Citations

ABSTRACTIntroduction: The utility of F‐waves in assessing radiculopathies is debated. The aim of this study is to determine the frequency of abnormal minimum tibial F‐wave latencies compared to an F‐estimate and an absolute reference value in patients with electromyography (EMG) confirmed S1 radiculopathies. Methods: A retrospective review of F‐waves in patients with an EMG‐confirmed isolated S1 radiculopathy was performed. The minimum and mean latencies of 8 tibial F‐waves were compared with the calculated F‐estimate and to an absolute reference value, and the frequencies of abnormal responses were determined. Results: Of the 50 patients with an S1 radiculopathy, 4% had prolongation of the minimum reproducible F‐wave latency, and 8% had prolongation of the mean latency relative to the calculated F‐estimate. Conclusions: The minimum and mean F‐wave latencies are infrequently abnormal when compared with an estimated F‐wave latency in S1 radiculopathies and are insensitive in the assessment of S1 nerve root injury. Muscle Nerve 49: 809–813, 2014

Abstract Hypoxia-inducible factor 1 (HIF-1) is a master transcription factor that controls cellular homeostasis. Although its activation benefits normal tissue, HIF-1 activation in tumors is a major risk factor for angiogenesis, therapeutic resistance, and poor prognosis. HIF-1 activity is usually suppressed under normoxic conditions because of rapid oxygen-dependent degradation of HIF-1α. Here, we show that, under normoxic conditions, HIF-1α is upregulated in tumor cells in response to doxorubicin, a chemotherapeutic agent used to treat many cancers. In addition, doxorubicin enhanced VEGF secretion by normoxic tumor cells and stimulated tumor angiogenesis. Doxorubicin-induced accumulation of HIF-1α in normoxic cells was caused by increased expression and activation of STAT1, the activation of which stimulated expression of iNOS and its synthesis of nitric oxide (NO) in tumor cells. Mechanistic investigations established that blocking NO synthesis or STAT1 activation was sufficient to attenuate the HIF-1α accumulation induced by doxorubicin in normoxic cancer cells. To our knowledge, this is the first report that a chemotherapeutic drug can induce HIF-1α accumulation in normoxic cells, an efficacy-limiting activity. Our results argue that HIF-1α-targeting strategies may enhance doxorubicin efficacy. More generally, they suggest a broader perspective on the design of combination chemotherapy approaches with immediate clinical impact. Cancer Res; 73(20); 6230–42. ©2013 AACR.

Abstract Motivation: Batch effects are due to probe-specific systematic variation between groups of samples (batches) resulting from experimental features that are not of biological interest. Principal component analysis (PCA) is commonly used as a visual tool to determine whether batch effects exist after applying a global normalization method. However, PCA yields linear combinations of the variables that contribute maximum variance and thus will not necessarily detect batch effects if they are not the largest source of variability in the data. Results: We present an extension of PCA to quantify the existence of batch effects, called guided PCA (gPCA). We describe a test statistic that uses gPCA to test whether a batch effect exists. We apply our proposed test statistic derived using gPCA to simulated data and to two copy number variation case studies: the first study consisted of 614 samples from a breast cancer family study using Illumina Human 660 bead-chip arrays, whereas the second case study consisted of 703 samples from a family blood pressure study that used Affymetrix SNP Array 6.0. We demonstrate that our statistic has good statistical properties and is able to identify significant batch effects in two copy number variation case studies. Conclusion: We developed a new statistic that uses gPCA to identify whether batch effects exist in high-throughput genomic data. Although our examples pertain to copy number data, gPCA is general and can be used on other data types as well. Availability and implementation: The gPCA R package (Available via CRAN) provides functionality and data to perform the methods in this article. Contact: reesese@vcu.edu or eckel@mayo.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Epileptic encephalopathy syndromes are disorders in which the epileptiform abnormalities are thought to contribute to a progressive cerebral dysfunction. Characteristic electroencephalogram findings have an important diagnostic value in classification of epileptic encephalopathy syndromes. In this paper, we focus on electroencephalogram findings of childhood epileptic encephalopathy syndromes and provide sample illustrations.