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      Molecular Processes in Stress Urinary Incontinence: A Systematic Review of Human and Animal Studies

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          Abstract

          Stress urinary incontinence (SUI) is a common and burdensome condition. Because of the large knowledge gap around the molecular processes involved in its pathophysiology, the aim of this review was to provide a systematic overview of genetic variants, gene and protein expression changes related to SUI in human and animal studies. On 5 January 2021, a systematic search was performed in Pubmed, Embase, Web of Science, and the Cochrane library. The screening process and quality assessment were performed in duplicate, using predefined inclusion criteria and different quality assessment tools for human and animal studies respectively. The extracted data were grouped in themes per outcome measure, according to their functions in cellular processes, and synthesized in a narrative review. Finally, 107 studies were included, of which 35 used animal models (rats and mice). Resulting from the most examined processes, the evidence suggests that SUI is associated with altered extracellular matrix metabolism, estrogen receptors, oxidative stress, apoptosis, inflammation, neurodegenerative processes, and muscle cell differentiation and contractility. Due to heterogeneity in the studies (e.g., in examined tissues), the precise contribution of the associated genes and proteins in relation to SUI pathophysiology remained unclear. Future research should focus on possible contributors to these alterations.

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          ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions

          Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomised Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.
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            The PRISMA 2020 statement: An updated guideline for reporting systematic reviews

            The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
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              How to perform a meta-analysis with R: a practical tutorial

              Meta-analysis is of fundamental importance to obtain an unbiased assessment of the available evidence. In general, the use of meta-analysis has been increasing over the last three decades with mental health as a major research topic. It is then essential to well understand its methodology and interpret its results. In this publication, we describe how to perform a meta-analysis with the freely available statistical software environment R, using a working example taken from the field of mental health. R package meta is used to conduct standard meta-analysis. Sensitivity analyses for missing binary outcome data and potential selection bias are conducted with R package metasens. All essential R commands are provided and clearly described to conduct and report analyses. The working example considers a binary outcome: we show how to conduct a fixed effect and random effects meta-analysis and subgroup analysis, produce a forest and funnel plot and to test and adjust for funnel plot asymmetry. All these steps work similar for other outcome types. R represents a powerful and flexible tool to conduct meta-analyses. This publication gives a brief glimpse into the topic and provides directions to more advanced meta-analysis methods available in R.
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                Author and article information

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                Journal
                IJMCFK
                International Journal of Molecular Sciences
                IJMS
                MDPI AG
                1422-0067
                March 2022
                March 21 2022
                : 23
                : 6
                : 3401
                Article
                10.3390/ijms23063401
                35328824
                625aaae8-2827-4319-940c-356302dbfd13
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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