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The article presents a study on the capability of S100B to regulate apoptosis in the post-myocardial infarction (MI) myocardium in the U.S. The study intends to investigate the function of S100B and receptor for advanced glycation end products (RAGE). The findings reveal that the interaction of RAGE and its ligand S100 B after myocardial infarction may have a potential function in myocyte apoptosis.

Amphoterin is a ubiquitous and highly conserved protein previously considered solely as a chromatin-associated, nuclear molecule. Amphoterin is released into the extracellular space by various cell types, and plays an important role in the regulation of cell migration, differentiation, tumorigenesis and inflammation. This paper reviews recent research on the mechanistic background underlying the biology of secreted amphoterin, with an emphasis on the role of amphoterin as an autocrine/paracrine regulator of cell migration. [ABSTRACT FROM AUTHOR]

Abstract Background Periodic phenomena are widespread in biology. The problem of finding periodicity in biological time series can be viewed as a multiple hypothesis testing of the spectral content of a given time series. The exact noise characteristics are unknown in many bioinformatics applications. Furthermore, the observed time series can exhibit other non-idealities, such as outliers, short length and distortion from the original wave form. Hence, the computational methods should preferably be robust against such anomalies in the data. Results We propose a general-purpose robust testing procedure for finding periodic sequences in multiple time series data. The proposed method is based on a robust spectral estimator which is incorporated into the hypothesis testing framework using a so-called g-statistic together with correction for multiple testing. This results in a robust testing procedure which is insensitive to heavy contamination of outliers, missing-values, short time series, nonlinear distortions, and is completely insensitive to any monotone nonlinear distortions. The performance of the methods is evaluated by performing extensive simulations. In addition, we compare the proposed method with another recent statistical signal detection estimator that uses Fisher's test, based on the Gaussian noise assumption. The results demonstrate that the proposed robust method provides remarkably better robustness properties. Moreover, the performance of the proposed method is preferable also in the standard Gaussian case. We validate the performance of the proposed method on real data on which the method performs very favorably. Conclusion As the time series measured from biological systems are usually short and prone to contain different kinds of non-idealities, we are very optimistic about the multitude of possible applications for our proposed robust statistical periodicity detection method. Availability The presented methods have been implemented in Matlab and in R. Codes are available on request. Supplementary material is available at: http://www.cs.tut.fi/sgn/csb/robustperiodic/.