Distinguishing cancerous from non-cancerous cells through analysis of electrical noise

Since 1984, electric cell-substrate impedance sensing (ECIS) has been used to monitor cell behavior in tissue culture and has proven sensitive to cell morphological changes and cell motility. We have taken ECIS measurements on several cultures of non-cancerous (HOSE) and cancerous (SKOV) human ovarian surface epithelial cells. By analyzing the noise in real and imaginary electrical impedance, we demonstrate that it is possible to distinguish the two cell types purely from signatures of their electrical noise. Our measures include power-spectral exponents, Hurst and detrended fluctuation analysis, and estimates of correlation time; principal-component analysis combines all the measures. The noise from both cancerous and non-cancerous cultures shows correlations on many time scales, but these correlations are stronger for the non-cancerous cells.Comment: 8 pages, 4 figures; submitted to PR

The Population Structure of Ten Newfoundland Outports

This is the published version. Copyright 2000 Wayne State University Press.Island populations are most informative in the study of the genetic structure of human aggregates. These populations are often of small size, thus violating the Hardy-Weinberg assumption of infinite size. Some geographically isolated island populations are further subdivided by religion, ethnicity, and socioeconomic factors, reducing their effective sizes and facilitating genetic changes due to stochastic processes. Because of extreme geographic and social isolation, fishing communities or outports of Newfoundland have been investigated for genetic micro-differentiation through the founder effect and genetic drift (Crawford et al. 1995). The purpose of this paper is to examine the population structure of 10 Newfoundland outports using the allelic frequencies derived from 12 red cell antigens. To achieve this goal, first we calculated gene frequencies using maximum-likelihood estimation procedures. Second, we used /{-matrix methods to explore population differentiation. Third, we regressed mean per-locus heterozygosity on genetic distance from the gene frequency centroid to identify the most isolated populations. On the basis of this information, the three outports of Seal Cove, Island Harbor, and Tilting were found to be genetically differentiated from the other small populations. Moreover, religious and geographic subdivisions appear to explain the observed genetic variation

Multiresolution analysis of active region magnetic structure and its correlation with the Mt. Wilson classification and flaring activity

Two different multi-resolution analyses are used to decompose the structure of active region magnetic flux into concentrations of different size scales. Lines separating these opposite polarity regions of flux at each size scale are found. These lines are used as a mask on a map of the magnetic field gradient to sample the local gradient between opposite polarity regions of given scale sizes. It is shown that the maximum, average and standard deviation of the magnetic flux gradient for alpha, beta, beta-gamma and beta-gamma-delta active regions increase in the order listed, and that the order is maintained over all length-scales. This study demonstrates that, on average, the Mt. Wilson classification encodes the notion of activity over all length-scales in the active region, and not just those length-scales at which the strongest flux gradients are found. Further, it is also shown that the average gradients in the field, and the average length-scale at which they occur, also increase in the same order. Finally, there are significant differences in the gradient distribution, between flaring and non-flaring active regions, which are maintained over all length-scales. It is also shown that the average gradient content of active regions that have large flares (GOES class 'M' and above) is larger than that for active regions containing flares of all flare sizes; this difference is also maintained at all length-scales.Comment: Accepted for publication in Solar Physic

Socio-Economic Status and Pregnancy Outcome: An Australian Study

A prospective cohort of 8556 pregnant women attending the Mater Misericordiae Mothers' Hospital in Brisbane was examined to consider the impact of socio-economic status on pregnancy outcome. The indicators of socio-economic status selected were family income, maternal education and paternal occupational status. Pregnancy outcomes considered were preterm delivery, low birthweight, low birthweight for gestational age, and perinatal death. Subsidiary analyses were also undertaken for Apgar scores, time to establish respiration, need for mechanical respiration and admission to intensive care. Before adjustment, the main consistent association was between the occupational status of the father and three measures of perinatal morbidity. Initial adjustment for the mother's socio-demographic background and weight/height ratio reduced the strength and statistical significance of the above associations, while further adjustment for lifestyle variations between the three status groups further reduced the above associations to marginal statistical significance. The findings suggest that observed class differences in pregnancy outcome are attributable to the mother's personal characteristics (height/weight, parity) and her lifestyle

Differential exposure and reactivity to interpersonal stress predict sex differences in adolescent depression

This study tested the hypothesis that higher rates of depression in adolescent girls are explained by their greater exposure and reactivity to stress in the interpersonal domain in a large sample of 15-year-olds. Findings indicate that adolescent girls experienced higher levels of total and interpersonal episodic stress, whereas boys experienced higher levels of chronic stress (academic and close friendship domains). Higher rates of depression in girls were explained by their greater exposure to total stress, particularly interpersonal episodic stress. Adolescent girls were also more reactive (more likely to become depressed) to both total and interpersonal episodic stress. The findings suggest that girls experience higher levels of episodic stress and are more reactive to these stressors, increasing their likelihood of becoming depressed compared to boys. Results were discussed in terms of girls' greater interpersonal focus and implications for understanding sex differences in depression

Real-world experience among patients with relapsed/refractory mantle cell lymphoma after Bruton tyrosine kinase inhibitor failure in Europe: The SCHOLAR-2 retrospective chart review study

Mantle cell lymphoma (MCL) after relapse is associated with poor prognosis. No standard of care exists and available evidence for treatments is limited, particularly in patients who fail Bruton tyrosine kinase inhibitor (BTKi) therapy. This multicentre retrospective chart review study, SCHOLAR-2, addresses this knowledge gap and reports on data collected from 240 patients with relapsed/refractory MCL in Europe who were treated with BTKi-based therapy between July 2012 and July 2018, and had experienced disease progression while on BTKi therapy or discontinued BTKi therapy due to intolerance. The median overall survival (OS) from initiation of first BTKi therapy was 14.6 months (95% confidence interval [CI] 11.6–20.0) in the overall cohort, 5.5 months (95% CI 3.9–8.2) in 91 patients without post-BTKi therapy, and 23.8 months (95% CI 18.9–30.1) in 149 patients who received post-BTKi therapy (excluding chimeric antigen receptor T-cell treatment). In the latter group, patients received a median of one (range, one to seven) line of post-BTKi therapy, with lenalidomide-containing regimens and bendamustine plus rituximab being the most frequently administered; the median OS from initiation of first post-BTKi therapy was 9.7 months (95% CI 6.3–12.7). These results provide a benchmark for survival in patients with R/R MCL receiving salvage therapy after BTKi failure

Prediction of Obesity in Children at 5 years: A Cohort Study

Objective To examine determinants of moderate and severe obesity in children at 5 years of age. Methodology A prospective cohort of mothers were enrolled at first antenatal visit, and interviewed shortly after delivery, at 6 months and 5 years. Detailed health, psychological and social questionnaires were completed at each phase by mothers, and child health questionnaires at 6 months and 5 years. At 5 years 4062 children were assessed physically, the Peabody Picture Vocabulary Test administered and mothers completed a modified Child Behaviour Checklist. Moderate obesity was defined as BMI between 85th and 94th percentiles inclusively, and severe obesity as a BMI greater than the 94th percentile. Results Independent predictors of severe obesity at 5 years were birthweight, female gender, maternal BMI and paternal BMI. Moderate obesity at 5 years was predicted by birthweight, paternal BMI and sleeplessness at 6 months, while small for gestational age (SGA) status and feeding problems at 6 months were protective factors for moderate obesity. Obesity was not associated with problems of language comprehension or behaviour. Conclusions Findings of this study suggest that biological rather than psychosocial factors are the major determinants of obesity at 5 years

State-of-the art data normalization methods improve NMR-based metabolomic analysis

Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples