We investigated various factors, including cryoprotective agents (CPAs), diluents, and freezing rates, to develop an optimal cryopreservation protocol for Epinephelus akaara sperm. In experiments using 10% dimethyl sulfoxide (DMSO), glycerol, and methanol with various diluents, 10% DMSO and 300 mM glucose yielded the highest post-thaw sperm motility. The combination of 10% glycerol and 300 mM sucrose yielded significantly higher post-thaw sperm motility than did combinations using other diluents. Glycerol and DMSO at a concentration of 10% as CPAs with 300 mM glucose as the diluent resulted in the highest MSR and sperm activity index (SAI). An investigation to determine the effects of glycerol and DMSO concentrations on post-thaw sperm survival rate revealed no significant differences among 5, 10, 15, and 20% concentrations of either CPA. In assessing the effects of CPA concentration on the fertilization rate, the 10% concentration yielded the highest fertilization rate (81.4 ± 4.3%) in DMSO, whereas 15% was the optimal concentration for glycerol (fertilization rate = 66.7 ± 6.1%). The hatching rate was also highest in 10% DMSO (40.1 ± 0.4%) and in 15% glycerol (27.8 ± 2.3%). In conclusion, the optimal rates of post-thaw sperm motility, fertilization, and hatching were achieved when E. akaara sperm were cryopreserved in a diluent of 300 mM glucose with 10% DMSO as the CPA at a freezing rate of -5 °C/min. We therefore recommend this protocol for the cryopreservation of E. akaara sperm.
Recent introduction of a learning algorithm for cDNA microarray analysis has permitted to select feature set to accurately distinguish human cancers according to their pathological judgments. Here, we demonstrate that hepatitis B virus-positive hepatocellular carcinoma (HCC) could successfully be identified from non-tumor liver tissues by supervised learning analysis of gene expression profiling. Through learning and cross-validating HCC sample set, we could identify an optimized set of 44 genes to discriminate the status of HCC from non-tumor liver tissues. In an analysis of other blind-tested HCC sample sets, this feature set was found to be statistically significant, indicating the reproducibility of our molecular discrimination approach with the defined genes. One prominent finding was an asymmetrical distribution pattern of expression profiling in HCC, in which the number of down-regulated genes was greater than that of up-regulated genes. In conclusion, the present findings indicate that application of learning algorithm to HCC may establish a reliable feature set of genes to be useful for therapeutic target of HCC, and that the asymmetric expression pattern may emphasize the importance of suppressed genes in HCC.
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