Hepatic proteomic assessment of oral ingestion of titanium dioxide nano fiber (TDNF) in Sprague Dawley rats.

Titanium dioxide nanofibers (TDNF) have been widely employed in pigments, sunscreens, paints, ointments, toothpaste and photocatalytic splitting of water. However, their potential toxicity has not been thoroughly examined. The goal of the present study is to examine hepatic effects associated with the ingestion of TDNF. TDNF was fabricated via electrospinning method and characterized. Six to seven weeks old male Sprague Dawley rats ingested (oral gavage) a total of 0 ppm, 40, 60 ppm TDNF for two weeks. After sacrifice, the liver was assessed for cellular effects using proteomic approach. The fibers diameter ranged from 0.18 - 0.29 µm, forming clusters and majority of the fibers were in the rutile phase. Proteomics assessment revealed more that more than 400 hundred proteins in the liver may be affected. These proteins are involved in such processes as catalysis of fatty acids by CoA, homocysteine metabolism, beta oxidation and the condensation of carbamoyl phosphate in the urea cycle among others. Further analysis of the protein associations showed that 325 biological processes, 140 molecular functions and 70 cellular components appear to be affected from the ingestion of TNDF. Quantitative analysis of specific mRNA transcripts indicated CMBL, GSTM1 and SDS were differentially expressed.

The Use of Penalized Regression Analysis to Identify County-Level Demographic and Socioeconomic Variables Predictive of Increased COVID-19 Cumulative Case Rates in the State of Georgia.

Systemic inequity concerning the social determinants of health has been known to affect morbidity and mortality for decades. Significant attention has focused on the individual-level demographic and co-morbid factors associated with rates and mortality of COVID-19. However, less attention has been given to the county-level social determinants of health that are the main drivers of health inequities. To identify the degree to which social determinants of health predict COVID-19 cumulative case rates at the county-level in Georgia, we performed a sequential, cross-sectional ecologic analysis using a diverse set of socioeconomic and demographic variables. Lasso regression was used to identify variables from collinear groups. Twelve variables correlated to cumulative case rates (for cases reported by 1 August 2020) with an adjusted r squared of 0.4525. As time progressed in the pandemic, correlation of demographic and socioeconomic factors to cumulative case rates increased, as did number of variables selected. Findings indicate the social determinants of health and demographic factors continue to predict case rates of COVID-19 at the county-level as the pandemic evolves. This research contributes to the growing body of evidence that health disparities continue to widen, disproportionality affecting vulnerable populations.