To investigate the therapeutic effect of different doses of low energy shock wave therapy (LESWT) on the erectile dysfunction (ED) in streptozotocin (STZ) induced diabetic rats. SD rats (n = 75) were randomly divided into 5 groups (normal control, diabetic control, 3 different dose LESWT treated diabetic groups). Diabetic rats were induced by intra-peritoneal injection of STZ (60 mg/kg) and rats with fasting blood glucose ≥ 300 mg/dL were selected as diabetic models. Twelve weeks later, different doses of LESWT (100, 200 and 300 shocks each time) treatment on penises were used to treat ED (7.33 MPa, 2 shocks/s) three times a week for two weeks. The erectile function was evaluated by intracavernous pressure (ICP) after 1 week washout period. Then the penises were harvested for histological study. The results showed LESWT could significantly improve the erectile function of diabetic rats, increase smooth muscle and endothelial contents, up-regulate the expression of α-SMA, vWF, nNOS and VEGF, and down- regulate the expression of RAGE in corpus cavernosum. The therapeutic effect might relate to treatment dose positively, and the maximal therapeutic effect was noted in the LESWT300 group. Consequently, 300 shocks each time might be the ideal LESWT dose for diabetic ED treatment.

Icariin and icariside II (ICA II), 2 active components isolated from herba epimedii, have a closely structural relationship. There is evidence that icariin may be useful in the treatment of erectile dysfunction (ED); however, the study on the therapeutic efficacy of ICA II on ED is currently scant. We investigated the effects of ICA II on improving erectile function of rats with streptozocin-induced diabetes. Fifty 8-week-old Sprague-Dawley rats were randomly distributed into normal control and diabetic groups. Diabetes was induced by a onetime intraperitoneal injection of streptozocin (60 mg/kg). Three days later, the diabetic rats were randomly divided into 4 groups including a saline-treated placebo arm and 3 ICA II-treated models (1, 5, and 10 mg/kg/d). After 3 months, penile hemodynamics was measured by cavernous nerve electrostimulation (CNE) with real time intracorporal pressure assessment. Penises were harvested with subsequent histological examination (picrosirius red stain, Hart elastin stain, and immunohistochemical stain) and Western blots to explore the expression of the nitric oxide-cyclic guanosine monophosphate (NOcGMP) and transforming growth factor b1 (TGFb1)/Smad2 signaling pathways. Diabetes significantly attenuated erectile responses to CNE. Diabetic rats had decreased corpus cavernosum smooth muscle/collagen ratio and endothelial cell content relative to the control group. The ratio of collagen I to III was significantly lower in the corpora of diabetic rats; furthermore, cavernous elastic fibers were fragmented in the diabetic animals. Neuronal nitric oxide synthase (nNOS), endothelial nitric oxide synthase, and vascular endothelial growth factor were expressed at lower levels in the diabetic group; ICA II-treated diabetic rats had higher expression in the penis relative to placebo-treated diabetic animals. Both the TGFb1/Smad2/connective tissue growth factor (CTGF) signaling pathway and apoptosis were down-regulated in the penis from ICA II-treated rats. ICA II treatment attenuates diabetes-related impairment of penile hemodynamics, likely by increasing smooth muscle, endothelial function, and nNOS expression. ICA II could alter corpus cavernosum fibrous-muscular pathological structure in diabetic rats, which could be regulated by the TGFb1/Smad2/CTGF and NO-cGMP signaling pathways.

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To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow predictors, such as recurrent neural network (RNN) and long short-term memory (LSTM), which has demonstrated its promising results. In this paper, different from existing work, we study the temporal convolutional network (TCN) and propose a deep learning framework based on TCN model for short-term city-wide traffic forecast to accurately capture the temporal and spatial evolution of traffic flow. Moreover, we design the model with the Taguchi method to develop an optimized structure of the TCN model, which not only reduces the number of experiments, but also yields high accuracy of forecasting results. With the real-world traffic flow data collected from highways in Birmingham City of U.K., we compare our model with four deep learning based models including LSTM models, GRU models, SAE models, DeepTrend and CNN-LSTM models in terms of the mean absolute error (MAE) and mean relative error (MRE) regarding the actual flow data. The experimental results demonstrate that our framework achieves the state-of-art performance with superior accuracy in short-term traffic flow forecasting. INDEX TERMS Deep learning, temporal convolutional networks, short-term forecasting.