Diffusion-weighted MRI: influence of intravoxel fat signal and breast density on breast tumor conspicuity and apparent diffusion coefficient measurements

Promising recent investigations have shown that breast malignancies exhibit restricted diffusion on diffusion-weighted imaging (DWI) and may be distinguished from normal tissue and benign lesions in the breast based on differences in apparent diffusion coefficient (ADC) values. In this study, we assessed the influence of intravoxel fat signal on breast diffusion measures by comparing ADC values obtained using a diffusion-weighted single shot fast spin-echo sequence with and without fat suppression. The influence of breast density on ADC measures was also evaluated. ADC values were calculated for both tumor and normal fibroglandular tissue in a group of 21 women with diagnosed breast cancer. There were systematic underestimations of ADC for both tumor and normal breast tissue due to intravoxel contribution from fat signal on non–fat-suppressed DWI. This ADC underestimation was more pronounced for normal tissue values (mean difference=40%) than for tumors (mean difference=27%, P<.001) and was worse in women with low breast tissue density vs. those with extremely dense breasts (P<.05 for both tumor and normal tissue). Tumor conspicuity measured by contrast-to-noise ratio was significantly higher on ADC maps created with fat suppression and was not significantly associated with breast density. In summary, robust fat suppression is important for accurate breast ADC measures and optimal lesion conspicuity on DWI.     

Determination of the mineral composition of fresh bovine milk from the milk-producing areas located in the State of Sergipe in Brazil and evaluation employing exploratory analysis

In this work, the mineral composition of fresh bovine milk obtained from the milk-producing areas of the Brazilian State of Sergipe was examined. A dry-ashed digestion method and the ICP OES technique were used for the quantification of mineral elements (e.g., Ca, Mg, K, Na, P, Sr and Zn) in 27 samples of milk collected from properties located in milk-producing areas around Nossa Senhora da Glória. The following ranges of values (% m/v) were obtained: 0.063 to 0.117 for Ca; 0.060 to 0.114 for P; 0.024 to 0.064 for Na; and 0.087 to 0.164 for K. The ranges of values (mg L^− 1) for the other mineral elements were also found: 0.68 to 1.89 for Sr; 2.46 to 5.73 for Zn; and 54.2 to 109.9 for Mg. Additionally, the exploratory evaluation of the 27 milk samples was performed using principal component analysis (PCA) involving seven variables dealing with the effect of different management systems (conventional and organic) on milk composition. The results show that there are indeed differences between the mineral composition of milk from properties that use organic practices and those that use conventional management practices.     

XPS analysis of bio‐organic systems

A survey of the literature is made for the XPS analysis of food products (mainly spray‐dried powders, which reveal a considerable surface enrichment in lipids) and of microorganisms and related systems (extracellular polymer substances and biofilms). This survey is used as a background for discussions and recommendations regarding methodology. Sample preparation methods reviewed are freeze drying, analysis of frozen hydrated specimens and adsorption of surface‐active biocompounds on model substrates. Peak decomposition is a way to increase the wealth of information provided by the XPS spectra. It should be performed after a check that sample charge stabilization is satisfactory. Moreover, ensuring the precision needed to make comparisons within sets of samples may involve a trade‐off between imposing constraints and generating information. The examination of correlations between spectral data in the light of chemical guidelines is useful to validate or improve peak decomposition and component assignment, and may also upgrade the chemical information regarding speciation. Further upgrading may be achieved by expressing marker XPS data in terms of concentrations of compounds of interest. Different methods of computation are discussed, providing a composition in terms of ingredients, classes of biochemical compounds, or various organic and inorganic compounds. As an alternative or complement to this deterministic approach, multivariate analysis of suitable spectral windows provides spectral components, which may be used for comparing samples, and which may have a direct chemical relevance or be used to identify features of interest. Copyright © 2011 John Wiley & Sons, Ltd.     

Ultrasound-assisted extraction technique for establishing selenium contents in breast cancer biopsies by Zeeman-electrothermal atomic absorption spectrometry using multi-injection

A solid-liquid extraction method is developed to establish the contents of selenium in breast cancer biopsies. The method is based on the ultrasound-assisted extraction of selenium from pretreated biopsies prior to Se determination by atomic absorption spectrometry with longitudinal-Zeeman background correction. Fifty-one breast biopsies were collected from the Cies Hospital (Vigo, Spain), 32 of which correspond to tumor tissue and 19 to normal tissue (parenchyma). Difficulties arising from the samples analyzed, i.e. small samples mass (50-100 mg), extremely low Se contents and sample texture modification including tissue hardening due to formaldehyde preservation are addressed and overcome. High intensity sonication using a probe together with addition of hydrogen peroxide succeeded in completely extracting Se from biopsies. The multiple injection technique was useful to tackle the low Se contents present in some biopsies. The detection limit was 25 ng g⁻¹ of Se and the precision, expressed as relative standard deviation, was less than 10%. Se contents ranged from 0.08 to 0.4 μg g⁻¹ for parenchyma samples and from 0.09 to 0.8 μg g⁻¹ for tumor samples. In general, Se levels in tumor biopsies were higher as compared with the adjacent normal tissue in 19 patients by a factor of up to 6. Analytical data confirmed Se accumulation in the breast tumors.     

Network-based ranking methods for prediction of novel disease associated microRNAs

BackgroundMany studies have shown roles of microRNAs on human disease and a number of computational methods have been proposed to predict such associations by ranking candidate microRNAs according to their relevance to a disease. Among them, machine learning-based methods usually have a limitation in specifying non-disease microRNAs as negative training samples. Meanwhile, network-based methods are becoming dominant since they well exploit a “disease module” principle in microRNA functional similarity networks. Of which, random walk with restart (RWR) algorithm-based method is currently state-of-the-art. The use of this algorithm was inspired from its success in predicting disease gene because the “disease module” principle also exists in protein interaction networks. Besides, many algorithms designed for webpage ranking have been successfully applied in ranking disease candidate genes because web networks share topological properties with protein interaction networks. However, these algorithms have not yet been utilized for disease microRNA prediction.MethodsWe constructed microRNA functional similarity networks based on shared targets of microRNAs, and then we integrated them with a microRNA functional synergistic network, which was recently identified. After analyzing topological properties of these networks, in addition to RWR, we assessed the performance of (i) PRINCE (PRIoritizatioN and Complex Elucidation), which was proposed for disease gene prediction; (ii) PageRank with Priors (PRP) and K-Step Markov (KSM), which were used for studying web networks; and (iii) a neighborhood-based algorithm.ResultsAnalyses on topological properties showed that all microRNA functional similarity networks are small-worldness and scale-free. The performance of each algorithm was assessed based on average AUC values on 35 disease phenotypes and average rankings of newly discovered disease microRNAs. As a result, the performance on the integrated network was better than that on individual ones. In addition, the performance of PRINCE, PRP and KSM was comparable with that of RWR, whereas it was worst for the neighborhood-based algorithm. Moreover, all the algorithms were stable with the change of parameters. Final, using the integrated network, we predicted six novel miRNAs (i.e., hsa-miR-101, hsa-miR-181d, hsa-miR-192, hsa-miR-423-3p, hsa-miR-484 and hsa-miR-98) associated with breast cancer.ConclusionsNetwork-based ranking algorithms, which were successfully applied for either disease gene prediction or for studying social/web networks, can be also used effectively for disease microRNA prediction.     

HPLC-MS/MS测定氯羟吡啶在牛奶中的残留

建立了牛奶中氯羟吡啶残留检测的高效液相色谱-串联质谱方法(HPLC-MS/MS)。样品经乙腈提取,碱性氧化铝固相萃取柱(Alumina-B)净化,以乙腈+0.1%甲酸水溶液作为流动相洗脱,采用电喷雾离子源(ESI),多反应监测(MRM)方式进行采集,外标法定量。结果表明,氯羟吡啶在1~200ng·m L-1浓度范围内呈现良好的线性关系,相关系数R2大于0.99,方法检测限为0.2μg·kg-1,从5、10和50μg·kg-13个水平添加浓度检测结果可以看出,方法回收率在93.4%~102.6%,相对标准偏差不大于6.5%。     

聚多巴胺修饰棉花固定金属离子用于磷酸化多肽的富集

本文通过多巴胺自聚合在天然的棉花纤维表面,构建了仿生聚多巴胺(PDA)膜层,然后利用儿茶酚羟基固定Ti~(4+),设计并合成了一种固定金属亲合色谱(Immobilized Metal Ion Affinity Chromatography,IMAC)材料Cotton@PDA-Ti~(4+),并将其用于磷酸化多肽的富集。该材料机械性能好,化学性能稳定和生物相容性好,且制备过程简单,通过简易的In-pipet-tip固相萃取(SPE)装置使整个富集操作过程简便快速。实验结果表明,Cotton@PDA-Ti~(4+)不仅可以从简单的蛋白酶解物(β-casein)中富集磷酸化多肽,并且在含有大量非磷酸化多肽的复杂体系样品中对磷酸化多肽也表现出良好的选择性。另外,利用Cotton@PDA-Ti~(4+)对磷酸化多肽进行富集也有较高的效率。我们将该材料应用于实际样品,如人体血清以及脱脂牛奶酶解物中磷酸化多肽的富集,均表现出了较好的选择性。说明该方法有可能用于磷酸化蛋白质组的全分析。     

A study of enhanced ion formation from metal‐semiconductor complexes in atmospheric pressure laser desorption/ionization mass spectrometry

The study of the key parameters impacted surface‐assisted laser desorption/ionization‐mass spectrometry is of broad interest. In previous studies, it has been shown that surface‐assisted laser desorption/ionization‐mass spectrometry is a complex process depending on multiple factors. In the presented study, we showed that neither porosity, light absorbance nor surface hydrophobicity alone influence the enhancement phenomena observed from the hybrid metal‐semiconductor complexes versus individual targets, but small changes in the analyte attaching to the target significantly affect laser desorption ionization‐efficiency. By means of Raman spectroscopy and scanning electron microscopy, it was revealed that the formation of an amorphous analyte layer after drying on a solid substrate was essential for the enhanced laser desorption ionization‐signal observed from the hybrid metal‐semiconductor targets, and the crystallization properties of the analyte appeared as a function of the substrate. Obtained results were used for the screening of regular and lactose‐free milk samples through the hybrid metal‐semiconductor target. Copyright © 2016 John Wiley & Sons, Ltd.     

Lactic acid bacteria in dairy food: Surface characterization and interactions with food matrix components

This review gives an overview of the importance of interactions occurring in dairy matrices between Lactic Acid Bacteria and milk components. Dairy products are important sources of biological active compounds of particular relevance to human health. These compounds include immunoglobulins, whey proteins and peptides, polar lipids, and lactic acid bacteria including probiotics. A better understanding of interactions between bioactive components and their delivery matrix may successfully improve their transport to their target site of action. Pioneering research on probiotic lactic acid bacteria has mainly focused on their host effects. However, very little is known about their interaction with dairy ingredients. Such knowledge could contribute to designing new and more efficient dairy food, and to better understand relationships between milk constituents. The purpose of this review is first to provide an overview of the current knowledge about the biomolecules produced on bacterial surface and the composition of the dairy matter. In order to understand how bacteria interact with dairy molecules, adhesion mechanisms are subsequently reviewed with a special focus on the environmental conditions affecting bacterial adhesion. Methods dedicated to investigate the bacterial surface and to decipher interactions between bacteria and abiotic dairy components are also detailed. Finally, relevant industrial implications of these interactions are presented and discussed.     

Synthesis of Patent Blue derivatized hydrophilic dendrons dedicated to sentinel node detection in breast cancer

In the last decade, methods for the precise localization of sentinel lymph node (SLN) have drawn tremendous attention by oncology surgeons and researchers in the field of medical diagnosis. The accurate identification and characterization of lymph nodes by imaging has important therapeutic and prognostic significance in patients with newly diagnosed cancers. The SLN is the first lymph node that receives lymphatic drainage from the site of a primary tumor. Two biocompatible dendronized phosphonates, one bearing a Patent Blue (PB VF) dye at its periphery, where synthesized. Indeed, such a blue dye is currently injected to label the lymph node system for its per-operative detection. Therefore, developing chemistry of Patent Blue VF to optimize early diagnosis is of great current interest.