2,497 research outputs found
Salvianolic Acid B Prevents Arsenic Trioxide-Induced Cardiotoxicity In Vivo and Enhances Its Anticancer Activity In Vitro
Clinical attempts to reduce the cardiotoxicity of arsenic trioxide (ATO) without compromising its anticancer activities remain to be an unresolved issue. In this study, we determined whether Sal B can protect against ATO-induced cardiac toxicity in vivo and increase the toxicity of ATO toward cancer cells. Combination treatment of Sal B and ATO was investigated using BALB/c mice and human hepatoma (HepG2) cells and human cervical cancer (HeLa) cells. The results showed that the combination treatment significantly improved the ATO-induced loss of cardiac function, attenuated damage of cardiomyocytic structure, and suppressed the ATO-induced release of cardiac enzymes into serum in BALB/c mouse models. The expression levels of Bcl-2 and p-Akt in the mice treated with ATO alone were reduced, whereas those in the mice given the combination treatment were similar to those in the control mice. Moreover, the combination treatment significantly enhanced the ATO-induced cytotoxicity and apoptosis of HepG2 cells and HeLa cells. Increases in apoptotic marker cleaved poly (ADP-ribose) polymerase and decreases in procaspase-3 expressions were observed through western blot. Taken together, these observations indicate that the combination treatment of Sal B and ATO is potentially applicable for treating cancer with reduced cardiotoxic side effects
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation
Multivariate time series forecasting with hierarchical structure is pervasive
in real-world applications, demanding not only predicting each level of the
hierarchy, but also reconciling all forecasts to ensure coherency, i.e., the
forecasts should satisfy the hierarchical aggregation constraints. Moreover,
the disparities of statistical characteristics between levels can be huge,
worsened by non-Gaussian distributions and non-linear correlations. To this
extent, we propose a novel end-to-end hierarchical time series forecasting
model, based on conditioned normalizing flow-based autoregressive transformer
reconciliation, to represent complex data distribution while simultaneously
reconciling the forecasts to ensure coherency. Unlike other state-of-the-art
methods, we achieve the forecasting and reconciliation simultaneously without
requiring any explicit post-processing step. In addition, by harnessing the
power of deep model, we do not rely on any assumption such as unbiased
estimates or Gaussian distribution. Our evaluation experiments are conducted on
four real-world hierarchical datasets from different industrial domains (three
public ones and a dataset from the application servers of Alipay's data center)
and the preliminary results demonstrate efficacy of our proposed method
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