当前位置: X-MOL 学术Am. J. Respir. Crit. Care Med. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Childhood Asthma: Advances Using Machine Learning and Mechanistic Studies.
American Journal of Respiratory and Critical Care Medicine ( IF 24.7 ) Pub Date : 2019-02-15 , DOI: 10.1164/rccm.201810-1956ci
Sejal Saglani 1 , Adnan Custovic 2
Affiliation  

A paradigm shift brought by the recognition that childhood asthma is an aggregated diagnosis that comprises several different endotypes underpinned by different pathophysiology, coupled with advances in understanding potentially important causal mechanisms, offers a real opportunity for a step change to reduce the burden of the disease on individual children, families, and society. Data-driven methodologies facilitate the discovery of "hidden" structures within "big healthcare data" to help generate new hypotheses. These findings can be translated into clinical practice by linking discovered "phenotypes" to specific mechanisms and clinical presentations. Epidemiological studies have provided important clues about mechanistic avenues that should be pursued to identify interventions to prevent the development or alter the natural history of asthma-related diseases. Findings from cohort studies followed by mechanistic studies in humans and in neonatal mouse models provided evidence that environments such as traditional farming may offer protection by modulating innate immune responses and that impaired innate immunity may increase susceptibility. The key question of which component of these exposures can be translated into interventions requires confirmation. Increasing mechanistic evidence is demonstrating that shaping the microbiome in early life may modulate immune function to confer protection. Iterative dialogue and continuous interaction between experts with different but complementary skill sets, including data scientists who generate information about the hidden structures within "big data" assets, and medical professionals, epidemiologists, basic scientists, and geneticists who provide critical clinical and mechanistic insights about the mechanisms underpinning the architecture of the heterogeneity, are keys to delivering mechanism-based stratified treatments and prevention.

中文翻译:

儿童哮喘:使用机器学习和机械研究的进展。

由于认识到儿童哮喘是一种综合诊断,包括由不同病理生理学支持的几种不同内型,再加上对潜在重要因果机制的理解取得了进展,从而带来了范式转变,为逐步改变以减轻疾病负担提供了真正的机会。个别儿童、家庭和社会。数据驱动的方法有助于发现“大医疗数据”中的“隐藏”结构,以帮助产生新的假设。通过将发现的“表型”与特定机制和临床表现联系起来,这些发现可以转化为临床实践。流行病学研究提供了有关机制途径的重要线索,应采取这些途径来确定干预措施以防止哮喘相关疾病的发展或改变其自然史。队列研究的结果以及人类和新生小鼠模型的机械研究提供的证据表明,传统农业等环境可以通过调节先天免疫反应提供保护,而先天免疫受损可能会增加易感性。这些暴露的哪些部分可以转化为干预措施的关键问题需要确认。越来越多的机械证据表明,在生命早期塑造微生物组可能会调节免疫功能以提供保护。
更新日期:2019-11-01
down
wechat
bug