Carnosine has been shown to be present in the skeletal muscle and in the brain of a variety of animals and humans. Despite the various physiological functions assigned to this metabolite, its exact role remains unclear. It has been suggested that carnosine plays a role in buffering in the intracellular physiological pHi range in skeletal muscle as a result of accepting hydrogen ions released in the development of fatigue during intensive exercise. It is thus postulated that the concentration of carnosine is an indicator for the extent of the buffering capacity. However, the determination of the concentration of this metabolite has only been performed by means of muscle biopsy, which is an invasive procedure. In this paper, we utilized proton magnetic resonance spectroscopy (1H MRS) in order to perform absolute quantification of carnosine in vivo non-invasively. The method was verified by phantom experiments and in vivo measurements in the calf muscles of athletes and untrained volunteers. The measured mean concentrations in the soleus and the gastrocnemius muscles were found to be 2.81 +/- 0.57/4.8 +/- 1.59 mM (mean +/- SD) for athletes and 2.58 +/- 0.65/3.3 +/- 0.32 mM for untrained volunteers, respectively. These values are in agreement with previously reported biopsy-based results. Our results suggest that 1H MRS can provide an alternative method for non-invasively determining carnosine concentration in human calf muscle in vivo.
In regression models, the design variable has primarily been treated as a nonstochastic variable. In numerous situations, however, the design variable is stochastic. The estimation and hypothesis testing problems in such situations are considered. Real life examples are given.
In this study we compared the efficiency and robustness of several estimators, namely, the least squares (LS) estimators, the Huber and Tukey Mestimators, the S-estimators and the MM-estimators for the parameters of the general linear regression (GLR) model via simulation. First, the programs for each method were written by using Matlab. Then, an extensive simulation study was conducted under several models. The results are consistent with the literature but some important points were also found to be remarked. As the literature suggests, in general, the MM-estimators are the most efficient estimators, and among the robust estimators discussed here, the S-estimators are the least efficient ones. Naturally, the LS estimators are badly affected by the deviations from the assumed model because of their sensitive nature. Moreover, it was found that while the LS estimator of the variance of the error term is unbiased, the robust estimators discussed here are generally biased. Additionally, the MM-estimator of the variance of the error term is less biased than the other robust estimators and its bias gets smaller faster as the sample size increases compared to the others. At the end of the study, to be more illustrative, two real life data examples were given with the related comments.
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