Comparison of multidetector-row computed tomography and duplex Doppler ultrasonography in detecting atherosclerotic carotid plaques complicated with intraplaque hemorrhage [Usporedba višeslojne kompjuterizirane tomografije i duplex Doppler ultrazvuka u otkrivanju aterosklerotskih karotidnih plakova kompliciranih krvarenjem u plak ]

This study compared sensitivity and specificity of multidetector-row computed tomography and duplex Doppler ultrasonography in detecting atherosclerotic carotid plaques complicated with intraplaque hemorrhage. Carotid plaques from 50 patients operated for carotid artery stenosis were analyzed. Carotid endarterectomy was performed within one week of diagnostic evaluation. Results of multidetector-row computed tomography and duplex Doppler ultrasonography diagnostic evaluation were compared with results of histological analysis of the same plaque areas. American Heart Association classification of atherosclerotic plaques was applied for histological classification. Median tissue density of carotid plaques complicated with intraplaque hemorrhage was 14.7 Hounsfield units. Median tissue density of noncalcified segments of uncomplicated plaques was 54.3 Hounsfield units (p = 0.00003). The highest tissue density observed for complicated plaques was 31.8 Hounsfield units. Multidetector-row computed tomography detected plaques complicated with hemorrhage with sensitivity of 100% and specificity of 70.4%, with tissue density of 33.8 Hounsfield units as a threshold value. Duplex Doppler ultrasonography plaque analysis based on visual in-line classification showed sensitivity of 21.7% and specificity of 89.6% in detecting plaques complicated with intraplaque hemorrhage. Multidetector-row computed tomography showed a very high level of sensitivity and a moderate level of specificity in detecting atherosclerotic carotid plaques complicated with hemorrhage. Duplex Doppler ultrasonography plaque analysis based on visual in-line classification showed a low level of sensitivity and a moderate-high level of specificity in detecting atherosclerotic carotid plaques complicated with hemorrhage

Detection of secretory IgA antibodies against gliadin and human tissue transglutaminase in stool to screen for coeliac disease in children: validation study

Objective To evaluate two commercial stool tests for detection of secretory IgA antibodies against gliadin and human tissue transglutaminase for diagnosis of coeliac disease in children with symptoms.Setting Tertiary care children's hospital.Participants Coded stool samples from 20 children with newly diagnosed coeliac disease and 64 controls. Six children with coeliac disease had stool tests every two weeks for three months after starting a gluten-free diet.Main outcome measures Secretory IgA antibodies against gliadin and human tissue transglutaminase in stool samples, determined in duplicate by using recommended cut-off limits.Results Sensitivity of faecal antibodies against human tissue transglutaminase was 10% (95% confidence interval 1% to 32%), and specificity was 98% (91% to 100%). For antibodies against gliadin, sensitivity was 6% (0% to 29%) and specificity was 97% (89% to 100%). Optimisation of cut-off limits by receiver operating characteristic analysis and use of results of both tests increased sensitivity to 82%, but specificity decreased to 58%. All follow-up stool tests remained negative, except for two positive anti-gliadin results in one patient, six and 10 weeks after the gluten-free diet was started.Conclusions Neither stool test was suitable for screening for coeliac disease in children with symptoms

A theory for the tissue specificity of BRCA1/2 related and other hereditary cancers

Women who inherit a defective BRCA1 or BRCA2 gene have risks for breast and ovarian cancer that are so high and seem so selective that many mutation carriers choose to have prophylactic surgery. There has been much conjecture to explain such apparently striking tissue specificity. All these suggestions share the assumption that some disabled function of normal tumor suppressor genes leads to a tissue specific cancer response. Here the idea is proposed and tested that major determinants of where BRCA1/2 hereditary cancers occur are related to tissue specificity of the cancer pathogen, the agent that causes chronic inflammation or the carcinogen. The target tissue may have receptors for the pathogen, become selectively exposed to an inflammatory process or to a carcinogen such as during digestion, metabolism or elimination. An innate genomic deficit in a tumor suppressor gene impairs normal responses to these extrinsic challenges and exacerbates the susceptibility to disease in organ targets. This hypothesis also fits data for several tumor suppressors beyond BRCA1/2. A major advantage of this model is that it suggests there may be some options in addition to prophylactic surgery

Single cell analysis reveals the involvement of the long non-coding RNA Pvt1 in the modulation of muscle atrophy and mitochondrial network

Long non-coding RNAs (lncRNAs) are emerging as important players in the regulation of several aspects of cellular biology. For a better comprehension of their function, it is fundamental to determine their tissue or cell specificity and to identify their subcellular localization. In fact, the activity of lncRNAs may vary according to cell and tissue specificity and subcellular compartmentalization. Myofibers are the smallest complete contractile system of skeletal muscle influencing its contraction velocity and metabolism. How lncRNAs are expressed in different myofibers, participate in metabolism regulation and muscle atrophy or how they are compartmentalized within a single myofiber is still unknown. We compiled a comprehensive catalog of lncRNAs expressed in skeletal muscle, associating the fiber-type specificity and subcellular location to each of them, and demonstrating that many lncRNAs can be involved in the biological processes de-regulated during muscle atrophy. We demonstrated that the lncRNA Pvt1, activated early during muscle atrophy, impacts mitochondrial respiration and morphology and affects mito/autophagy, apoptosis and myofiber size in vivo. This work corroborates the importance of lncRNAs in the regulation of metabolism and neuromuscular pathologies and offers a valuable resource to study the metabolism in single cells characterized by pronounced plasticity

A multiple-instance scoring method to predict tissue-specific cis-regulatory motifs and regions

Transcription is the central process of gene regulation. In higher eukaryotes, the transcription of a gene is usually regulated by multiple cis-regulatory regions (CRRs). In different tissues, different transcription factors bind to their cis-regulatory motifs in these CRRs to drive tissue-specific expression patterns of their target genes. By combining the genome-wide gene expression data with the genomic sequence data, we proposed multiple-instance scoring (MIS) method to predict the tissue-specific motifs and the corresponding CRRs. The method is mainly based on the assumption that only a subset of CRRs of the expressed gene should function in the studied tissue. By testing on the simulated datasets and the fly muscle dataset, MIS can identify true motifs when noise is high and shows higher specificity for predicting the tissue-specific functions of CRRs

Submission of Evidence on Online Violence Against Women to the UN Special Rapporteur on Violence Against Women, its Causes and Consequences, Dr Dubravka Šimonović

Figure S1. B3GALNT2 levels determined by W.B. and ROC curve. a–c Relative mRNA expression of B3GALNT2 in HCC tumor tissues and normal liver tissues obtained from GSE76427, GSE36376, and TCGA-LIHC datasets. d Western blot analysis of B3GALNT2 levels in 24 pairs of HCC tissues. T HCC tumor tissue, N adjacent non-tumor tissue. e ROC curve analysis of the sensitivity and specificity for the predictive value of TNM model, B3GALNT2 expression, and the combination model. (TIFF 546 kb

Detection of myocardial scar from the VCG using a supervised learning approach

This paper addresses the possibility of detecting presence of scar tissue in the myocardium through the in- vestigation of vectorcardiogram (VCG) characteristics. Scarred myocardium is the result of myocardial infarction (MI) due to ischemia and creates a substrate for the manifestation of fatal arrhythmias. Our efforts are focused on the development of a classification scheme for the early screening of patients for the presence of scar. More specifically, a supervised learning model based on the extracted VCG features is proposed and validated through comprehensive testing analysis. The achieved accuracy of 82.36% (sensitivity 84.31%, specificity 77.36%) indicates the potential of the proposed screening mechanism for detecting the presence/absence of scar tissue

SPECS: a non-parametric method to identify tissue-specific molecular features for unbalanced sample groups

Background To understand biology and differences among various tissues or cell types, one typically searches for molecular features that display characteristic abundance patterns. Several specificity metrics have been introduced to identify tissue-specific molecular features, but these either require an equal number of replicates per tissue or they can’t handle replicates at all. Results We describe a non-parametric specificity score that is compatible with unequal sample group sizes. To demonstrate its usefulness, the specificity score was calculated on all GTEx samples, detecting known and novel tissue-specific genes. A webtool was developed to browse these results for genes or tissues of interest. An example python implementation of SPECS is available at https://github.com/celineeveraert/SPECS. The precalculated SPECS results on the GTEx data are available through a user-friendly browser at specs.cmgg.be. Conclusions SPECS is a non-parametric method that identifies known and novel specific-expressed genes. In addition, SPECS could be adopted for other features and applications

A Network of SLC and ABC Transporter and DME Genes Involved in Remote Sensing and Signaling in the Gut-Liver-Kidney Axis.

Genes central to drug absorption, distribution, metabolism and elimination (ADME) also regulate numerous endogenous molecules. The Remote Sensing and Signaling Hypothesis argues that an ADME gene-centered network-including SLC and ABC "drug" transporters, "drug" metabolizing enzymes (DMEs), and regulatory genes-is essential for inter-organ communication via metabolites, signaling molecules, antioxidants, gut microbiome products, uremic solutes, and uremic toxins. By cross-tissue co-expression network analysis, the gut, liver, and kidney (GLK) formed highly connected tissue-specific clusters of SLC transporters, ABC transporters, and DMEs. SLC22, SLC25 and SLC35 families were network hubs, having more inter-organ and intra-organ connections than other families. Analysis of the GLK network revealed key physiological pathways (e.g., involving bile acids and uric acid). A search for additional genes interacting with the network identified HNF4α, HNF1α, and PXR. Knockout gene expression data confirmed ~60-70% of predictions of ADME gene regulation by these transcription factors. Using the GLK network and known ADME genes, we built a tentative gut-liver-kidney "remote sensing and signaling network" consisting of SLC and ABC transporters, as well as DMEs and regulatory proteins. Together with protein-protein interactions to prioritize likely functional connections, this network suggests how multi-specificity combines with oligo-specificity and mono-specificity to regulate homeostasis of numerous endogenous small molecules