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Keloid are a fibroproliferative disorder caused by abnormal healing of skin, specifically reticular dermis, when subjected to pathological or inflammatory scars demonstrating redness, elevation above the skin surface, extension beyond the original wound margins and resulting in an unappealing cosmetic appearance. The severity of keloids and risk of developing keloids scars are subjected to elevation by other contributing factors such as systemic diseases, general health conditions, genetic disorders, lifestyle and natural environment. In particular, recently, daily physical work interpreted into mechanical force as well as the interplay between mechanical factors such as stress, strain and stiffness have been reported to strongly modulate the cellular behaviour of keloid formation, affect their location and shape in keloids. Herein, we review the extensive literature on the effects of these factors on keloids and the contributing predisposing mechanisms. Early understanding of these participating factors and their effects in developing keloids may raise the patient awareness in preventing keloids incidence and controlling its severity. Moreover, further studies into their association with keloids as well as considering strategies to control such factors may help clinicians to prevent keloids and widen the therapeutic options.
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.
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.
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.
OSF Registries (https://doi.org/10.17605/OSF.IO/T54G8).
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