In this task, you are given an abstract of article. Your task is to generate title for this article. Preferred titles are under thirty words.

We hypothesized that serum cystatin C can be a more predictable marker of arterial stiffness than serum creatinine and creatinine-based glomerular filtration rate (GFR). The aim of this study is to evaluate whether serum cystatin C is related to arterial stiffness independently of serum creatinine in subjects for whom serum creatinine is normal. A total of 2,018 individuals (1,120 males, 898 females) were enrolled. Mean brachial-ankle pulse wave velocity (baPWV) was used as a marker of arterial stiffness and sex-specific analysis was performed. A positive relationship between baPWV and serum cystatin C (Y=1109.0548+329.9102X, r2=0.056, p<0.001) was found in males. Stepwise multivariate regression analysis in males showed that age, waist circumference, heart rate, cystatin C level, triglyceride level, and fasting glucose were independent contributors to baPWV. In females, a positive relationship between baPWV and serum cystatin C (Y=1035.7828+402.2970X, r2=0.090, p<0.001) was found. Stepwise multivariate regression analysis showed that age, heart rate, cystatin C level, fasting glucose and insulin level were independent contributors to baPWV. Age, heart rate, fasting glucose and serum cystatin C were the significant variables in both genders that contributed to baPWV. In conclusion, this study confirmed that serum cystatin C was related to pulse wave velocity even in subjects with normal serum creatinine. This finding suggested that cystatin C could be a more predictable marker of arterial stiffness than serum creatinine and creatinine-based GFR.
Serum Cystatin C Is Related to Pulse Wave Velocity Even in Subjects with Normal Serum Creatinine

TP53 is the most frequently mutated gene in human cancer. Functionally, p53 is activated by a host of stress stimuli and, in turn, governs an exquisitely complex anti-proliferative transcriptional program that touches upon a bewildering array of biological responses. Despite the many unveiled facets of the p53 network, a clear appreciation of how and in what contexts p53 exerts its diverse effects remains unclear. How can we interpret p53's disparate activities and the consequences of its dysfunction to understand how cell type, mutation profile, and epigenetic cell state dictate outcomes, and how might we restore its tumor-suppressive activities in cancer?
Putting p53 in Context

Information on global human movement patterns is central to spatial epidemiological models used to predict the behavior of influenza and other infectious diseases. Yet it remains difficult to test which modes of dispersal drive pathogen spread at various geographic scales using standard epidemiological data alone. Evolutionary analyses of pathogen genome sequences increasingly provide insights into the spatial dynamics of influenza viruses, but to date they have largely neglected the wealth of information on human mobility, mainly because no statistical framework exists within which viral gene sequences and empirical data on host movement can be combined. Here, we address this problem by applying a phylogeographic approach to elucidate the global spread of human influenza subtype H3N2 and assess its ability to predict the spatial spread of human influenza A viruses worldwide. Using a framework that estimates the migration history of human influenza while simultaneously testing and quantifying a range of potential predictive variables of spatial spread, we show that the global dynamics of influenza H3N2 are driven by air passenger flows, whereas at more local scales spread is also determined by processes that correlate with geographic distance. Our analyses further confirm a central role for mainland China and Southeast Asia in maintaining a source population for global influenza diversity. By comparing model output with the known pandemic expansion of H1N1 during 2009, we demonstrate that predictions of influenza spatial spread are most accurate when data on human mobility and viral evolution are integrated. In conclusion, the global dynamics of influenza viruses are best explained by combining human mobility data with the spatial information inherent in sampled viral genomes. The integrated approach introduced here offers great potential for epidemiological surveillance through phylogeographic reconstructions and for improving predictive models of disease control.
Unifying Viral Genetics and Human Transportation Data to Predict the Global Transmission Dynamics of Human Influenza H3N2