The Prevalence of Cyclospora cayetanensis and Cryptosporidium spp. in Turkish patients infected with HIV-1

Opportunistic infections such as cryptosporidiosis and cyclosporiasis are commonly encountered in patients with acquired immunodeficiency syndrome (AIDS). We investigated the existence of opportunistic protozoans that significantly affect the quality of life in HIV-1 infected patients using conventional and molecular methods. The study group comprised 115 HIV-1 positive patients. In the identification of Cyclospora cayetanensis and Cryptosporidium, the formol-ether precipitation method was used and smears were evaluated in optical microscope by staining modified Ziehl-Neelsen (ZN). The primers and probes used for PCR were Heat shock protein 70 for C. cayetanensis and the oocysts wall protein for Cryptosporidium spp.. Cyclospora and Cryptosporidium spp. oocysts were detected in one and two patients, respectively, by staining, whereas we detected C. cayetanensis in three patients out of 115 (2.6%) by PCR, and Cryptosporidium spp. in a further three patients (2.6%). C. cayetensis was detected in patients with CD4 counts of 64 cells/mu m, 182 cells/mu m and 287 cells/mu m, respectively. Cryptosporidium spp. was detected in patients with CD4 counts of 176 cells/mu m, 241 cells/mu m and 669 cells/mu m. As conclusion, PCR method is faster and more sensitive than microscopic methods and to screen intestinal pathogens routinely in patients infected with HIV should not be neglected in developing countries like Turkey

Application of higher order statistics/spectra in biomedical signals - A review

For many decades correlation and power spectrum have been primary tools for digital signal processing applications in the biomedical area. The information contained in the power spectrum is essentially that of the autocorrelation sequence; which is sufficient for complete statistical descriptions of Gaussian signals of known means. However, there are practical situations where one needs to look beyond autocorrelation of a signal to extract information regarding deviation from Gaussianity and the presence of phase relations. Higher order spectra, also known as polyspectra, are spectral representations of higher order statistics, i.e. moments and cumulants of third order and beyond. HOS (higher order statistics or higher order spectra) can detect deviations from linearity, stationarity or Gaussianity in the signal. Most of the biomedical signals are non-linear, non-stationary and non-Gaussian in nature and therefore it can be more advantageous to analyze them with HOS compared to the use of second order correlations and power spectra. In this paper we have discussed the application of HOS for different bio-signals. HOS methods of analysis are explained using a typical heart rate variability (HRV) signal and applications to other signals are reviewed