Quercetin significantly induced the activity of P-gp and this induced effect was more obvious in MDR1 3435 TT individuals.

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Purpose: To investigate whether subconjunctival bevacizumab help prevent corneal graft neovascularization and prolong the graft survival of patients with chemical burns. Methods: We performed a prospective nonrandomized comparative case series study. Twenty-six eyes received subconjunctival bevacizumab (10 mg/0.4 mL) once and topical immunosuppressive agents after sclerocorneal lamellar keratoplasty as the treatment, and 13 eyes received a topical immunosuppressant alone and served as the control group. The main outcomes were a cumulative probability of graft survival, development of corneal neovascularization, and complications. Results: The postoperative follow-up time was 14.3 months (range, 2–62 mo). The cumulative graft survival time was significantly longer in the treatment group than that in the control group (42.9 ± 5.9 vs. 4.8 ± 0.7 mo; log rank < 0.001). In the treatment group, 19 of the 26 grafts (73.1%) survived as transparent with a mean follow-up of 18.7 ± 3.0 months. At the end of the follow-up, 4 grafts remained free of neovascularization, 2 developed edema without neovascularization, and 15 remained transparent with a stable ocular surface and some neovascular vessels in the peripheral transplant interface. The other 5 grafts became opaque and neovascularized. In the control group, all grafts became opaque and neovascularized within the follow-up period (5.5 ± 0.7 mo). During the follow-up, a corneal epithelial defect developed in 9 eyes in the treatment group and 7 in the control group. Conclusions: Early application of subconjunctival bevacizumab after sclerocorneal lamellar keratoplasty can significantly prevent corneal neovascularization and promote graft survival for severe late-stage ocular chemical burns.

Lake water level is an essential indicator of environmental changes caused by natural and human factors. The water level of Poyang Lake, the largest freshwater lake in China, has exhibited a dramatic variation for the past few years, especially after the completion of the Three Gorges Dam (TGD). However, there is a lack of more accurate assessment of the effect of the TGD on the Poyang Lake water level (PLWL) at finer temporal scales (e.g., the daily scale). Here, we used three machine learning models, namely, an Artificial Neural Network (ANN), a Nonlinear Autoregressive model with eXogenous input (NARX), and a Gated Recurrent Unit (GRU), to simulate the daily lake level during 2003-2016. We found that machine learning models with historical memory (i.e., the GRU model) are more suitable for simulating the PLWL under the influence of the TGD. The GRU-based results show that the lake level is significantly affected by the TGD regulation in the different operation stages and in different periods. Although the TGD has had a slight but not very significant impact on the yearly decline of the PLWL, the blocking or releasing of water at the TGD at certain moments has caused large changes in the lake level. This machine-learning-based study sheds light on the interactions between Poyang Lake and the Yangtze River regulated by the TGD.

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