KIF2A silencing inhibits the proliferation and migration of breast cancer cells and correlates with unfavorable prognosis in breast cancer

Background; Kinesin family member 2a (KIF2A), a type of motor protein found in eukaryotic cells, is associated with development and progression of various human cancers. The role of KIF2A during breast cancer tumorigenesis and progression was studied. Methods; Immunohistochemical staining, real time RT-PCR and western blot were used to examine the expression of KIF2A in cancer tissues and adjacent normal tissues from breast cancer patients. Patients’ survival in relation to KIF2A expression was estimated using the Kaplan–Meier survival and multivariate analysis. Breast cancer cell line, MDA-MB-231 was used to study the proliferation, migration and invasion of cells following KIF2A-siRNA transfection. Results; The expression of KIF2A in cancer tissues was higher than that in normal adjacent tissues from the same patient (P < 0.05). KIF2A expression in cancer tissue with lymph node metastasis and HER2 positive cancer were higher than that in cancer tissue without (P < 0.05). A negative correlation was found between KIF2A expression levels in breast cancer and the survival time of breast cancer patients (P < 0.05). In addition, multivariate analysis indicated that KIF2A was an independent prognostic for outcome in breast cancer (OR: 16.55, 95% CI: 2.216-123.631, P = 0.006). The proliferation, migration and invasion of cancer cells in vitro were suppressed by KIF2A gene silencing (P < 0.05). Conclusions; KIF2A may play an important role in breast cancer progression and is potentially a novel predictive and prognostic marker for breast cancer

HIV drug resistance in HIV positive individuals under antiretroviral treatment in Shandong Province, China

<div><p>The efficacy of antiretroviral drugs is limited by the development of drug resistance. Therefore, it is important to examine HIV drug resistance following the nationwide implementation of drug resistance testing in China since 2009. We conducted drug resistance testing in patients who were already on or new to HIV antiretroviral therapy (ART) in Shandong Province, China, from 2011 to 2013, and grouped them based on the presence or absence of drug resistance to determine the effects of age, gender, ethnicity, marital status, educational level, route of transmission and treatment status on drug resistance. We then examined levels of drug resistance the following year. The drug resistance rates of HIV patients on ART in Shandong from 2011 to 2013 were 3.45% (21/608), 3.38% (31/916), and 4.29% (54/1259), per year, respectively. <i>M184V</i> was the most frequently found point mutation, conferring resistance to the nucleoside reverse transcriptase inhibitor, while <i>Y181C</i>, <i>G190A</i>, <i>K103N</i> and <i>V179D/E/F</i> were the most frequent point mutations conferring resistance to the non-nucleoside reverse transcriptase inhibitor. In addition, the protease inhibitor drug resistance mutations <i>I54V</i> and <i>V82A</i> were identified for the first time in Shandong Province. Primary resistance accounts for 20% of the impact factors for drug resistance. Furthermore, it was found that educational level and treatment regimen were high-risk factors for drug resistance in 2011 (P<0.05), while treatment regimen was a high risk factor for drug resistance in 2012 and 2013 (P<0.05). Among the 106 drug-resistant patients, 77 received immediate adjustment of treatment regimen following testing, and 69 (89.6%) showed a reduction in drug resistance the following year. HIV drug resistance has a low prevalence in Shandong Province. However, patients on second line ART regimens and those with low educational level need continuous monitoring. Active drug resistance testing can effectively prevent the development of drug resistance.</p></div

Detection of primary resistance in 10 HIV drug-resistant patients.

<p>Detection of primary resistance in 10 HIV drug-resistant patients.</p

Drug resistance mutations and frequencies among HIV patients in 2011–2013.

<p>Note: *indicates second round primer. The drug resistance mutations labeled ‘Other’ indicate that they are related to drug resistance, but the mutations alone do not result in drug resistance.</p

Analysis of impact factors for drug resistance in 2012.

<p>Analysis of impact factors for drug resistance in 2012.</p

Primers for detecting drug resistance.

<p>Primers for detecting drug resistance.</p

A large CVaR-based portfolio selection model with weight constraints

Although the traditional CVaR-based portfolio methods are successfully used in practice, the size of a portfolio with thousands of assets makes optimizing them difficult, if not impossible to solve. In this article we introduce a large CVaR-based portfolio selection method by imposing weight constraints on the standard CVaR-based portfolio selection model, which effectively avoids extreme positions often emerging in traditional methods. We propose to solve the large CVaR-based portfolio model with weight constraints using penalized quantile regression techniques, which overcomes the difficulties of large scale optimization in traditional methods. We illustrate the method via empirical analysis of optimal portfolios on Shanghai and Shenzhen 300 (HS300) index and Shanghai Stock Exchange Composite (SSEC) index of China. The empirical results show that our method is efficient to solve a large portfolio selection and performs well in dispersing tail risk of a portfolio by only using a small amount of financial assets