Biomedical subjects

Yuko Nagata

Publications and source records attributed to Yuko Nagata.

4 recordsLinked to original sources

PubMed2005

Seminal plasma inhibin-B level is a useful predictor of the success of conventional testicular sperm extraction in patients with non-obstructive azoospermia.

AIM: The value of serum inhibin-B as a predictor of the presence of testicular spermatozoa is still controversial. The purpose of this study is to evaluate the predictive value of the seminal plasma inhibin-B level, which might more directly reflect the secretion by Sertoli cells, and to discriminate between successful and failed testicular sperm extraction (TESE) in non-obstructive azoospermia. METHODS: Sixty-two patients with non-obstructive azoospermia were examined at the Department of Obstetrics and Gynecology at Niigata University Hospital, Niigata, Japan. The level of inhibin-B was measured using a two-site enzyme-linked immunoassay. RESULTS: Testicular sperm were successfully retrieved in 17 of 62 patients (27.4%). The serum levels of follicle-stimulating hormone (FSH) were significantly lower and the serum and seminal inhibin-B concentrations were significantly higher in the successful TESE group compared with the failed TESE group. According to the receiver operating characteristics (ROC) curve analysis, the best discriminating seminal plasma inhibin-B level was 27.0 pg/mL (sensitivity 88.2%, specificity 93.3%). The best discriminating serum inhibin-B level was 34.0 pg/mL (sensitivity 70.6%, specificity 95.6%). The area under the ROC curve for seminal plasma inhibin-B was significantly larger than that for FSH and testicular volume. Using multivariate logistic regression analysis, only seminal plasma inhibin-B was an independent predictor of the presence of spermatozoa on TESE. CONCLUSION: Seminal plasma inhibin-B level is a useful predictor of the presence of testicular sperm in men with non-obstructive azoospermia.

Adult↗

PubMed2004

Transcriptional profiling of genes responsive to abscisic acid and gibberellin in rice: phenotyping and comparative analysis between rice and Arabidopsis.

We collected and completely sequenced 32,127 full-length complementary DNA clones from Oryza sativa L. ssp. japonica cv. "Nipponbare." Mapping of these clones to genomic DNA revealed approximately 20,500 transcriptional units (TUs) in the rice genome. For each TU, we selected 60-mers using an algorithm that took into account some DNA conditions such as base composition and sequence complexity. Using in situ synthesis technology, we constructed oligonucleotide arrays with these TUs on glass slides. We targeted RNAs prepared from normally grown rice callus and from callus treated with abscisic acid (ABA) or gibberellin (GA). We identified 200 ABA-responsive and 301 GA-responsive genes, many of which had never before been annotated as ABA or GA responsive in other expression analysis. Comparison of these genes revealed antagonistic regulation of almost all by both hormones; these had previously been annotated as being responsible for protein storage and defense against pathogens. Comparison of the cis-elements of genes responsive to one or antagonistic to both hormones revealed that the antagonistic genes had cis-elements related to ABA and GA responses. The genes responsive to only one hormone were rich in cis-elements that supported ABA and GA responses. In a search for the phenotypes of mutants in which a retrotransposon was inserted in these hormone-responsive genes, we identified phenotypes related to seed formation or plant height, including sterility, vivipary, and dwarfism. In comparison of cis-elements for hormone response genes between rice and Arabidopsis thaliana, we identified cis-elements for dehydration-stress response as Arabidopsis specific and for protein storage as rice specific.

Abscisic Acid↗

PubMed2003

Genomics approach to abscisic acid- and gibberellin-responsive genes in rice.

We used an 8987-EST collection to construct a cDNA microarray system with various genomics information (full-length cDNA, expression profile, high accuracy genome sequence, phenotype, genetic map, and physical map) in rice. This array was used as a probe to hybridize target RNAs prepared from normally grown callus of rice and from callus treated for 6 hr or 3 days with the hormones abscisic acid (ABA) or gibberellin (GA). We identified 509 clones, including many clones that had never been annotated as ABA-or GA-responsive. These genes included not only ABA- or GA-responsive genes but also genes responsive to other physiological conditions such as pathogen infection, heat shock, and metal ion stress. Comparison of ABA- and GA-responsive genes revealed antagonistic regulation for these genes by both hormones except for one defense-related gene, thionin. The gene for thionin was up-regulated by both hormone treatments for 3 days. The upstream regions of all the genes that were regulated by both hormones had cis-elements for ABA and GA response. We performed a clustering analysis of genes regulated by both hormones and various expression profiles that showed three notable clusters (seed tissues, low temperature and sugar starvation, and thionin-gene related). A comparison of the cis-elements for hormone response genes between rice and Arabidopsis thaliana, we identified cis-elements for dehydration-stress response or for expression of amylase gene as Arabidopsis gene-specific or rice gene-specific, respectively.

Abscisic Acid↗

PubMed2003

Optimization of a fermentation medium using neural networks and genetic algorithms.

Artificial neural networks and genetic algorithms are used to model and optimize a fermentation medium for the production of the enzyme hydantoinase by Agrobacterium radiobacter. Experimental data reported in the literature were used to build two neural network models. The concentrations of four medium components served as inputs to the neural network models, and hydantoinase or cell concentration served as a single output of each model. Genetic algorithms were used to optimize the input space of the neural network models to find the optimum settings for maximum enzyme and cell production. Using this procedure, two artificial intelligence techniques have been effectively integrated to create a powerful tool for process modeling and optimization.

Algorithms↗