Template-based automatic breast segmentation on MRI by excluding the chest region.

PurposeMethods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template-based method.MethodsNonfat-suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as the template, and the remaining 30 cases were used for testing. Unlike most model-based breast segmentation methods that use the breast region as the template, the chest body region on a middle slice was used as the template. Within the chest template, three body landmarks (thoracic spine and bilateral boundary of the pectoral muscle) were identified for performing the initial V-shape cut to determine the posterior lateral boundary of the breast. The chest template was mapped to each subject's image space to obtain a subject-specific chest model for exclusion. On the remaining image, the chest wall muscle was identified and excluded to obtain clean breast segmentation. The chest and muscle boundaries determined on the middle slice were used as the reference for the segmentation of adjacent slices, and the process continued superiorly and inferiorly until all 3D slices were segmented. The segmentation results were evaluated by an experienced radiologist to mark voxels that were wrongly included or excluded for error analysis.ResultsThe breast volumes measured by the proposed algorithm were very close to the radiologist's corrected volumes, showing a % difference ranging from 0.01% to 3.04% in 30 tested subjects with a mean of 0.86% ± 0.72%. The total error was calculated by adding the inclusion and the exclusion errors (so they did not cancel each other out), which ranged from 0.05% to 6.75% with a mean of 3.05% ± 1.93%. The fibroglandular tissue segmented within the breast region determined by the algorithm and the radiologist were also very close, showing a % difference ranging from 0.02% to 2.52% with a mean of 1.03% ± 1.03%. The total error by adding the inclusion and exclusion errors ranged from 0.16% to 11.8%, with a mean of 2.89% ± 2.55%.ConclusionsThe automatic chest template-based breast MRI segmentation method worked well for cases with different body and breast shapes and different density patterns. Compared to the radiologist-established truth, the mean difference in segmented breast volume was approximately 1%, and the total error by considering the additive inclusion and exclusion errors was approximately 3%. This method may provide a reliable tool for MRI-based segmentation of breast density

Flexible Ureteroscopic Management of Horseshoe Kidney Renal Calculi

ABSTRACTPurpose:To evaluate the clinical efficacy of flexible ureteroscope (F-URS) combined with holmium laser lithotripter in treating renal calculi in horseshoe kidney.Materials and Methods:From November 2010 to December 2013, the medical history and charts of sixteen patients (mean age 42.9±11.6 years, range 26-66 years), including 13 males and 3 females were analyzed retrospectively. Mean stone burden was 29±8 mm (range 17-42 mm2). Mean stone digitized surface area (DSA) was 321±94 mm2 (range 180-538 mm2). Under spinal anesthesia in a modified lithotomy position with the head down, rigid ureteroscope was placed firstly into the ureter to reach the level of the pelvis, a zebra guide wire was inserted and following the removal of the rigid ureteroscope, an ureteral access sheath was positioned along the guide wire, then passed the URF P-5 flexible ureteroscope into the renal cavities over the guidewire. After locating the stones, holmium laser lithotripsy was performed.Results:The average operative time was 92±16 minutes (range 74-127 min.). No major complications were encountered. Ten patients obtained stone-free status with one session, four obtained stone-free status after two sessions. Single session stone-free rate was 62.5%, overall stone-free rate was 87.5%. Two patients have small residual stones in the lower pole.Conclusions:F-URS combined with holmium laser lithotripter and nitinol basket, is safe and effective in dealing with moderate stone diameter (<30 mm) in HSKs with high clearance rates and low complication rates

H3K4me3 redistribution from TSS to miRNA-coding region affects miRNA expression in LPS-conditioned moDCs.

<p>(<b>A</b>) Proportion of H3K4me3 redistribution around the TSSs in the LPS- (LPS) or TGF-β(TGFβ)-upregulated miRNA with H3K4me3 modification. (<b>B</b>) H3K4me3 redistribution around the TSSs in the LPS-upregulated miRNA with H3K4me3 modification. For ChIP-Seq analysis, the MoDCs were exposed to LPS (100 ng/ml) for 24 hrs. The gene structure was downloaded from the UCSC Genome Brower. Enriched regions were found in the UCSC Genome Browser (<a href="http://genome.ucsc.edu" target="_blank">http://genome.ucsc.edu</a>.). Histone modification peaks were detected by CHIPOTle. The miRNA positions were predicted by Ensembl. The definitive and putative miRNATSSs were inferred from <a href="http://mirstart.mbc.nctu.edu.tw/browse.php" target="_blank">http://mirstart.mbc.nctu.edu.tw/browse.php</a>. (<b>C</b>) qRT-PCR analysis of microRNA in LPS-conditioned DCs. moDCs were exposed to LPS (100 ng/ml) for 24 hrs, and the total RNAs was extracted from LPS-conditioned DCs. R.E, relative expression. The qRT-PCR data were representative of three healthy donors. The arrow points in the direction of gene transcription. *, P<0.05; **, P<0.01.</p

LPS associated factors affects the expression of miRNAs with H3K4me3 redistribution.

<p>(<b>A</b>) qRT-PCR analyses of epigenetic factors RBBP4, RBBP7, HDAC1, MLL, RBBP5, EED, EZH2, JHM1D and SUZ12. Total RNA was extracted from moDCs following exposure to LPS (100 ng/ml) for 24 hrs and the expressions of RBBP4, RBBP7, HDAC1, MLL, RBBP5, EED, EZH2, JHM1D and SUZ12 were analyzed by qRT-PCR. (<b>B</b>) qRT-PCR analyses of miR-146a, miR-155, miR-29 and miR-210 in siRNA-transfected moDCs. MoDCs were transfected with siRNAs against RBBP4 (siRBBP4), RBBP7 (siRBBP7), HDAC1 (siHDAC1) or with control siRNA (siMock). After 48 hrs, total RNAs were extracted and analyzed by qRT-PCR. (<b>C</b>) qRT-PCR analyses of miR-146a and miR-155 in siRNA-transfected moDCs. moDCs were respectively transfected with siRNAs against p300 (siP300), p65 (sip65), p50 (siP50), or with control siRNA (siMock). After 48 hrs, total RNA was extracted and analyzed by qRT-PCR. (<b>D</b>) TNFα ELISA analyses of supernatants from siRBBP4 transfected moDCs. The supernatants were collected from siRBBP4 transfected moDCs and the TNFα concentrations were analyzed by ELISA (R&D, USA). (<b>E</b>) Flow cytometry analysis of CD86. siRBBP4 transfected moDCs were stained with anti-CD86 (IT2.2) and analyzed by flow cytometry. Grey, isotypic control. *, P<0.05; **, P<0.01.</p

Template-based automatic breast segmentation on MRI by excluding the chest region

Purpose: Methods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template-based method. Methods: Nonfat-suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as the template, and the remaining 30 cases were used for testing. Unlike most model-based breast segmentation methods that use the breast region as the template, the chest body region on a middle slice was used as the template. Within the chest template, three body landmarks (thoracic spine and bilateral boundary of the pectoral muscle) were identified for performing the initial V-shape cut to determine the posterior lateral boundary of the breast. The chest template was mapped to each subject's image space to obtain a subject-specific chest model for exclusion. On the remaining image, the chest wall muscle was identified and excluded to obtain clean breast segmentation. The chest and muscle boundaries determined on the middle slice were used as the reference for the segmentation of adjacent slices, and the process continued superiorly and inferiorly until all 3D slices were segmented. The segmentation results were evaluated by an experienced radiologist to mark voxels that were wrongly included or excluded for error analysis. Results: The breast volumes measured by the proposed algorithm were very close to the radiologist's corrected volumes, showing a % difference ranging from 0.01% to 3.04% in 30 tested subjects with a mean of 0.86% ± 0.72%. The total error was calculated by adding the inclusion and the exclusion errors (so they did not cancel each other out), which ranged from 0.05% to 6.75% with a mean of 3.05% ± 1.93%. The fibroglandular tissue segmented within the breast region determined by the algorithm and the radiologist were also very close, showing a % difference ranging from 0.02% to 2.52% with a mean of 1.03% ± 1.03%. The total error by adding the inclusion and exclusion errors ranged from 0.16% to 11.8%, with a mean of 2.89% ± 2.55%. Conclusions: The automatic chest template-based breast MRI segmentation method worked well for cases with different body and breast shapes and different density patterns. Compared to the radiologist-established truth, the mean difference in segmented breast volume was approximately 1%, and the total error by considering the additive inclusion and exclusion errors was approximately 3%. This method may provide a reliable tool for MRI-based segmentation of breast density

Dendritic Cell-Associated miRNAs Are Modulated via Chromatin Remodeling in Response to Different Environments

<div><p>Introduction</p><p>Epigenetic modification plays a critical role in regulating gene expression. To understand how epigenetic modification alters miRNA expression in monocyte-derived dendritic cells (moDCs) in different environments, we analyzed the connections between H3K4me3 and H3K27me3 modification and the expression of miRNAs in LPS- and TGF-β-conditioned moDCs.</p><p>Results</p><p>In moDCs, H3K4me3 modification was strongly associated with the expression of activating miRNAs, whereas H3K27me3 was related to repressive miRNAs. The regulation of miRNA expression by H3K4me3 and H3K27me3 was further confirmed by silencing or inhibiting methyltransferases or methylation-associated factors in LPS- and TGF-β-conditioned moDCs. siRNAs targeting H3K4me3-associated mixed lineage leukemia (MLL) and retinoblastoma binding protein 5 (RBBP5) reduced H3K4me3 enrichment and downregulated miRNA expression; conversely, silencing H3K27me3-associated enhancer of zeste homolog 2 (EZH2) and embryonic ectoderm development (EED) genes upregulated the DC-associated miRNAs. However, LPS-mediated miRNAs were often associated with H3K4me3 redistribution from the transcription start site (TSS) to the miRNA-coding region. Silencing LPS-associated NF-κB p65 and CBP/p300 not only inhibited H3K4m3 redistribution but also reduced miRNA expression. LPS-upregulated RBBP4 and RBBP7, which are involved in chromatin remodeling, also affected the redistribution of H3K4me3 and reduced the expression of miRNAs.</p><p>Conclusion</p><p>In LPS- and TGF-β-conditioned moDCs, miRNAs may be modulated not only by H3K4m3 and H3K27me3 modification but also by redistribution of H3K4me3 around the transcriptional start site of miRNAs. Thus, H3K4me3 and H3K27me3 epigenetic modification may play an important role in regulating DC differentiation and function in the presence of tumor or inflammatory environments.</p></div

Regulation of miRNA expression in LPS-conditioned moDCs.

<p>When moDCs are exposed to LPS, activated NF-κB p65 induces H3K4me3 redistribution from the TSS to miRNA-coding region by interacting with p300 and RBBP4/RBBP7. Meanwhile, activated NF-κB transcription factors (such as p65, p52, p50, C-rel and CrelB) bind to promoter regions and initiate miRNA expression. AP-1 components Jun and c-fos, which are activated by LPS, also bind with the miR-146a and miR-155 promoters to induce miR-155 and miR-146a gene expression.</p