No abstract

No abstract

The present study attempts to identify the optimal time duration for the administration of Ad-MSCs, in order to maximize its therapeutic benefits, and compare the degree of fibrosis among three different administration time points using the RILF rat model system. Ad-MSCs were delivered to Sprague-Dawley rats through the tail vein at the following different time points after thorax irradiation: two hours, seven days, and two hours + seven days. Post Ad-MSCs transplantation and the histopathological analysis of the lungs were performed along with analysis of inflammatory cytokine levels, including interleukin (IL)-1, IL-2, IL-6, IL-10 and tumor necrosis factor-α (TNF-α). In particular, pro-fibrotic factors (TGF-β1 and α-SMA) were also evaluated in serum and lung tissues. In addition, it was also determined whether Ad-MSCs had any role in inhibiting the transition of type II alveolar epithelial cells into fibroblasts in the lungs of injured rats. The present results demonstrated that the intravenous delivery of Ad-MSCs twice at the 2-hour and 7-day (R + MSC 2h+7d group) was effective in reducing lung fibrosis for long term durations, when compared with single delivery either at the two-hour or 7-day time points. In addition, a marked anti-inflammatory effect was also observed in RILF rats in the R + MSC 2h+7d group, as indicated by the reduced serum levels of pro-inflammatory cytokines (TNF-α, IL-1 and IL-6) and increased levels of anti-inflammatory cytokines IL-10 and IL-2. Rats that were delivered twice with Ad-MSCs (R + MSC 2h+7d group) exhibited significantly reduced TGF-β1 and α-SMA levels, in contrast to rats in the R + MSC 7d or R + MSC 2h groups, after four weeks. Furthermore, it was also noted that after four weeks, Ad-MSCs increased the number of lung epithelial cells (SP-C) and inhibited the lung fibroblastic cells (α-SMA) of rats in the R + MSC 2h and R + MSC 2h+7d groups. The present study concluded that two injections of Ad-MSCs (R + MSC 2h+7d group) appear to be optimal for therapeutic efficacy and safety during RILF.

No abstract

Metabolic syndrome (MetS) is characterized by the concurrence of multiple metabolic disorders resulting in the increased risk of a variety of diseases related to disrupted metabolism homeostasis. The prevalence of MetS has reached a pandemic level worldwide. In recent years, extensive amount of data have been generated throughout the research targeted or related to the condition with techniques including high-throughput screening and artificial intelligence, and with these “big data”, the prevention of MetS could be pushed to an earlier stage with different data source, data mining tools and analytic tools at different levels. In this review we briefly summarize the recent advances in the study of “big data” applications in the three-level disease prevention for MetS, and illustrate how these technologies could contribute tobetter preventive strategies.