Inhibitory effect of liposomal quercetin on acute hepatitis and hepatic fibrosis induced by concanavalin A

Immune response plays an important role in the development of hepatic fibrosis. In the present study, we investigated the effects of quercetin on hepatitis and hepatic fibrosis induced by immunological mechanism. In the acute hepatitis model, quercetin (2.5 mg/kg) was injected iv into mice 30 min after concanavalin A (Con A) challenge. Mice were sacrificed 4 or 24 h after Con A injection, and aminotransferase tests and histopathological sections were performed. Treatment with quercetin significantly decreased the levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Consistent with this observation, treatment with quercetin markedly attenuated the pathologic changes in the liver. A hepatic fibrosis model was also generated in mice by Con A challenge once a week for 6 consecutive weeks. Mice in the experimental group were treated with daily iv injections of quercetin (0.5 mg/kg). Histopathological analyses revealed that treatment with quercetin markedly decreased collagen deposition, pseudolobuli development, and hepatic stellate cells activation. We also examined the effects of quercetin on the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and transforming growth factor beta (TGF-β) pathways by immunohistochemistry and real-time reverse transcriptase-polymerase chain reaction (RT-PCR). NF-κB and TGF-β production was decreased after treatment with quercetin, indicating that the antifibrotic effect of quercetin is associated with its ability to modulate NF-κB and TGF-β production. These results suggest that quercetin may be an effective therapeutic strategy in the treatment of patients with liver damage and fibrosis.

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.