Role of PPARγ in dyslipidemia and altered pulmonary functioning in mice following ozone exposure.

Exposure to ozone causes decrements in pulmonary function, a response associated with alterations in lung lipids. Pulmonary lipid homeostasis is dependent on the activity of peroxisome proliferator activated receptor gamma (PPARγ), a nuclear receptor that regulates lipid uptake and catabolism by alveolar macrophages (AMs). Herein, we assessed the role of PPARγ in ozone-induced dyslipidemia and aberrant lung function in mice. Exposure of mice to ozone (0.8 ppm, 3 h) resulted in a significant reduction in lung hysteresivity at 72 h post exposure; this correlated with increases in levels of total phospholipids, specifically cholesteryl esters, ceramides, phosphatidylcholines, phosphorylethanolamines, sphingomyelins, and di- and triacylglycerols in lung lining fluid. This was accompanied by a reduction in relative surfactant protein-B (SP-B) content, consistent with surfactant dysfunction. Administration of the PPARγ agonist, rosiglitazone (5 mg/kg/day, i.p.) reduced total lung lipids, increased relative amounts of SP-B, and normalized pulmonary function in ozone-exposed mice. This was associated with increases in lung macrophage expression of CD36, a scavenger receptor important in lipid uptake and a transcriptional target of PPARγ. These findings highlight the role of alveolar lipids as regulators of surfactant activity and pulmonary function following ozone exposure and suggest that targeting lipid uptake by lung macrophages may be an efficacious approach for treating altered respiratory mechanics.

Impacts of the COVID-19 pandemic on people with spinal cord injury.

PURPOSE: Few studies have examined the impacts of the COVID-19 pandemic on the lives of people with spinal cord injury (SCI), a population uniquely vulnerable to pandemic-related stressors. This study examines the impact of the pandemic on three life domains (psychosocial health, health and health behavior, and social participation) and identifies risk factors for adverse psychosocial health impacts in a sample of people with SCI. METHOD: A diverse sample of 346 adults with SCI completed a survey assessing demographic, disability, health, and social characteristics, and perceived impacts of the pandemic. RESULTS: Many respondents reported no change on items reflecting psychosocial health, health and health behavior, and social participation; however, among those reporting change, more reported negative than positive impacts. Negative impacts were most striking with regard to psychosocial health and social engagement, with approximately half reporting a worsening of stress, depression, anxiety, and loneliness and a reduction in face-to-face interactions and participation in life roles. Regression analyses revealed that those at greater risk of adverse psychosocial impacts were women, were non-Black, were in poorer health, had greater unmet care needs, and were less satisfied with their social roles and activities. CONCLUSIONS: Although not universal, negative impacts were reported by many respondents 9-15 months into the pandemic. Future research should examine the impacts of the pandemic over time and on a wider range of outcomes. Such research could generate substantial benefits in understanding, preventing, or minimizing the adverse effects of the evolving pandemic and future public health emergencies in people with SCI. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

In silico estimates of tissue components in surgical samples based on expression profiling data.

Tissue samples from many diseases have been used for gene expression profiling studies, but these samples often vary widely in the cell types they contain. Such variation could confound efforts to correlate expression with clinical parameters. In principle, the proportion of each major tissue component can be estimated from the profiling data and used to triage samples before studying correlations with disease parameters. Four large gene expression microarray data sets from prostate cancer, whose tissue components were estimated by pathologists, were used to test the performance of multivariate linear regression models for in silico prediction of major tissue components. Ten-fold cross-validation within each data set yielded average differences between the pathologists' predictions and the in silico predictions of 8% to 14% for the tumor component and 13% to 17% for the stroma component. Across independent data sets that used similar platforms and fresh frozen samples, the average differences were 11% to 12% for tumor and 12% to 17% for stroma. When the models were applied to 219 arrays of "tumor-enriched" samples in the literature, almost one quarter were predicted to have 30% or less tumor cells. Furthermore, there was a 10.5% difference in the average predicted tumor content between 37 recurrent and 42 nonrecurrent cancer patients. As a result, genes that correlated with tissue percentage generally also correlated with recurrence. If such a correlation is not desired, then some samples might be removed to rebalance the data set or tissue percentages might be incorporated into the prediction algorithm. A web service, "CellPred," has been designed for the in silico prediction of sample tissue components based on expression data.