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The safety of chemicals, drugs, novel foods and genetically modified crops is often tested using repeat-dose sub-acute toxicity tests in rats or mice. It is important to avoid misinterpretations of the results as these tests are used to help determine safe exposure levels in humans. Treated and control groups are compared for a range of haematological, biochemical and other biomarkers which may indicate tissue damage or other adverse effects. However, the statistical analysis and presentation of such data poses problems due to the large number of statistical tests which are involved. Often, it is not clear whether a “statistically significant” effect is real or a false positive (type I error) due to sampling variation. The author's conclusions appear to be reached somewhat subjectively by the pattern of statistical significances, discounting those which they judge to be type I errors and ignoring any biomarker where the p-value is greater than p = 0.05. However, by using standardised effect sizes (SESs) a range of graphical methods and an over-all assessment of the mean absolute response can be made. The approach is an extension, not a replacement of existing methods. It is intended to assist toxicologists and regulators in the interpretation of the results. Here, the SES analysis has been applied to data from nine published sub-acute toxicity tests in order to compare the findings with those of the author's. Line plots, box plots and bar plots show the pattern of response. Dose-response relationships are easily seen. A “bootstrap” test compares the mean absolute differences across dose groups. In four out of seven papers where the no observed adverse effect level (NOAEL) was estimated by the authors, it was set too high according to the bootstrap test, suggesting that possible toxicity is under-estimated. [ABSTRACT FROM AUTHOR]

Background: Blood-based or urinary biomarkers may play a role in quantifying the future risk of type 2 diabetes (T2D) and in understanding possible aetiological pathways to disease. However, no systematic review has been conducted that has identified and provided an overview of available biomarkers for incident T2D. We aimed to systematically review the associations of biomarkers with risk of developing T2D and to highlight evidence gaps in the existing literature regarding the predictive and aetiological value of these biomarkers and to direct future research in this field. Methods and Findings: We systematically searched PubMed MEDLINE (January 2000 until March 2015) and Embase (until January 2016) databases for observational studies of biomarkers and incident T2D according to the 2009 PRISMA guidelines. We also searched availability of meta-analyses, Mendelian randomisation and prediction research for the identified biomarkers. We reviewed 3910 titles (705 abstracts) and 164 full papers and included 139 papers from 69 cohort studies that described the prospective relationships between 167 blood-based or urinary biomarkers and incident T2D. Only 35 biomarkers were reported in large scale studies with more than 1000 T2D cases, and thus the evidence for association was inconclusive for the majority of biomarkers. Fourteen biomarkers have been investigated using Mendelian randomisation approaches. Only for one biomarker was there strong observational evidence of association and evidence from genetic association studies that was compatible with an underlying causal association. In additional search for T2D prediction, we found only half of biomarkers were examined with formal evidence of predictive value for a minority of these biomarkers. Most biomarkers did not enhance the strength of prediction, but the strongest evidence for prediction was for biomarkers that quantify measures of glycaemia. Conclusions: This study presents an extensive review of the current state of the literature to inform the strategy for future interrogation of existing and newly described biomarkers for T2D. Many biomarkers have been reported to be associated with the risk of developing T2D. The evidence of their value in adding to understanding of causal pathways to disease is very limited so far. The utility of most biomarkers remains largely unknown in clinical prediction. Future research should focus on providing good genetic instruments across consortia for possible biomarkers in Mendelian randomisation, prioritising biomarkers for measurement in large-scale cohort studies and examining predictive utility of biomarkers for a given context. [ABSTRACT FROM AUTHOR]

Background: To allow early diagnosis and monitoring of disease progression, there is a need for biomarkers in amyotrophic lateral sclerosis (ALS). Neurofilaments (NF) are emerging protein biomarkers in other neurological diseases, and are of possible use in ALS. Objective: The aim of this study is to evaluate the utility of NF levels as blood or cerebrospinal fluid (CSF) biomarker in patients with ALS. Methods: A systematic search of Pubmed, Embase and Scopus was performed. Methodological quality assessment was applied to refine the final search results. Meta-analysis of the data was performed. Results: Level of NF heavy chain and light chains were significantly elevated in the CSF of ALS patients compared to healthy controls/controls without parenchymal central nervous system (CNS) involvement and ALS mimic disease patients. NF light chain level in CSF was higher in ALS patients than in neurological patients with CNS involvement (SMD = 1.352, P = 0.01). NF light chain concentration in blood was higher in ALS patients than healthy controls/controls without CNS involvement (SMD = 1.448, P<0.0001). NF heavy chain levels in CSF were negatively correlated disease duration and ALSFRS-R ((r = -0.447, P<0.0001; r = -0.486, P<0.0001). NF light chain levels in CSF were negatively correlated with disease duration (r = -0.273, P = 0.011). Conclusion: NF heavy and light chain levels have potential use as a marker of neural degeneration in ALS, but are not specific for the disease, and are more likely to be used as measures of disease progression. [ABSTRACT FROM AUTHOR]

Purpose: Many studies have reported that carbohydrate antigen 153 (CA153) in breast secretions (BS) can discriminate breast cancer (BC) patients from healthy individuals, indicating CA153 in BS as a potential index for BC. This meta-analysis aimed to evaluate the actual diagnostic value of CA153 in BS. Methods: Related papers were obtained from Pubmed, Embase, Scopus, Ovid, Sciverse, the Cochrane library, Chinese Biomedical literature Database (CBM), Technology of Chongqing (VIP), Wan Fang Data, and Chinese National Knowledge Infrastructure (CNKI). Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) of CA153 in BS for BC diagnosis were analyzed with the random effect model. SROC and the area under the curve (AUC) were applied to assess overall diagnostic efficiency. Results: This meta-analysis included five studies with a total of 329 BC patients and 381 healthy subjects. For CA153 in BS, the summary sensitivity, specificity, and DOR to diagnose BC were 0.63 (95% confidence interval (CI): 0.57∼0.68), 0.82 (95% CI: 0.78∼0.86), and 9.18 (95% CI: 4.22∼19.95), respectively. Furthermore, the AUC of BS CA153 in the diagnosis of BC was 0.8614. Conclusions: CA153 in BS is a valuable molecular marker in diagnosing BC and should be applied in standard clinical practices of BC screening upon confirmation of our findings in a larger prospective study. [ABSTRACT FROM AUTHOR]

How can we predict the occurrence of acute kidney injury (AKI) in cancer patients based on machine learning with serum creatinine data? Given irregular and heterogeneous clinical data, how can we make the most of it for accurate AKI prediction? AKI is a common and significant complication in cancer patients, and correlates with substantial morbidity and mortality. Since no effective treatment for AKI still exists, it is important to take timely preventive measures. While several approaches have been proposed for predicting AKI, their scope and applicability are limited as they either assume regular data measured over a short hospital stay, or do not fully utilize heterogeneous data. In this paper, we provide an AKI prediction model with a greater applicability, which relaxes the constraints of existing approaches, and fully utilizes irregular and heterogeneous data for learning the model. In a cohort of 21,022 cancer patients who were registered into Korea Central Cancer Registry (KCCR) in Seoul National University Hospital between January 1, 2004 and December 31, 2013, our method achieves 0.7892 precision, 0.7506 recall, and 0.7576 F-measure in predicting whether a patient will develop AKI during the next 14 days. [ABSTRACT FROM AUTHOR]

Event-related potential (ERP) studies feature among the most cited papers in the field of body representation, with recent research highlighting the potential of ERPs as neuropsychiatric biomarkers. Despite this, investigation into how reliable early visual ERPs and body-sensitive effects are over time has been overlooked. This study therefore aimed to assess the stability of early body-sensitive effects and visual P1, N1 and VPP responses. Participants were asked to identify pictures of their own bodies, other bodies and houses during an EEG test session that was completed at the same time, once a week, for four consecutive weeks. Results showed that amplitude and latency of early visual components and their associated body-sensitive effects were stable over the 4-week period. Furthermore, correlational analyses revealed that VPP component amplitude might be more reliable than VPP latency and specific electrode sites might be more robust indicators of body-sensitive cortical activity than others. These findings suggest that visual P1, N1 and VPP responses, alongside body-sensitive N1/VPP effects, are robust indications of neuronal activity. We conclude that these components are eligible to be considered as electrophysiological biomarkers relevant to body representation. [ABSTRACT FROM AUTHOR]

Classification models built on biological expression data are increasingly used to predict distinct disease subtypes. Selected features that separate sample groups can be the candidates of biomarkers, helping us to discover biological functions/pathways. However, three challenges are associated with building a robust classification and feature selection model: 1) the number of significant biomarkers is much smaller than that of measured features for which the search will be exhaustive; 2) current biological expression data are big in both sample size and feature size which will worsen the scalability of any search algorithms; and 3) expression profiles of certain features are typically highly correlated which may prevent to distinguish the predominant features. Unfortunately, most of the existing algorithms are partially addressing part of these challenges but not as a whole. In this paper, we propose a unified framework to address the above challenges. The classification and feature selection problem is first formulated as a nonconvex optimisation problem. Then the problem is relaxed and solved iteratively by a sequence of convex optimisation procedures which can be distributed computed and therefore allows the efficient implementation on advanced infrastructures. To illustrate the competence of our method over others, we first analyse a randomly generated simulation dataset under various conditions. We then analyse a real gene expression dataset on embryonal tumour. Further downstream analysis, such as functional annotation and pathway analysis, are performed on the selected features which elucidate several biological findings. [ABSTRACT FROM AUTHOR]

Background: DSC is used to determine thermally-induced conformational changes of biomolecules within a blood plasma sample. Recent research has indicated that DSC curves (or thermograms) may have different characteristics based on disease status and, thus, may be useful as a monitoring and diagnostic tool for some diseases. Since thermograms are curves measured over a range of temperature values, they are considered functional data. In this paper we apply functional data analysis techniques to analyze differential scanning calorimetry (DSC) data from individuals from the Lupus Family Registry and Repository (LFRR). The aim was to assess the effect of lupus disease status as well as additional covariates on the thermogram profiles, and use FD analysis methods to create models for classifying lupus vs. control patients on the basis of the thermogram curves. Methods: Thermograms were collected for 300 lupus patients and 300 controls without lupus who were matched with diseased individuals based on sex, race, and age. First, functional regression with a functional response (DSC) and categorical predictor (disease status) was used to determine how thermogram curve structure varied according to disease status and other covariates including sex, race, and year of birth. Next, functional logistic regression with disease status as the response and functional principal component analysis (FPCA) scores as the predictors was used to model the effect of thermogram structure on disease status prediction. The prediction accuracy for patients with Osteoarthritis and Rheumatoid Arthritis but without Lupus was also calculated to determine the ability of the classifier to differentiate between Lupus and other diseases. Data were divided 1000 times into separate 2/3 training and 1/3 test data for evaluation of predictions. Finally, derivatives of thermogram curves were included in the models to determine whether they aided in prediction of disease status. Results: Functional regression with thermogram as a functional response and disease status as predictor showed a clear separation in thermogram curve structure between cases and controls. The logistic regression model with FPCA scores as the predictors gave the most accurate results with a mean 79.22% correct classification rate with a mean sensitivity = 79.70%, and specificity = 81.48%. The model correctly classified OA and RA patients without Lupus as controls at a rate of 75.92% on average with a mean sensitivity = 79.70% and specificity = 77.6%. Regression models including FPCA scores for derivative curves did not perform as well, nor did regression models including covariates. Conclusion: Changes in thermograms observed in the disease state likely reflect covalent modifications of plasma proteins or changes in large protein-protein interacting networks resulting in the stabilization of plasma proteins towards thermal denaturation. By relating functional principal components from thermograms to disease status, our Functional Principal Component Analysis model provides results that are more easily interpretable compared to prior studies. Further, the model could also potentially be coupled with other biomarkers to improve diagnostic classification for lupus. [ABSTRACT FROM AUTHOR]

Proteomic analysis of cancers' stages has provided new opportunities for the development of novel, highly sensitive diagnostic tools which helps early detection of cancer. This paper introduces a new feature ranking approach called FRMT. FRMT is based on the Technique for Order of Preference by Similarity to Ideal Solution method (TOPSIS) which select the most discriminative proteins from proteomics data for cancer staging. In this approach, outcomes of 10 feature selection techniques were combined by TOPSIS method, to select the final discriminative proteins from seven different proteomic databases of protein expression profiles. In the proposed workflow, feature selection methods and protein expressions have been considered as criteria and alternatives in TOPSIS, respectively. The proposed method is tested on seven various classifier models in a 10-fold cross validation procedure that repeated 30 times on the seven cancer datasets. The obtained results proved the higher stability and superior classification performance of method in comparison with other methods, and it is less sensitive to the applied classifier. Moreover, the final introduced proteins are informative and have the potential for application in the real medical practice. [ABSTRACT FROM AUTHOR]

In this paper, high-throughput RNA-sequencing (RNA-seq) was used to search for transcriptional biomarkers for β2-agonists. In combination with drug mechanisms, a smaller group of genes with higher detection accuracy was screened out. Unknown samples were first predicted by this group of genes, and liquid chromatograph tandem mass spectrometer (LC-MS/MS) was applied to positive samples to validate the biomarkers. The results of principal component analysis (PCA), hierarchical cluster analysis (HCA) and discriminant analysis (DA) indicated that the eight genes screened by high-throughput RNA-seq were able to distinguish samples in the experimental group and control group. Compared with the nine genes selected from an earlier literature, 17 genes including these nine genes were proven to have a more satisfactory effect, which validated the accuracy of gene selection by RNA-seq. Then, six key genes were selected from the 17 genes according to the variable importance in projection (VIP) value of greater than 1. The test results using the six genes and 17 genes were similar, revealing that the six genes were critical genes. By using the six genes, three positive samples possibly treated with drugs were screened out from 25 unknown samples through DA and partial least squares discriminant analysis (PLS-DA). Then, the three samples were verified by a standard method, and mapenterol was detected in a sample. Therefore, the six genes can be used as biomarkers to detect β2-agonists. Compared with the previous study, accurate detection of β2-agonists abuse using six key genes is an improvement method, which show great significance in the monitoring of β2-agonists abuse in animal husbandry. [ABSTRACT FROM AUTHOR]

Background: Arteriovenous fistula (AVF) failure is a significant cause of morbidity and expense in patients on maintenance haemodialysis (HD). Circulating biomarkers could be valuable in detecting patients at risk of AVF failure and may identify targets to improve AVF outcome. Currently there is little consensus on the relationship between circulating biomarkers and AVF failure. The aim of this systematic review was to identify circulating biomarkers associated with AVF failure. Methods: Studies evaluating the association between circulating biomarkers and the presence or risk of AVF failure were systematically identified from the MEDLINE, EMBASE and Cochrane Library databases. No restrictions on the type of study were imposed. Concentrations of circulating biomarkers of routine HD patients with and without AVF failure were recorded and meta-analyses were performed on biomarkers that were assessed in three or more studies with a composite population of at least 100 participants. Biomarker concentrations were synthesized into inverse-variance random-effects models to calculate standardized mean differences (SMD) and 95% confidence intervals (CI). Results: Thirteen studies comprising a combined population of 1512 participants were included after screening 2835 unique abstracts. These studies collectively investigated 48 biomarkers, predominantly circulating molecules which were assessed as part of routine clinical care. Meta-analysis was performed on twelve eligible biomarkers. No significant association between any of the assessed biomarkers and AVF failure was observed. Conclusion: This paper is the first systematic review of biomarkers associated with AVF failure. Our results suggest that blood markers currently assessed do not identify an at-risk AVF. Further, rigorously designed studies assessing biological plausible biomarkers are needed to clarify whether assessment of circulating markers can be of any clinical value. PROSPERO registration number CRD42016033845. [ABSTRACT FROM AUTHOR]

Shotgun metagenomic analysis of the human associated microbiome provides a rich set of microbial features for prediction and biomarker discovery in the context of human diseases and health conditions. However, the use of such high-resolution microbial features presents new challenges, and validated computational tools for learning tasks are lacking. Moreover, classification rules have scarcely been validated in independent studies, posing questions about the generality and generalization of disease-predictive models across cohorts. In this paper, we comprehensively assess approaches to metagenomics-based prediction tasks and for quantitative assessment of the strength of potential microbiome-phenotype associations. We develop a computational framework for prediction tasks using quantitative microbiome profiles, including species-level relative abundances and presence of strain-specific markers. A comprehensive meta-analysis, with particular emphasis on generalization across cohorts, was performed in a collection of 2424 publicly available metagenomic samples from eight large-scale studies. Cross-validation revealed good disease-prediction capabilities, which were in general improved by feature selection and use of strain-specific markers instead of species-level taxonomic abundance. In cross-study analysis, models transferred between studies were in some cases less accurate than models tested by within-study cross-validation. Interestingly, the addition of healthy (control) samples from other studies to training sets improved disease prediction capabilities. Some microbial species (most notably Streptococcus anginosus) seem to characterize general dysbiotic states of the microbiome rather than connections with a specific disease. Our results in modelling features of the “healthy” microbiome can be considered a first step toward defining general microbial dysbiosis. The software framework, microbiome profiles, and metadata for thousands of samples are publicly available at . [ABSTRACT FROM AUTHOR]

The sleep onset process (SOP) is a dynamic process correlated with a multitude of behavioral and physiological markers. A principled analysis of the SOP can serve as a foundation for answering questions of fundamental importance in basic neuroscience and sleep medicine. Unfortunately, current methods for analyzing the SOP fail to account for the overwhelming evidence that the wake/sleep transition is governed by continuous, dynamic physiological processes. Instead, current practices coarsely discretize sleep both in terms of state, where it is viewed as a binary (wake or sleep) process, and in time, where it is viewed as a single time point derived from subjectively scored stages in 30-second epochs, effectively eliminating SOP dynamics from the analysis. These methods also fail to integrate information from both behavioral and physiological data. It is thus imperative to resolve the mismatch between the physiological evidence and analysis methodologies. In this paper, we develop a statistically and physiologically principled dynamic framework and empirical SOP model, combining simultaneously-recorded physiological measurements with behavioral data from a novel breathing task requiring no arousing external sensory stimuli. We fit the model using data from healthy subjects, and estimate the instantaneous probability that a subject is awake during the SOP. The model successfully tracked physiological and behavioral dynamics for individual nights, and significantly outperformed the instantaneous transition models implicit in clinical definitions of sleep onset. Our framework also provides a principled means for cross-subject data alignment as a function of wake probability, allowing us to characterize and compare SOP dynamics across different populations. This analysis enabled us to quantitatively compare the EEG of subjects showing reduced alpha power with the remaining subjects at identical response probabilities. Thus, by incorporating both physiological and behavioral dynamics into our model framework, the dynamics of our analyses can finally match those observed during the SOP. [ABSTRACT FROM AUTHOR]

Predicting a transition point in behavioral data should take into account the complexity of the signal being influenced by contextual factors. In this paper, we propose to analyze changes in the embedding dimension as contextual information indicating a proceeding transitive point, called OPtimal Embedding tRANsition Detection (OPERAND). Three texts were processed and translated to time-series of emotional polarity. It was found that changes in the embedding dimension proceeded transition points in the data. These preliminary results encourage further research into changes in the embedding dimension as generic markers of an approaching transition point. [ABSTRACT FROM AUTHOR]

Background: In this research, an meta-analysis was performed for assessment of the associations between O6-methyguanine-DNA methyltransferase (MGMT) promoter hypermethylation possessing low-grade intraepithelial lesion (LSIL), high-grade intraepithelial lesion (HSIL), cervical cancer (CC), and clinicopathological characters of CC. Methods: Literature selection were conducted through searching PubMed, Web of science, EMBASE, China National Knowledge Infrastructure and Wanfang databases (up to November 2018). An assessment of associations between MGMT methylation and LSIL, HSIL, CC risk and clinicopathological characteristics was performed through pooled odds ratios (ORs) with relevant 95% confidence intervals (CIs). Subgroup analyses, meta-regressions and Galbraith plots were conducted to conduct an exploration on the possible sources of heterogeneity. The genome-wide DNA methylation array studies were extracted from Gene Expression Omnibus (GEO) databases for validation of these outcomes. Results: In this meta-analysis of 25 published articles, MGMT hypermethylation gradually elevated the rates among control group (12.16%), LSIL (20.92%), HSIL (36.33%) and CC (41.50%) specimens. MGMT promoter methylation was significant associated with the increased risk of LSIL by 1.74-fold (P<0.001), HSIL by 3.71-fold (P<0.001) and CC by 7.08-fold (P<0.001) compared with control. A significant association between MGMT promoter methylation with FIGO stage was also found (OR = 2.81, 95% CI: 1.79–4.41, p<0.001). The results of GEO datasets showed that 5 CpG sites in MGMT with a great diagnostic value for the screening of cervical cancer. Conclusion: The meta-analysis indicated the association between MGMT promoter hypermethylation and squamous intraepithelial lesion and cervical cancer. MGMT methylation detection might have a potential value to be an epigenetic marker for the clinical diagnosis of cervical cancer. [ABSTRACT FROM AUTHOR]

Background: Pleural fluid homocysteine (HCY) can be useful for diagnosis of malignant pleural effusion (MPE). There are no published studies comparing the diagnostic accuracy of HCY with other tumour markers in pleural fluid for diagnosis of MPE. The aim was to compare the accuracy of HCY with that of carcinoembryonic antigen (CEA), cancer antigen (CA) 15.3, CA19.9 and CA125 in pleural fluid and to develop a probabilistic model using these biomarkers to differentiate benign (BPE) from MPE. Methods: Patients with pleural effusion were randomly included. HCY, CEA, CA15.3, CEA19.9 and CA125 were quantified in pleural fluid. Patients were classified into two groups: MPE or BPE. By applying logistic regression analysis, a multivariate probabilistic model was developed using pleural fluid biomarkers. The diagnostic accuracy was determined by receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). Results: Population of study comprised 133 patients (72 males and 61 females) aged between 1 and 96 years (median = 70 years), 81 BPE and 52 MPE. The logistic regression analysis included HCY (p<0.0001) and CEA (p = 0.0022) in the probabilistic model and excluded the other tumour markers. The probabilistic model was: HCY+CEA = Probability(%) = 100×(1+e-z)-1, where Z = 0.5471×[HCY]+0.3846×[CEA]–8.2671. The AUCs were 0.606, 0.703, 0.778, 0.800, 0.846 and 0.948 for CA125, CA19.9, CEA, CA15.3, HCY and HCY+CEA, respectively. Conclusions: Pleural fluid HCY has higher accuracy for diagnosis of MPE than CEA, CA15.3, CA19.9 and CA125. The combination of HCY and CEA concentrations in pleural fluid significantly improves the diagnostic accuracy of the test. [ABSTRACT FROM AUTHOR]

Background: Acute kidney injury (AKI) is an adverse event that carries significant morbidity. Given that interventions after AKI occurrence have poor performance, there is substantial interest in prediction of AKI prior to its diagnosis. However, integration of real-time prognostic modeling into the electronic health record (EHR) has been challenging, as complex models increase the risk of error and complicate deployment. Our goal in this study was to create an implementable predictive model to accurately predict AKI in hospitalized patients and could be easily integrated within an existing EHR system.Methods and Findings: We performed a retrospective analysis looking at data of 169,859 hospitalized adults admitted to one of three study hospitals in the United States (in New Haven and Bridgeport, Connecticut) from December 2012 to February 2016. Demographics, medical comorbidities, hospital procedures, medications, and laboratory data were used to develop a model to predict AKI within 24 hours of a given observation. Outcomes of AKI severity, requirement for renal replacement therapy, and mortality were also measured and predicted. Models were trained using discrete-time logistic regression in a subset of Hospital 1, internally validated in the remainder of Hospital 1, and externally validated in Hospital 2 and Hospital 3. Model performance was assessed via the area under the receiver-operator characteristic (ROC) curve (AUC). The training set cohort contained 60,701 patients, and the internal validation set contained 30,599 patients. External validation data sets contained 43,534 and 35,025 patients. Patients in the overall cohort were generally older (median age ranging from 61 to 68 across hospitals); 44%-49% were male, 16%-20% were black, and 23%-29% were admitted to surgical wards. In the training set and external validation set, 19.1% and 18.9% of patients, respectively, developed AKI. The full model, including all covariates, had good ability to predict imminent AKI for the validation set, sustained AKI, dialysis, and death with AUCs of 0.74 (95% CI 0.73-0.74), 0.77 (95% CI 0.76-0.78), 0.79 (95% CI 0.73-0.85), and 0.69 (95% CI 0.67-0.72), respectively. A simple model using only readily available, time-updated laboratory values had very similar predictive performance to the complete model. The main limitation of this study is that it is observational in nature; thus, we are unable to conclude a causal relationship between covariates and AKI and do not provide an optimal treatment strategy for those predicted to develop AKI.Conclusions: In this study, we observed that a simple model using readily available laboratory data could be developed to predict imminent AKI with good discrimination. This model may lend itself well to integration into the EHR without sacrificing the performance seen in more complex models. [ABSTRACT FROM AUTHOR]

Background: In recent years, many studies focused on the association between the microRNAs (miRNAs) and the risk of systemic lupus erythematosus (SLE), especially miRNA-21 (miR-21). We aimed to investigate the role of circulating miRNAs, especially the miR-21, as a biomarker in detecting SLE. Methods: We searched PubMed, EMBASE, the Cochrane Central Register of Controlled Trials, and China National Knowledge Infrastructure through Mar 3th, 2019. We performed this meta-analysis in a fixed/random-effect model using Meta-disc 1.4 and STATA 15.1. Results: A total of 17 relevant studies were eligible to analyze pooled accuracy. The overall performance of total mixed miRNAs (TmiRs) detection was: pooled sensitivity, 0.71 (95% confidence interval [CI], 0.69 to 0.72); pooled specificity, 0.81 (95%CI, 0.79 to 0.83); and area under the summary receiver operating characteristic curves value (SROC), 0.8797. The miR-21 detection was: pooled sensitivity, 0.68 (95%CI, 0.62 to 0.74); pooled specificity, 0.77 (95%CI, 0.69 to 0.84); and SROC, 0.8281. The meta-regression analysis showed that the type of samples was the sources of heterogeneity. The subgroup analysis suggested that detection in plasma group had the largest AUC of SROC in all the subgroups: pooled sensitivity, 0.8 (95%CI, 0.78 to 0.82); pooled specificity, 0.83 (95%CI, 0.8 to 0.86); and SROC, 0.9068. Conclusions: Our meta-analysis demonstrated that circulating miRNAs might be potential novel biomarkers for detecting SLE, especially miR-21. Moreover, plasma is recommended as the clinical specimen for diagnostic detection. [ABSTRACT FROM AUTHOR]

Background: This meta-analysis presents evidence regarding the diagnostic accuracy of mammaglobin detected using the RT-PCR technique, related to the presence of sentinel node metastasis in breast cancer patients. Methods: The following databases were consulted: Cochrane, Lilacs, Scielo, Hinary, PubMed, Elsevier, Embase, ProQuest, the Universidad del Rosario´s Centro de Recursos Para el Aprendizaje y la Investigación (CRAI-UR) [Resource Center for Learning and Research], and the Google Scholar search engine. The quality of the studies was assessed using the QUADAS-2 and CASpe tools. The selected studies presented the necessary data to calculate diagnostic validity index of mammaglobin detection using RT-PCR, compared with the reference standard test. Global values for the sensitivity, specificity, positive predictive value, negative predictive value, probability ratios, diagnostic ORs, and summary ROC curves of this meta-analysis were obtained using the Meta-DiSc 1.4 program. Results: Initially, 731 articles were obtained; but only 25 were included in the meta-analysis. Sensitivity was 84% (95% CI: 83% - 86%), and specificity was 92% (95% CI: 91% - 93%). Positive and negative predictive values were 9.26 (95% CI: 6.47–13.26) and 0.17 (95% CI: 0.13–0.23), respectively. The diagnostic OR was 66.34 (95% CI: 42.52–103.52). The predictive area under the sROC curve was 94.78 (Q = 0.8876). Conclusions: The evaluated diagnostic index showed that the expression of the mammaglobin biomarker has diagnostic prediction for detecting lymph node metastasis in breast cancer patients, when analyzed using RT-PCR, although more than 50% heterogeneity was found. [ABSTRACT FROM AUTHOR]

Background: Preeclampsia (PE) is a leading cause of maternal and perinatal morbidity and mortality worldwide. Although predictive multiparametric screening is being developed, it is not applicable to nulliparous women, and is not applied to low-risk women. As PE is considered a heterogenous disorder, it is unlikely that any single multiparametric screening protocol containing a small group of biomarkers could have the required accuracy to predict all PE subgroups. Given the etiology of PE is complex and not fully understood, it begs the question, whether the search for biomarkers based on the predominant view of impaired placentation involving factors predominately implicated in angiogenesis and inflammation, has been too limiting. Here we highlight the enormous potential of state-of-the-art, high-throughput proteomics, to provide a comprehensive and unbiased approach to biomarker identification. Methods and findings: Our literature search identified 1336 articles; after review, 45 studies with proteomic data from PE women that were eligible for inclusion. From 710 proteins with altered abundance, we identified 13 common circulating proteins, some of which had not been previously considered as prospective biomarkers of PE. An additional search of the literature for original publications testing any of the 13 common proteins using non-proteomic techniques was also undertaken. Strikingly, 9 of these common proteins had been independently evaluated in PE studies as potential biomarkers. Conclusion: This study highlights the potential of using high-throughput data sets, which are comprehensive and without bias, to identify a profile of proteins that may improve predictions of PE and understanding of its etiology. We bring to the attention of the medical and research communities that the strengths and advantages of using data from high-throughput studies for biomarker discovery would be increased dramatically, if first and second trimester samples were collected for proteomics, and if standardized guidelines for patient reporting and data collection were implemented. [ABSTRACT FROM AUTHOR]

Panniculitis and vitiligo-like lesions have been recently identified as rare cutaneous side effects of the combination of BRAF and MEK inhibitors, a standard of care in metastatic and locally advanced BRAF V600 mutated melanoma. An immune-mediated mechanism has been advocated in the pathogenesis of these skin lesions. Herein we retrospectively reviewed our institutional experience with the aim to explore the association between the occurrence of panniculitis and vitiligo-like lesions during combination therapy with dabrafenib (D) and trametinib (T) and outcome of advanced melanoma patients. Among 52 consecutive BRAF V600 mutated melanoma patients submitted to DT in our center, 12 (23%) developed immune related skin lesions (IRSLs): 8 panniculitis and 4 vitiligo. Patients with IRSLs diagnosis obtained a better disease response (83% versus 25%) (p = 0.001) than their counterpart and had a longer progression free survival and overall survival. The association of IRSLs and lower risk of disease progression (HR 0.19; CI 95% 0.04–0.90; p = 0.043) was confirmed after adjusting for major prognostic factors in multivariate analysis. IRSLs might represent an easy predictive surrogate marker for treatment response and favourable outcome in melanoma patients submitted to DT combination therapy. [ABSTRACT FROM AUTHOR]

Objectives: Brain-derived neurotrophic factor (BDNF) has been associated with the psychopathology of both major depressive disorder (MDD) and schizophrenia (SZ). However, studies focusing on the accuracy of BDNF levels to differentiate between these patients and healthy controls (HCs) have been rare. Methods: Over a discrete ten-year period, we investigated serum BDNF levels in patients with MDD or SZ and compared them to HCs. Results: We found serum BDNF levels in 224 samples with SZ to be lower than those in 390 HCs samples (p = 0.007), but not lower than those in the 273 samples with MDD. Male MDD patients tended to have lower BDNF levels compared to male HCs (p = 0.083). The receiver operating characteristic curve analysis demonstrated that BDNF levels were moderately accurate in differentiating male MDD patients and female patients with SZ from HCs (AUC = 0.652 and 0.623, respectively). The most adequate cut-off points for BDNF level were 5.11 ng/ml (sensitivity = 81.1%, specificity = 48.5%) and 5.88 ng/ml (sensitivity = 74.1%, specificity = 57.4%), respectively. Conclusions: Our results support that BDNF demonstrated moderate accuracy in distinguishing male patients with MDD and female patients with SZ from HCs. In the future, greater samples would be required to further confirm these results. [ABSTRACT FROM AUTHOR]

Many biological data acquisition platforms suffer from inadvertent inclusion of biologically irrelevant variance in analyzed data, collectively termed batch effects. Batch effects can lead to difficulties in downstream analysis by lowering the power to detect biologically interesting differences and can in certain instances lead to false discoveries. They are especially troublesome in predictive modelling where samples in training sets and test sets are often completely correlated with batches. In this article, we present BARA, a normalization method for adjusting batch effects in predictive modelling. BARA utilizes a few reference samples to adjust for batch effects in a compressed data space spanned by the training set. We evaluate BARA using a collection of publicly available datasets and three different prediction models, and compare its performance to already existing methods developed for similar purposes. The results show that data normalized with BARA generates high and consistent prediction performances. Further, they suggest that BARA produces reliable performances independent of the examined classifiers. We therefore conclude that BARA has great potential to facilitate the development of predictive assays where test sets and training sets are correlated with batch. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this study was to build and assess the performance of survival prediction models using the gender-specific informative risk factors for patients with left ventricular systolic dysfunction. Methods: A lasso approach was used to decide the informative predictors for building semi-parametric proportional hazards Cox model. Separate models were built for all patients [N = 299], male patients [Nmale = 194 (64.88%)], and female patients [Nfemale = 105 (35.12%)], to observe the risk factors associated with the individual’s risk of death. The likelihood- ratio test was used to test the goodness of fit of the selected model, and the C-index was used to assess the predictive performance of the selected model(s) with respect to the overall model with all observed risk factors. Results: The survival prediction model for females is notably different from that for males. For males, smoking, diabetes, and anaemia, whereas for females, ejection fraction, sodium, and platelets count are non-informative with zero regression coefficients. The goodness of fit of the selected models with respect to the general model with all observed risk factors is tested using the likelihood-ratio test. The results are in favor of the selected models with p-values 0.51,0.61, and 0.70 for all patients, male patients, and female patients, respectively. The same values of C-index for the full model and the selected models for overall data, for males, and for females (0.72, 0.73, and 0.77 for overall data, male data, and female data, respectively) indicate that the selected models are as good as the corresponding overall models regarding their predictive performance. Conclusion: There is a substantial difference in the survival prediction models for heart failure (HF) of male and female patients in this study. More studies are needed in Pakistan for confirming this striking male-female difference regarding the potential risk factors to predict survival with heart failure. [ABSTRACT FROM AUTHOR]

Background: Nasopharyngeal cancer (NPC), despite being one of the most malignant head and neck carcinomas (HNC), lacks comprehensive prognostic biomarkers that predict patient survival. Therefore, this systematic review and meta-analysis is aimed to evaluate the potential prognostic value of miRNAs as prognostic biomarkers in NPC. Methods: PRISMA guidelines were used to conduct this systematic review and meta-analysis study. Permutations of multiple “search key-words” were used for the search strategy, which was limited to articles published between January 2012 and March 2018. The retrieved articles were meticulously searched with multi-level screening by two reviewers and confirmed by other reviewers. Meta-analysis was performed using Hazard Ratios (HR) and associated 95% Confidence Interval (CI) of survival obtained from previously published studies. Publication bias was assessed by Egger’s bias indicator test and funnel plot symmetry. Results: A total of 5069 patients across 21 studies were considered eligible for inclusion in the systematic review, with 65 miRNAs being evaluated in the subsequent meta-analysis. Most articles included in this study originated from China and one study from North Africa. The forest plot was generated using cumulated survival data, resulting in a pooled HR value of 1.196 (95% CI: 0.893–1.601) indicating that the upregulated miRNAs increased the likelihood of death of NPC patients by 19%. Conclusion: To our knowledge, this is the first meta-analysis that examines the prognostic effectiveness of miRNAs as biomarkers in NPC patients. We noted that the combined effect estimate of HR across multiple studies indicated that increased miRNA expression in NPC potentially leads to poor overall survival. However, further large-scale prospective studies on the clinical significance of the miRNAs, with sizable cohorts are necessary in order to obtain conclusive results. [ABSTRACT FROM AUTHOR]

Background: Managing nephropathy associated with diabetes mellitus warrant investigation of relevant biomarkers in predicting this condition. Adiponectin (ADP) may hold promise as a biomarker for diabetic nephropathy (DN). In this study, we examine associations of ADP with DN by meta-analyzing relevant literature. We also examined the predictive potential of ADP and estimate progression of DN. Methods: Multi-database literature searches and serial omissions of articles yielded 13 studies for inclusion in the meta-analysis. We compared ADP levels between controls/ normoalbuminuria and cases with micro- and macroalbuminuria (MI and MA, respectively) as well as MI versus MA using standardized mean differences (SMD). Associations of ADP with DN were indicated with the P-value considered significant at ≤ 0.05. Subgrouping was based on diabetes type (1 and 2). Predictive potential of ADP was explored with AUC (area under the curve) derived from Receiver Operating Characteristic curve analysis. Results and conclusion: At high P-values of <10−5, overall and subgroup outcomes indicated ADP associations with DN (up to SMD = 1.89–2.26, respectively). However, heterogeneity of the initial SMD effects (up to I2 = 99%) warranted examination of their sources which with the Galbraith plot method, either eliminated or reduced their heterogeneity, signifying combinability of the studies. This feature along with consistency of significant associations, robust outcomes and significant AUC values provide good evidence of the associative and predictive roles of ADP in DN. [ABSTRACT FROM AUTHOR]

Accurate assessment of the association between continuous variables such as gene expression and survival is a critical aspect of precision medicine. In this report, we provide a review of some of the available survival analysis and validation tools by referencing published studies that have utilized these tools. We have identified pitfalls associated with the assumptions inherent in those applications that have the potential to impact scientific research through their potential bias. In order to overcome these pitfalls, we have developed a novel method that enables the logrank test method to handle continuous variables that comprehensively evaluates survival association with derived aggregate statistics. This is accomplished by exhaustively considering all the cutpoints across the full expression gradient. Direct side-by-side comparisons, global ROC analysis, and evaluation of the ability to capture relevant biological themes based on current understanding of RAS biology all demonstrated that the new method shows better consistency between multiple datasets of the same disease, better reproducibility and robustness, and better detection power to uncover biological relevance within the selected datasets over the available survival analysis methods on univariate gene expression and penalized linear model-based methods. [ABSTRACT FROM AUTHOR]

Background: Mitochondria are energy-producing structure of the cell and help to maintain redox environment. In cardiovascular disease, the number of mitochondrial DNA (mtDNA) will changes accordingly compare to normal condition. Some investigators ask whether it has a clear association between mtDNA and cardiovascular disease with its adverse events. Thus, we conduct the meta-analysis to assess the role of circulating mtDNA in evaluating cardiovascular disease. Methods: The meta-analysis was conducted in accordance with a predetermined protocol following the recommendations of Cochrane Handbook of Systematic Reviews. We searched the Pubmed, Embase, the Cochrane Central Register of Controlled Trials and World Health Organization clinical trials registry center to identify relevant studies up to the end of October 2017. Data were analyzed using STATA. Besides, publication bias and meta-regression analysis were also conducted. Results: We collected results from 5 articles for further analyses with 8,252 cases and 20,904 control. The normalized mtDNA copy number level is lower in cardiovascular disease (CVD) than the control groups with a pooled standard mean difference (SMD) of -0.36(95%CI,-0.65 to -0.08); The pooled odds ratio (OR) for CVD proportion associated with a 1-SD (standard deviation) decrease in mtDNA copy number level is 1.23 (95% CI,1.06–1.42); The OR for CVD patients with mtDNA copy number lower than median level is 1.88(95% CI,1.65–2.13); The OR for CVD patients with mtDNA copy number located in the lowest quartile part is 2.15(95% CI, 1.46–3.18); the OR between mtDNA copy number and the risk of sudden cardiac death (SCD) is 1.83(95% CI, 1.22–2.74). Conclusion: Although inter-study variability, the overall performance test of mtDNA for evaluating CVD and SCD revealed that the mtDNA copy number presented the potential to be a biomarker for CVD and SCD prediction. Given that, the fewer copies of mtDNA, the higher the risk of CVD. [ABSTRACT FROM AUTHOR]

Motivation: The measurement of disease biomarkers in easily–obtained bodily fluids has opened the door to a new type of non–invasive medical diagnostics. New technologies are being developed and fine–tuned in order to make this possibility a reality. One such technology is Field Asymmetric Ion Mobility Spectrometry (FAIMS), which allows the measurement of volatile organic compounds (VOCs) in biological samples such as urine. These VOCs are known to contain a range of information on the relevant person’s metabolism and can in principle be used for disease diagnostic purposes. Key to the effective use of such data are well–developed data processing pipelines, which are necessary to extract the most useful data from the complex underlying biological structure. Results: In this study, we present a new data analysis pipeline for FAIMS data, and demonstrate a number of improvements over previously used methods. We evaluate the effect of a series of candidate operational steps during data processing, such as the use of wavelet transforms, principal component analysis (PCA), and classifier ensembles. We also demonstrate the use of FAIMS data in our pipeline to diagnose diabetes on the basis of a simple urine sample using machine learning classifiers. We present results for data generated from a case-control study of 115 urine samples, collected from 72 type II diabetic patients, with 43 healthy volunteers as negative controls. The resulting pipeline combines the steps that resulted in the best classification model performance. These include the use of a two–dimensional discrete wavelet transform, and the Wilcoxon rank–sum test for feature selection. We are able to achieve a best ROC curve AUC of 0.825 (0.747–0.9, 95% CI) for classification of diabetes vs control. We also note that this result is robust to changes in the data pipeline and different analysis runs, with AUC > 0.80 achieved in a range of cases. This is a substantial improvement in performance over previously used data processing methods in this area. Our ability to make strong statements about FAIMS ability to diagnose diabetes is sadly limited, as we found confounding effects from the demographics when including these data in the pipeline. The demographics alone produced a best AUC of 0.87 (0.795–0.94, 95% CI). While the combination of the demographics and FAIMS data resulted in an improvement on the AUC (0.907; 0.848–0.97, 95% CI), it did not prove to be a significant difference. Nevertheless, the pipeline itself shows a significant improvement in performance over more basic methods which have been used with FAIMS data in the past. [ABSTRACT FROM AUTHOR]

Background: The high prevalence of metabolic syndrome (MetS) and cardiovascular diseases (CVD) is observed among Kazakhs in Xinjiang. Because MetS may significantly predict the occurrence of CVD, the inclusion of CVD-related indicators in metabolic network may improve the predictive ability for a CVD-risk model for Kazakhs in Xinjiang. Methods: The study included 2,644 subjects who were followed for 5 years or longer. CVD cases were identified via medical records of the local hospitals from April 2016 to August 2017. Factor analysis was performed in 706 subjects (267 men and 439 women) with MetS to extract CVD-related potential factors from 18 biomarkers tested in a routine health check-up, served as a synthetic predictor (SP). We evaluated the predictive ability of the CVD-risk model using age and SP, logistic regression discrimination for internal validation (n = 384; men = 164, women = 220) and external validation (n = 219; men = 89, women = 130), calculated the probability of CVD for each participant, and receiver operating characteristic curves. Results: According to the diagnostic criteria of JIS, the prevalence of MetS in Kazakh was 30.9%. Seven potential factors with a similar pattern were obtained from men and women and comprised the CVD predictors. When predicting CVD in the internal validation, the area under the curve (AUC) were 0.857 (95%CI 0.807–0.898) for men and 0.852 (95%CI 0.809–0.889) for women, respectively. In the external validation, the AUC to predict CVD were 0.914 (95%CI 0.832–0.963) for men and 0.848 (95%CI 0.774–0.905) for women. It is suggested that SP might serve as a useful tool in identifying CVD with in Kazakhs, especially for Kazakhs men. Conclusions: Among 7 potential factors were extracted from 18 biomarkrs in Kazakhs with MetS, and SP may be used for CVD risk assessment. [ABSTRACT FROM AUTHOR]

Selye defined stress as the nonspecific response of the body to any demand and thus an inherent element of all diseases. He reported that rats show adrenal hypertrophy, thymicolymphatic atrophy, and gastrointestinal ulceration, referred to as the stress triad, upon repeated exposure to nocuous agents. However, Selye’s stress triad as well as its extended version including reduced body weight gain, increased plasma glucocorticoid (GC) concentrations, and GC resistance of target cells do not represent reliable discriminatory biomarkers for chronic stress. To address this, we collected multivariate biological data from male mice exposed either to the preclinically validated chronic subordinate colony housing (CSC) paradigm or to single-housed control (SHC) condition. We then used principal component analysis (PCA), top scoring pairs (tsp) and support vector machines (SVM) analyses to identify markers that discriminate between chronically stressed and non-stressed mice. PCA segregated stressed and non-stressed mice, with high loading for some of Selye’s stress triad parameters. The tsp analysis, a simple and highly interpretable statistical approach, identified left adrenal weight and relative thymus weight as the pair with the highest discrimination score and prediction accuracy validated by a blinded dataset (92% p-value < 0.0001; SVM model = 83% accuracy and p-value < 0.0001). This finding clearly shows that simultaneous consideration of these two parameters can be used as a reliable biomarker of chronic stress status. Furthermore, our analysis highlights that the tsp approach is a very powerful method whose application extends beyond what has previously been reported. [ABSTRACT FROM AUTHOR]

MicroRNAs are small non-coding RNAs that influence gene expression by binding to the 3’ UTR of target mRNAs in order to repress protein synthesis. Soon after discovery, microRNA dysregulation has been associated to several pathologies. In particular, they have often been reported as differentially expressed in healthy and tumor samples. This fact suggested that microRNAs are likely to be good candidate biomarkers for cancer diagnosis and personalized medicine. With the advent of Next-Generation Sequencing (NGS), measuring the expression level of the whole miRNAome at once is now routine. Yet, the collaborative effort of sharing data opens to the possibility of population analyses. This context motivated us to perform an in-silico study to distill cancer-specific panels of microRNAs that can serve as biomarkers. We observed that the problem of finding biomarkers can be modeled as a two-class classification task where, given the miRNAomes of a population of healthy and cancerous samples, we want to find the subset of microRNAs that leads to the highest classification accuracy. We fulfill this task leveraging on a sensible combination of data mining tools. In particular, we used: differential evolution for candidate selection, component analysis to preserve the relationships among miRNAs, and SVM for sample classification. We identified 10 cancer-specific panels whose classification accuracy is always higher than 92%. These panels have a very little overlap suggesting that miRNAs are not only predictive of the onset of cancer, but can be used for classification purposes as well. We experimentally validated the contribution of each of the employed tools to the selection of discriminating miRNAs. Moreover, we tested the significance of each panel for the corresponding cancer type. In particular, enrichment analysis showed that the selected miRNAs are involved in oncogenesis pathways, while survival analysis proved that miRNAs can be used to evaluate cancer severity. Summarizing: results demonstrated that our method is able to produce cancer-specific panels that are promising candidates for a subsequent in vitro validation. [ABSTRACT FROM AUTHOR]

CIP2A, cancerous inhibitor of protein phosphatase 2A, was initially recognized as an oncoprotein. Recently several studies revealed that CIP2A could function as a prognosis biomarker, however, the result remained not comprehensive, partly due to small number of patients included individually. Here we carried out a meta-analysis of published studies to assess the prognostic significance of CIP2A in solid tumors. All eligible studies were identified through searching PubMed, Embase and Web of Science database. In this meta-analysis, 22 studies involving 4,579 participants were included, and we verified that CIP2A over-expression was significantly related with poor overall survival (pooled HR = 1.844, 95% CI = 1.528–2.225, P<0.001) and short disease free survival (pooled HR = 1.808, 95% CI = 1.591–2.055, P<0.001) in solid tumors. Additionally, subgroup analysis suggested that the trend of a poor overall survival with an increased CIP2A expression was present in East-Asian and European patients, as well as in lung cancer and colorectal cancer. To sum up, CIP2A over-expression was associated with poor survival in human solid tumors and might be a predictive factor of poor prognosis. [ABSTRACT FROM AUTHOR]

In omics experiments, variable selection involves a large number of metabolites/ genes and a small number of samples (the n < p problem). The ultimate goal is often the identification of one, or a few features that are different among conditions- a biomarker. Complicating biomarker identification, the p variables often contain a correlation structure due to the biology of the experiment making identifying causal compounds from correlated compounds difficult. Additionally, there may be elements in the experimental design (blocks, batches) that introduce structure in the data. While this problem has been discussed in the literature and various strategies proposed, the over fitting problems concomitant with such approaches are rarely acknowledged. Instead of viewing a single omics experiment as a definitive test for a biomarker, an unrealistic analytical goal, we propose to view such studies as screening studies where the goal of the study is to reduce the number of features present in the second round of testing, and to limit the Type II error. Using this perspective, the performance of LASSO, ridge regression and Elastic Net was compared with the performance of an ANOVA via a simulation study and two real data comparisons. Interestingly, a dramatic increase in the number of features had no effect on Type I error for the ANOVA approach. ANOVA, even without multiple test correction, has a low false positive rates in the scenarios tested. The Elastic Net has an inflated Type I error (from 10 to 50%) for small numbers of features which increases with sample size. The Type II error rate for the ANOVA is comparable or lower than that for the Elastic Net leading us to conclude that an ANOVA is an effective analytical tool for the initial screening of features in omics experiments. [ABSTRACT FROM AUTHOR]

Introduction: Bladder cancer (BC) is one of the most common urologic malignancies and it is urgently needed to identify novel potential prognostic biomarkers for predicting prognosis and progression of patients with BC in clinical practice. Previous research has revealed that long noncoding RNAs (lncRNAs) played critical roles in BC, and may serve as novel potential prognostic biomarkers in patients with BC. Therefore, we conducted this meta-analysis to clarify the prognostic potential of lncRNAs in BC patients. Methods: A comprehensive search was performed in PubMed, Web of Science, and China National Knowledge Infrastructure (CNKI). According to the predefined exclusion and inclusion criteria, a total of 9 recently published articles comprising 13 lncRNAs and 666 BC patients were included into this meta-analysis. We analyzed the hazard ratios (HRs) and 95% confidence intervals (CIs) to determine the relationship between lncRNAs expression and survival outcomes. We also analyzed the odds ratio (ORs) and 95% confidence intervals (CIs) to assess the association between lncRNAs expression and clinicopathological characteristics, including histological grade, gender, multifocality, tumor size, and tumor stage. Results: Our results revealed that high lncRNAs expression was associated with shorter overall survival in Asian BC patients (pooled HR = 2.32, 95% CI: 1.35–4.00, P = 0.002, random-effect). High lncRNAs expression levels were significantly associated with histological grade (G2-G3 vs. G1: OR = 3.857, 95%CI: 1.293–11.502, P = 0.015, random-effect). Conclusions: In summary, this meta-analysis has demonstrated that lncRNAs could be used as potential prognostic markers for BC and high lncRNAs expression could predict poor prognosis among Asian BC patients. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to summarize and discuss the similarities and differences in inflammatory biomarkers in postoperative delirium (POD) and cognitive dysfunction (POCD). Methods: A systematic retrieval of literature up to June 2017 in PubMed, Embase, the Cochrane Library, the China National Knowledge Infrastructure database, and the Wanfang database was conducted. Extracted data were analyzed with STATA (version 14). The standardized mean difference (SMD) and the 95% confidence interval (95% CI) of each indicator were calculated using a random effect model. We also performed tests of heterogeneity, sensitivity analysis, assessments of bias, and meta-regression in this meta-analysis. Results: A total of 54 observational studies were included. By meta-analysis we found significantly increased C-reactive protein (CRP) (9 studies, SMD 0.883, 95% CI 0.130 to 1.637, P = 0.022 in POD; 10 studies, SMD -0.133, 95% CI -0.512 to 0.246, P = 0.429 in POCD) and interleukin (IL)-6 (7 studies, SMD 0.386, 95% CI 0.054 to 0.717, P = 0.022 in POD; 16 studies, SMD 0.089, 95% CI -0.133 to 0.311, P = 0.433 in POCD) concentrations in both POD and POCD patients. We also found that the SMDs of CRP and IL-6 from POCD patients were positively correlated with surgery type in the meta-regression (CRP: Coefficient = 1.555365, P = 0.001, 10 studies; IL-6: Coefficient = -0.6455521, P = 0.086, 16 studies). Conclusion: Available evidence from medium-to-high quality observational studies suggests that POD and POCD are indeed correlated with the concentration of peripheral and cerebrospinal fluid (CSF) inflammatory markers. Some of these markers, such as CRP and IL-6, play roles in both POD and POCD, while others are specific to either one of them. [ABSTRACT FROM AUTHOR]

Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses (‘gained’ genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease. [ABSTRACT FROM AUTHOR]

Deciding who should receive a liver transplant (LT) depends on both urgency and utility. Most survival scores are validated through discriminative tests, which compare predicted outcomes between patients. Assessing post-transplant survival utility is not discriminate, but should be “calibrated” to be effective. There are currently no such calibrated models. We developed and validated a novel calibrated model to predict individual survival after LT for Primary Sclerosing Cholangitis (PSC). We applied a software tool, PSSP, to adult patients in the Scientific Registry of Transplant Recipients (n = 2769) who received a LT for PSC between 2002 and 2013; this produced a model for predicting individual survival distributions for novel patients. We also developed an appropriate evaluation measure, D-calibration, to validate this model. The learned PSSP model showed an excellent D-calibration (p = 1.0), and passed the single-time calibration test (Hosmer-Lemeshow p-value of over 0.05) at 0.25, 1, 5 and 10 years. In contrast, the model based on traditional Cox regression showed worse calibration on long-term survival and failed at 10 years (Hosmer-Lemeshow p value = 0.027). The calculator and visualizer are available at: . In conclusion we present a new tool that accurately estimates individual post liver transplantation survival. [ABSTRACT FROM AUTHOR]

Aim: Among the three adiponectin isoforms, a lower ratio of high molecular weight (HMW) adiponectin to total adiponectin (TA) is well known to cause insulin resistance and type 2 diabetes (T2D). However, how the levels of other adiponectin isoforms, such as the middle molecular weight (MMW) and low molecular weight (LMW) isoforms, and their relative ratio to TA change in T2D subjects has not been determined. Therefore, we investigated the association of these adiponectin-related parameters with T2D. Methods: We examined the associations between adiponectin-related parameters and diabetes in a group of 394 T2D subjects and 374 controls (1st group) randomly selected from among the participants in our previous study. The associations between these parameters and the HOMA-IR in a 2nd group, consisting of the subjects remaining in the 1st group after the exclusion of subjects receiving diabetic medication, were also examined. Result: In the 1st group, after adjusting for confounding factor, the levels of all the adiponectin isoforms and the HMW/TA ratio were significantly lower among the diabetic subjects than among the controls (all P values < 0.01). On the contrary, the LMW/TA ratio was significantly higher among the diabetic subjects (P < 0.01) and was positively associated with T2D (odds ratio = 8.64, P < 0.01). In the 2nd group, the HMW/TA ratio was inversely associated with the HOMA-IR; however, the LMW/TA ratio was positively associated with the HOMA-IR (β for LMW/TA ratio = 0.89, SE = 0.24, P < 0.001), similar to the association with T2D. The MMW/TA ratio was not associated with T2D or the HOMA-IR. Conclusion: The current investigation demonstrated that, unlike the reduction in the levels of all the adiponectin isoforms and the HMW/TA ratio, an increased LMW/TA ratio was associated with T2D through its relation to insulin resistance. [ABSTRACT FROM AUTHOR]

Glioma is the most common malignant intracranial tumour. Recently, several publications have suggested that miRNAs can be used as potential diagnostic biomarkers of glioma. Here we performed a meta-analysis to identify the diagnostic accuracy of differentially expressed circulating miRNAs in gliomas. Using PubMed, Medline and Cochrane databases, we searched for studies which evaluated a single or panel of miRNAs from circulating blood as potential biomarkers of glioma. Sixteen publications involving 23 studies of miRNAs from serum or plasma met our criteria and were included in this meta-analysis. The pooled diagnostic parameters were calculated by random effect models and overall diagnostic performance of altered miRNAs was illustrated by the summary receiver operator characteristic (SROC) curves. The pooled sensitivity, specificity, positive likelihood ratio (PLR) and negative likelihood ratio (NLR) from each study were calculated. The pooled PLR, NLR and Diagnostic Odds Ratio were 6.39 (95% CI, 4.61–8.87), 0.15 (95% CI, 0.11–0.21) and 41.91 (95% CI, 23.15–75.88), respectively. The pooled sensitivity, specificity and area under the curve (AUC) were 0.87 (95% CI, 0.82–0.91), 0.86 (95% CI, 0.82–0.90) and 0.93 (95% CI, 0.91–0.95), respectively. This meta-analysis demonstrated that circulating miRNAs are capable of distinguishing glioma from healthy controls. Circulating miRNAs are promising diagnostic biomarkers for glioma and can potentially be used as a non-invasive early detection. [ABSTRACT FROM AUTHOR]

Background: The prognostic role of human epidermal growth factor receptor 2 (HER2) in ovarian cancer has been investigated in previous studies, but the results remain controversial. Here we present a meta-analysis to systematically review the association between HER2 expression and ovarian cancer prognosis. Method: Observational studies published until July 2017 were searched in Pubmed, Embase, and Cochrane library databases. Hazard ratios (HRs) for survival with 95% confidence intervals (CIs), subgroup analyses, publication bias and sensitivity analyses were implemented under a standard manner. Estimates of overall survival (OS), progress-free survival (PFS) and disease-free survival (DFS) were weighted and pooled using Der Simonian-Laird random-effect model. Result: Thirty-four studies that include 5180 ovarian cancer patients were collected for analysis. Expression of HER2 was negatively correlated with clinical prognosis of overall survival (HR = 1.57, 95% CI: 1.31 to 1.89, P < 0.001) and disease-free survival / progress-free survival (HR = 1.26, 95% CI = 1.06 to 1.49) in ovarian cancers. The association between HER2 expression and poor ovarian cancer prognosis in overall survival was also statistically significant in subgroups of unclassified ovarian cancer, Caucasian population and Asian population, while irrespective of detection method. Conclusion: HER2 expression was related with poor prognosis in ovarian cancer patients and can be used as a predicting cancer prognostic biomarker in ovarian cancer patients. [ABSTRACT FROM AUTHOR]

Background: Salmonella Typhi and Salmonella Paratyphi A are the agents of enteric (typhoid) fever; both can establish chronic carriage in the gallbladder. Chronic Salmonella carriers are typically asymptomatic, intermittently shedding bacteria in the feces, and contributing to disease transmission. Detecting chronic carriers is of public health relevance in areas where enteric fever is endemic, but there are no routinely used methods for prospectively identifying those carrying Salmonella in their gallbladder. Methodology/Principal findings: Here we aimed to identify biomarkers of Salmonella carriage using metabolite profiling. We performed metabolite profiling on plasma from Nepali patients undergoing cholecystectomy with confirmed S. Typhi or S. Paratyphi A gallbladder carriage (and non-carriage controls) using two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GCxGC-TOFMS) and supervised pattern recognition modeling. We were able to significantly discriminate Salmonella carriage samples from non-carriage control samples. We were also able to detect differential signatures between S. Typhi and S. Paratyphi A carriers. We additionally compared carriage metabolite profiles with profiles generated during acute infection; these data revealed substantial heterogeneity between metabolites associated with acute enteric fever and chronic carriage. Lastly, we found that Salmonella carriers could be significantly distinguished from non-carriage controls using only five metabolites, indicating the potential of these metabolites as diagnostic markers for detecting chronic Salmonella carriers. Conclusions/Significance: Our novel approach has highlighted the potential of using metabolomics to search for diagnostic markers of chronic Salmonella carriage. We suggest further epidemiological investigations of these potential biomarkers in alternative endemic enteric fever settings. [ABSTRACT FROM AUTHOR]

Identifying a reliable biomarker may accelerate diagnosis of multiple sclerosis (MS) and lead to early management of the disease. Accumulating evidence suggest that cerebrospinal fluid (CSF) and peripheral blood concentration of osteopontin (OPN) may have diagnostic and prognostic value in MS. We conducted a systematic review and meta-analysis of studies that measured peripheral blood and CSF levels of OPN in MS patients and controls to evaluate the diagnostic potential of this biomarker better. We searched PubMed, Web of Science and Scopus databases to find articles that measured OPN concentration in peripheral blood and CSF samples from MS patients up to October 19, 2016. Q statistic tests and the I2 index were applied for heterogeneity assessment. If the I2 index was less than 40%, the fixed-effects model was used for meta-analysis. Random-effects meta-analysis was chosen if the I2 value was greater than 40%. After removal of duplicates, 918 articles were identified, and 27 of them fulfilled the inclusion criteria. We included 22 eligible studies in the final meta-analysis. MS patients, in general, had considerably higher levels of OPN in their CSF and blood when compared to all types of controls (p<0.05). When the comparisons were made between different subtypes of MS patients and controls, the results pointed to significantly higher levels of OPN in CSF of MS subgroups (p<0.05). All subtypes of MS patients, except CIS patients, had increased blood levels of OPN compared to controls (p<0.05). In the second set of meta-analyses, we compared the peripheral blood and CSF concentrations of OPN between MS patient subtypes. CIS patients had significantly lower levels of OPN both in their peripheral blood and CSF compared to patients with progressive subtypes of MS (p<0.05). CSF concentration of OPN was significantly higher among RRMS patients compared to the CIS patients and SPMS patients (P<0.05). Finally, patients with active MS had significantly higher OPN levels in their CSF compared to patients with stable disease (P = 0.007). The result of this study confirms that increased levels of OPN exist in CSF and peripheral blood of MS patients and strengthens the evidence regarding the clinical utility of OPN as a promising and validated biomarker for MS. [ABSTRACT FROM AUTHOR]

Predicting novel microRNA (miRNA)-disease associations is clinically significant due to miRNAs’ potential roles of diagnostic biomarkers and therapeutic targets for various human diseases. Previous studies have demonstrated the viability of utilizing different types of biological data to computationally infer new disease-related miRNAs. Yet researchers face the challenge of how to effectively integrate diverse datasets and make reliable predictions. In this study, we presented a computational model named Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction (LRSSLMDA), which projected miRNAs/diseases’ statistical feature profile and graph theoretical feature profile to a common subspace. It used Laplacian regularization to preserve the local structures of the training data and a L1-norm constraint to select important miRNA/disease features for prediction. The strength of dimensionality reduction enabled the model to be easily extended to much higher dimensional datasets than those exploited in this study. Experimental results showed that LRSSLMDA outperformed ten previous models: the AUC of 0.9178 in global leave-one-out cross validation (LOOCV) and the AUC of 0.8418 in local LOOCV indicated the model’s superior prediction accuracy; and the average AUC of 0.9181+/-0.0004 in 5-fold cross validation justified its accuracy and stability. In addition, three types of case studies further demonstrated its predictive power. Potential miRNAs related to Colon Neoplasms, Lymphoma, Kidney Neoplasms, Esophageal Neoplasms and Breast Neoplasms were predicted by LRSSLMDA. Respectively, 98%, 88%, 96%, 98% and 98% out of the top 50 predictions were validated by experimental evidences. Therefore, we conclude that LRSSLMDA would be a valuable computational tool for miRNA-disease association prediction. [ABSTRACT FROM AUTHOR]

Introduction: The low incidence of oesophageal adenocarcinoma (EAC) in Barrett's oesophagus (BE) patients reinforces the need for risk stratification tools to make BE surveillance more effective. Therefore, we have undertaken a systematic review and meta-analysis of published studies on immunohistochemical (IHC) biomarkers in BE to determine the value of IHC biomarkers as neoplastic predictors in BE surveillance. Materials and methods: We searched MEDLINE, EMBASE, Web of Science, CENTRAL, Pubmed publisher, and Google scholar. All studies on IHC biomarkers in BE surveillance were included. ORs were extracted and meta-analyses performed with a random effects model. Results: 16 different IHC biomarkers were studied in 36 studies. These studies included 425 cases and 1835 controls. A meta- analysis was performed for p53, aspergillus oryzae lectin (AOL), Cyclin A, Cyclin D and alpha-methylacyl-CoA racemase. Aberrant p53 expression was significantly associated with an increased risk of neoplastic progression with an OR of 3.18 (95% CI 1.68 to 6.03). This association was confirmed for both non-dysplastic BE and BE with low-grade dysplasia (LGD). Another promising biomarker to predict neoplastic progression was AOL, with an OR of 3.04 (95% CI 2.05 to 4.49). Discussion: Use of p53 IHC staining may improve risk stratification in BE surveillance. Aberrant p53 expression in BE patients appeared to be associated with a significantly increased risk of neoplastic progression for both non-dysplastic and LGD BE patients. [ABSTRACT FROM AUTHOR]

Background: To reduce over-diagnosis of chronic kidney disease (CKD) resulting from the inaccuracy of creatinine-based estimates of glomerular filtration rate (GFR), UK and international guidelines recommend that cystatin-C-based estimates of GFR be used to confirm or exclude the diagnosis in people with GFR 45-59 ml/min/1.73 m2 and no albuminuria (CKD G3aA1). Whilst there is good evidence for cystatin C being a marker of GFR and risk in people with CKD, its use to define CKD in this manner has not been evaluated in primary care, the setting in which most people with GFR in this range are managed.Methods and Findings: A total of 1,741 people with CKD G3a or G3b defined by 2 estimated GFR (eGFR) values more than 90 days apart were recruited to the Renal Risk in Derby study between June 2008 and March 2010. Using Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations, we compared GFR estimated from creatinine (eGFRcreat), cystatin C (eGFRcys), and both (eGFRcreat-cys) at baseline and over 5 years of follow-up. We analysed the proportion of participants with CKD G3aA1 reclassified to 'no CKD' or more advanced CKD with the latter two equations. We further assessed the impact of using cystatin-C-based eGFR in risk prediction equations for CKD progression and all-cause mortality and investigated non-GFR determinants of eGFRcys. Finally, we estimated the cost implications of implementing National Institute for Health and Care Excellence (NICE) guidance to use eGFRcys to confirm the diagnosis in people classified as CKD G3aA1 by eGFRcreat. Mean eGFRcys was significantly lower than mean eGFRcreat (45.1 ml/min/1.73 m2, 95% CI 44.4 to 45.9, versus 53.6 ml/min/1.73 m2, 95% CI 53.0 to 54.1, P < 0.001). eGFRcys reclassified 7.7% (50 of 653) of those with CKD G3aA1 by eGFRcreat to eGFR ≥ 60 ml/min/1.73 m2. However, a much greater proportion (59.0%, 385 of 653) were classified to an eGFR category indicating more severe CKD. A similar pattern was seen using eGFRcreat-cys, but lower proportions were reclassified. Change in eGFRcreat and eGFRcys over 5 years were weakly correlated (r = 0.33, P < 0.001), but eGFRcys identified more people as having CKD progression (18.2% versus 10.5%). Multivariable analysis using eGFRcreat as an independent variable identified age, smoking status, body mass index, haemoglobin, serum uric acid, serum albumin, albuminuria, and C reactive protein as non-GFR determinants of eGFRcys. Use of eGFRcys or eGFRcreat-cys did not improve discrimination in risk prediction models for CKD progression and all-cause mortality compared to similar models with eGFRcreat. Application of the NICE guidance, which assumed cost savings, to participants with CKD G3aA1 increased the cost of monitoring by £23 per patient, which if extrapolated to be applied throughout England would increase the cost of testing and monitoring CKD by approximately £31 million per year. Limitations of this study include the lack of a measured GFR and the potential lack of ethnic diversity in the study cohort.Conclusions: Implementation of current guidelines on eGFRcys testing in our study population of older people in primary care resulted in only a small reduction in diagnosed CKD but classified a greater proportion as having more advanced CKD than eGFRcreat. Use of eGFRcys did not improve risk prediction in this population and was associated with increased cost. Our data therefore do not support implementation of these recommendations in primary care. Further studies are warranted to define the most appropriate clinical application of eGFRcys and eGFRcreat-cys. [ABSTRACT FROM AUTHOR]

Objectives: There has been renewed interest in lactate as a risk biomarker in sepsis and septic shock. However, the ability of the odds ratio (OR) and change in the area under the receiver operator characteristic curve (AUC-ROC) to assess biomarker added-value has been questioned. Design, setting and participants: A sepsis cohort was identified from the ICU database of an Australian tertiary referral hospital using APACHE III diagnostic codes. Demographic information, APACHE III scores, 24-hour post-admission patient lactate levels, and hospital mortality were accessed. Measurements and main results: Hospital mortality was modelled using a base predictive logistic regression model and sequential addition of admission lactate, lactate clearance ([lactateadmission—lactatefinal]/lactateadmission), and area under the lactate-time curve (LTC). Added-value was assessed using lactate index OR; AUC-ROC difference (base-model versus lactate index addition); net (mortality) reclassification index (NRI; range -2 to +2); and net benefit (NB), the number of true positives per patient adjusted for the number of false positives. The data set comprised 717 patients with mean(SD) age and APACHE III score 61.1(16.5) years and 68.3(28.2) respectively; 59.2% were male. Admission lactate was 2.3(2.5) mmol/l; with lactate of ≥ 4 mmol/L (37% hospital mortality) in 17% and patients with lactate < 4 mmol/L having 18% hospital mortality. The admission base-model had an AUC-ROC = 0.81 with admission lactate OR = 1.127 (95%CI: 1.038, 1.224), AUC-ROC difference of 0.0032 (-0.0037, 0.01615; P = 0.61), and NRI 0.240(0.030, 0.464). The over-time model had an AUC-ROC = 0.86 with (i) clearance OR = 0.771, 95%CI: 0.578, 1.030; P = 0.08; AUC-ROC difference 0.001 (-0.003, 0.014; P = 0.78), and NRI 0.109(-0.193, 0.425) and (ii) LTC OR = 0.997, 95%CI: 0.989, 1.005, P = 0.49; AUC-ROC difference 0.004 (-0.002, 0.004; P = 0.34), and NRI 0.111(-0.222, 0.403). NB was not incremented by any lactate index. Conclusions: Lactate added-value assessment is dependent upon the performance of the underlying predictive model and should incorporate risk reclassification and net benefit measures. [ABSTRACT FROM AUTHOR]

Background: The heart rate (HR) response to exercise provides useful information about the autonomic function and has prognostic value, but its reproducibility over a long period of time, a critical requirement for using it as a clinical biomarker, is undetermined. Aim: To determine the intra-individual reproducibility of HR dynamics during sub-maximum exercise and one minute recovery. Methods: 1187 individuals from the Cardio physical fitness assessment test of the UK Biobank repeated a standard exercise stress test twice (recall time 34.2 ± 2.8 months) and were prospectively studied. Results: 821 individuals complied with inclusion criteria for reproducibility analysis, including peak workload differences between assessments ≤10 W. Intra-individual correlation between HR profile during the first and the second assessment was very high and higher than inter-individual correlation (0.92±0.08 vs 0.87±0.11, p<0.01). Intra-individual correlation of indices describing HR dynamics was: ρ = 0.81 for maximum HR during exercise; ρ = 0.71 for minimum HR during recovery; ρ = 0.70 for HR changes during both exercise and recovery; Intra-individual correlation was higher for these indices of HR dynamics than for resting HR (ρ = 0.64). Bland-Altman plots demonstrated good agreement between HR indices estimated during the first and second assessment. A small but consistent bias was registered for all repeated measurements. The intra-individual consistency of abnormal values was about 60–70%. Conclusions: The HR dynamics during exercise and recovery are reproducible over a period of 3 years, with moderate to strong intra-individual reproducibility of abnormal values. [ABSTRACT FROM AUTHOR]

A decline in β-cell function is a prerequisite for the development of type 2 diabetes, yet the level of β-cell function in individuals at risk of the condition is rarely measured. This is due, in part, to the fact that current methods for assessing β-cell function are inaccurate, prone to error, labor-intensive, or affected by glucose-lowering therapy. The aim of the current study was to identify novel circulating biomarkers to monitor β-cell function and to identify individuals at high risk of developing β-cell dysfunction. In a nested case-control study from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) cohort (n = 1157), proteomics and miRNA profiling were performed on fasting plasma samples from 43 individuals who progressed to impaired glucose tolerance (IGT) and 43 controls who maintained normal glucose tolerance (NGT) over three years. Groups were matched at baseline for age, gender, body mass index (BMI), insulin sensitivity (euglycemic clamp) and β-cell glucose sensitivity (mathematical modeling). Proteomic profiling was performed using the SomaLogic platform (Colorado, USA); miRNA expression was performed using a modified RT-PCR protocol (Regulus Therapeutics, California, USA). Results showed differentially expressed proteins and miRNAs including some with known links to type 2 diabetes, such as adiponectin, but also novel biomarkers and pathways. In cross sectional analysis at year 3, the top differentially expressed biomarkers in people with IGT/ reduced β-cell glucose sensitivity were adiponectin, alpha1-antitrypsin (known to regulate adiponectin levels), endocan, miR-181a, miR-342, and miR-323. At baseline, adiponectin, cathepsin D and NCAM.L1 (proteins expressed by pancreatic β-cells) were significantly lower in those that progressed to IGT. Many of the novel prognostic biomarker candidates were within the epithelial-mesenchymal transition (EMT) pathway: for example, Noggin, DLL4 and miR-181a. Further validation studies are required in additional clinical cohorts and in patients with type 2 diabetes, but these results identify novel pathways and biomarkers that may have utility in monitoring β-cell function and/ or predicting future decline, allowing more targeted efforts to prevent and intercept type 2 diabetes. [ABSTRACT FROM AUTHOR]

Background: Myocardial fibrosis is a potential pathophysiological mechanism leading to systemic right ventricular (SRV) deterioration. We hypothesize that circulating levels of collagen deposition markers are elevated in patients with SRV remodeling and this elevation may have a predictive value. Methods: We prospectively evaluated 56 patients with D-TGA after the atrial switch procedure (mean age 25.6 ± 4.8, range 18–37 years; 67% males). Serum levels of procollagen type III amino-terminal propeptide (PIIINP), collagen type I carboxy-terminal telopeptide (CITP), procollagen type I N-terminal propeptide (PINP), matrix metalloproteinase (MMP 1, MMP 9) and a tissue inhibitor of matrix metalloproteinase (TIMP 1) and N-terminal pro-brain natriuretic peptide (NT-pro-BNP) were measured and compared with healthy controls. The relationship between these serum markers, echocardiographic and cardiac magnetic resonance parameters and the outcome at a follow-up of 61 months (range, 24–85 months) was determined. Results: Compared with the healthy control group, the study group had significantly higher levels of TIMP1, PIIINP, CITP, PINP and NT-pro-BNP (p<0.05, each). The levels of PIIINP and CITP were significantly higher among patients with an SRV mass index above the mean value. The level of PIIINP was significantly higher among patients with an SRV EDV index above the mean value. CITP was significantly elevated in SRV late gadolinium enhanced (LGE) positive patients, compared to patients without SRV LGE. MMP9 and TIMP1 predicted an adverse clinical outcome on univariate Cox proportional hazard survival analysis in addition to well proven predictors of outcome (SRV EF and NYHA). Conclusions: We demonstrated a pattern of altered collagen turnover adversely related with the indices of SRV remodeling and an adverse clinical outcome in patients with SRV. [ABSTRACT FROM AUTHOR]