Objectives Idiopathic intracranial hypertension (IIH) is characterized by elevated intracranial pressure of unknown etiology and venous sinus stenting may be an optional treatment. We aimed to evaluate the effects of venous sinus stenting on visual function, intracranial pressure, and trans‐stenotic pressure gradient of the patients with IIH and to determine effects of baseline BMI or weight changes on subjective vision outcome and intracranial pressure. Methods From July 2009 to Aug 2016, 88 eligible patients with IIH and venous sinus stenosis who underwent stenting were retrospectively studied. Results In this study, 67 women and 21 men were included with an average age of 39.01 (18–60) years. The average BMI was 26.75 kg/m 2 . Here, 66 (75.9%) patients had papilledema, 39 had impaired vision before stenting; 57 patients were followed‐up, 48 (84.2%) showed significant subjective improvement or recovery in visual acuity, 4 (7.0%) patients reported no significant change in visual functions, and 5 (8.8%) suffered permanent vision loss. The cerebrospinal fluid opening pressure and trans‐stenotic pressure gradient were significantly decreased postoperatively. Baseline BMI was associated with pre‐and postoperative trans‐stenotic pressure gradients, as well as changes in cerebrospinal fluid opening pressure. However, baseline BMI and body weight changes during follow‐up were not necessarily associated with subjective visual outcomes after stenting. Stenting efficacy was limited in patients with severe preoperative optic symptoms. Conclusions Venous sinus stenting represented an effective treatment for resolving visual dysfunction and intracranial pressure associated with venous sinus stenosis. BMI seemed to be associated with intracranial pressure but not subjective visual outcomes after stenting.

Background To evaluate the EZR (ezrin) gene expression in breast cancer and correlation with the prognosis through bioinformatics analysis and immunohistochemistry assay. Methods EZR gene expression in breast cancer and corresponding normal breast tissue was compared in the TCGA database. Protein‐protein interaction (PPI) network relevant EZR was established through the STRING database. The correlation between EZR expression and prognosis of breast cancer was analyzed by the log‐rank analysis from the TCGA. Ezrin protein (coded by EZR) expression was also examined by immunohistochemistry assay in 120 breast cancer patients. Results EZR expression level in tumor tissue was significantly upregulated compared to that of normal breast tissue of breast cancer patients (P < 0.05). In the PPI analysis, there were 51 nodes and 455 edges in the network. The top 10 hub genes of the network were identified. High expression of EZR mRNA was correlated with poor overall survival (OS) of the breast cancer patients (HR = 1.40, P = 0.038). However, the disease‐free survival (DFS) of breast cancer patients did not correlate with the EZR mRNA level (HR = 0.86, P = 0.44). The ezrin protein expression was positive with uniform brown‐yellow granules in the cell membrane, cavity surface and cytoplasm of the breast cancer cells. Of the included 120 cancer samples, 98 cases were positive for ezrin expression and 22 were negative. No correlation was found between ezrin expression site and patients’ clinicopathological features. Conclusion EZR is upregulated in breast cancer and can be used as potential biomarker for overall survival.

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We present a simulation-and-regression method for solving dynamic portfolio allocation problems in the presence of general transaction costs, liquidity costs and market impacts. This method extends the classical least squares Monte Carlo algorithm to incorporate switching costs, corresponding to transaction costs and transient liquidity costs, as well as multiple endogenous state variables, namely the portfolio value and the asset prices subject to permanent market impacts. To do so, we improve the accuracy of the control randomization approach in the case of discrete controls, and propose a global iteration procedure to further improve the allocation estimates. We validate our numerical method by solving a realistic cash-and-stock portfolio with a power-law liquidity model.We quantify the certainty equivalent losses associated with ignoring liquidity effects, and illustrate how our dynamic allocation protects the investor's capital under illiquid market conditions. Lastly, we analyze, under different liquidity conditions, the sensitivities of certainty equivalent returns and optimal allocations with respect to trading volume, stock price volatility, initial investment amount, risk-aversion level and investment horizon.The effect of liquidity on the design of dynamic multi-period portfolio selection methods (a.k.a. asset allocation, portfolio optimization or portfolio management) has drawn great attention from academics and practitioners alike. Liquidity affects portfolio allocation in two main ways: temporary liquidity cost and permanent market impact. Liquidity cost, also known as implementation shortfall, temporary market impact or transitory market impact, is the difference between the realized transaction price and the pre-transaction price. Market impact is the permanent shift in the asset price after a transaction, due to the post-transaction "resilience" of the limit order book. These liquidity effects depend on several factors, such as the nature of the exchange platform, the duration of the trade execution, the transaction volume, the asset volatility and so on. Up to now, liquidity modeling for dynamic portfolio selection has been impeded by the intractability of analytical solutions and by the limited capability of numerical methods to handle endogenous stochastic prices. The purpose of the present paper is to introduce a new simulation-and-regression method capable of handling multivariate portfolio allocation problems under general transaction costs, liquidity costs and market impacts. The original literature on dynamic portfolio selection started with simple problems without transaction costs. The seminal papers, Mossin (1968), Samuelson (1969), Merton (1969) and Merton (1971) provide closed-form solutions of optimal asset allocation strategies for long-term investors. In reality though, every transaction incurs commission fee (or brokerage cost), and several improvements have therefore been proposed to account for transaction cost. Examples of closed-form solutions are Davis and Norman (1990), Shr...