In weeks to months following implantation, neointimal hyperplasia (NIH) in vein grafts (VGs) transitions from a cellularized to a decellularized phenotype. The inhibition of early cellular proliferation failed to improve long-term VG patency. We have previously demonstrated that transforming growth factor-beta(1) (TGF-beta(1))/connective tissue growth factor (CTGF) pathways mediate a conversion of fibroblasts to myofibroblasts in the early VG (<2 wk). We hypothesize that these similar pathways drive fibrosis observed in the late VG lesion. Within rabbit VGs, real-time RT-PCR, Western blot analysis, ELISA, and immunohistochemistry were used to examine TGF-beta/CTGF pathways in late (1-6 mo) NIH. All VGs exhibited a steady NIH growth (P = 0.006) with significant reduction in cellularity (P = 0.01) over time. Substantial TGF-beta profibrotic activities, as evidenced by enhanced TGF-beta(1) activation, TGF-beta receptor types I (activin receptor-like kinase 5)-to-II receptor ratio, SMAD2/3 phosphorylation, and CTGF production, persisted throughout the observation period. An increased matrix synthesis was accompanied by a temporal reduction of matrix metalloproteinase-2 (P = 0.001) and -9 (P < 0.001) activity. VG NIH is characterized by a conversion from a proproliferative to a profibrotic morphology. An enhanced signaling via TGF-beta/CTGF coupled with reduced matrix metalloproteinase activities promotes progressive fibrotic NIH expansion. The modulation of late TGF-beta/CTGF signaling may offer a novel therapeutic strategy to improve the long-term VG durability.

In this diverse sample of patients, survival among Blacks is the shortest after HCC diagnosis. Survival differences reflect a more advanced tumor stage at presentation rather than severity of underlying liver disease precluding treatment. Improving survival in minority populations, in whom HCC incidence is rapidly increasing, requires identification and modification of factors contributing to late-stage presentation.

In order to cope with the energy crisis, the concept of an energy internet (EI) has been proposed as a novel energy structure with high efficiency which allows full play to the advantages of multi-energy coupling. In order to adapt to the multi-energy coupled energy structure and achieve flexible conversion and interaction of multi-energy, the concept of energy routing centers (ERCs) is proposed. A two-layered structure of an ERC is established. Multi-energy conversion devices and connection ports with monitoring functions are integrated in the physical layer which allows multi-energy flow with high flexibility. As for the EI with several ERCs connected to each other, energy flows among them are managed by an energy routing controller located in the information layer. In order to improve the efficiency and reduce the operating cost and environmental cost of the proposed EI, an optimal multi-energy management-based energy routing design problem is researched. Specifically, the voltages of the ERC ports are managed to regulate the power flow on the connection lines and are restricted on account of security operations. An artificial neural network (ANN)-based reinforcement learning algorithm was proposed to manage the optimal energy routing path. Simulations were done to verify the effectiveness of the proposed method.

Heavy metal pollution in the air, water, and soil has attracted substantial interest recently; however, assessment of the total human environmental exposure remains limited. Therefore, determining the total human environmental exposure is imperative for the management and control of heavy metal pollution. This study assessed the total environmental exposure levels of heavy metals as well as the exposure contributions of air, water, and soil, focusing on Hg, Cd, As, Pb, and Cr. Data from 3,855 volunteers from the cities of Taiyuan, Dalian, Shanghai, Wuhan, Chengdu, and Lanzhou allowed for comparison of the exposures in urban and rural areas. The levels of total human environmental exposure of Hg, Cd, As, Pb, and Cr were 1.82 × 10−6 mg/(kg·d), 1.58 × 10−6 mg/(kg·d), 3.87 × 10−5 mg/(kg·d), 1.79 × 10−5 and 7.47 × 10−5 mg/(kg·d), respectively. There were regional, urban-rural, sex, and age differences in the levels of heavy metal exposure. Water pollution was determined to be the largest contributor to heavy metal exposure, accounting for 97.87%, 92.50%, 80.51%, 76.16% and 79.46% of the Hg, Cd, As, Pb, and Cr, followed by air and soil pollution. These results can provide data to inform environmental protection policies and identify the priority pollutants that can help identify and prevent health risks due to overexposure to these heavy metal pollutants.

Air pollution is a major environmental health problem. The study of interaction between air pollution and human will benefit to the human health and well-being of community. Both a model for assessing population relative risk of air pollution exposure (MAPRRAPE) and air pollution concentration methods were applied in a case study to determine the optimal method in evaluating risk of population exposure to Sulfur Dioxide (SO2). The framework for building the MAPRRAPE was described in detail. Then, the spatial patterns of population by demographic characteristics exposed to SO2 from industrial, vehicle, and the mixture of industrial and vehicle pollution sources, as well as an in-depth quantitative investigation using correlation analysis were studied for further source appointment. The results showed that the MAPRRAPE was more reliable than air pollution concentration model in determining population exposure risks by demographic characteristics. The high risk areas of whites exposed to SO2 were larger than blacks and the other races due to a large number of whites, and other age groups exposed to SO2 were larger than children and the old people. In addition, the correlation analyses showed that the relative risks of population by demographic characteristics exposed to SO2 had a more significant correlation with vehicle pollution source than industrial pollution source. The results of source appointment thus demonstrated that vehicle pollution source was the main pollution source. This study suggests that there is a clear need for the implementation of programs and services that will reduce population exposed to air pollution with focusing on densely populated areas for an ultimate improvement of community health status and the environmental conditions.

No abstract

No abstract

Due to the uncertainty of the power load and the randomness of distributed generations, low-voltage direct current (LVDC) bus voltage fluctuation will greatly affect the safety of an energy router-enabled direct current (DC) microgrid. In this paper, a power feedforward control strategy based on a dual active bridge (DAB) DC/DC converter in an energy router-based DC Microgrid is proposed. Based on this strategy, the LVDC bus voltage is controlled by virtual inertia control of the DC microgrid, instead of by the DAB converter. Thus, two benefits of the proposed strategy can be achieved: the power feedforward control can be realized, to mitigate the voltage fluctuation range of the LVDC bus; and the modulation algorithm in the DAB converter can be simplified. Experimental results verify the correctness and effectiveness of the proposed control method.