Haemodynamics-driven developmental pruning of brain vasculature in zebrafish.

The brain blood vasculature consists of a highly ramified vessel network that is tailored to meet its physiological functions. How the brain vasculature is formed has long been fascinating biologists. Here we report that the developing vasculature in the zebrafish midbrain undergoes not only angiogenesis but also extensive vessel pruning, which is driven by changes in blood flow. This pruning process shapes the initial exuberant interconnected meshwork into a simplified architecture. Using in vivo long-term serial confocal imaging of the same zebrafish larvae during 1.5-7.5 d post-fertilization, we found that the early formed midbrain vasculature consisted of many vessel loops and higher order segments. Vessel pruning occurred preferentially at loop-forming segments via a process mainly involving lateral migration of endothelial cells (ECs) from pruned to unpruned segments rather than EC apoptosis, leading to gradual reduction in the vasculature complexity with development. Compared to unpruned ones, pruned segments exhibited a low and variable blood flow, which further decreased irreversibly prior to the onset of pruning. Local blockade of blood flow with micro-bead obstruction led to vessel pruning, whereas increasing blood flow by noradrenergic elevation of heartbeat impeded the pruning process. Furthermore, the occurrence of vessel pruning could be largely predicted by haemodynamics-based numerical simulation of vasculature refinement. Thus, changes of blood flow drive vessel pruning via lateral migration of ECs, leading to the simplification of the vasculature and possibly efficient routing of blood flow in the developing brain.

Plant antifungal proteins and their applications in agriculture.

Fungi are far more complex organisms than viruses or bacteria and can develop numerous diseases in plants that cause loss of a substantial portion of the crop every year. Plants have developed various mechanisms to defend themselves against these fungi which include the production of low-molecular-weight secondary metabolites and proteins and peptides with antifungal activity. In this review, families of plant antifungal proteins (AFPs) including defensins, lectins, and several others will be summarized. Moreover, the application of AFPs in agriculture will also be analyzed.

A new ion-exchange adsorbent with paramagnetic properties for the separation of genomic DNA.

A new ion-exchange adsorbent (IEA) derived from Fe(3)O(4)/SiO(2)-GPTMS-DEAE with paramagnetic properties was prepared. Fe(3)O(4) nanoparticles were firstly prepared in water-in-oil microemulsion. The magnetic Fe(3)O(4) particles were modified in situ by hydrolysis and condensation reactions with tetraethoxysilane (TEOS) to form the core-shell Fe(3)O(4)/SiO(2). The modified particles were further treated by 3-glycidoxypropyltrimethoxysilane (GPTMS) to form Fe(3)O(4)/SiO(2)-GPTMS nanoparticles. Fe(3)O(4)/SiO(2)-GPTMS-DEAE nanoparticles (IEA) were finally obtained through the condensation reaction between the Cl of diethylaminoethyl chloride-HCl (DEAE) and the epoxy groups of GPTMS in the Fe(3)O(4)/SiO(2)-GPTMS. The obtained IEA has features of paramagnetic and ion exchange properties because of the Fe(3)O(4) nanoparticles and protonated organic amine in the sample. The intermediates and final product obtained in the synthesis process were characterized. The separation result of genomic DNA from blood indicated that Fe(3)O(4)/SiO(2)-GPTMS-DEAE nanoparticles have outstanding advantages in operation, selectivity, and capacity.

Epidermal growth factor down-regulates the expression of human hepatic stimulator substance via CCAAT/enhancer-binding protein ß in HepG2 cells.

hHSS (human hepatic stimulator substance), acting as a hepatotrophic growth factor, promotes liver regeneration. However, the regulatory mechanisms for hHSS transcription are still poorly understood. In the present study, we investigated transcription of hHSS triggered by EGF (epidermal growth factor) and the role of C/EBPß (CCAAT/enhancer-binding protein ß) as a potential core factor responsible for hHSS transcription in HepG2 cells. The results show that EGF suppresses hHSS mRNA expression at early time points. Using a promoter deletion assay, we identified a proximal region (-358/-212) that is required for EGF suppression. Overexpression of C/EBPß enhances EGF suppression of hHSS, and mutation of the C/EBPß-binding site at -292/-279 or siRNA (short interfering RNA) interference abolishes EGF suppression. Furthermore, using an electrophoretic mobility-shift assay and chromatin immunoprecipitation analysis, we found that C/EBPß specifically binds to the -292/-279 site that is responsible for EGF inhibition. Moreover, using a knockin (overexpression) and knockdown strategy (siRNA), we confirmed that C/EBPß is a key factor responsible for inhibition of hHSS mRNA expression. Pre-treatment with an inhibitor of JNK (c-Jun N-terminal kinase) or down-regulation of JNK1 with specific siRNA reverses EGF-inhibited hHSS expression. Our results provide a crucial regulatory mechanism for EGF in hHSS transcription within the promoter proximal region.

Fibroglandular Tissue and Background Parenchymal Enhancement on Breast MR Imaging Correlates With Breast Cancer.

OBJECTIVES: To evaluate the association of breast cancer with both the background parenchymal enhancement intensity and volume (BPEI and BPEV, respectively) and the amount of fibroglandular tissue (FGT) using an automatic quantitative assessment method in breast magnetic resonance imaging (MRI). MATERIALS AND METHODS: Among 17,274 women who underwent breast MRI, 132 normal women (control group), 132 women with benign breast lesions (benign group), and 132 women with breast cancer (cancer group) were randomly selected and matched by age and menopausal status. The area under the receiver operating characteristic curve (AUC) was compared in Cancer vs Control and Cancer vs Benign groups to assess the discriminative ability of BPEI, BPEV and FGT. RESULTS: Compared with the control groups, the cancer group showed a significant difference in BPEV with a maximum AUC of 0.715 and 0.684 for patients in premenopausal and postmenopausal subgroup, respectively. And the cancer group showed a significant difference in BPEV with a maximum AUC of 0.622 and 0.633 for patients in premenopausal and postmenopausal subgroup, respectively, when compared with the benign group. FGT showed no significant difference when breast cancer group was compared with normal control and benign lesion group, respectively. Compared with the control groups, BPEI showed a slight difference in the cancer group. Compared with the benign group, no significant difference was seen in cancer group. CONCLUSION: Increased BPEV is correlated with a high risk of breast cancer While FGT is not.

Segmentation of whole breast and fibroglandular tissue using nnU-Net in dynamic contrast enhanced MR images.

PURPOSE: Segmentation of the whole breast and fibroglandular tissue (FGT) is important for quantitatively analyzing the breast cancer risk in the dynamic contrast-enhanced magnetic resonance (DCE-MR) images. The purpose of this study is to improve the accuracy and efficiency of the segmentation of the whole breast and FGT in 3-D fat-suppressed DCE-MR images with a versatile deep learning (DL) framework. METHODS: We randomly collected 100 breast DCE-MR scans from Shanghai Cancer Hospital of Fudan University. The MR scans in the dataset were different in both the spatial resolution and the MR scanners employed. Furthermore, four breast density categories were assessed by radiologists based on Breast Imaging Reporting and Data System (BI-RADS) of American College of Radiology. The dataset was separated into the training and the testing sets, while keeping a balanced distribution of scans with different imaging parameters and density categories. The nnU-Net has been recently proposed to automatically adapt preprocessing strategies and network architectures for a given medical image dataset, thus showing a great potential in the systematic adaptation of DL methods to different datasets. In this study, we applied the nnU-Net to segment the whole breast and FGT in 3-D fat-suppressed DCE-MR images. Five-fold cross validation was employed to train and validate the segmentation method. RESULTS: The segmentation performance was evaluated with the volume and surface agreement metrics between the DL-based automatic and the manually delineated masks, as quantified with the following measures: the average Dice volume overlap (0.968 ± 0.017 and 0.877 ± 0.081), the average surface distances (0.201 ± 0.080 mm and 0.310 ± 0.043 mm), and the Pearson correlation coefficient of masks (0.995 and 0.972) between the automatic and the manually delineated masks, as calculated for the whole breast and the FGT segmentation, respectively. The correlation coefficient between the breast densities obtained with the DL-based segmentation and the manual delineation was 0.981. There was a positive bias of 0.8% (DL-based relative to manual) in breast density measurement with the Bland-Altman plot. The execution time of the DL-based segmentation was approximately 20 s for the whole breast segmentation and 15 s for the FGT segmentation. CONCLUSIONS: Our DL-based segmentation framework using nnU-Net could robustly achieve high accuracy and efficiency across variable MR imaging settings without extra pre- or post-processing procedures. It would be useful for developing DCE-MR-based CAD systems to quantify breast cancer risk and to be integrated into the clinical workflow.

A cascade-network framework for integrated registration of liver DCE-MR images.

Registration of hepatic dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) is an important task for evaluation of transarterial chemoembolization (TACE) or radiofrequency ablation by quantifying enhancing viable residue tumor against necrosis. However, intensity changes due to contrast agents combined with spatial deformations render technical challenges for accurate registration of DCE-MRI, and traditional deformable registration methods using mutual information are often computationally intensive in order to tolerate such intensity enhancement and shape deformation variability. To address this problem, we propose a cascade network framework composed of a de-enhancement network (DE-Net) and a registration network (Reg-Net) to first remove contrast enhancement effects and then register the liver images in different phases. In experiments, we used DCE-MRI series of 97 patients from Renji Hospital of Shanghai Jiaotong University and registered the arterial phase and the portal venous phase images onto the pre-contrast phases. The performance of the cascade network framework was compared with that of the traditional registration method SyN in the ANTs toolkit and Reg-Net without DE-Net. The results showed that the proposed method achieved comparable registration performance with SyN but significantly improved the efficiency.

2-[3-(1H-Benzimidazol-2-yl)prop-yl]-1H-benzimidazol-3-ium 3,5-dicarb-oxy-benzoate-benzene-1,3,5-tricarb-oxy-lic acid-water (1/1/1).

The title compound, C(17)H(17)N(4) (+)·C(9)H(5)O(6) (-)·C(9)H(6)O(6)·H(2)O, contains a protonated 2,2'-(1,3-propanedi-yl)bis-(1H-benzimidazole) cation, a deprotonated benzene-1,3,5-tricarb-oxy-lic acid anion, a neutral benzene-1,3,5-tricarb-oxy-lic acid mol-ecule and a water mol-ecule, which are linked together through N-H⋯O, O-H⋯O and weak C-H⋯O hydrogen bonds into almost double sheets parallel to (4). These hydrogen-bonded sheets are packed in the crystal with the formation of centrosymmetric voids of 25.5 Å(3), which are filled by the water mol-ecules.

Quantitative assessment of background parenchymal enhancement in breast magnetic resonance images predicts the risk of breast cancer.

The objective of this study was to evaluate the association betweenthe quantitative assessment of background parenchymal enhancement rate (BPER) and breast cancer. From 14,033 consecutive patients who underwent breast MRI in our center, we randomly selected 101 normal controls. Then, we selected 101 women with benign breast lesions and 101 women with breast cancer who were matched for age and menstruation status. We evaluated BPER at early (2 minutes), medium (4 minutes) and late (6 minutes) enhanced time phases of breast MRI for quantitative assessment. Odds ratios (ORs) for risk of breast cancer were calculated using the receiver operating curve. The BPER increased in a time-dependent manner after enhancement in both premenopausal and postmenopausal women. Premenopausal women had higher BPER than postmenopausal women at early, medium and late enhanced phases. In the normal population, the OR for probability of breast cancer for premenopausal women with high BPER was 4.1 (95% CI: 1.7-9.7) and 4.6 (95% CI: 1.7-12.0) for postmenopausal women. The OR of breast cancer morbidity in premenopausal women with high BPER was 2.6 (95% CI: 1.1-6.4) and 2.8 (95% CI: 1.2-6.1) for postmenopausal women. The BPER was found to be a predictive factor of breast cancer morbidity. Different time phases should be used to assess BPER in premenopausal and postmenopausal women.

Fully automated segmentation of whole breast using dynamic programming in dynamic contrast enhanced MR images.

PURPOSE: Amount of fibroglandular tissue (FGT) and level of background parenchymal enhancement (BPE) in breast dynamic contrast enhanced magnetic resonance images (DCE-MRI) are suggested as strong indices for assessing breast cancer risk. Whole breast segmentation is the first important task for quantitative analysis of FGT and BPE in three-dimensional (3-D) DCE-MRI. The purpose of this study is to develop and evaluate a fully automated technique for accurate segmentation of the whole breast in 3-D fat-suppressed DCE-MRI. METHODS: The whole breast segmentation consisted of two major steps, i.e., the delineation of chest wall line and breast skin line. First, a sectional dynamic programming method was employed to trace the upper and/or lower boundaries of the chest wall by use of the positive and/or negative gradient within a band along the chest wall in each 2-D slice. Second, another dynamic programming was applied to delineate the skin-air boundary slice-by-slice based on the saturated gradient of the enhanced image obtained with the prior statistical distribution of gray levels of the breast skin line. Starting from the central slice, these two steps employed a Gaussian function to limit the search range of boundaries in adjacent slices based on the continuity of chest wall line and breast skin line. Finally, local breast skin line detection was applied around armpit to complete the whole breast segmentation. The method was validated with a representative dataset of 100 3-D breast DCE-MRI scans through objective quantification and subjective evaluation. The MR scans in the dataset were acquired with four MR scanners in five spatial resolutions. The cases were assessed with four breast density ratings by radiologists based on Breast Imaging Reporting and Data System (BI-RADS) of American College of Radiology. RESULTS: Our segmentation algorithm achieved a Dice volume overlap measure of 95.8 ± 1.2% and volume difference measure of 8.4 ± 2.4% between the automatically and manually segmented breast regions. Moreover, the root-mean-square distances between the automatically and manually segmented boundaries for the chest wall line and the breast skin line were 0.40 ± 0.15 mm and 0.89 ± 0.21 mm respectively. The segmentation algorithm took approximately 1.0 min to segment the breasts in a MR scan of 160 slices. CONCLUSIONS: Our fully automated method could robustly achieve high segmentation accuracy and efficiency. It would be useful for developing CAD systems for quantitative analysis of FGT and BPE in 3-D DCE-MRI.

Association Between Background Parenchymal Enhancement and Pathologic Complete Remission Throughout the Neoadjuvant Chemotherapy in Breast Cancer Patients.

PURPOSE: To retrospectively investigate the quantitative background parenchymal enhancement (BPE) of the contralateral normal breast in patients with unilateral invasive breast cancer throughout multiple monitoring points of neoadjuvant chemotherapy (NAC) and to further determine whether BPE is associated with tumor response, especially at the early stage of NAC. MATERIALS AND METHODS: A total of 90 patients with unilateral breast cancer who then received six or eight cycles of NAC before surgery were analyzed retrospectively. BPE was measured in dynamic contrast-enhanced MRI at baseline and after 2nd, 4th, and 6th NAC, respectively. Correlation between BPE and tumor size was analyzed, and the association between pathologic complete remission (pCR) and BPE was also analyzed. RESULTS: The BPE of contralateral normal breast showed a constant reduction throughout NAC therapy regardless of the menopausal status (P<.001 in all). Both the BPEs and the changes of BPE in each of the three monitoring points were significantly correlated with those in tumor size (P<.05 in all), and the reduction of BPE after 2nd NAC had the largest diagnostic value for pCR (AUC=0.726, P<.001), particularly in hormonal receptor (HR)-negative patients (OR=0.243, 95%CI=0.083 to 0.706, P=.009). CONCLUSION: The BPE of contralateral normal breast had a constant decreased tendency similar to the change of tumor size in NAC. Reduction of BPE at the early stage of NAC was positively associated with pCR, especially in HR-negative status.

Multiple myeloma-associated iliopsoas muscular amyloidoma first presenting with bilateral femoral nerve entrapment.

We report a 38-year-old man of multiple myeloma with bilateral femoral nerve entrapment caused by bilateral huge protruding masses in the inguinal areas. The masses were identified as iliopsoas muscular amyloidoma after the operation. He was diagnosed with multiple myeloma 1 year ago before he was admitted to our hospital. He complained of muscle weakness in the bilateral thigh and protruding lumps in the bilateral inguinal areas with tenderness for 6 month. The pelvic MRI revealed round masses in the iliopsoas muscles of bilateral inguinal areas. To implement the nerve decompression, the resection of the bilateral masses was done. The pathological result showed Congo red-positive substance with green birefringence to polarized light in a dense fibrous background. Before the operation, six cycles of chemotherapy with VAD (vincristine, adriamycin, dexamethasone) and two cycles of chemotherapy with PAD (bortezomib, adriamycin, dexamethasone) regimen were performed. One month after the operations, one cycle of chemotherapy with PADT (bortezomib, adriamycin, dexamethasone, thalidomide) regimen was used and the patient reached complete remission. The function of the bilateral femoral nerves restored to normal 7 months after the operation with a Karnofsky score of 100. Twenty-two months follow-up showed that there was no evidence of the recurrence of the iliopsoas muscular amyloidoma and no progression of multiple myeloma.

Breast mass segmentation in mammography using plane fitting and dynamic programming.

RATIONALE AND OBJECTIVES: Segmentation is an important and challenging task in a computer-aided diagnosis (CAD) system. Accurate segmentation could improve the accuracy in lesion detection and characterization. The objective of this study is to develop and test a new segmentation method that aims at improving the performance level of breast mass segmentation in mammography, which could be used to provide accurate features for classification. MATERIALS AND METHODS: This automated segmentation method consists of two main steps and combines the edge gradient, the pixel intensity, as well as the shape characteristics of the lesions to achieve good segmentation results. First, a plane fitting method was applied to a background-trend corrected region-of-interest (ROI) of a mass to obtain the edge candidate points. Second, dynamic programming technique was used to find the "optimal" contour of the mass from the edge candidate points. Area-based similarity measures based on the radiologist's manually marked annotation and the segmented region were employed as criteria to evaluate the performance level of the segmentation method. With the evaluation criteria, the new method was compared with 1) the dynamic programming method developed by Timp and Karssemeijer, and 2) the normalized cut segmentation method, based on 337 ROIs extracted from a publicly available image database. RESULTS: The experimental results indicate that our segmentation method can achieve a higher performance level than the other two methods, and the improvements in segmentation performance level were statistically significant. For instance, the mean overlap percentage for the new algorithm was 0.71, whereas those for Timp's dynamic programming method and the normalized cut segmentation method were 0.63 (P < .001) and 0.61 (P < .001), respectively. CONCLUSIONS: We developed a new segmentation method by use of plane fitting and dynamic programming, which achieved a relatively high performance level. The new segmentation method would be useful for improving the accuracy of computerized detection and classification of breast cancer in mammography.

Automated detection of breast mass spiculation levels and evaluation of scheme performance.

RATIONALE AND OBJECTIVES: Although the spiculation levels of breast mass boundaries are a primary sign of malignancy for masses detected on mammography, developing an automated computerized method to detect spiculation levels and quantitatively evaluating the performance of such a method is a difficult task. The objectives of this study were to (1) develop and test a new method to improve mass segmentation and detect mass boundary spiculation levels and (2) assess the performance of this method using a relatively large imaging data set. MATERIALS AND METHODS: The fully automated method developed for this study includes three image-processing steps. In the first step, the principle of maximum entropy is applied in the selected region of interest (ROI) after correcting the background trend to enhance the initial outlines of a mass. In the second step, an active-contour model is used to refine the initial outlines. In the third step, spiculated lines connected to the mass boundary are detected and identified using a special line detector. A quantitative spiculation index is computed to assess the degree of spiculation. To develop and evaluate this automated method, 211 ROIs depicting masses were extracted from a publicly available image database. Among these ROIs, 106 depicted circumscribed mass regions and 105 involved spiculated mass regions. The performance of the method was evaluated using receiver-operating characteristic (ROC) analysis. RESULTS: The computed area under the ROC curve, when applying the method to the data set, was 0.701 +/- 0.027. By setting up a threshold at a spiculation index of 5.0, the method achieved an overall classification accuracy of 66.4%, with 54.3% sensitivity and 78.3% specificity. CONCLUSIONS: In this study, a new computerized method with a number of unique characteristics was developed to detect spiculated mass regions, and a simple spiculation index was applied to quantify mass spiculation levels. Although this quantitative index can be used to distinguish between spiculated and circumscribed masses, the results also suggest that the automated detection of mass spiculation levels remains a technical challenge.