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Quantitative assessment of gestational sac shape: the gestational sac shape score.

ABSTRACT: OBJECTIVE:To develop a quantitative method for characterizing gestational sac shape. METHODS:Twenty first-trimester gestational sacs in normal pregnancies were studied with three-dimensional (3D) ultrasonography. The 3D coordinates of surface-point sets were obtained for each sac using 30-, 15- and six-slice sampling. Cubic spline interpolation was used with the 15- and six-slice surface-point samples to generate coordinates for those 30-slice surface points not measured. Interpolated and measured values, the latter from the 30-slice sample, were compared and the percent error calculated. Cubic spline interpolation was used to determine the coordinates of a standard surface-point sample (3660) for each sac in each slice sample. These coordinate data were used to give each sac a standard configuration by moving its center of gravity to the origin, aligning its inertial axes along the coordinate axes and converting its volume to 1.0 mL. In this form, a volume shape descriptor could be generated for each sac that was then transformed into a vector containing only shape information. The 20 shape vectors of each slice sample were subjected to principal components analysis, and principal component scores (PCSs) calculated. The first four PCSs were used to define a gestational sac shape score (GSSS-30, GSSS-15 or GSSS-6) for each sac in a given slice sample. The characteristics of each set of GSSSs were determined and those for the GSSS-15 and GSSS-6 were compared with the GSSS-30 characteristics. RESULTS:Cubic spline interpolations were very accurate in most cases, with means close to 0%, and approximately 95% of the errors being less than 10%. GSSS-30 accounted for 67.6% of the shape variance, had a mean of zero and an SD of 1.1, was normally distributed and was not related to menstrual age (R=-0.16, P=0.51). GSSS-15 and GSSS-6 had essentially the same characteristics. No significant differences between individual GSSS-30 values and those for GSSS-15 or GSSS-6 were found, indicating the absence of a slice sample effect. CONCLUSION:Using sophisticated mathematical methods, the gestational sac shape, initially represented by the 3D coordinates of 3660 surface points, was converted to a single number, the GSSS. This score had the appropriate properties for quantitatively characterizing normal, first-trimester gestational sac shapes. As it can be obtained from as few as six slices, it should be useful in many clinical situations. This novel approach has the potential for providing quantitative shape information about a variety of biological shapes and how they change over time.

SUBMITTER: Deter RL

PROVIDER: S-EPMC3516405 | biostudies-literature | 2007 May

REPOSITORIES: biostudies-literature

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Quantitative assessment of gestational sac shape: the gestational sac shape score.

Deter R L RL Li J J Lee W W Liu S S Romero R R

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology 20070501 5

<h4>Objective</h4>To develop a quantitative method for characterizing gestational sac shape.<h4>Methods</h4>Twenty first-trimester gestational sacs in normal pregnancies were studied with three-dimensional (3D) ultrasonography. The 3D coordinates of surface-point sets were obtained for each sac using 30-, 15- and six-slice sampling. Cubic spline interpolation was used with the 15- and six-slice surface-point samples to generate coordinates for those 30-slice surface points not measured. Interpol ...[more]

PMID: 17444551

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Project description:Accurate predictions of the DNA binding specificities of transcription factors (TFs) are necessary for understanding gene regulatory mechanisms. Traditionally, predictive models are built based on nucleotide sequence features. Here, we employed three- dimensional DNA shape information obtained on a high-throughput basis to integrate intuitive DNA structural features into the modeling of TF binding specificities using support vector regression. We performed quantitative predictions of DNA binding specificities, using the DREAM5 dataset for 65 mouse TFs and genomic-context protein binding microarray data for three human basic helix-loop-helix TFs. DNA shape-augmented models compared favorably with sequence-based models for these predictions. Although both k-mer and DNA shape features encoded the interdependencies between nucleotide positions of the binding site, using DNA shape features reduced the dimensionality of the feature space compared to k-mer use. Finally, analyzing the weights of DNA shape-augmented models uncovered TF family- specific structural readout mechanisms that were not obvious from the nucleotide sequence. Three genomic-context protein binding microarray (gcPBM) experiments of human transcription factors were performed. Briefly, the gcPBMs involved binding his-tagged transcription factors c-Myc, Max, and Mad1(Mxd1) to double-stranded 180K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Briefly, we represent three categories of 36-bp sequences: 1) bound probes, 2) unbound probes (or negative controls), and 3) test probes. Bound probes corresponded to genomic regions bound in vivo by c-Myc, Max, or Mad2 (ChIP-seq P < 10^(-10) in HeLaS3 or K562 celld (ENCODE)) that contain at least two consecutive 8-mers with universal PBM E-score > 0.4 (Munteanu and Gordan, LNCS 2013). All putative binding sites occur at the same position within the probes on the array. M-bM-^@M-^\UnboundM-bM-^@M-^] probes corresponded to genomic regions with ChIP-seq P < 10^(-10) and a maximum 8-mer E-score < 0.2. We also designed test probes that contain, within constant flanking regions, all nnCACGTGnn 10-mers and 18 nnnCACGTGnnn 12-mers (where n = A, C, G, or T). Each DNA sequence represented on the array is present in 6 replicate spots. We report the gcPBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).

Project description:ObjectiveGestational diabetes mellitus (GDM) affects a significant number of women. Evidence regarding the association between GDM and offspring body mass index (BMI) is unclear due to small samples and lack of adequate confounding control. The objective of this study was to investigate the association between GDM and offspring BMI z-scores from birth to early adolescence and to examine the role of maternal pre-gestational BMI in this relationship.DesignProspective study.SettingPelotas 2004 Birth Cohort, Brazil.ParticipantsCohort participants that were followed-up from birth up to early adolescence (~3500) and their mothers.Primary outcome measuresBMI z-scores at birth, 3, 12, 24, 48 months and 6 and 11 years of age, calculated according to the WHO growth charts.ResultsUnadjusted and adjusted linear regressions were performed and interaction terms between maternal pre-gestational BMI and GDM were included. Prevalence of self-reported GDM was 2.6% (95% CI 2.1% to 3.1%). The offspring BMI z-scores (SD) at birth, 3, 12, 24, 48 months and at 6 and 11 years were 0.10 (1.12), -0.47 (1.10), 0.59 (1.10), 0.59 (1.08), 0.78 (1.32), 0.70 (1.43) and 0.75 (1.41), respectively. Unadjusted regression models showed positive associations between GDM and offspring BMI z-scores at birth, 6 and 11 years. After adjustment, the associations attenuated towards the null. Statistical evidence of effect modification between maternal pre-gestational BMI and GDM was observed at birth (p=0.007), with the association between GDM and offspring BMI z-score being apparent only in those children born to overweight or obese mothers (?=0.72, 95%?CI 0.30 to 1.14 and ?=0.61, 95%?CI 0.20 to 1.01, respectively).ConclusionsWe observed that in the association between GDM and offspring BMI z-scores, there is a predominant role for maternal nutritional status before pregnancy and that the association between GDM and newborn's BMI is apparent only among those born to overweight or obese mothers.

Dataset Information

Quantitative assessment of gestational sac shape: the gestational sac shape score.

Publications

Quantitative assessment of gestational sac shape: the gestational sac shape score.

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