenced clinicians\u27 use of the CDS tool. RESULTS: The following categories of factors that impacted clinicians\u27 use of the CDS were identified: (1) aspects of the CDS tool\u27s design (2) clinicians\u27 understanding (or misunderstanding) of the CDS or referral process, (3) the busy nature of the emergency department environment, (4) clinicians\u27 perceptions of the patient and their associated fall risk, and (5) the opacity of the referral process. Additionally, clinician education was done to address any misconceptions about the CDS tool or referral process, for example, demonstrating how simple it is to place a referral via the CDS and clarifying which clinic the referral goes to. CONCLUSIONS: Our study demonstrates the use of academic detailing for supporting the implementation of health information technologies, allowing us to identify factors that impacted clinicians\u27 use of the CDS while concurrently educating clinicians to ensure the correct understanding of the CDS tool and intervention. Thus, academic detailing can inform both real-time adjustments of a tool\u27s implementation, for example, refinement of the language used to introduce the tool, and larger scale redesign of the CDS tool to better fit the dynamic work environment of clinicians

Combining Citizen Science and Genomics to Investigate Tick, Pathogen, and Commensal Microbiome at Single-Tick Resolution

The prevalence of tickborne diseases worldwide is increasing virtually unchecked due to the lack of effective control strategies. The transmission dynamics of tickborne pathogens are influenced by the tick microbiome, tick co-infection with other pathogens, and environmental features. Understanding this complex system could lead to new strategies for pathogen control, but will require large-scale, high-resolution data. Here, we introduce Project Acari, a citizen science-based project to assay, at single-tick resolution, species, pathogen infection status, microbiome profile, and environmental conditions of tens of thousands of ticks collected from numerous sites across the United States. In the first phase of the project, we collected more than 2,400 ticks wild-caught by citizen scientists and developed high-throughput methods to process and sequence them individually. Applying these methods to 192 Ixodes scapularis ticks collected in a region with a high incidence of Lyme disease, we found that 62% were colonized by Borrelia burgdorferi, the Lyme disease pathogen. In contrast to previous reports, we did not find an association between the microbiome diversity of a tick and its probability of carrying B. burgdorferi. However, we did find undescribed associations between B. burgdorferi carriage and the presence of specific microbial taxa within individual ticks. Our findings underscore the power of coupling citizen science with high-throughput processing to reveal pathogen dynamics. Our approach can be extended for massively parallel screening of individual ticks, offering a powerful tool to elucidate the ecology of tickborne disease and to guide pathogen-control initiatives

Effectiveness of an Emergency Department-Based Machine Learning Clinical Decision Support Tool to Prevent Outpatient Falls Among Older Adults: Protocol for a Quasi-Experimental Study

Background Emergency department (ED) providers are important collaborators in preventing falls for older adults because they are often the first health care providers to see a patient after a fall and because at-home falls are often preceded by previous ED visits. Previous work has shown that ED referrals to falls interventions can reduce the risk of an at-home fall by 38%. Screening patients at risk for a fall can be time-consuming and difficult to implement in the ED setting. Machine learning (ML) and clinical decision support (CDS) offer the potential of automating the screening process. However, it remains unclear whether automation of screening and referrals can reduce the risk of future falls among older patients. Objective The goal of this paper is to describe a research protocol for evaluating the effectiveness of an automated screening and referral intervention. These findings will inform ongoing discussions about the use of ML and artificial intelligence to augment medical decision-making. Methods To assess the effectiveness of our program for patients receiving the falls risk intervention, our primary analysis will be to obtain referral completion rates at 3 different EDs. We will use a quasi-experimental design known as a sharp regression discontinuity with regard to intent-to-treat, since the intervention is administered to patients whose risk score falls above a threshold. A conditional logistic regression model will be built to describe 6-month fall risk at each site as a function of the intervention, patient demographics, and risk score. The odds ratio of a return visit for a fall and the 95% CI will be estimated by comparing those identified as high risk by the ML-based CDS (ML-CDS) and those who were not but had a similar risk profile. Results The ML-CDS tool under study has been implemented at 2 of the 3 EDs in our study. As of April 2023, a total of 1326 patient encounters have been flagged for providers, and 339 unique patients have been referred to the mobility and falls clinic. To date, 15% (45/339) of patients have scheduled an appointment with the clinic. Conclusions This study seeks to quantify the impact of an ML-CDS intervention on patient behavior and outcomes. Our end-to-end data set allows for a more meaningful analysis of patient outcomes than other studies focused on interim outcomes, and our multisite implementation plan will demonstrate applicability to a broad population and the possibility to adapt the intervention to other EDs and achieve similar results. Our statistical methodology, regression discontinuity design, allows for causal inference from observational data and a staggered implementation strategy allows for the identification of secular trends that could affect causal associations and allow mitigation as necessary. Trial Registration ClinicalTrials.gov NCT05810064; https://www.clinicaltrials.gov/study/NCT05810064 International Registered Report Identifier (IRRID) DERR1-10.2196/4812

Neurofibromatosis type 2 protein co-localizes with elements of the cytoskeleton

The product of the neurofibromatosis type 2 (NF2) tumor suppressor gene is a 595-amino-acid protein bearing resemblance to a family of band-4.1-related proteins. These proteins, including ezrin, radixin, and moesin, probably function as molecular linking proteins, connecting the cytoskeleton to the cell membrane. On the grounds of the homology to the ezrin, radixin, and moesin proteins and on the basis of its predicted secondary structure, the NF2 protein is also thought to act as a cytoskeleton-cell membrane linking protein. Using monoclonal antibodies to amino- and carboxyl-terminal synthetic NF2 peptides we demonstrate the co-localization of the NF2 protein with elements of the cytoskeleton in a COS cell model system and in cultured human cells. Furthermore, the presence of the NF2 protein in tissue sections is shown. The monoclonal antibodies specifically stain smooth muscle cells and the stratum granulosum of the human epidermis. In cultured smooth muscle cells the NF2 protein co-localizes with actin stress fibers. Immunoelectron microscopy demonstrates the presence of the NF2 protein associated with keratohyalin granules and to a lesser extent with intermediate filaments in the human epidermis. We conclude that the NF2 protein is indeed associated with multiple elements of the cytoskeleton.</p