A systematic literature review of preference-based health related quality-of-life measures applied and validated for use in childhood and adolescent populations in sub-Saharan Africa

Objectives: Consideration of health status in children and adolescents now includes broader concepts such as health-related quality-of-life (HRQoL). Globally, there is a need for relevant preference-based HRQoL measures (PBMs) for use in children and adolescents, yet measurement of HRQoL in these groups presents particular challenges. This article systematically reviews the available generic childhood PBMs and their application and cross-cultural validation in sub-Saharan African (sSA). Methods: A systematic review of published literature from January 1, 1990, to February 8, 2017, was conducted using MEDLINE (through OvidSP), EMBASE (OvidSP), EconLit (EBSCOhost), PsycINFO, Web of Science, and PubMed. Results: A total of 220 full-text articles were included in a qualitative synthesis. Ten generic childhood PBMs were identified, of which 9 were adapted from adult versions and only 1 was developed specifically for children. None of the measures were originally developed in sSA or other resource-constrained settings. The Health Utilities Index Mark 3 (HUI3) and the EQ-5D-Y were the only measures that had been applied in sSA settings. Further, the HUI3 and the EQ-5D-Y were the only generic childhood PBM that attempted to establish cross-cultural validation in sSA. Five of the 6 of these validation studies were conducted using the EQ-5D-Y in a single country, South Africa. Conclusions: The findings show that application of generic childhood PBMs in sSA settings has hitherto been limited to the HUI3 and EQ-5D-Y. Most adaptations of existing measures take an absolutist approach, which assumes that measures can be used across cultures. Nevertheless, there is also need to ensure linguistic and conceptual equivalence and undertake validation across a range of sSA cultural contexts

Notch Initiates the Endothelial-to-Mesenchymal Transition in the Atrioventricular Canal through Autocrine Activation of Soluble Guanylyl Cyclase

SummaryThe heart is the most common site of congenital defects, and valvuloseptal defects are the most common of the cardiac anomalies seen in the newborn. The process of endothelial-to-mesenchymal transition (EndMT) in the cardiac cushions is a required step during early valve development, and Notch signaling is required for this process. Here we show that Notch activation induces the transcription of both subunits of the soluble guanylyl cyclase (sGC) heterodimer, GUCY1A3 and GUCY1B3, which form the nitric oxide receptor. In parallel, Notch also promotes nitric oxide (NO) production by inducing Activin A, thereby activating a PI3-kinase/Akt pathway to phosphorylate eNOS. We thus show that the activation of sGC by NO through a Notch-dependent autocrine loop is necessary to drive early EndMT in the developing atrioventricular canal (AVC)

Tissue Stromal Vascular Fraction Improves Early Scar Healing:A Prospective Randomized Multicenter Clinical Trial

Background Wound healing and scar formation depends on a plethora of factors. Given the impact of abnormal scar formation, interventions aimed to improve scar formation would be most advantageous. The tissue stromal vascular fraction (tSVF) of adipose tissue is composed of a heterogenous mixture of cells embedded in extracellular matrix. It contains growth factors and cytokines involved in wound-healing processes, eg, parenchymal proliferation, inflammation, angiogenesis, and matrix remodeling.Objectives The aim of this study was to investigate the hypothesis that tSVF reduces postsurgical scar formation.Methods This prospective, double-blind, placebo-controlled, randomized trial was conducted between 2016 and 2020. Forty mammoplasty patients were enrolled and followed for 1 year. At the end of the mammoplasty procedure, all patients received tSVF in the lateral 5 cm of the horizontal scar of 1 breast and a placebo injection in the contralateral breast to serve as an intrapatient control. Primary outcome was scar quality measure by the Patient and Observer Scar Assessment Scale (POSAS). Secondary outcomes were obtained from photographic evaluation and histologic analysis of scar tissue samples.Results Thirty-four of 40 patients completed follow-up. At 6 months postoperation, injection of tSVF had significantly improved postoperative scar appearance as assessed by the POSAS questionnaire. No difference was observed at 12 months postoperation. No improvement was seen based on the evaluation of photographs and histologic analysis of postoperative scars between both groups.Conclusions Injection of tSVF resulted in improved wound healing and reduced scar formation at 6 months postoperation, without any noticeable advantageous effects seen at 12 months.</p

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Novel genetic loci associated with hippocampal volume

The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg =-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness

Cerebral small vessel disease genomics and its implications across the lifespan

White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.Peer reviewe

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder