Effect of intra-abdominal volume increment on kidneys in minipigs with intra-abdominal hypertension after hemorrhagic shock and resuscitation

Background: To investigate the effect of intra-abdominal volume increment(IAVI) on intra-abdominal hypertension(IAH) in the kidneys.Methods: Eight minipigs were successfully established as IAH models and were randomly divided into two groups: the IAVI group and the sham-operated group. The intravesical pressure, inferior vena cava pressure and urine volume were measured before shock, 2 h after IAH, and 22 h after surgery, respectively. The following indices were measured: serum creatinine, urea nitrogen, renal cortical thickness, ratio of abdominal anteroposterior diameter/transverse diameter, renal thickness, diameter of the renal sinus and the wet/dry ratio of renal tissues.Results: The intravesical pressure(IVP) of the 8 minipig IAH models was calculated to be 21.16±4.63 mmHg. There was a significant increase in the abdominal anteroposterior diameter/transverse diameter ratio. The minipigs in the IAVI group survived during the observational period, whereas 2 minipigs died at 18 h and 20 h in the sham-operated group. Twenty-two hours after surgery, the animals in the IAVI group displayed increased urinary volume(UV) and decreased Cr and Ur and remarkable decreases of VP and IVCP. After IAH, the renal cortical thickness and the renal thickness increased significantly. The renal wet/dry ratio in the sham-operated group was higher than that in the IAVI group.Conclusion: IAVI helps to control renal dysfunction after IAH, which may be related to lowering the intra-abdominal pressure, thus alleviating renal edema and blood stasis.

E-commerce business model mining and prediction

We study the problem of business model mining and prediction in the e-commerce context. Unlike most existing approaches where this is typically formulated as a regression problem or a time-series prediction problem, we take a different formulation to this problem by noting that these existing approaches fail to consider the potential relationships both among the consumers （consumer influence） and among the shops （competitions or collaborations）. Taking this observation into consideration, we propose a new method for e-commerce business model mining and prediction, called EBMM, which combines regression with community analysis. The challenge is that the links in the network are typically not directly observed, which is addressed by applying information diffusion theory through the consumer-shop network. Extensive evaluations using Alibaba Group e-commerce data demonstrate the promise and superiority of EBMM to the state-of-the-art methods in terms of business model mining and prediction.