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Accurate and early prediction of the outcome of an in vitro fertilization (IVF) treatment is important for both patients and physicians. The most common question asked by IVF patients is ''What are my chances of conceiving?'' The answer to this difficult question typically considers patient age, day 3 serum follicle stimulating hormone (FSH) levels, and infertility diagnosis. However, many more parameters are known to affect IVF outcome. It is difficult for the clinician to recognize trends and intuitively decide how to optimize success rates for each infertile couple. This paper presents a hybrid intelligence method which integrating genetic algorithm and decision learning techniques for knowledge mining of an IVF medical database. The proposed method can not only assist the IVF physician in predicting the IVF outcome, but also find useful knowledge that can help the IVF physician tailor the IVF treatment to the individual patient with the aim of improving the pregnancy success rate. The twenty-eight most significant attributes for determining the pregnancy rate (e.g., patient's age, number of embryo transferred, number of frozen embryos, and culture days of embryo) and their combinative relationships (represented by if-then rules) were identified through the proposed method. The knowledge discovered in this study is currently accepted as an interesting discovery from the viewpoint of domain experts. For the results from this study to be conveniently accessed by IVF physicians and patients, an expert system tool equipped with the proposed IVF outcome prediction model was built.

Retinoic acid (RA)-induced endodermal differentiation of F9 teratocarcinoma cells is accompanied by altered of many genes, including the retinoic acid receptor (RAR) alpha, beta, and gamma and retinoic x receptor (RXR) alpha and gamma genes. In addition, RA enhances the binding of nuclear receptors to the RA responsive elements and increases the trans-activation of the RA-responsive genes in F9 cells. These data suggest that RA increases the overall function of RA receptors in F9 cells. However, the cell line F9E, established by long-term exposure of F9 cells to RA, is insensitive to RA. These cells expressed the genes for keratin 8 and tissue specific plasminogen activator (t-PA), which are characteristic of endoderm cells. Northern blot analysis showed that F9E cells expressed high basal levels of RAR and RXR mRNAs, but genes including the RARs and RXRs, which are normally regulated by RA in F9 cells, were no longer regulated in F9E cells. Further, F9E cells were not sensitive to the antiproliferative effect of RA. In these cells, high levels of RA receptors constitutively bound to RA responsive elements, but RA could neither increase the amount of RA receptor binding to the RA responsive elements, nor enhance the transcription of the RA target genes. These findings provide a possible explanation for insensitivity of endodermal cells to the actions of RA. Our data also indicate that the levels of endogenous RA receptors do not predict the degree of RA sensitivity, and that RA responsiveness might be a function of the cell's ability to regulate RA receptor genes.

Retinoic acid (RA), a well-known inducer of differentiation, has been shown to regulate its own receptor gene expression in F9 teratocarcinoma cells. The homologous regulation of receptors by RA might be critical for RA-induced F9 cell differentiation. F9 cell lines from two different laboratories, named F9-1 and F9-2, were compared for retinoic acid receptor (RAR) and retinoid x receptor (RXR) gene expression in response to RA. The data show that both F9-1 and F9-2 cell lines are embryonal carcinoma cells, but of different phenotypes and different sensitivity to RA. In F9-1 cells, RA regulates all three RARs (alpha, beta, and gamma), two RXRs (alpha and gamma), two activin receptors (ActR II and IIB), and tissue-specific plasminogen activator (t-PA) gene expression. In F9-2 cells RA regulates only the RAR beta, RXR alpha, and t-PA genes. The induction of mRNA levels was much higher in F9-1 than in F9-2 cells. Different basal RAR gamma and RXR gamma mRNA levels were also noted. In these two cell lines F9-2 cells expressed greater amounts of RAR gamma 1, gamma 2, and gamma 3 mRNA isoforms, but lacked RXR gamma mRNA compared with F9-1 cells. Since RAR gamma 1 has been shown to exert an antagonistic effect on other types of RA receptors, the decreased sensitivity of F9-2 cells to RA might be due to its high level of RAR gamma 1 and/or low level of RXR gamma. This notion was in part supported by gel shift assay which demonstrated constitutive binding of RAR gamma to a RA responsive element (RAR beta E) in F9-2 cells. Further, the binding of nuclear protein to RAR beta E was increased upon RA treatment in F9-1 cells, but not in F9-2 cells. These differences in the regulation of RA receptors might determine the sensitivity of the two substrains of F9 cells to RA.

Objectives To examine the expression of estrogen receptor (ER) messenger ribonucleic acid in the human ovary. To detect ER gene transcripts in human oocytes; and to study the possible autocrine effect of estrogen (E) in granulosa/cumulus cells. Design Human ovaries from benign gynecological surgeries as well as the oocytes, cumulus-oocyte complexes, and granulosa/cumulus cells from the in vitro fertilization program were used to detect ER gene transcripts using the highly sensitive reverse transcriptase-polymerase chain reaction technique. Setting Molecular biology laboratory. Results The ER gene is expressed in human ovary. Estrogen receptor transcripts were detected in human cumulus-oocyte complexes and oocytes but not in granulosa/cumulus cells. Conclusions These results suggest a lack of receptor-mediated autocrine effect of E during folliculogenesis and that E, secreted by granulosa/cumulus cells, may exert a paracrine effect to influence oocyte maturation and fertilization competence directly.

A single early quantitative hCG measurement 16 to 18 days after timed insemination has prognostic value with regard to pregnancy outcome in an asymptomatic, infertile population. Further, there is a statistically significant difference in the predictive value for hCG levels in patients less than or equal to 35 years compared with patients greater than 35 years of age. Interestingly, the same age-dependent phenomenon was observed when analyzing the predictive value of sonographically detected fetal heart motion at 5 weeks post-ovulation. We suggest that any investigation on pregnancy outcome, or its prediction should consider the impact of maternal age as a potentially significant covariate.