Cerebrotendinous xanthomatosis (CTX) is a rare, difficult to diagnose genetic disorder of bile acid (BA) synthesis that can cause progressive neurological damage and premature death. Detection of CTX in the newborn period would be beneficial since an effective oral therapy for CTX is available to prevent disease progression. There is no suitable test to screen newborn dried bloodspots (DBS) for CTX. Blood screening for CTX is currently performed by GC-MS measurement of elevated 5α-cholestanol. We present here LC-ESI/MS/MS methodology utilizing keto derivatization with (O-(3-trimethylammonium-propyl) hydroxylamine) reagent to enable sensitive detection of ketosterol BA precursors that accumulate in CTX. The availability of isotopically enriched derivatization reagent allowed ready tagging of ketosterols to generate internal standards for isotope dilution quantification. Ketosterols were quantified and their utility as markers for CTX compared to 5α-cholestanol. 7α12α-Dihydroxy-4-cholesten-3-one provided the best discrimination between CTX and unaffected samples. In two CTX newborn DBS concentrations of this ketosterol (120-214 ng/ml) were around 10-fold higher than in unaffected newborn DBS (16.4±6.0 ng/ml), such that its quantification provides a test with potential to screen newborn DBS for CTX. Early detection and intervention through newborn screening would greatly benefit those affected with CTX, preventing morbidity and mortality.
We investigated links between persuasive behavior and theory of mind (ToM) understanding using a novel naturalistic peer persuasion task in which children were invited to convince an interactive puppet to eat raw broccoli or brush his teeth. Sixty-three 3- to 8-year-olds (M age = 6 years, 6 months) took part in the persuasion task and were also given a battery of first-order and advanced false belief tests. As predicted, the number of independent persuasive arguments children produced was significantly associated with false belief scores, even after controlling for age and verbal ability. (PsycINFO Database Record © 2013 APA, all rights reserved).
Dynamic Contrast-Enhanced Ultrasound (DCE-US) for the Characterization of Hepatocellular Carcinoma and Cholangiocellular Carcinoma
- Ultraschall in der Medizin (Stuttgart, Germany : 1980)
- Published over 3 years ago
Purpose: In a prospective study, we compared the different perfusion kinetics of HCC and ICC using dynamic contrast-enhanced ultrasound (DCE-US). Materials and Methods: Patients with proven HCC and ICC were included. Three-minute video clips of CEUS examinations (CPS - low MI mode) after a bolus injection of 1.2 ml SonoVue(®) were recorded and analyzed with quantification software (VueBox(®)). Parameters for the arterial contrast enhancement [rise time (RT), time-to-peak (TTP)] towards portal venous contrast enhancement [mean transit time (local) (mTTl) and fall time (FT)] were quantified. Furthermore, contrast wash-out after peak enhancement (PE) (40 s, 80 s, 100 s and 120 s after PE) was compared between HCC and ICC. Results: 43 patients with proven HCC (n = 23 HCC; cirrhosis n = 16) and ICC (n = 20 ICC; Cirrhosis n = 6) were examined. No statistical difference of the arterial DCEUS parameters was found between HCC and ICC. Contrast enhancement of the portal venous and late phases showed significantly lower values in the ICC group indicating early wash-out of the contrast agent: mTTl (p = 0.0209): HCC 118.4 s (SD± 88.4); ICC 64.8 s (SD± 49.7). FT (p = 0.0433): HCC 42.5 s (SD± 27.7); ICC 27.7 s (SD± 16.2). The percental loss of intensity at a definite time point after PE was significantly higher in ICC than in HCC lesions. Conclusion: DCE-US is able to detect and quantify differences in perfusion kinetics between HCC and ICC. Whereas arterial contrast enhancement patterns may overlap between HCC and ICC, a timed characterization of wash-out kinetics may offer an additional tool to characterize HCC and ICC. The presence of a rapid loss of signal intensity in the early portal venous phase is significantly higher in ICC than in HCC lesions.
In-depth and reproducible protein measurement in many biological samples is often critical for pharmaceutical/biomedical proteomics but remains challenging. MS1-based quantification using quadrupole/ultra-high-field Orbitrap(Q/UHF-Orbitrap) holds great promise, but the critically important experimental approaches enabling reliable large-cohort analysis have long been overlooked. Here we described an IonStar experimental strategy achieving excellent quantitative quality of MS1 quantification. Key features include: i)an optimized, surfactant-aided sample preparation approach provides highly efficient(>75%recovery) and reproducible(<15%CV) peptide recovery across large cell/tissue cohorts; ii)a long column with modest gradient length (e.g. 2.5-hr) yields the optimal balance of depth/throughput; iii)a large-ID trap not only enables highly reproducible gradient delivery as for the first time observed via real-time conductivity monitoring, but also increases quantitative loading capacity by >8-fold and quantified >25% more proteins; iv) an optimized HCD-OT markedly outperforms HCD-IT when analyzing large cohorts with high loading amounts; v)selective removal of hydrophobic/hydrophilic matrix components using a novel selective trapping/delivery approach enables reproducible, robust LC-MS analysis of >100 biological samples in a single set, eliminating batch effect; vi)MS1 acquired at higher resolution(FWHM=120k) provides enhanced S/N and quantitative accuracy/precision for low-abundance species. We examined this pipeline by analyzing a 5-group, 20-samples biological benchmark sampleset, and quantified 6273 unique proteins(≥2-peptides/protein) under stringent cutoffs without fractionation, 6234(>99.4%) without missing data in any of the 20 samples. The strategy achieved high quantitative accuracy(3-6% media-error), low intra-group-variation(6-9% media intra-group CV) and low false-positive-biomarker-discovery-rates(3-8%) across the five groups, with quantified protein abundances spanning >6.5 orders of magnitudes. Finally, this strategy is straightforward, robust and broadly applicable in pharmaceutical/biomedical investigations.
Exercise is extremely beneficial to whole body health reducing the risk of a number of chronic human diseases. Some of these physiological benefits appear to be mediated via the secretion of peptide/protein hormones into the blood stream. The plasma peptidome contains the entire complement of low molecular weight endogenous peptides derived from secretion, protease activity and PTMs, and is a rich source of hormones. In the current study we have quantified the effects of intense exercise on the plasma peptidome to identify novel exercise regulated secretory factors in humans. We developed an optimised 2D-LC-MS/MS method and used multiple fragmentation methods including HCD and EThcD to analyse endogenous peptides. This resulted in quantification of 5,548 unique peptides during a time course of exercise and recovery. The plasma peptidome underwent dynamic and large changes during exercise on a time-scale of minutes with many rapidly reversible following exercise cessation. Among acutely regulated peptides, many were known hormones including insulin, glucagon, ghrelin, bradykinin, cholecystokinin and secretogranins validating the method. Prediction of bioactive peptides regulated with exercise identified C-terminal peptides from Transgelins, which were increased in plasma during exercise. In vitro experiments using synthetic peptides identified a role for transgelin peptides on the regulation of cell-cycle, extracellular matrix remodelling and cell migration. We investigated the effects of exercise on the regulation of PTMs and proteolytic processing by building a site-specific network of protease:substrate activity. Collectively, our deep peptidomic analysis of plasma revealed that exercise rapidly modulates the circulation of hundreds of bioactive peptides through a network of proteases and PTMs. These findings illustrate that peptidomics is an ideal method for quantifying changes in circulating factors on a global scale in response to physiological perturbations such as exercise. This will likely be a key method for pinpointing exercise regulated factors that generate health benefits.
We introduce Salmon, a lightweight method for quantifying transcript abundance from RNA-seq reads. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure. It is the first transcriptome-wide quantifier to correct for fragment GC-content bias, which, as we demonstrate here, substantially improves the accuracy of abundance estimates and the sensitivity of subsequent differential expression analysis.
Paper-based immunoassays, usually in the form of lateral flow tests, are currently the standard platform for home diagnostics. However, conventional lateral tests are often complicated by severe non-specific adsorption of detector particles when applied to test samples containing salivary fluid. It is believed that a high concentration of proteinaceous substances in salivary fluid causes particle aggregation and adhesion. In this study, we developed a stacking flow platform for single-step detection of a target antibody in salivary fluid. Stacking flow circumvents the need for separate sample pre-treatments, such as filtration or centrifugation, which are often required prior to testing saliva samples using paper-based immunoassays. This is achieved by guiding the samples and reagents to the test strip through different paths. By doing so, salivary substances that interfere with the particle-based sensing system are removed before they come into contact with the detection reagents, which greatly reduces the background. In addition, the stacking flow configuration enables uniform flow with a unique flow regulator, which leads to even test lines with good quantification capability, enabling the detection of ~20 ng mL(-1) α-fetoprotein in the serum. We have successfully applied the stacking flow device to detect dengue-specific immunoglobulins that are present in salivary fluid.
Acute myocardial infarct (AMI) size depicted by late gadolinium enhancement cardiovascular magnetic resonance (CMR) is increasingly used as an efficacy endpoint in randomized trials comparing AMI therapies. Infarct size is quantified using manual planimetry (MANUAL), visual scoring (VISUAL), or automated techniques using signal-intensity thresholding (AUTO). Although AUTO is considered the most reproducible, prior studies did not account for the subjective determination of endocardial/epicardial borders, which all methods require. For MANUAL and VISUAL, prior studies did not address how to treat intermediate signal intensities due to partial volume.
Whole-organism chemical screening can circumvent bottlenecks that impede drug discovery. However, in vivo screens have not attained throughput capacities possible with in vitro assays. We therefore developed a method enabling in vivo high-throughput screening (HTS) in zebrafish, termed automated reporter quantification in vivo (ARQiv). Here, ARQiv was combined with robotics to fully actualize whole-organism HTS (ARQiv-HTS). In a primary screen, this platform quantified cell-specific fluorescent reporters in >500,000 transgenic zebrafish larvae to identify FDA-approved drugs that increased the number of insulin-producing β cells in the pancreas. Twenty-four drugs were confirmed as inducers of endocrine differentiation and/or stimulators of β-cell proliferation. Further, we discovered novel roles for NF-κB signaling in regulating endocrine differentiation and for serotonergic signaling in selectively stimulating β-cell proliferation. These studies demonstrate the power of ARQiv-HTS for drug discovery and provide unique insights into signaling pathways controlling β-cell mass, potential therapeutic targets for treating diabetes.
As next generation sequencing technologies are getting more efficient and less expensive, RNA-Seq is becoming a widely used technique for transcriptome studies. Computational analysis of RNA-Seq data often starts with the mapping of millions of short reads back to the genome or transcriptome, a process in which some reads are found to map equally well to multiple genomic locations (multimapping reads). We have developed the Minimum Unique Length Tool (MULTo), a framework for efficient and comprehensive representation of mappability information, through identification of the shortest possible length required for each genomic coordinate to become unique in the genome and transcriptome. Using the minimum unique length information, we have compared different uniqueness compensation approaches for transcript expression level quantification and demonstrate that the best compensation is achieved by discarding multimapping reads and correctly adjusting gene model lengths. We have also explored uniqueness within specific regions of the mouse genome and enhancer mapping experiments. Finally, by making MULTo available to the community we hope to facilitate the use of uniqueness compensation in RNA-Seq analysis and to eliminate the need to make additional mappability files.