Medicinal mushrooms have been traditionally used as food nutrient supplements in China for thousands of years. The present study aimed to evaluate the immunomodulatory activities of Ganoderma sinense (GS), an allied species of G. lucidum, using human peripheral blood mononuclear cells (PBMC). Our results showed that the polysaccharide-enriched fraction of GS hot water extract (400 μg/ml) exhibited significant stimulatory effects on PBMC proliferation. When the fruiting bodies of GS were divided into pileus and stipe parts and were separately extracted, the GS stipe polysaccharide-enriched fraction (50-400 μg/ml) showed concentration-dependent immunostimulating effects in PBMC. The productions of tumor necrosis factor-α, interleukin (IL)-10, and transforming growth factor -β were significantly enhanced by this fraction. In addition, the proportion of CD14(+) monocyte subpopulation within the PBMC was specifically increased. The IL-10 and IL-12 productions in monocyte-derived dendritic cells were significantly enhanced by GS stipe fraction. The composition of monosaccharides of this fraction was determined by ultra performance liquid chromatography and ion exchange chromatography. Our study demonstrated for the first time the immunostimulatory effects of GS stipe polysaccharide-enriched fraction on PBMC and dendritic cells. The findings revealed the potential use of GS (especially including the stipes of fruiting bodies) as adjuvant nutrient supplements for patients, who are receiving immunosuppressive chemotherapies.

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Side population (SP) refers to a group of cells, which is capable to efflux Hoechst 33342, a DNA-binding dye. SP cells exist both in normal and tumor tissues. Although SP abundance has been used as an indicator for disease prognostic and drug screening in many research projects, few studies have systematically examined the factors influencing SP analysis. In this study we aim to develop a more thorough understanding of the multiple factors involved in SP analysis including Hoechst 33342 staining and cell culture. RPMI-8226, a high SP percentage (SP%) human myeloma cell line was employed here. The results showed that SP% was subject to staining conditions including: viable cell proportion, dye concentration, staining cell density, incubation duration, staining volume, and mix interval. In addition, SP% was highest in day one after passage, while dropped steadily over time. This study shows that both staining conditions and culture duration can significantly affect SP%. In this case, any conclusions based on SP% should be interpreted cautiously. The relation between culture duration and SP% suggests that the incidence of SP cells may be related to cell proliferation and cell cycle phase. Maintaining these technical variables consistently is essential in SP research.

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The separation technologies of 3D chromatograms have been researched for a long time to obtain spectra and chromatogram peaks for individual compounds. However, before applying most of the current methods, the number of compounds must be known in advance. Independent Component Analysis (ICA) is applied to separate 3D chromatograms without knowing the compounds' number in advance, but the existence of the noise component in the results makes it complex for computation. In this paper, a parallel model of Independent Component Analysis constrained by a 5-parameter Reference Curve (pICA5pRC) is proposed based on the ICA model. Introducing a priori knowledge from chromatogram peaks, the pICA5pRC model transformed the 3D chromatogram separation problem to a 5 parameters optimization issue. An algorithm named multi-target particle swarm optimization (mPSO) has been developed to solve the pICA5pRC model. Through simulations, the performance and explanation of our method were described. Through experiments, the practicability of our method is validated. The results show that: (1) our method could separate 3D chromatograms efficiently even with severe overlap without knowing the compounds' number in advance; (2) our method extracted chromatogram peaks from the dataset directly without noise components; (3) our method could be applied to the practical HPLC-DAD dataset.