Dataset Information

The Human Microbiota and Obesity: A Literature Systematic Review of In Vivo Models and Technical Approaches.

ABSTRACT: Obesity is a noncommunicable disease that affects a considerable part of humanity. Recently, it has been recognized that gut microbiota constitutes a fundamental factor in the triggering and development of a large number of pathologies, among which obesity is one of the most related to the processes of dysbiosis. In this review, different animal model approaches, methodologies, and genome scale metabolic databases were revisited to study the gut microbiota and its relationship with metabolic disease. As a data source, PubMed for English-language published material from 1 January 2013, to 22 August 2018, were screened. Some previous studies were included if they were considered classics or highly relevant. Studies that included innovative technical approaches or different in vivo or in vitro models for the study of the relationship between gut microbiota and obesity were selected after a 16-different-keyword exhaustive search. A clear panorama of the current available options for the study of microbiota's influence on obesity, both for animal model election and technical approaches, is presented to the researcher. All the knowledge generated from the study of the microbiota opens the possibility of considering fecal transplantation as a relevant therapeutic alternative for obesity and other metabolic disease treatment.

SUBMITTER: Carrera-Quintanar L

PROVIDER: S-EPMC6320813 | biostudies-other | 2018 Nov

REPOSITORIES: biostudies-other

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The Human Microbiota and Obesity: A Literature Systematic Review of In Vivo Models and Technical Approaches.

Carrera-Quintanar Lucrecia L Ortuño-Sahagún Daniel D Franco-Arroyo Noel N NN Viveros-Paredes Juan M JM Zepeda-Morales Adelaida S AS Lopez-Roa Rocio I RI

International journal of molecular sciences 20181130 12

Obesity is a noncommunicable disease that affects a considerable part of humanity. Recently, it has been recognized that gut microbiota constitutes a fundamental factor in the triggering and development of a large number of pathologies, among which obesity is one of the most related to the processes of dysbiosis. In this review, different animal model approaches, methodologies, and genome scale metabolic databases were revisited to study the gut microbiota and its relationship with metabolic dis ...[more]

PMID: 30513674

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Project description:BackgroundPrimary care in England is facing increasing pressure due to the increasing number and complexity of consultations and the declining number of doctors per head of population. The improvement of primary care efficiency and productivity should be a priority, to ensure that future investments in the medical workforce can cope with the increasingly large and complex demand for care.ObjectivesThis paper presents a systematic literature review of studies that define or measure efficiency in primary care in high-income settings. The review of the existing definitions of primary care efficiency and their limitations will inform future research on the measurement of efficiency in primary care in England and its determinants.MethodsLiterature searches were performed on Embase, Medline, and EconLit in January 2020. The records that passed the screening were reviewed in full text, and data on the study settings, the efficiency definition, and the efficiency analysis were extracted.ResultsOf the 2590 non-duplicate records retrieved from the searches, 38 papers were included in the analysis. The volume of the literature on primary care efficiency has evolved significantly from the 1980s, with the majority of the published studies focussing on European health systems. The setting most often analysed was primary care centres. Output was usually expressed using measures of primary care utilisation, with or without quality adjustments. Reference to the health outcomes achieved was, however, limited. Inputs were more commonly expressed in labour terms, while the exogenous variables related either to the characteristics of the patient population or the organisation of primary care. While all studies included an analysis of technical efficiency, consideration of allocative or cost efficiency or the determinants of productivity (e.g. technological change, skill mix) was rare.ConclusionsThe main limitations that future research on primary care efficiency should address relate to the definition of output. Current approaches to measure the impact on health and the multiple dimensions of output are not sufficient to represent the valued output of primary care. In light of the recent changes in the model of primary care delivery in England, future research should also investigate the impact of technological change on productivity and the scope for substitution across staff roles.

Project description:BackgroundAlthough the past decade has witnessed the development of many self-management mobile health (mHealth) apps that enable users to monitor their health and activities independently, there is a general lack of empirical evidence on the functional and technical aspects of self-management mHealth apps from a software engineering perspective.ObjectiveThis study aims to systematically identify the characteristics and challenges of self-management mHealth apps, focusing on functionalities, design, development, and evaluation methods, as well as to specify the differences and similarities between published research papers and commercial and open-source apps.MethodsThis research was divided into 3 main phases to achieve the expected goal. The first phase involved reviewing peer-reviewed academic research papers from 7 digital libraries, and the second phase involved reviewing and evaluating apps available on Android and iOS app stores using the Mobile Application Rating Scale. Finally, the third phase involved analyzing and evaluating open-source apps from GitHub.ResultsIn total, 52 research papers, 42 app store apps, and 24 open-source apps were analyzed, synthesized, and reported. We found that the development of self-management mHealth apps requires significant time, effort, and cost because of their complexity and specific requirements, such as the use of machine learning algorithms, external services, and built-in technologies. In general, self-management mHealth apps are similar in their focus, user interface components, navigation and structure, services and technologies, authentication features, and architecture and patterns. However, they differ in terms of the use of machine learning, processing techniques, key functionalities, inference of machine learning knowledge, logging mechanisms, evaluation techniques, and challenges.ConclusionsSelf-management mHealth apps may offer an essential means of managing users' health, expecting to assist users in continuously monitoring their health and encourage them to adopt healthy habits. However, developing an efficient and intelligent self-management mHealth app with the ability to reduce resource consumption and processing time, as well as increase performance, is still under research and development. In addition, there is a need to find an automated process for evaluating and selecting suitable machine learning algorithms for the self-management of mHealth apps. We believe that these issues can be avoided or significantly reduced by using a model-driven engineering approach with a decision support system to accelerate and ameliorate the development process and quality of self-management mHealth apps.

Dataset Information

The Human Microbiota and Obesity: A Literature Systematic Review of In Vivo Models and Technical Approaches.

Publications

The Human Microbiota and Obesity: A Literature Systematic Review of In Vivo Models and Technical Approaches.

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