The impacts of the number of prefreeze and postthaw blastomeres on embryo implantation potential: A systematic analysis.

To systematically analyze the potential of embryo implantation through comparison between the number of surviving blastomeres, the growth, and implantation rate.Retrospective analysis on implantation rate and the growth of prefreeze-postthaw embryos with different blastomeres in 1487 frozen embryo transfer cycles.In groups of postthaw embryos without damage, implantation rate and the average number of blastomere growth increased significantly with increasing number of blastomeres. The implantation rate and the number of blastomeres of embryos with 8-8c (the number of blastomeres in prefreeze embryo-the number of blastomeres in postthaw embryo) continued to grow at a significantly higher rate than that of 5-5c and 6-6c (P < .05). In groups of embryos with the same number of blastomeres before freezing and with partial damage after resuscitation, the implantation rates were lower and the average numbers of blastomere growth reduced as the number of damaged blastomeres increased. For embryos with good quality before freezing, 1 to 3 damaged blastomeres in postthawed embryos did not affect the development and implantation rate. Both implantation rate and growth rate of embryos with 8-6c were significantly higher than those of embryos with 6-6c (P < .05).The number of surviving blastomeres and growth in frozen-thawed embryos could be important index to predict embryo development potential and clinical outcome of implantation. For embryos with good quality, a small amount of damaged blastomeres would not weaken embryo development potential and implantation rate after being thawed.

The peripheral blood transcriptome identifies dysregulation of inflammatory response genes in polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is a common endocrine disorder in women, resulting in ovulation failure and other metabolic problems. However, the underlying mechanisms of it remain largely uncertain due to the complexity of clinical manifestations. This systemic disorder is involved in endocrine, metabolism, immune system and many organs, and few studies have explored peripheral blood transcriptome in patients with PCOS. We performed gene expression profiling of peripheral blood from 8 PCOS patients and eight healthy women with microarray. The significance analysis of microarray (SAM) software was employed to screen the differentially expressed genes (DEGs) and gene ontology (GO) was used for functional enrichment analysis. In total, 181 DEGs with fold-changes >2.0 and q-values <0.05 were identified between the two groups. Among them, 149 were up-regulated and 32 down-regulated in PCOS. Unsupervised clustering of expressed genes could readily differentiate PCOS from control. More importantly, inflammatory response pathway including 14 dysregulated genes was highly enriched in PCOS. Furthermore, 10 DEGs were validated using quantitative reverse-transcription PCR (qRT-PCR) assays. Our study provides independent evidence for the involvement of systemic inflammatory response in PCOS and it may facilitate a greater understanding of this complex disease.

[Time interval from the end of sperm processing to artificial intrauterine in semination with husband's sperm correlates to the rate of clinical pregnancy].

OBJECTIVE: To investigate the influence of the time interval from the end of semen processing to artificial intrauterine in semination with husband's sperm (AIH-IUI) on the rate of clinical pregnancy. METHODS: This study involved 191 AIH-IUI cycles with the same ovulation induction protocol. After Percoll density gradient centrifugation, we divided the sperm into four groups based on the incubation time: 0-19, 20-39, 40-59, and 60-80 min, and again into another four groups according to the total progressively motile sperm count (TPMC): (0-9), (10-20), (21-30), and > 30 x 10(6). We analyzed the correlation of the clinical pregnancy rate with the time interval from the end of sperm processing to AIH-IUI and with other influencing factors, such as maternal age, infertility duration, and semen quality. RESULTS: The rate of clinical pregnancy was significantly higher in the 20-39 min group (18.3%) than in the 0-19, 40-59, and 60-80 min groups (12.7, 11.4 and 9.1%) (all P < 0.05). The (10-20) x 10(6) group achieved a remarkably higher pregnancy rate (16.7%) than the (0-9), (21-30), and > 30 x 10(6) groups (0, 11.4, and 8.3%) (all P < 0.05). Logistic multivariate analysis showed that the rate of clinical pregnancy was decreased with the increased age of the women (OR 0.89, 95% CI 0.83-0.94) but significantly elevated in the 20-39 min group (OR 2.11, 95% CI 1.34-3.13) and of (10-20) x 10(6) group (OR 2.06, 95% CI 1.32-3.46). CONCLUSION: The time interval from the end of sperm processing to AIH-IUI is a most significant factor influencing the rate of clinical pregnancy of AIH-IUI.

[Differences between CD3⁺ TCRvα24⁺ NKT cell and CD3⁺ TCRvß11⁺ NKT cell in PBMC].

AIM: Clarified the differences between CD3(+);TCRvα24(+); NKT cells and CD3(+);TCRvß11(+); NKT cells in their frequencies, subpopulations, phenotypes and biological functions, so as to fully understand the effects of NKT cells in immune responses. METHODS: PBMCs from blood donors were isolated and cell surface markers (CD3, TCRvα24, TCRvß11, CD4, CD8, CD45RA, CD62L, CCR7) and intracellular cytokines (IL-4, IFN-γ) were detected by flow cytometry directly or after stimulation with PMA plus Ionomycin. RESULTS: The mean frequencies of CD3(+);TCRvα24(+); NKT cells and CD3(+);TCRvß11(+); NKT cells in PBMCs were 0.63% and 0.43% and they varied according to individuals. A small population of NKT cells coexpressed TCRvα24 and TCRvß11. The subpopulations of CD4(+); NKT 64.35%, CD8(+); NKT 19.04%, CD4(-);CD8(-); NKT 17.18% in human CD3(+);TCRvα24(+); NKT cells and CD4(+); NKT 53.69%, CD8(+); NKT 18.99%, CD4(-);CD8(-); NKT 29.74% in CD3(+);TCRvß11(+); NKT cells could be identified based upon the expressions of CD4 and CD8 molecules. There were no significant differences between relative subtypes. The frequency of CD45RA(+);CD3(+);TCRvß11(+); NKT cells(71.14%) was higher than the frequency of CD45RA(+);CD3(+);TCRvα24(+); NKT cells and the differences between them were significant. The differences between the frequencies of CD62L(+);CD3(+);TCRvα24(+); NKT cells(46.26%) and CD62L(+);CD3(+);TCRvß11(+); NKT cells(42.36%), the frequencies of CCR7(+);CD3(+);TCRvα24(+); NKT cells(9.24%) and CCR7(+);CD3(+);TCRvß11(+); NKT cells(8.22%) were not significant. There were no significant differences in the secretions of IL-4 by CD3(+);TCRvα24(+); NKT cells(13.01%) and CD3(+);TCRvß11(+); NKT cells(6.62%), and IFN-γ by CD3(+);TCRvα24(+); NKT cells(38.12%) and CD3(+);TCRvß11(+); NKT cells(26.95%). However, there were significant differences between the mean frequency of IFN-γ(+);IL-4(+);CD3(+);TCRvα24(+); NKT cells(12.65%) and that of IFN-γ(+);IL-4(+);CD3(+);TCRvß11(+); NKT cells(3.02%). CONCLUSION: There were some differences between CD3(+);TCRvα24(+); NKT cells and CD3(+);TCRvß11(+); NKT cells in their frequencies, phenotypes and productions of cytokines. In all, although their frequencies were low, the complicated phenotypes and high secretions of cytokines(IL-4 and IFN-γ) assigned NKT cells immunoregulatory effects.