A balanced redox state is critical for proper healing. Although human chronic wounds are characterized by high levels of oxidative stress (OS), whether OS levels are critical for chronic wound development is not known. For these studies, we used our chronic wound model in diabetic mice that has similar characteristics as human chronic wounds, including naturally developed biofilm. We hypothesize that OS levels in wound tissues are critical for chronic wound initiation and development. We show that increased OS levels in the wound correlate with increased chronicity. Moreover, without increased OS levels, biofilm taken from chronic wounds and placed in new excision wounds do not create chronic wounds. Similarly, high OS levels in the wound tissue in the absence of the skin microbiome do not lead to chronic wounds. These findings show that both high OS levels and bacteria are needed for chronic wound initiation and development. In conclusion, OS levels in the wound at time of injury are critical for biofilm formation and chronic wound development and may be a good predictor of the degree of wound chronicity. Treating such wounds might be accomplished by managing OS levels with antioxidants combined with manipulation of the skin microbiome after debridement.
The context of comparing two different groups of subjects that are measured repeatedly over time is considered. Our specific focus is on highly variable count data which have a nonnegligible frequency of zeros and have time trends that are difficult to characterize. These challenges are often present when analyzing bacteria or gene expression data sets. Traditional longitudinal data analysis methods, including generalized estimating equations, can be challenged by the features present in these types of data sets. We propose a Bayesian methodology that effectively confronts these challenges. A key feature of the methodology is the use of Gaussian processes to flexibly model the time trends. Inference procedures based on both sharp and interval null hypotheses are discussed, including for the important hypotheses that test for group differences at individual time points. The proposed methodology is illustrated with next‐generation sequencing (NGS) data sets corresponding to two different experimental conditions. In particular, the method is applied to a case study containing bacteria counts of mice with chronic and nonchronic wounds to identify potential wound‐healing probiotics. The methodology can be applied to similar NGS data sets comparing two groups of subjects.
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