StAR: a simple tool for the statistical comparison of ROC curves

Differential methylation of the promoter and first exon of the RASSF1A gene in hepatocarcinogenesis

AIM

Aberrant methylation of the promoter, P2, and the first exon, E1, regions of the tumor suppressor gene RASSF1A, have been associated with hepatocellular carcinoma (HCC), albeit with poor specificity. This study analyzed the methylation profiles of P1, P2 and E1 regions of the gene to identify the region of which methylation most specifically corresponds to HCC and to evaluate the potential of this methylated region as a biomarker in urine for HCC screening.

METHODS

Bisulfite DNA sequencing and quantitative methylation-specific polymerase chain reaction assays were performed to compare methylation of the 56 CpG sites in regions P1, P2 and E1 in DNA isolated from normal, hepatitic, cirrhotic, adjacent non-HCC, and HCC liver tissue and urine samples for the characterization of hypermethylation of the RASSF1A gene as a biomarker for HCC screening.

RESULTS

In tissue, comparing HCC (n = 120) with cirrhosis and hepatitis together (n = 70), methylation of P1 had an area under the receiver operating characteristics curve (AUROC) of 0.90, whereas methylation of E1 and P2 had AUROC of 0.84 and 0.72, respectively. At 90% sensitivity, specificity for P1 methylation was 72.9% versus 38.6% for E1 and 27.1% for P2. Methylated P1 DNA was detected in urine in association with cirrhosis and HCC. It had a sensitivity of 81.8% for α-fetoprotein negative HCC.

CONCLUSION

Among the three regions analyzed, methylation of P1 is the most specific for HCC and holds great promise as a DNA marker in urine for screening of cirrhosis and HCC.

Genome-wide prediction of prokaryotic two-component system networks using a sequence-based meta-predictor

BACKGROUND

Two component systems (TCS) are signalling complexes manifested by a histidine kinase (receptor) and a response regulator (effector). They are the most abundant signalling pathways in prokaryotes and control a wide range of biological processes. The pairing of these two components is highly specific, often requiring costly and time-consuming experimental characterisation. Therefore, there is considerable interest in developing accurate prediction tools to lessen the burden of experimental work and cope with the ever-increasing amount of genomic information.

RESULTS

We present a novel meta-predictor, MetaPred2CS, which is based on a support vector machine. MetaPred2CS integrates six sequence-based prediction methods: in-silico two-hybrid, mirror-tree, gene fusion, phylogenetic profiling, gene neighbourhood, and gene operon. To benchmark MetaPred2CS, we also compiled a novel high-quality training dataset of experimentally deduced TCS protein pairs for k-fold cross validation, to act as a gold standard for TCS partnership predictions. Combining individual predictions using MetaPred2CS improved performance when compared to the individual methods and in comparison with a current state-of-the-art meta-predictor.

CONCLUSION

We have developed MetaPred2CS, a support vector machine-based metapredictor for prokaryotic TCS protein pairings. Central to the success of MetaPred2CS is a strategy of integrating individual predictors that improves the overall prediction accuracy, with the in-silico two-hybrid method contributing most to performance. MetaPred2CS outperformed other available systems in our benchmark tests, and is available online at http://metapred2cs.ibers.aber.ac.uk, along with our gold standard dataset of TCS interaction pairs.

Urinary neutrophil gelatinase-associated lipocalin time course during cardiac surgery

Background:

NGAL is one of the most promising AKI biomarkers in cardiac surgery. However, the best timing to dose it and the reference values are still matter of discussion.

Aim of the Study:

We performed a uNGAL perioperative time course, to better understand its perioperative kinetics and its role in AKI diagnosis.

Setting of the Study:

San Raffaele University Hospital, cardiac surgery department.

Material and Methods:

We enrolled in this prospective observational study 19 patients undergoing cardiac surgery with cardiopulmonary bypass (CPB). Based on preoperative characteristics, they were divided in low-risk and high-risk patients. uNGAL measurements were collected at pre-defined times before, during, and up to 24 hours after surgery.

Statistical Analysis:

Data were analysed by use of SAS 1999-2001 program or IBM SPSS Statistics.

Results:

In low-risk patients, uNGAL had the highest value immediately after general anesthesia induction (basal dosage: uNGAL: 12.20ng×ml^-1, IQR 14.00). It later decreased significantly (3.40 ng×ml^-1, IQR 4.80; P = 0.006) during CPB, and finally return to its original value 24 hours after surgery. In high-risk patients, uNGAL increased immediately after surgery; it had the highest value on ICU arrival (38,20 ng×ml^-1; IQR 133,10) and remained high for several hours. A difference in uNGAL levels between the two groups was already observed at the end of surgery, but it became statistically significant on ICU arrival (P = 0.002).

Conclusion:

This study helps to better understand the different kinetics of this new biomarker in low-risk and high-risk cardiac patients.

Inclusion bodies of recombinant Epstein-Barr virus capsid antigen p18 as potential immobilized antigens in enzyme immunoassays for detection of nasopharyngeal carcinoma

BACKGROUND

Development of indirect enzyme-linked immunosorbent assays (ELISAs) often utilizes synthetic peptides or recombinant proteins from Escherichia coli as immobilized antigens. Because inclusion bodies (IBs) formed during recombinant protein expression in E. coli are commonly thought as misfolded aggregates, only refolded proteins from IBs are used to develop new or in-house diagnostic assays. However, the promising utilities of IBs as nanomaterials and immobilized enzymes as shown in recent studies have led us to explore the potential use of IBs of recombinant Epstein-Barr virus viral capsid antigen p18 (VCA p18) as immobilized antigens in ELISAs for serologic detection of nasopharyngeal carcinoma (NPC).

METHODS

Thioredoxin fusion VCA p18 (VCA-Trx) and IBs of VCA p18 without fusion tags (VCA-IBs) were purified from E. coli. The diagnostic performances of IgG/VCA-IBs, IgG/VCA-Denat-IBs (using VCA-IBs coated in 8mol/l urea), IgG/VCA-Trx, and IgG/VCA-Peptide assays were compared by screening 100 NPC case-control pairs.

RESULTS

The IgG/VCA-Denat-IBs assay showed the best area under the receiver operating characteristic curve (AUC: 0.802; p<0.05), while the AUCs for the IgG/VCA-IBs, IgG/VCA-Trx, and IgG/VCA-Peptide assays were comparable (AUC: 0.740, 0.727, and 0.741, respectively).

CONCLUSION

We improved the diagnostic performance of the ELISA significantly using IBs of recombinant VCA p18.

A multigene array for measurable residual disease detection in AML patients undergoing SCT

AML is a diagnosis encompassing a diverse group of myeloid malignancies. Heterogeneous genetic etiology, together with the potential for oligoclonality within the individual patient, have made the identification of a single high-sensitivity marker of disease burden challenging. We developed a multiple gene measurable residual disease (MG-MRD) RQ-PCR array for the high-sensitivity detection of AML, retrospectively tested on 74 patients who underwent allo-SCT at the NHLBI in the period 1994-2012. MG-MRD testing on peripheral blood samples prior to transplantation demonstrated excellent concordance with traditional BM-based evaluation and improved risk stratification for post-transplant relapse and OS outcomes. Pre-SCT assessment by MG-MRD predicted all clinical relapses occurring in the first 100 days after allo-SCT compared with 57% sensitivity using WT1 RQ-PCR alone. Nine patients who were negative for WT1 prior to transplantation were correctly reclassified into a high-risk MG-MRD-positive group, associated with 100% post-transplant mortality. This study provides proof of principle that a multiple gene approach may be superior to the use of WT1 expression alone for AML residual disease detection.

MiR-205 and MiR-375 microRNA assays to distinguish squamous cell carcinoma from adenocarcinoma in lung cancer biopsies

INTRODUCTION

Identification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) histology of non-small-cell lung cancer (NSCLC) in biopsies is clinically important but can be inaccurate by routine histopathologic examination. We quantify this inaccuracy at a cancer center, and evaluate the utility of a microRNA-based method to histotype AC/SCC in biopsies.

METHODS

RNA was extracted from tissue sections with greater than 90% tumor content that were macro- or micro-dissected from formalin-fixed, paraffin-embedded biopsy specimens. MicroRNAs in RNA from the biopsies and from resected tumors were quantified by TaqMan reverse transcription-polymerase chain reaction assays and normalized against the RNU6B housekeeping RNA. Publicly available microRNA expression datasets were examined.

RESULTS

NSCLC subtyping of small biopsy specimens by routine histopathologic examination either failed or mistyped the histology of 21% of 190 cases. Using 77 resectates, an reverse transcription-polymerase chain reaction-based assay of microRNAs miR-21, miR-205, and miR-375 was developed to identify AC and SCC subtypes of NSCLC. This method identified the AC/SCC histotypes of 25 biopsies with an accuracy of 96%, and correctly histotyped all 12 cases for which the histology had been mistyped by routine histopathologic examination of the biopsy. Examination of publicly available datasets identified miR-205 and miR-375 as microRNAs with the best ability to histotype AC and SCC, and that levels of the two microRNAs in AC or SCC are unaffected by the pathologic stage of the tumor or the age or race of the patient.

CONCLUSIONS

Histotypic microRNA assays can aid the subtyping of NSCLC biopsies as AC or SCC by standard histopathologic methods.

Serum HE4 detects recurrent endometrial cancer in patients undergoing routine clinical surveillance

Background

The purpose of this study was to evaluate serum HE4 as a biomarker to detect recurrent disease during follow-up of patients with endometrial adenocarcinoma (EAC).

Methods

We performed a retrospective analysis of 98 EAC patients treated at Innsbruck Medical University, between 1999 and 2009. Twenty-six patients developed recurrent disease. Median follow-up was 5 years. Serum HE4 and CA125 levels were analyzed using the ARCHITECT assay (Abbott, Wiesbaden, Germany) pre-operatively (baseline), post-operative (interval) and after histological confirmation of recurrent disease or when patients returned for clinical review with no evidence of recurrent disease (recurrence/final)). Receiver operator curves (ROC), Spearman rank correlation coefficient, chi-squared and Mann–Whitney tests were used for statistical analysis.

Results

HE4 levels decreased after initial treatment (p = 0.001) and increased again at recurrence (p = 0.002). HE4 was elevated (>70 pmol/L) in 21 of 26 (81%) and CA125 was elevated (>35 U/ml) in 12 of 26 (46%) patients at recurrence. In endometrioid histology (n = 69) serum HE4 measured during follow up (Area under the curve (AUC) = 0.87, 95%CI 0.79-0.95) was a better indicator of recurrence than CA125 (AUC = 0.67, 95%CI 0.52-0.83). A HE4 level of 70 pmol/L was associated with a sensitivity of 84%, a specificity of 74% and a negative predictive value of 93% when assessing for recurrent endometrioid EAC.

Conclusion

This is a preliminary description of HE4 serum levels measured during routine follow up of EAC patients. Serum HE4 measured during clinical follow-up may identify recurrent disease particularly in patients with endometrioid histology. Further prospective validation of HE4 is warranted.

Prediction of long-term segmental and global functional recovery of hibernating myocardium after revascularisation based on low dose dobutamine and late gadolinium enhancement cardiovascular magnetic resonance

BACKGROUND

This study sought to evaluate the relation between long-term segmental and global functional outcome after revascularisation in patients with chronic ischaemic left ventricular dysfunction (LVD) and baseline markers of viability: late gadolinium enhancement (LGE) transmurality and contractile reserve (CR).

METHODS

Forty-two patients with chronic ischaemic LVD underwent low-dose dobutamine- (LDD) and late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) before surgical or percutaneous revascularisation. Regional and global left ventricular (LV) functions and LGE were repeatedly assessed 6 ± 1 and 35 ± 6 months after revascularisation. In total, 319 at baseline dysfunctional and successfully revascularised segments were available for statistical analysis.

RESULTS

The likelihood of long-term functional improvement was directly related to the presence of CR and inversely related to both the LGE and the degree of contractile dysfunction at baseline. The time course of functional improvement was protracted, with significantly more delay in segments with more extensive LGE (p = 0.005) and more severe contractile dysfunction at baseline (p = 0.002). The presence of CR was the predictor of earlier functional improvement (p < 0.0001). Using a definition of viable segment as a segment without any LGE or with any LGE and producing CR during LDD stimulation, ≥ 55% of viable segments from all dysfunctional and revascularised segments in a patient was the only independent predictor of significant improvement (≥ 5%) in the left ventricular ejection fraction (LVEF) after revascularisation, with a 72% sensitivity and an 80% specificity (AUC 0.76, p = 0.014). Reverse LV remodelling was observed in patients who had a significant amount of viable myocardium successfully revascularised.

CONCLUSIONS

In patients with chronic ischaemic LVD, improvement of dysfunctional but viable myocardium can be considerably delayed. Both the likelihood and the time course of functional improvement are related to the LGE, CR and the degree of contractile dysfunction at baseline. At 35 ± 6 months after revascularisation, patients with ≥55% of viable segments from all dysfunctional and revascularised segments significantly improve LVEF and experience reverse LV remodelling. A combination of LDD-CMR and LGE-CMR is a simple and powerful tool for identifying which patients with impaired LV function will benefit from revascularisation.

Serum peptide pattern that differentially diagnoses hepatitis B virus-related hepatocellular carcinoma from liver cirrhosis

BACKGROUND

Although alpha-fetoprotein (AFP) is a useful serologic marker of hepatocellular carcinoma (HCC), it is not sufficiently sensitive to differentiate HCC and liver cirrhosis (LC) caused by hepatitis B virus (HBV) infection.

AIMS

The aim is to discover novel noninvasive specific serum biomarkers for the differential diagnosis of HBV-related HCC and LC.

METHODS

With a highly optimized peptide extraction and matrix-assisted laser desorption/ionization time of flight/time of flight mass spectrometric approach, we investigated serum peptide profiles of 80 HCC and 67 LC patients. Three supervised machine learning methods were employed to construct classifiers. Receiver operator curves were plotted to evaluate the performance of classifiers.

RESULTS

With a support vector machine-based strategy, we picked nine peaks with m/z ratios of 819.49, 1076.14, 1341.72, 2551.44, 3156.44, 3812.88, 4184.26, 4465.92, and 4776.41 to construct the classifier. We proposed a novel method for distinguishing HCC from cirrhosis, based on a multilayer perceptron (MLP) method. We obtained a sensitivity of 90.0%, specificity of 79.4%, and overall accuracy of 85.1% on an independent test set. The combination of the MLP model and serum AFP level outperformed serum AFP marker alone in distinguishing HCC patients from LC patients. In this experience, sensitivity increased from 62.5% to 87.5%, and specificity increased from 79.4% to 88.2%.

CONCLUSIONS

Our results indicate that the MLP model is a novel and useful serum peptide pattern for distinguishing HCC and LC. The peptidome signature alone or together with serum AFP determination may be a more effective method for early diagnosis of HCC in patients with HBV-related LC.

A new clinical multivariable model that predicts postoperative acute kidney injury: impact of endogenous ouabain

BACKGROUND

Acute kidney injury (AKI) is an important complication of cardiac surgery. Recently, elevated levels of endogenous ouabain (EO), an adrenal stress hormone with haemodynamic and renal effects, have been associated with worse renal outcome after cardiac surgery. Our aim was to develop and evaluate a new risk model of AKI using simple preoperative clinical parameters and to investigate the utility of EO.

METHODS

The primary outcome was AKI according to Acute Kidney Injury Network stage II or III. We selected the Northern New England Cardiovascular Disease Study Group (NNECDSG) as a reference model. We built a new internal predictive risk model considering common clinical variables (CLIN-RISK), compared this model with the NNECDSG model and determined whether the addition of preoperative plasma EO improved prediction of AKI.

RESULTS

All models were tested on >800 patients admitted for elective cardiac surgery in our hospital. Seventy-nine patients developed AKI (9.9%). Preoperative EO levels were strongly associated with the incidence of AKI and clinical complication (total ICU stay and in-hospital mortality). The NNECDSG model was confirmed as a good predictor of AKI (AUC 0.74, comparable to the NNECDSG reference population). Our CLIN-RISK model had improved predictive power for AKI (AUC 0.79, CI 95% 0.73-0.84). Furthermore, addition of preoperative EO levels to both clinical models improved AUC to 0.79 and to 0.83, respectively (ΔAUC +0.05 and +0.04, respectively, P < 0.01).

CONCLUSION

In a population where the predictive power of the NNECDSG model was confirmed, CLIN-RISK was more powerful. Both clinical models were further improved by the addition of preoperative plasma EO levels. These new models provide improved predictability of the relative risk for the development of AKI following cardiac surgery and suggest that EO is a marker for renal vascular injury.

On the association of common and rare genetic variation influencing body mass index: a combined SNP and CNV analysis

Background

As the architecture of complex traits incorporates a widening spectrum of genetic variation, analyses integrating common and rare variation are needed. Body mass index (BMI) represents a model trait, since common variation shows robust association but accounts for a fraction of the heritability. A combined analysis of single nucleotide polymorphisms (SNP) and copy number variation (CNV) was performed using 1850 European and 498 African-Americans from the Study of Addiction: Genetics and Environment. Genetic risk sum scores (GRSS) were constructed using 32 BMI-validated SNPs and aggregate-risk methods were compared: count versus weighted and proxy versus imputation.

Results

The weighted SNP-GRSS constructed from imputed probabilities of risk alleles performed best and was highly associated with BMI (p = 4.3×10⁻¹⁶) accounting for 3% of the phenotypic variance. In addition to BMI-validated SNPs, common and rare BMI/obesity-associated CNVs were identified from the literature. Of the 84 CNVs previously reported, only 21-kilobase deletions on 16p12.3 showed evidence for association with BMI (p = 0.003, frequency = 16.9%), with two CNVs nominally associated with class II obesity, 1p36.1 duplications (OR = 3.1, p = 0.009, frequency 1.2%) and 5q13.2 deletions (OR = 1.5, p = 0.048, frequency 7.7%). All other CNVs, individually and in aggregate, were not associated with BMI or obesity. The combined model, including covariates, SNP-GRSS, and 16p12.3 deletion accounted for 11.5% of phenotypic variance in BMI (3.2% from genetic effects). Models significantly predicted obesity classification with maximum discriminative ability for morbid-obesity (p = 3.15×10⁻¹⁸).

Conclusion

Results show that incorporating validated effect sizes and allelic probabilities improve prediction algorithms. Although rare-CNVs did not account for significant phenotypic variation, results provide a framework for integrated analyses.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-368) contains supplementary material, which is available to authorized users.

Systematic detection of internal symmetry in proteins using CE-Symm

Symmetry is an important feature of protein tertiary and quaternary structures that has been associated with protein folding, function, evolution, and stability. Its emergence and ensuing prevalence has been attributed to gene duplications, fusion events, and subsequent evolutionary drift in sequence. This process maintains structural similarity and is further supported by this study. To further investigate the question of how internal symmetry evolved, how symmetry and function are related, and the overall frequency of internal symmetry, we developed an algorithm, CE-Symm, to detect pseudo-symmetry within the tertiary structure of protein chains. Using a large manually curated benchmark of 1007 protein domains, we show that CE-Symm performs significantly better than previous approaches. We use CE-Symm to build a census of symmetry among domain superfamilies in SCOP and note that 18% of all superfamilies are pseudo-symmetric. Our results indicate that more domains are pseudo-symmetric than previously estimated. We establish a number of recurring types of symmetry-function relationships and describe several characteristic cases in detail. With the use of the Enzyme Commission classification, symmetry was found to be enriched in some enzyme classes but depleted in others. CE-Symm thus provides a methodology for a more complete and detailed study of the role of symmetry in tertiary protein structure [availability: CE-Symm can be run from the Web at http://source.rcsb.org/jfatcatserver/symmetry.jsp. Source code and software binaries are also available under the GNU Lesser General Public License (version 2.1) at https://github.com/rcsb/symmetry. An interactive census of domains identified as symmetric by CE-Symm is available from http://source.rcsb.org/jfatcatserver/scopResults.jsp].

Two new mathematical models for prediction of early mortality risk in coronary artery bypass graft surgery

OBJECTIVES

The aim of this study was to develop new models for prediction of short-term mortality risk in on-pump coronary artery bypass grafting (CABG) surgery using decision tree (DT) methods.

METHODS

Between September 2005 and April 2006, 948 consecutive patients underwent CABG surgery at Rajaie Heart Center. Potential risk factors were reviewed and univariate and multivariate analysis for short-term mortality were performed. The whole dataset was divided into mutually exclusive subsets. An entropy error fuzzy decision tree (EEFDT) and an entropy error crisp decision tree (EECDT) were implemented using 650 (68.6%) patient data and tested with 298 (31.4%) patient data. Ten times hold-out cross validation was done and the area under the receiver operative characteristic curve (AUC) was reported as model performance. The results were compared with the logistic regression (LR) model and EuroSCORE.

RESULTS

The overall short-term mortality rate was 3.8%, and was statistically higher in women than men (P<.001). The final EEFDT selected 19 variables and resulted in a tree with 39 nodes, 20 conditional rules, and AUC of 0.90±0.008. The final EECDT selected 15 variables and resulted in a tree with 35 nodes, 18 conditional rules, and AUC of 0.86±0.008. The LR model selected 10 variables and resulted in an AUC of 0.78±0.008; the AUC for EuroSCORE was 0.77±0.003. There were no differences in the discriminatory power of EEFDT and EECDT (P=.066) and their performance was superior to LR and EuroSCORE.

CONCLUSIONS

EEFDT, EECDT, LR, and EuroSCORE had clinical acceptance but the performance and accuracy of the DTs were superior to the other models.

Proteome screening of pleural effusions identifies galectin 1 as a diagnostic biomarker and highlights several prognostic biomarkers for malignant mesothelioma

Malignant mesothelioma is an aggressive asbestos-induced cancer, and affected patients have a median survival of approximately one year after diagnosis. It is often difficult to reach a conclusive diagnosis, and ancillary measurements of soluble biomarkers could increase diagnostic accuracy. Unfortunately, few soluble mesothelioma biomarkers are suitable for clinical application. Here we screened the effusion proteomes of mesothelioma and lung adenocarcinoma patients to identify novel soluble mesothelioma biomarkers. We performed quantitative mass-spectrometry-based proteomics using isobaric tags for quantification and used narrow-range immobilized pH gradient/high-resolution isoelectric focusing (pH 4–4.25) prior to analysis by means of nano liquid chromatography coupled to MS/MS. More than 1,300 proteins were identified in pleural effusions from patients with malignant mesothelioma (n = 6), lung adenocarcinoma (n = 6), or benign mesotheliosis (n = 7). Data are available via ProteomeXchange with identifier PXD000531. The identified proteins included a set of known mesothelioma markers and proteins that regulate hallmarks of cancer such as invasion, angiogenesis, and immune evasion, plus several new candidate proteins. Seven candidates (aldo-keto reductase 1B10, apolipoprotein C-I, galectin 1, myosin-VIIb, superoxide dismutase 2, tenascin C, and thrombospondin 1) were validated by enzyme-linked immunosorbent assays in a larger group of patients with mesothelioma (n = 37) or metastatic carcinomas (n = 25) and in effusions from patients with benign, reactive conditions (n = 16). Galectin 1 was identified as overexpressed in effusions from lung adenocarcinoma relative to mesothelioma and was validated as an excellent predictor for metastatic carcinomas against malignant mesothelioma. Galectin 1, aldo-keto reductase 1B10, and apolipoprotein C-I were all identified as potential prognostic biomarkers for malignant mesothelioma. This analysis of the effusion proteome furthers our understanding of malignant mesothelioma, identified galectin 1 as a potential diagnostic biomarker, and highlighted several possible prognostic biomarkers of this disease.

An MLP classifier for prediction of HBV-induced liver cirrhosis using routinely available clinical parameters

Background. Liver cirrhosis (LC) is the final stage of most of chronic liver diseases and is almost caused by chronic hepatitis B (CHB) in China. Liver biopsy is the reference method for the evaluation of liver cirrhosis. However, it is an invasive procedure with inherent risk. The aim of this study was to construct a new classifier based on the routine clinical markers for the prediction of HBV-induced LC. Subjects and Methods. We collected routine clinical parameters from 124 LC patients with CHB and 115 with CHB. Training set (n = 120) and test set (n = 119) were built for model construction and evaluation, respectively. Results. We describe a new classifier, MLP, for prediction of LC with CHB. MLP was built with seven routinely available clinical parameters, including age, ALT, AST, PT, PLT, HGB, and RDW. With optimal cutoff, we obtained a sensitivity of 95.2%, a specificity of 84.2%, and an overall accuracy of 89.9% on an independent test set, which were superior to those of FIB-4 and APRI. Conclusions. Our study suggests that the MLP classifier can be implemented for discriminating LC and non-LC cohorts by using machine learning method based on the routine available clinical parameters. It could be used for clinical practice in HBV-induced LC assessment.

Leukemia prediction using sparse logistic regression

We describe a supervised prediction method for diagnosis of acute myeloid leukemia (AML) from patient samples based on flow cytometry measurements. We use a data driven approach with machine learning methods to train a computational model that takes in flow cytometry measurements from a single patient and gives a confidence score of the patient being AML-positive. Our solution is based on an [Formula: see text] regularized logistic regression model that aggregates AML test statistics calculated from individual test tubes with different cell populations and fluorescent markers. The model construction is entirely data driven and no prior biological knowledge is used. The described solution scored a 100% classification accuracy in the DREAM6/FlowCAP2 Molecular Classification of Acute Myeloid Leukaemia Challenge against a golden standard consisting of 20 AML-positive and 160 healthy patients. Here we perform a more extensive validation of the prediction model performance and further improve and simplify our original method showing that statistically equal results can be obtained by using simple average marker intensities as features in the logistic regression model. In addition to the logistic regression based model, we also present other classification models and compare their performance quantitatively. The key benefit in our prediction method compared to other solutions with similar performance is that our model only uses a small fraction of the flow cytometry measurements making our solution highly economical.

Effectiveness of FISK, an invasiveness screening tool for non-native freshwater fishes, to perform risk identification assessments in the Iberian Peninsula

Risk assessments are crucial for identifying and mitigating impacts from biological invasions. The Fish Invasiveness Scoring Kit (FISK) is a risk identification (screening) tool for freshwater fishes consisting of two subject areas: biogeography/history and biology/ecology. According to the outcomes, species can be classified under particular risk categories. The aim of this study was to apply FISK to the Iberian Peninsula, a Mediterranean climate region highly important for freshwater fish conservation due to a high level of endemism. In total, 89 fish species were assessed by three independent assessors. Results from receiver operating characteristic analysis showed that FISK can discriminate reliably between noninvasive and invasive fishes for Iberia, with a threshold of 20.25, similar to those obtained in several regions around the world. Based on mean scores, no species was categorized as "low risk," 50 species as "medium risk," 17 as "moderately high risk," 11 as "high risk," and 11 as "very high risk." The highest scoring species was goldfish Carassius auratus. Mean certainty in response was above the category "mostly certain," ranging from tinfoil barb Barbonymus schwanenfeldii with the lowest certainty to eastern mosquitofish Gambusia holbrooki with the highest level. Pair-wise comparison showed significant differences between one assessor and the other two on mean certainty, with these two assessors showing a high coincidence rate for the species categorization. Overall, the results suggest that FISK is a useful and viable tool for assessing risks posed by non-native fish in the Iberian Peninsula and contributes to a "watch list" in this region.

The role of transient elastography in the detection of liver disease in patients with chronic pancreatitis

BACKGROUND & AIMS

Quantification of liver stiffness with transient elastography (TE) is validated for staging hepatic fibrosis in chronic hepatitis C infection. The current study was aimed to assess the diagnostic performance of liver stiffness measurement for the determination of fibrosis stage in patients with chronic pancreatitis.

METHODS

Thirty consecutive patients with chronic pancreatitis and increased liver enzyme were enrolled over a 2.5-year period. Eight liver living donor candidates were recruited to serve as internal controls. The TE values were compared with non-invasive fibrosis scoring systems including aspartate transaminase (AST)/alanine aminotransferase (ALT) ratio, APRI, non-alcoholic fatty liver disease NAFLD score, FIB-4 index and to liver histology.

RESULTS

TE was successful in all patients. Stiffness values ranged from 3.1 to 69 kPa (mean 16.9). Liver stiffness was correlated with fibrosis stage (Spearman's correlation 0.73, P < 0.0001). Areas under receiver operator characteristics curves for fibrosis F = 4 were 0.92 for TE, 0.87 for FIB-4 index, 0.81 for APRI, 0.73 for NAFLD score and 0.71 for AST/ALT ratio. Optimal stiffness cut-off values for diagnosis fibrosis F = 4 was 10.9 kPa, with 90% sensitivity, 85% specificity and 86% accuracy.

CONCLUSION

Our study provides for the first time evidence that liver stiffness in patients with chronic pancreatitis and concomitant cholestasis can be measured by TE.

Cortical hand bone porosity and its association with distal radius fracture in middle aged and elderly women

OBJECTIVE

Reduced bone mineral density (BMD), assessed by Dual Energy X-ray absorptiometry (DXA), is a well-known risk factor for fragility fracture. A large proportion of patients with fracture have only slightly reduced BMD. Assessment of other bone structure features than BMD may improve identification of individuals at increased fracture risk. Digital X-ray radiogrammetry (DXR), which is a feasible tool for measurement of metacarpal cortical bone density, also gives an estimate of cortical bone porosity. Our primary aim was to explore the association between cortical porosity in the hand assessed by DXR and distal radius fracture.

METHODS

This case-control study included 123 women >50 years with distal radius fracture, and 170 controls. DXR was used to measure metacarpal BMD (DXR-BMD), cortical porosity (DXR-porosity), thickness (DXR-CT) and bone width (DXR-W) of the hand. Femoral neck BMD was measured by DXA.

RESULTS

The fracture group had a statistically significant lower DXR-BMD (0.492 vs. 0.524 g/cm(2) p<0.001), higher cortical DXR-porosity (0.01256 vs. 0.01093, p<0.001), less DXR-CT (0.148 vs. 0.161 cm, p<0.001) and lower femoral neck DXA-BMD (0.789 vs. 0.844 g/cm(2), p = 0.001) than the controls. In logistic regression analysis adjusted for age, a significant association with distal radius fracture (OR, 95% CI) was found for body mass index (0.930, 0.880-0.983), DXA-BMD (0.996, 0.995-0.999), DXR-BMD (0.990, 0.985-0.998), DXR-porosity (1.468, 1.278-1.687) and DXR-CT (0.997, 0.996-0.999). In an adjusted model, DXR-porosity remained the only variable associated with distal radius fracture (1.415, 1.194-1.677).

CONCLUSION

DXR derived porosity is associated with fracture at distal radius and might be a sensitive marker for skeletal fragility.

Preoperative endogenous ouabain predicts acute kidney injury in cardiac surgery patients

OBJECTIVES

Acute kidney injury is a frequent complication of cardiac surgery and increases morbidity and mortality. As preoperative biomarkers predicting the development of acute kidney injury are not available, we have tested the hypothesis that preoperative plasma levels of endogenous ouabain may function as this type of biomarker.

RATIONALE AND DESIGN

Endogenous ouabain is an adrenal stress hormone associated with adverse cardiovascular outcomes. Its involvement in acute kidney injury is unknown. With studies in patients and animal settings, including isolated podocytes, we tested the above mentioned hypothesis.

PATIENTS

Preoperative endogenous ouabain was measured in 407 patients admitted for elective cardiac surgery and in a validation population of 219 other patients. We also studied the effect of prolonged elevations of circulating exogenous ouabain on renal parameters in rats and the influence of ouabain on podocyte proteins both "in vivo" and "in vitro."

MAIN RESULTS

In the first group of patients, acute kidney injury (2.8%, 8.3%, 20.3%, p < 0.001) and ICU stay (1.4±0.38, 1.7±0.41, 2.4±0.59 days, p = 0.014) increased with each incremental preoperative endogenous ouabain tertile. In a linear regression analysis, the circulating endogenous ouabain value before surgery was the strongest predictor of acute kidney injury. In the validation cohort, acute kidney injury (0%, 5.9%, 8.2%, p < 0.0001) and ICU stay (1.2±0.09, 1.4±0.23, 2.2±0.77 days, p = 0.003) increased with the preoperative endogenous ouabain tertile. Values for preoperative endogenous ouabain significantly improved (area under curve: 0.85) risk prediction over the clinical score alone as measured by integrate discrimination improvement and net reclassification improvement. Finally, in the rat model, elevated circulating ouabain reduced creatinine clearance (-18%, p < 0.05), increased urinary protein excretion (+ 54%, p < 0.05), and reduced expression of podocyte nephrin (-29%, p < 0.01). This last finding was replicated ex vivo by incubating podocyte primary cell cultures with low-dose ouabain.

CONCLUSIONS

Preoperative plasma endogenous ouabain levels are powerful biomarkers of acute kidney injury and postoperative complications and may be a direct cause of podocyte damage.

Two classifiers based on serum peptide pattern for prediction of HBV-induced liver cirrhosis using MALDI-TOF MS

Chronic infection with hepatitis B virus (HBV) is associated with the majority of cases of liver cirrhosis (LC) in China. Although liver biopsy is the reference method for evaluation of cirrhosis, it is an invasive procedure with inherent risk. The aim of this study is to discover novel noninvasive specific serum biomarkers for the diagnosis of HBV-induced LC. We performed bead fractionation/MALDI-TOF MS analysis on sera from patients with LC. Thirteen feature peaks which had optimal discriminatory performance were obtained by using support-vector-machine-(SVM-) based strategy. Based on the previous results, five supervised machine learning methods were employed to construct classifiers that discriminated proteomic spectra of patients with HBV-induced LC from those of controls. Here, we describe two novel methods for prediction of HBV-induced LC, termed LC-NB and LC-MLP, respectively. We obtained a sensitivity of 90.9%, a specificity of 94.9%, and overall accuracy of 93.8% on an independent test set. Comparisons with the existing methods showed that LC-NB and LC-MLP held better accuracy. Our study suggests that potential serum biomarkers can be determined for discriminating LC and non-LC cohorts by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. These two classifiers could be used for clinical practice in HBV-induced LC assessment.

Dissecting sources of quantitative gene expression pattern divergence between Drosophila species

Gene expression patterns can diverge between species due to changes in a gene's regulatory DNA or changes in the proteins, e.g., transcription factors (TFs), that regulate the gene. We developed a modeling framework to uncover the sources of expression differences in blastoderm embryos of three Drosophila species, focusing on the regulatory circuit controlling expression of the hunchback (hb) posterior stripe. Using this framework and cellular-resolution expression measurements of hb and its regulating TFs, we found that changes in the expression patterns of hb's TFs account for much of the expression divergence. We confirmed our predictions using transgenic D. melanogaster lines, which demonstrate that this set of orthologous cis-regulatory elements (CREs) direct similar, but not identical, expression patterns. We related expression pattern differences to sequence changes in the CRE using a calculation of the CRE's TF binding site content. By applying this calculation in both the transgenic and endogenous contexts, we found that changes in binding site content affect sensitivity to regulating TFs and that compensatory evolution may occur in circuit components other than the CRE.

PanelComposer: a web-based panel construction tool for multivariate analysis of disease biomarker candidates

Measuring and evaluating diagnostic efficiency is important in biomarker discovery and validation. The receiver operating characteristic (ROC) curve is a graphical plot for assessing the performance of a classifier or predictor that can be used to test the sensitivity and specificity of diagnostic biomarkers. In this study, we describe PanelComposer, a Web-based software tool that uses statistical results from proteomic expression data and validates biomarker candidates based on ROC curves and the area under the ROC curve (AUC) values using a logistic regression model and provides an ordered list that includes ROC graphs and AUC values for proteins (individually or in combination). This tool allows users to easily compare and assess the effectiveness and diagnostic efficiency of single or multiprotein biomarker candidates. PanelComposer is available publicly at http://panelcomposer.proteomix.org/ and is compatible with major Web browsers.

MicroRNA expression profiles of whole blood in lung adenocarcinoma

The association of lung cancer with changes in microRNAs in plasma shown in multiple studies suggests a utility for circulating microRNA biomarkers in non-invasive detection of the disease. We examined if presence of lung cancer is reflected in whole blood microRNA expression as well, possibly because of a systemic response. Locked nucleic acid microarrays were used to quantify the global expression of microRNAs in whole blood of 22 patients with lung adenocarcinoma and 23 controls, ten of whom had a radiographically detected non-cancerous lung nodule and the other 13 were at high risk for developing lung cancer because of a smoking history of >20 pack-years. Cases and controls differed significantly for age with a mean difference of 10.7 years, but not for gender, race, smoking history, blood hemoglobin, platelet count, or white blood cell count. Of 1282 quantified human microRNAs, 395 (31%) were identified as expressed in the study’s subjects, with 96 (24%) differentially expressed between cases and controls. Classification analyses of microRNA expression data were performed using linear kernel support vector machines (SVM) and top-scoring pairs (TSP) methods, and classifiers to identify presence of lung adenocarcinoma were internally cross-validated. In leave-one-out cross-validation, the TSP classifiers had sensitivity and specificity of 91% and 100%, respectively. The values with SVM were both 91%. In a Monte Carlo cross-validation, average sensitivity and specificity values were 86% and 97%, respectively, with TSP, and 88% and 89%, respectively, with SVM. MicroRNAs miR-190b, miR-630, miR-942, and miR-1284 were the most frequent constituents of the classifiers generated during the analyses. These results suggest that whole blood microRNA expression profiles can be used to distinguish lung cancer cases from clinically relevant controls. Further studies are needed to validate this observation, including in non-adenocarcinomatous lung cancers, and to clarify upon the confounding effect of age.

SVMTriP: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity

Identifying protein surface regions preferentially recognizable by antibodies (antigenic epitopes) is at the heart of new immuno-diagnostic reagent discovery and vaccine design, and computational methods for antigenic epitope prediction provide crucial means to serve this purpose. Many linear B-cell epitope prediction methods were developed, such as BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, and BEST, towards this goal. However, effective immunological research demands more robust performance of the prediction method than what the current algorithms could provide. In this work, a new method to predict linear antigenic epitopes is developed; Support Vector Machine has been utilized by combining the Tri-peptide similarity and Propensity scores (SVMTriP). Applied to non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieves a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value is 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. Moreover, SVMTriP is capable of recognizing viral peptides from a human protein sequence background. A web server based on our method is constructed for public use. The server and all datasets used in the current study are available at http://sysbio.unl.edu/SVMTriP.

Baseline tumor oxygen saturation correlates with a pathologic complete response in breast cancer patients undergoing neoadjuvant chemotherapy

Tissue hemoglobin oxygen saturation (i.e., oxygenation) is a functional imaging endpoint that can reveal variations in tissue hypoxia, which may be predictive of pathologic response in subjects undergoing neoadjuvant chemotherapy. In this study, we used diffuse optical spectroscopic imaging (DOSI) to measure concentrations of oxyhemoglobin (ctO(2)Hb), deoxy-hemoglobin (ctHHb), total Hb (ctTHb = ctO(2)Hb + ctHHb), and oxygen saturation (stO(2) = ctO(2)Hb/ctTHb) in tumor and contralateral normal tissue from 41 patients with locally advanced primary breast cancer. Measurements were acquired before the start of neoadjuvant chemotherapy. Optically derived parameters were analyzed separately and in combination with clinical biomarkers to evaluate correlations with pathologic response. Discriminant analysis was conducted to determine the ability of optical and clinical biomarkers to classify subjects into response groups. Twelve (28.6%) of 42 tumors achieved pathologic complete response (pCR) and 30 (71.4%) were non-pCR. Tumor measurements in pCR subjects had higher stO(2) levels (median 77.8%) than those in non-pCR individuals (median 72.3%, P = 0.01). There were no significant differences in baseline ctO(2)Hb, ctHHb, and ctTHb between response groups. An optimal tumor oxygenation threshold of stO(2) = 76.7% was determined for pCR versus non-pCR (sensitivity = 75.0%, specificity = 73.3%). Multivariate discriminant analysis combining estrogen receptor staining and stO(2) further improved the classification of pCR versus non-pCR (sensitivity = 100%, specificity = 85.7%). These results show that elevated baseline tumor stO(2) are correlated with a pCR. Noninvasive DOSI scans combined with histopathology subtyping may aid in stratification of individual patients with breast cancer before neoadjuvant chemotherapy.

Biomine: predicting links between biological entities using network models of heterogeneous databases

BACKGROUND

Biological databases contain large amounts of data concerning the functions and associations of genes and proteins. Integration of data from several such databases into a single repository can aid the discovery of previously unknown connections spanning multiple types of relationships and databases.

RESULTS

Biomine is a system that integrates cross-references from several biological databases into a graph model with multiple types of edges, such as protein interactions, gene-disease associations and gene ontology annotations. Edges are weighted based on their type, reliability, and informativeness. We present Biomine and evaluate its performance in link prediction, where the goal is to predict pairs of nodes that will be connected in the future, based on current data. In particular, we formulate protein interaction prediction and disease gene prioritization tasks as instances of link prediction. The predictions are based on a proximity measure computed on the integrated graph. We consider and experiment with several such measures, and perform a parameter optimization procedure where different edge types are weighted to optimize link prediction accuracy. We also propose a novel method for disease-gene prioritization, defined as finding a subset of candidate genes that cluster together in the graph. We experimentally evaluate Biomine by predicting future annotations in the source databases and prioritizing lists of putative disease genes.

CONCLUSIONS

The experimental results show that Biomine has strong potential for predicting links when a set of selected candidate links is available. The predictions obtained using the entire Biomine dataset are shown to clearly outperform ones obtained using any single source of data alone, when different types of links are suitably weighted. In the gene prioritization task, an established reference set of disease-associated genes is useful, but the results show that under favorable conditions, Biomine can also perform well when no such information is available.The Biomine system is a proof of concept. Its current version contains 1.1 million entities and 8.1 million relations between them, with focus on human genetics. Some of its functionalities are available in a public query interface at http://biomine.cs.helsinki.fi, allowing searching for and visualizing connections between given biological entities.

L1pred: a sequence-based prediction tool for catalytic residues in enzymes with the L1-logreg classifier

To understand enzyme functions, identifying the catalytic residues is a usual first step. Moreover, knowledge about catalytic residues is also useful for protein engineering and drug-design. However, to experimentally identify catalytic residues remains challenging for reasons of time and cost. Therefore, computational methods have been explored to predict catalytic residues. Here, we developed a new algorithm, L1pred, for catalytic residue prediction, by using the L1-logreg classifier to integrate eight sequence-based scoring functions. We tested L1pred and compared it against several existing sequence-based methods on carefully designed datasets Data604 and Data63. With ten-fold cross-validation, L1pred showed the area under precision-recall curve (AUPR) and the area under ROC curve (AUC) of 0.2198 and 0.9494 on the training dataset, Data604, respectively. In addition, on the independent test dataset, Data63, it showed the AUPR and AUC values of 0.2636 and 0.9375, respectively. Compared with other sequence-based methods, L1pred showed the best performance on both datasets. We also analyzed the importance of each attribute in the algorithm, and found that all the scores contributed more or less equally to the L1pred performance.

HIV-1 phenotypic reverse transcriptase inhibitor drug resistance test interpretation is not dependent on the subtype of the virus backbone

To date, the majority of HIV-1 phenotypic resistance testing has been performed with subtype B virus backbones (e.g. HXB2). However, the relevance of using this backbone to determine resistance in non-subtype B HIV-1 viruses still needs to be assessed. From 114 HIV-1 subtype C clinical samples (36 ARV-naïve, 78 ARV-exposed), pol amplicons were produced and analyzed for phenotypic resistance using both a subtype B- and C-backbone in which the pol fragment was deleted. Phenotypic resistance was assessed in resulting recombinant virus stocks (RVS) for a series of antiretroviral drugs (ARV's) and expressed as fold change (FC), yielding 1660 FC comparisons. These Antivirogram® derived FC values were categorized as having resistant or sensitive susceptibility based on biological cut-off values (BCOs). The concordance between resistance calls obtained for the same clinical sample but derived from two different backbones (i.e. B and C) accounted for 86.1% (1429/1660) of the FC comparisons. However, when taking the assay variability into account, 95.8% (1590/1660) of the phenotypic data could be considered as being concordant with respect to their resistance call. No difference in the capacity to detect resistance associated with M184V, K103N and V106M mutations was noted between the two backbones. The following was concluded: (i) A high level of concordance was shown between the two backbone phenotypic resistance profiles; (ii) Assay variability is largely responsible for discordant results (i.e. for FC values close to BCO); (iii) Confidence intervals should be given around the BCO's, when assessing resistance in HIV-1 subtype C; (iv) No systematic resistance under- or overcalling of subtype C amplicons in the B-backbone was observed; (v) Virus backbone subtype sequence variability outside the pol region does not contribute to phenotypic FC values. In conclusion the HXB2 virus backbone remains an acceptable vector for phenotyping HIV-1 subtype C pol amplicons.

Prediction of incident type 2 diabetes mellitus based on a twenty-year follow-up of the Ventimiglia heart study

A novel algorithm to predict incident type 2 diabetes mellitus (iT2DM) is presented considering data from a 20-year prospective study in a Southern Italy population. Eight hundred and fifty-eight out of 1,351 subjects (24-85 years range of age) were selected. Incident type 2 diabetes was diagnosed in 103 patients in a 20-year follow-up. The Finnish Diabetes Risk Score (FINDRISC) and the Framingham Offspring Study simple clinical model (FOS) have been used as reference algorithms. Two custom algorithms have been created using Cox parametric hazard models followed by PROBIT analyses: the first one (VHSRISK) includes all the study subjects and the second one (VHS95RISK) evaluates separately subjects with baseline fasting blood glucose (FBG) above/below 5.2 mmol/L (95 mg/dL). The 44 iT2DM cases below 5.2 mmol/L of baseline FBG were predicted by high LDL cholesterol, metabolic syndrome (ATPIII criteria), BMI > 30 kg/m(2), and high factor VII activity. The 59 cases above the FBG threshold were predicted by FBG classes, hypertension, and age. ROC areas for iT2DM prediction were: FINDRISC = 0.759, FOS = 0.762, VHSRISK = 0.789, and VHS95RISK = 0.803. In a Mediterranean population, the use of a custom generated algorithm evaluating separately low/high FBG subjects improves the prediction of iT2DM in subjects classified at lower risk by common estimation algorithms.

Extending signaling pathways with protein-interaction networks. Application to apoptosis

Cells exploit signaling pathways during responses to environmental changes, and these processes are often modulated during disease. Particularly, relevant human pathologies such as cancer or viral infections require downregulating apoptosis signaling pathways to progress. As a result, the identification of proteins responsible for these changes is essential for the diagnostics and development of therapeutics. Transferring functional annotation within protein interaction networks has proven useful to identify such proteins, although this is not a trivial task. Here, we used different scoring methods to transfer annotation from 53 well-studied members of the human apoptosis pathways (as known by 2005) to their protein interactors. All scoring methods produced significant predictions (compared to a random negative model), but its number was too large to be useful. Thus, we made a final prediction using specific combinations of scoring methods and compared it to the proteins related to apoptosis signaling pathways during the last 5 years. We propose 273 candidate proteins that may be relevant in apoptosis signaling pathways. Although some of them have known functions consistent with their proposed apoptotsis involvement, the majority have not been annotated yet, leaving room for further experimental studies. We provide our predictions at http://sbi.imim.es/web/Apoptosis.php.

Cystatin C as a predictor of all-cause mortality and myocardial infarction in patients with non-ST-elevation acute coronary syndrome

OBJECTIVES

To investigate the predictive value of cystatin C among patients diagnosed with non-ST-elevation acute coronary syndrome (nSTE-ACS).

DESIGN AND METHODS

Admission serum samples from 245 nSTE-ACS patients were measured with a novel cystatin C immunoassay based on a dry-reagent, double monoclonal design. Creatinine concentrations, estimated glomerular filtration rates (eGFR) and one-year follow-up data were available for these patients.

RESULTS

During the follow-up period, 34 (14%) of patients had myocardial infarction (MI) and 25 (11%) died. Increased serum cystatin C was an independent predictor of all-cause mortality and combined events (all-cause mortality and MI) after adjustment to non-biomarker baseline factors, hazard ratio (HR) 2.19 (per increase of 1 tertile; 95% Cl 1.28-3.78, p=0.0046) and 1.75 (1.22-2.51, p=0.0024), respectively. Corresponding values for eGFR were 2.56 (1.43-4.59, p=0.0016) and 1.76 (1.23-2.53, p=0.0022), respectively. Creatinine was not an independent predictor of endpoints (p>0.05).

CONCLUSIONS

Cystatin C was associated with an increased risk of death and combined events in patients with nSTE-ACS.

Troponin T Measurements by High-Sensitivity vs Conventional Assays for Risk Stratification in Acute Dyspnea

BACKGROUND

Cardiac troponin T measured by a high-sensitivity assay (hs-cTnT) recently proved to be of prognostic value in several populations. The hs-cTnT assay may also improve risk stratification in acute dyspnea.

METHODS

We prospectively studied the prognostic value of hs-cTnT in 678 consecutive patients presenting to the emergency department with acute dyspnea. On the basis of conventional cardiac troponin T assay (cTnT) and hs-cTnT assay measurements, patients were divided into 3 categories: (1) neither assay increased (cTnT &lt;0.03 μg/L, hs-cTnT &lt;0.016 μg/L), (2) only hs-cTnT increased ≥0.016 μg/L (cTnT &lt;0.03 μg/L), and (3) both assays increased (cTnT ≥0.03 μg/L, hs-cTnT ≥0.016 μg/L). Moreover, the prognostic value of hs-cTnT was investigated if cTnT was not detectable (&lt;0.01).

RESULTS

One hundred seventy-two patients were in the lowest, 282 patients in the middle, and 223 patients in the highest troponin category. Patients in the second and third categories had significantly higher mortality compared to those in the first category (90-day mortality rate 2%, 10%, and 26% in groups 1, 2, and 3, respectively, P &lt; 0.001; 1-year mortality rate 9%, 21%, and 39%, P &lt; 0.001). Importantly, in patients with undetectable cTnT (n = 347, 51%), increased hs-cTnT indicated worse outcome [90-day mortality, odds ratio 4.26 (95% CI 1.19–15.21); 1-year mortality, hazard ratio 2.27 (1.19–4.36), P = 0.013], whereas N-terminal pro–brain-type natriuretic peptide (NT-proBNP) was not predictive of short-term outcome.

CONCLUSIONS

hs-cTnT is associated with mortality in patients presenting with acute dyspnea. hs-cTnT concentrations provide additional prognostic information to cTnT and NT-proBNP testing in patients with cTnT concentrations below the detection limit. In particular, the hs-cTnT cutoff of 0.016 μg/L enables identification of low-risk patients.

Validation of the revised Addenbrooke's Cognitive Examination (ACE-R) for detecting mild cognitive impairment and dementia in a Japanese population

BACKGROUND

Early detection of dementia will be important for implementation of disease-modifying treatments in the near future. We aimed to investigate the diagnostic validity and reliability of the Japanese version of the revised Addenbrooke's Cognitive Examination (ACE-R J) for identifying mild cognitive impairment (MCI) and dementia.

METHODS

We translated and adapted the original ACE-R for use with a Japanese population. Standard tests for evaluating cognitive decline and dementing disorders were applied. A total of 242 subjects (controls = 73, MCI = 39, dementia = 130) participated in this study.

RESULTS

The optimal cut-off scores of ACE-R J for detecting MCI and dementia were 88/89 (sensitivity 0.87, specificity 0.92) and 82/83 (sensitivity 0.99, specificity 0.99) respectively. ACE-R J was superior to the Mini-Mental State Examination in the detection of MCI (area under the curve (AUC): 0.952 vs. 0.868), while the accuracy of the two instruments did not differ significantly in identifying dementia (AUC: 0.999 vs. 0.993). The inter-rater reliability (ICC = 0.999), test-retest reliability (ICC = 0.883), and internal consistency (Cronbach's α = 0.903) of ACE-R J were excellent.

CONCLUSION

ACE-R J proved to be an accurate cognitive instrument for detecting MCI and mild dementia. Further neuropsychological evaluation is required for the differential diagnosis of dementia subtypes.

Risk stratification of patients suspected of coronary artery disease: comparison of five different models

OBJECTIVE

To compare the performance of five risk models (Diamond-Forrester, the updated Diamond-Forrester, Morise, Duke, and a new model designated COronary Risk SCORE (CORSCORE) in predicting significant coronary artery disease (CAD) in patients with chest pain suggestive of stable angina pectoris.

METHODS

Retrospective cohort for creation of CORSCORE by means of logistic regression analysis. Prospective cohort for validation of the five risk models using receiver operating characteristics (ROC) curve analysis, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Significant CAD was defined as lumen area diameter reduction ≥50% at coronary angiography. All risk models include information on age, sex, and symptoms. In addition the Duke, Morise, and CORSCORE models include information on tobacco use and hypercholesterolemia. Duke and Morise also include information on diabetes. History of myocardial infarction is used by the Duke and CORSCORE models whereas hypertension is included in the Morise and CORSCORE models. The Duke model includes information on electrocardiogram (ECG) changes and the Morise model includes information on family history, body mass index, obesity, and oestrogen status.

RESULTS

4781 retrospective and 633 prospective consecutive patients referred for coronary angiography were included. The area under the ROC for the updated Diamond-Forrester, Duke, and CORSCORE was significantly larger than for the Diamond-Forrester (p≤0.001). The IDI was significantly higher for the Duke as compared to all other models (p≤0.006).

CONCLUSION

The Duke, updated Diamond-Forrester, and CORSCORE risk models are most efficient in predicting CAD in a contemporary cohort of patients with symptoms suggestive of angina. The updated Diamond-Forrester may most operational in daily clinical practice since it is calculated from the lowest number of clinical variables.

Noninvasive work of breathing improves prediction of post-extubation outcome

PURPOSE

We hypothesized that non-invasively determined work of breathing per minute (WOB(N)/min) (esophageal balloon not required) may be useful for predicting extubation outcome, i.e., appropriate work of breathing values may be associated with extubation success, while inappropriately increased values may be associated with failure.

METHODS

Adult candidates for extubation were divided into a training set (n = 38) to determine threshold values of indices for assessing extubation and a prospective validation set (n = 59) to determine the predictive power of the threshold values for patients successfully extubated and those who failed extubation. All were evaluated for extubation during a spontaneous breathing trial (5 cmH(2)O pressure support ventilation, 5 cmH(2)O positive end expiratory pressure) using routine clinical practice standards. WOB(N)/min data were blinded to attending physicians. Area under the receiver operating characteristic curves (AUC), sensitivity, specificity, and positive and negative predictive values of all extubation indices were determined.

RESULTS

AUC for WOB(N)/min was 0.96 and significantly greater (p < 0.05) than AUC for breathing frequency at 0.81, tidal volume at 0.61, breathing frequency-to-tidal volume ratio at 0.73, and other traditionally used indices. WOB(N)/min had a specificity of 0.83, the highest sensitivity at 0.96, positive predictive value at 0.84, and negative predictive value at 0.96 compared to all indices. For 95% of those successfully extubated, WOB(N)/min was ≤10 J/min.

CONCLUSIONS

WOB(N)/min had the greatest overall predictive accuracy for extubation compared to traditional indices. WOB(N)/min warrants consideration for use in a complementary manner with spontaneous breathing pattern data for predicting extubation outcome.

Diabetes antibody standardization program: first proficiency evaluation of assays for autoantibodies to zinc transporter 8

BACKGROUND

Zinc transporter 8 (ZnT8) is a recently identified major autoantigen in type 1 diabetes, and autoantibodies to ZnT8 (ZnT8A) are new markers for disease prediction and diagnosis. Here we report the results of the first international proficiency evaluation of ZnT8A assays by the Diabetes Antibody Standardization Program (DASP).

METHODS

After a pilot workshop in 2007, an expanded ZnT8A workshop was held in 2009, with 26 participating laboratories from 13 countries submitting results of 63 different assays. ZnT8A levels were measured in coded sera from 50 patients with newly diagnosed type 1 diabetes and 100 blood donor controls. Results were analyzed comparing area under the ROC curve (ROC-AUC), sensitivity adjusted to 95% specificity (AS95), concordance of sample ZnT8A positive or negative designation, and autoantibody levels.

RESULTS

ZnT8A radio binding assays (RBAs) based on combined immunoprecipitation of the 2 most frequent ZnT8 COOH-terminal domain polymorphic variants showed a median ROC-AUC of 0.848 [interquartile range (IQR) 0.796-0.878] and a median AS95 of 70% (IQR 60%-72%). These RBAs were more sensitive than assays using as antigen either 1 ZnT8 variant only or chimeric constructs joining NH(2)- and COOH-terminal domains, assays based on immunoprecipitation and bioluminescent detection, or assays based on immunofluorescent staining of cells transfected with full-length antigen.

CONCLUSIONS

The DASP workshop identified immunoprecipitation-based ZnT8A assays and antigen constructs that achieved both a high degree of sensitivity and specificity and were suitable for more widespread clinical application.

Phalangeal densitometry compared with dual energy X-ray absorptiometry for assessment of bone mineral density in elderly women

BACKGROUND

Reduced bone mineral density (BMD) is identified as a major risk factor for fracture. The World Health Organization criterion for diagnosis of osteoporosis (T-score ≤-2.5 SD) is based on dual energy X-ray absorptiometry (DXA) measurements. However DXA availability may be limited in some regions. In this study the ability of the phalangeal radiographic absorptiometry (RA) device, MetriScan, to identify women with reduced BMD at the femoral neck assessed by DXA was evaluated.

METHODS

The study population contained women with recent low-energy distal radius fracture and women recruited from the general population, all aged ≥50 years. A triage approach was applied in which two cut-offs for RA T-score were defined at which individuals with 90% sensitivity and 90% specificity could be identified to have or not have reduced BMD at the femoral neck defined as T-score ≤-2.5 SD.

RESULTS

The correlation between phalangeal RA BMD and femoral neck DXA BMD was r=0.65 (p<0.001). The upper and lower RA T-score cut-off was -1.5 SD and -2.9 SD. With the triage approach being used for the whole cohort, 34% would require a central DXA assessment to determine if the femoral neck T-score is below or above -2.5 SD.

CONCLUSION

The application of the RA MetriScan device can reduce the number of DXA assessments needed to detect reduced BMD. The device may thus be of clinical value if access to DXA is limited, as well as for screening purposes.

Improving the prediction of protein binding sites by combining heterogeneous data and Voronoi diagrams

BACKGROUND

Protein binding site prediction by computational means can yield valuable information that complements and guides experimental approaches to determine the structure of protein complexes. Predictions become even more relevant and timely given the current resolution of protein interaction maps, where there is a very large and still expanding gap between the available information on: (i) which proteins interact and (ii) how proteins interact. Proteins interact through exposed residues that present differential physicochemical properties, and these can be exploited to identify protein interfaces.

RESULTS

Here we present VORFFIP, a novel method for protein binding site prediction. The method makes use of broad set of heterogeneous data and defined of residue environment, by means of Voronoi Diagrams that are integrated by a two-steps Random Forest ensemble classifier. Four sets of residue features (structural, energy terms, sequence conservation, and crystallographic B-factors) used in different combinations together with three definitions of residue environment (Voronoi Diagrams, sequence sliding window, and Euclidian distance) have been analyzed in order to maximize the performance of the method.

CONCLUSIONS

The integration of different forms information such as structural features, energy term, evolutionary conservation and crystallographic B-factors, improves the performance of binding site prediction. Including the information of neighbouring residues also improves the prediction of protein interfaces. Among the different approaches that can be used to define the environment of exposed residues, Voronoi Diagrams provide the most accurate description. Finally, VORFFIP compares favourably to other methods reported in the recent literature.