Chronic Muscle Weakness and Mitochondrial Dysfunction in the Absence of Sustained Atrophy in a Preclinical Sepsis Model

Chronic critical illness is a global clinical issue affecting millions of sepsis survivors annually. Survivors report chronic skeletal muscle weakness and development of new functional limitations that persist for years. To delineate mechanisms of sepsis-induced chronic weakness, we first surpassed a critical barrier by establishing a murine model of sepsis with ICU-like interventions that allows for the study of survivors. We show that sepsis survivors have profound weakness for at least 1 month, even after recovery of muscle mass. Abnormal mitochondrial ultrastructure, impaired respiration and electron transport chain activities, and persistent protein oxidative damage were evident in the muscle of survivors. Our data suggest that sustained mitochondrial dysfunction, rather than atrophy alone, underlies chronic sepsis-induced muscle weakness. This study emphasizes that conventional efforts that aim to recover muscle quantity will likely remain ineffective for regaining strength and improving quality of life after sepsis until deficiencies in muscle quality are addressed

Metformin Enhances Autophagy and Normalizes Mitochondrial Function to Alleviate Aging-Associated Inflammation

Age is a non-modifiable risk factor for the inflammation that underlies age-associated diseases; thus, anti-inflammaging drugs hold promise for increasing health span. Cytokine profiling and bioinformatic analyses showed that Th17 cytokine production differentiates CD4+ T cells from lean, normoglycemic older and younger subjects, and mimics a diabetes-associated Th17 profile. T cells from older compared to younger subjects also had defects in autophagy and mitochondrial bioenergetics that associate with redox imbalance. Metformin ameliorated the Th17 inflammaging profile by increasing autophagy and improving mitochondrial bioenergetics. By contrast, autophagy-targeting siRNA disrupted redox balance in T cells from young subjects and activated the Th17 profile by activating the Th17 master regulator, STAT3, which in turn bound IL-17A and F promoters. Mitophagy-targeting siRNA failed to activate the Th17 profile. We conclude that metformin improves autophagy and mitochondrial function largely in parallel to ameliorate a newly defined inflammaging profile that echoes inflammation in diabetes

PAI-1 as a critical factor in the resolution of sepsis and acute kidney injury in old age

Elevated plasma levels of plasminogen activator inhibitor type 1 (PAI-1) are documented in patients with sepsis and levels positively correlate with disease severity and mortality. Our prior work demonstrated that PAI-1 in plasma is positively associated with acute kidney injury (AKI) in septic patients and mice. The objective of this study was to determine if PAI-1 is causally related to AKI and worse sepsis outcomes using a clinically-relevant and age-appropriate murine model of sepsis. Sepsis was induced by cecal slurry (CS)-injection to wild-type (WT, C57BL/6) and PAI-1 knockout (KO) mice at young (5–9 months) and old (18–22 months) age. Survival was monitored for at least 10 days or mice were euthanized for tissue collection at 24 or 48 h post-insult. Contrary to our expectation, PAI-1 KO mice at old age were significantly more sensitive to CS-induced sepsis compared to WT mice (24% vs. 65% survival, p = 0.0037). In comparison, loss of PAI-1 at young age had negligible effects on sepsis survival (86% vs. 88% survival, p = 0.8106) highlighting the importance of age as a biological variable. Injury to the kidney was the most apparent pathological consequence and occurred earlier in aged PAI-1 KO mice. Coagulation markers were unaffected by loss of PAI-1, suggesting thrombosis-independent mechanisms for PAI-1-mediated protection. In summary, although high PAI-1 levels are clinically associated with worse sepsis outcomes, loss of PAI-1 rendered mice more susceptible to kidney injury and death in a CS-induced model of sepsis using aged mice. These results implicate PAI-1 as a critical factor in the resolution of sepsis in old age

Deriving the Distributions and Developing Methods of Inference for R\u3csup\u3e2\u3c/sup\u3e-type Measures, with Applications to Big Data Analysis

As computing capabilities and cloud-enhanced data sharing has accelerated exponentially in the 21st century, our access to Big Data has revolutionized the way we see data around the world, from healthcare to investments to manufacturing to retail and supply-chain. In many areas of research, however, the cost of obtaining each data point makes more than just a few observations impossible. While machine learning and artificial intelligence (AI) are improving our ability to make predictions from datasets, we need better statistical methods to improve our ability to understand and translate models into meaningful and actionable insights. A central goal in the world of statistics and data science is the construction of linear regression models for continuous variables of interest. Often, our objective is to examine the impact of one or more explanatory variables, after adjusting for demographic variables or some other known/relevant covariate(s). While the traditional methodology uses a combination of partial F-tests and individual t-tests to determine statistical significance, we know that p-values obtained from such methods are heavily dependent on sample size. This is particularly problematic for large datasets or overpowered studies, where even the tiniest of effects will appear to be highly significant, or for extremely small datasets, where real effects may not reach statistical significance. The coefficient of partial determination (also known as partial R2) is widely used in the applied sciences to supplement hypothesis testing, but little work has been done to understand its statistical properties. In this dissertation, the exact, complete distribution of partial R2 is derived, accompanied by simulation studies and real-world data examples to show the advantages of adding coefficients of determination to the analysis of quantitative data models, regardless of sample size. Additionally, two novel inference methods are proposed for both R2 and partial R2, which build on these distributional results to provide better coverage and more focused intervals for models built using small- and medium-sized datasets

Social support for exercise from pregnancy to postpartum and the potential impact of a mobile application: A randomized control pilot trial in Southern United States

This study compared perceived social support among women of all body mass index (BMI) categories with an attempt to assess the efficacy of the BumptUp® mobile application to improve social support for exercise during pregnancy and postpartum. Thirty-five pregnant women living in Southern United States were included in the sample. The intervention group received access to the BumptUp® mobile application that was designed to promote physical activity during pregnancy and postpartum. The control group received an evidence-based educational brochure. Perceived social support for exercise was assessed at four-time points using the social support and exercise survey. Outcomes were evaluated at 23–25, 35–37 gestational weeks, and 6 and 12 weeks postpartum. Based on their pre-pregnancy weight and height, BMI was computed to categorize participants into lean, overweight, and obese groups. Social support across BMI categories and between control and intervention groups were compared using linear mixed-effect models. Women grouped in the overweight and obese BMI categories reported receiving significantly lower levels of social support for exercise than women in the lean category throughout pregnancy and postpartum during mid-pregnancy, late pregnancy, and at 12 weeks postpartum (p  0.05). Women with a pre-pregnancy BMI of overweight and obese received lower social support for exercise during pregnancy and postpartum. The efficacy of BumptUp® to improve perceived social support for exercise in pregnancy and postpartum was not evident in the results

A Pilot Study on the Impact of the BumptUp^® Mobile App on Physical Activity during and after Pregnancy

To combat maternal morbidity and mortality, interventions designed to increase physical activity levels during and after pregnancy are needed. Mobile phone-based interventions show considerable promise, and BumptUp® has been carefully developed to address the lack of exercise among pregnant and postpartum women. The primary goal of this pilot study was to test the potential efficacy of BumptUp® for improving physical activity among pregnant and postpartum women. A randomized controlled clinical trial was performed (N = 35) with women either receiving access to the mhealth app or an educational brochure. Physical activity and self-efficacy for exercise data were collected at baseline (in mid-pregnancy) and at three additional timepoints (late pregnancy, 6 and 12 weeks postpartum). For moderate-to-vigorous physical activity, a clear trend is observed as the mean estimated difference between groups increases from −0.35 (SE: 1.75) in mid-pregnancy to −0.81 (SE: 1.75) in late pregnancy. For self-efficacy for exercise, the estimated difference of means (control–intervention) changed from 0.96 (SE: 6.53) at baseline to −7.64 (SE: 6.66) in late pregnancy and remained at −6.41 (SE: 6.79) and −6.70 (SE: 6.96) at 6 and 12 weeks postpartum, respectively. When assessing the change in self-efficacy from mid-to -ate pregnancy only, there was a statistically significant difference between groups (p = 0.044). BumptUp® (version 1.0 (3)) shows potential for efficacy. Pilot data suggest key refinements to be made and a larger clinical trial is warranted

A Pilot Study on the Impact of the BumptUp® Mobile App on Physical Activity during and after Pregnancy

To combat maternal morbidity and mortality, interventions designed to increase physical activity levels during and after pregnancy are needed. Mobile phone-based interventions show considerable promise, and BumptUp® has been carefully developed to address the lack of exercise among pregnant and postpartum women. The primary goal of this pilot study was to test the potential efficacy of BumptUp® for improving physical activity among pregnant and postpartum women. A randomized controlled clinical trial was performed (N = 35) with women either receiving access to the mhealth app or an educational brochure. Physical activity and self-efficacy for exercise data were collected at baseline (in mid-pregnancy) and at three additional timepoints (late pregnancy, 6 and 12 weeks postpartum). For moderate-to-vigorous physical activity, a clear trend is observed as the mean estimated difference between groups increases from −0.35 (SE: 1.75) in mid-pregnancy to −0.81 (SE: 1.75) in late pregnancy. For self-efficacy for exercise, the estimated difference of means (control–intervention) changed from 0.96 (SE: 6.53) at baseline to −7.64 (SE: 6.66) in late pregnancy and remained at −6.41 (SE: 6.79) and −6.70 (SE: 6.96) at 6 and 12 weeks postpartum, respectively. When assessing the change in self-efficacy from mid-to -ate pregnancy only, there was a statistically significant difference between groups (p = 0.044). BumptUp® (version 1.0 (3)) shows potential for efficacy. Pilot data suggest key refinements to be made and a larger clinical trial is warranted