Physical activity and health of adults with cystic fibrosis

Williams, Craig A.

doi:10.1111/resp.12765

Cited by 2 publications

(2 citation statements)

References 8 publications

(8 reference statements)

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“…According to Menetrey et al (2021) , it was shown that Stenotrophomonas maltophilia (SM) is an emerging pathogen that shares important pathophysiological features with CF pathogens and that SM possesses a large number of adaptive strategies to persist in CF patients. In terms of exercise and health, Williams (2016) found that cystic fibrosis ( CF ) patients who exercised for more than 30 min per day had fewer hospitalizations and improved lung function after 12 months compared to CF patients who exercised for less than 30 min per day. Kalamara et al (2021) also suggested that daily exercise can improve aerobic capacity and slow down the decline of lung function in CF patients, and a combination of aerobic and anaerobic training may be the best training approach for CF patients.…”

Section: Methodsmentioning

confidence: 99%

MSIF-LNP: microbial and human health association prediction based on matrix factorization noise reduction for similarity fusion and bidirectional linear neighborhood label propagation

et al. 2023

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Studies have shown that microbes are closely related to human health. Clarifying the relationship between microbes and diseases that cause health problems can provide new solutions for the treatment, diagnosis, and prevention of diseases, and provide strong protection for human health. Currently, more and more similarity fusion methods are available to predict potential microbe-disease associations. However, existing methods have noise problems in the process of similarity fusion. To address this issue, we propose a method called MSIF-LNP that can efficiently and accurately identify potential connections between microbes and diseases, and thus clarify the relationship between microbes and human health. This method is based on matrix factorization denoising similarity fusion (MSIF) and bidirectional linear neighborhood propagation (LNP) techniques. First, we use non-linear iterative fusion to obtain a similarity network for microbes and diseases by fusing the initial microbe and disease similarities, and then reduce noise by using matrix factorization. Next, we use the initial microbe-disease association pairs as label information to perform linear neighborhood label propagation on the denoised similarity network of microbes and diseases. This enables us to obtain a score matrix for predicting microbe-disease relationships. We evaluate the predictive performance of MSIF-LNP and seven other advanced methods through 10-fold cross-validation, and the experimental results show that MSIF-LNP outperformed the other seven methods in terms of AUC. In addition, the analysis of Cystic fibrosis and Obesity cases further demonstrate the predictive ability of this method in practical applications.

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Section: Methodsmentioning

confidence: 99%

MSIF-LNP: microbial and human health association prediction based on matrix factorization noise reduction for similarity fusion and bidirectional linear neighborhood label propagation

et al. 2023

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“…The important findings from the present study were discussed by Williams in an editorial. 74 Exercise was a theme that was found in both the CF and the paediatric papers this year. A scientific letter from a group in Finland reported that exercise is associated with an increase in nasal patency in healthy children as well as children with atopic asthma and children with sensitization but not asthma.…”

Section: Claire E Wainwrightmentioning

confidence: 99%