Background: Recent studies suggest that lung microbiome dysbiosis, the disease associated disruption of the lung microbial community, might play a key role in chronic obstructive pulmonary disease (COPD) exacerbations. However, characterising temporal variability of the microbiome from large longitudinal COPD cohorts is needed to better understand this phenomenon.
Methods: We performed a 16S ribosomal RNA survey of microbiome on 716 sputum samples collected longitudinally at baseline and exacerbations from 281 subjects with COPD at three UK clinical centres as part of the COPDMAP consortium.
Results: The microbiome composition was similar among centres and between stable and exacerbations except for a small significant decrease of at exacerbations. The abundance of was negatively associated with bacterial alpha diversity. Microbiomes were distinct between exacerbations associated with bacteria versus eosinophilic airway inflammation. Dysbiosis at exacerbations, measured as significant within subject deviation of microbial composition relative to baseline, was present in 41% of exacerbations. Dysbiosis was associated with increased exacerbation severity indicated by a greater fall in forced expiratory volume in one second, forced vital capacity and a greater increase in CAT score, particularly in exacerbations with concurrent eosinophilic inflammation. There was a significant difference of temporal variability of microbial alpha and beta diversity among centres. The variation of beta diversity significantly decreased in those subjects with frequent historical exacerbations.
Conclusions: Microbial dysbiosis is a feature of some exacerbations and its presence, especially in concert with eosinophilic inflammation, is associated with more severe exacerbations indicated by a greater fall in lung function.
Trial Registration Number: Results, NCT01620645.
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http://dx.doi.org/10.1136/thoraxjnl-2017-210741 | DOI Listing |
Brain Struct Funct
January 2025
Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, HSF III, R1173, Baltimore, MD, 21202, USA.
The brain entropy (BEN) reflects the randomness of brain activity and is inversely related to its temporal coherence. In recent years, BEN has been found to be associated with a number of neurocognitive, biological, and sociodemographic variables such as fluid intelligence, age, sex, and education. However, evidence regarding the potential relationship between BEN and brain structure is still lacking.
View Article and Find Full Text PDFInt J Chron Obstruct Pulmon Dis
January 2025
Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Germany.
Background: Chronic obstructive pulmonary disease (COPD) affects breathing, speech production, and coughing. We evaluated a machine learning analysis of speech for classifying the disease severity of COPD.
Methods: In this single centre study, non-consecutive COPD patients were prospectively recruited for comparing their speech characteristics during and after an acute COPD exacerbation.
Front Sports Act Living
January 2025
University of Maia, Maia, Portugal.
Objective: The study aimed to increase our understanding of offensive transitions in football, focusing on the time spent executing them and how it is influenced by the period in which they occur and the instant score. The objective is to understand its temporal variables and how they influence the unfolding of the scoreline during a match.
Methodology: This study analyzes 1,649 goals resulting from offensive transitions in 1,151 games across three leagues categories: Top, Marginal, and Emerging leagues.
J Med Surg Public Health
December 2024
College of Nursing, Michigan State University, Michigan, Life Science, 1355 Bogue St Room A218, East Lansing, MI 48824, USA.
In-hospital cardiac arrest (IHCA) has been understudied relative to out-of-hospital cardiac arrest. Further, studies of IHCA have mainly focused on a limited number of pre-arrest patient characteristics (e.g.
View Article and Find Full Text PDFACS Omega
January 2025
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
It is of great significance to realize the accurate prediction of the key output response of the chemical synthetic ammonia process for optimizing system performance and operation monitoring. Because many key intermediate variables of complex systems are difficult to measure comprehensively, there are great difficulties and errors in mechanism analysis and identification modeling techniques. Based on random forest (RF) variable selection, a deep neural network combining temporal convolutional network (TCN) and transformer is proposed to predict the output variables of the synthetic ammonia process.
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