Abnormal social interactions in a Drosophila mutant of an autism candidate gene: Neuroligin 3

Social interactions are typically impaired in neuropsychiatric disorders such as autism, for which the genetic underpinnings are very complex. Social interactions can be modeled by analysis of behaviors, including social spacing, sociability, and aggression, in simpler organisms such as Drosophila melanogaster. Here, we examined the effects of mutants of the autism-related gene neuroligin 3 (nlg3) on fly social and non-social behaviors. Startled-induced negative geotaxis is affected by a loss of function nlg3 mutation. Social space and aggression are also altered in a sex-and social-experience-specific manner in nlg3 mutant flies. In light of the conserved roles that neuroligins play in social behavior, our results offer insight into the regulation of social behavior in other organisms, including humans

Enabling knowledge translation: implementation of a web-based tool for independent walking prediction after traumatic spinal cord injury

IntroductionSeveral clinical prediction rules (CPRs) have been published, but few are easily accessible or convenient for clinicians to use in practice. We aimed to develop, implement, and describe the process of building a web-based CPR for predicting independent walking 1-year after a traumatic spinal cord injury (TSCI).MethodsUsing the published and validated CPR, a front-end web application called “Ambulation” was built using HyperText Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript. A survey was created using QualtricsXM Software to gather insights on the application's usability and user experience. Website activity was monitored using Google Analytics. Ambulation was developed with a core team of seven clinicians and researchers. To refine the app's content, website design, and utility, 20 professionals from different disciplines, including persons with lived experience, were consulted.ResultsAfter 11 revisions, Ambulation was uploaded onto a unique web domain and launched (www.ambulation.ca) as a pilot with 30 clinicians (surgeons, physiatrists, and physiotherapists). The website consists of five web pages: Home, Calculation, Team, Contact, and Privacy Policy. Responses from the user survey (n = 6) were positive and provided insight into the usability of the tool and its clinical utility (e.g., helpful in discharge planning and rehabilitation), and the overall face validity of the CPR. Since its public release on February 7, 2022, to February 28, 2023, Ambulation had 594 total users, 565 (95.1%) new users, 26 (4.4%) returning users, 363 (61.1%) engaged sessions (i.e., the number of sessions that lasted 10 seconds/longer, had one/more conversion events e.g., performing the calculation, or two/more page or screen views), and the majority of the users originating from the United States (39.9%) and Canada (38.2%).DiscussionAmbulation is a CPR for predicting independent walking 1-year after TSCI and it can assist frontline clinicians with clinical decision-making (e.g., time to surgery or rehabilitation plan), patient education and goal setting soon after injury. This tool is an example of adapting a validated CPR for independent walking into an easily accessible and usable web-based tool for use in clinical practice. This study may help inform how other CPRs can be adopted into clinical practice

Electronic consultation use by advanced practice nurses in older adult care—A descriptive study of service utilization data

Abstract Aims and Objectives To describe characteristics of service utilization by advanced practice nurses (APNs) employing an electronic consultation (eConsult) service in their care for older adults. Background Canada's aging population is projected to place unprecedented demands on the healthcare system. APNs, which include clinical nurse specialists (CNSs) and nurse practitioners (NPs), are nurses with advanced knowledge who can independently provide age‐appropriate care. eConsult is a secure web‐based platform enabling asynchronous, provider‐to‐provider communication. APNs can send and receive eConsults to address patient‐specific concerns. Methods This is a retrospective analysis of eConsult utilization and user survey data for cases completed in 2019, reported in line with the STROBE guidelines. Eligible eConsults included those that had APN involvement (as a referrer or responder) and were concerning an older patient (≥65 years). Descriptive statistics were used to analyse service utilization and survey response data. Results Of 430 eligible eConsults, 421 (97.9%) were initiated by NPs and the rest by physicians. 23 (5.3%) were received by a CNS, of which 14 (3.3%) involved an NP‐to‐CNS exchange. Median specialist response interval was 0.9 days. 53% of eConsults was for dermatology, haematology, cardiology, gastroenterology and endocrinology. 73% of eConsults avoided a face‐to‐face referral after the consultation. In 90% of eConsults, APNs rated the service as helpful and/or educational. Conclusions Through eConsult, APNs can collaborate with each other and physicians to access and provide a breadth of advice facilitating timely specialist‐informed care for older patients, thus helping to alleviate some of the demands placed on the healthcare system. Relevance to Clinical Practice There is an opportunity for APNs to further adopt eConsult into their clinical practice, and this can, in turn, support the integration of the APN role in the health workforce. Patient or Public Contribution Current APN eConsult users were involved in the study design and interpretation of results

Natural Language Processing to Identify Digital Learning Tools in Postgraduate Family Medicine: Protocol for a Scoping Review

BackgroundThe COVID-19 pandemic has highlighted the growing need for digital learning tools in postgraduate family medicine training. Family medicine departments must understand and recognize the use and effectiveness of digital tools in order to integrate them into curricula and develop effective learning tools that fill gaps and meet the learning needs of trainees. ObjectiveThis scoping review will aim to explore and organize the breadth of knowledge regarding digital learning tools in family medicine training. MethodsThis scoping review follows the 6 stages of the methodological framework outlined first by Arksey and O’Malley, then refined by Levac et al, including a search of published academic literature in 6 databases (MEDLINE, ERIC, Education Source, Embase, Scopus, and Web of Science) and gray literature. Following title and abstract and full text screening, characteristics and main findings of the included studies and resources will be tabulated and summarized. Thematic analysis and natural language processing (NLP) will be conducted in parallel using a 9-step approach to identify common themes and synthesize the literature. Additionally, NLP will be employed for bibliometric and scientometric analysis of the identified literature. ResultsThe search strategy has been developed and launched. As of October 2021, we have completed stages 1, 2, and 3 of the scoping review. We identified 132 studies for inclusion through the academic literature search and 127 relevant studies in the gray literature search. Further refinement of the eligibility criteria and data extraction has been ongoing since September 2021. ConclusionsIn this scoping review, we will identify and consolidate information and evidence related to the use and effectiveness of existing digital learning tools in postgraduate family medicine training. Our findings will improve the understanding of the current landscape of digital learning tools, which will be of great value to educators and trainees interested in using existing tools, innovators looking to design digital learning tools that meet current needs, and researchers involved in the study of digital tools. Trial RegistrationOSF Registries osf.io/wju4k; https://osf.io/wju4k International Registered Report Identifier (IRRID)DERR1-10.2196/3457

Use of Artificial Intelligence in the Identification and Management of Frailty: A Scoping Review Protocol

Introduction Rapid population ageing and associated health issues such as frailty are a growing public health concern. While early identification and management of frailty may limit adverse health outcomes, the complex presentations of frailty pose challenges for clinicians. Artificial intelligence (AI) has emerged as a potential solution to support the early identification and management of frailty. In order to provide a comprehensive overview of current evidence regarding the development and use of AI technologies including machine learning and deep learning for the identification and management of frailty, this protocol outlines a scoping review aiming to identify and present available information in this area. Specifically, this protocol describes a review that will focus on the clinical tools and frameworks used to assess frailty, the outcomes that have been evaluated and the involvement of knowledge users in the development, implementation and evaluation of AI methods and tools for frailty care in clinical settings.Methods and analysis This scoping review protocol details a systematic search of eight major academic databases, including Medline, Embase, PsycInfo, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Ageline, Web of Science, Scopus and Institute of Electrical and Electronics Engineers (IEEE) Xplore using the framework developed by Arksey and O’Malley and enhanced by Levac et al and the Joanna Briggs Institute. The search strategy has been designed in consultation with a librarian. Two independent reviewers will screen titles and abstracts, followed by full texts, for eligibility and then chart the data using a piloted data charting form. Results will be collated and presented through a narrative summary, tables and figures.Ethics and dissemination Since this study is based on publicly available information, ethics approval is not required. Findings will be communicated with healthcare providers, caregivers, patients and research and health programme funders through peer-reviewed publications, presentations and an infographic.Registration details OSF Registries (https://doi.org/10.17605/OSF.IO/T54G8)