ROC Value Research Articles

Identification of novel acetylcholinesterase inhibitors through e-pharmacophore-based virtual screening and molecular dynamics simulations

Alzheimer’s disease (AD), a progressive neurodegenerative disorder is the most common cause of dementia among elderly people. To date, the successful therapeutic strategy to treat AD is maintaining the levels of acetylcholine via inhibiting acetylcholinesterase (AChE). The present study involves identification of newer AChE inhibitors by dual approach of e-pharmacophore and structure-based virtual screening of Asinex library. Robustness of docking protocol was validated by enrichment calculation with ROC value .71 and BEDROC value .028. Among 11 selected hits, ZINC72338524 with best MM-GBSA dG binding shows optimal range of CNS properties and ligand–AChE complex stability. Further, molecular dynamics study revealed its molecular interactions with Trp86, Phe338, and Tyr341 amino acid residues of catalytic anionic site and Tyr124, Ser125, and Trp286 amino acid residues of peripheral anionic site. Physicochemical properties and ADMET risk prediction indicates their potential in druggability and safety.

An Optimized Particle Swarm Optimization based ANN Model for Clinical Disease Prediction

Objective: Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) are two broadly used for rule generation process in data mining. ANN have been widely used for pattern extraction from medical databases. Methods: Heart disease is one of the leading death cause disease in all over the world in the present situation. A large number of research works have been carried out for diagnosing heart failures using data mining techniques. As the number of heart disease features increasing along with a number of instances, the traditional approaches such as PSO, ANN and BPSO are fail to predict the exact disease causing features. Findings: In this research work, an improved ANN approach model called Optimized Artificial Neural Network (OANN) was implemented on the medical data sets. In this paper, an Optimized Particle Swarm Optimization (PSO) technique is used for disease dimension reduction. Filter based Artificial Neural Network is used for classifying the disease type as positive or negative based on the disease features. Applications/Improvements: The performance of the proposed algorithm is analyzed by using the traditional approaches of Performance plot, Regression, ROC Value and Confusion Matrix. It is proved that the performance of the whole ANN network is optimized after the inclusion of proposed PSO for Feature Reduction.

Mapping regional patterns of large forest fires in Wildland–Urban Interface areas in Europe

Over recent decades, Land Use and Cover Change (LUCC) trends in many regions of Europe have reconfigured the landscape structures around many urban areas. In these areas, the proximity to landscape elements with high forest fuels has increased the fire risk to people and property. These Wildland–Urban Interface areas (WUI) can be defined as landscapes where anthropogenic urban land use and forest fuel mass come into contact. Mapping their extent is needed to prioritize fire risk control and inform local forest fire risk management strategies. This study proposes a method to map the extent and spatial patterns of the European WUI areas at continental scale. Using the European map of WUI areas, the hypothesis is tested that the distance from the nearest WUI area is related to the forest fire probability. Statistical relationships between the distance from the nearest WUI area, and large forest fire incidents from satellite remote sensing were subsequently modelled by logistic regression analysis. The first European scale map of the WUI extent and locations is presented. Country-specific positive and negative relationships of large fires and the proximity to the nearest WUI area are found. A regional-scale analysis shows a strong influence of the WUI zones on large fires in parts of the Mediterranean regions. Results indicate that the probability of large burned surfaces increases with diminishing WUI distance in touristic regions like Sardinia, Provence-Alpes-Côte d'Azur, or in regions with a strong peri-urban component as Catalunya, Comunidad de Madrid, Comunidad Valenciana. For the above regions, probability curves of large burned surfaces show statistical relationships (ROC value > 0.5) inside a 5000 m buffer of the nearest WUI. Wise land management can provide a valuable ecosystem service of fire risk reduction that is currently not explicitly included in ecosystem service valuations. The results re-emphasise the importance of including this ecosystem service in landscape valuations to account for the significant landscape function of reducing the risk of catastrophic large fires.

Are ET and PV Patients Two Similar Populations As Concern Thrombotic Risk Factors?

Thrombotic events are major complications in patients (pts) affected by Essential Thrombocytemia (ET) and Polycytemia Vera (PV). To compare thrombotic risk in these 2 groups, we evaluated retrospectively our database of 1249 ET and 623 PV pts diagnosed and followed in 11 hematological centers in the Latium region between 1/1980 and 12/2010: the diagnosis was done according to PVSG, WHO 2001 and 2008criteria based on the time of first observation. Baseline features of ET pts: 797F/452M,median age 62.9 yrs (range 19-96),median WBC count 8.8 x 109/L (range 1.2-57.7), median PLT count 812 x 109/L (range 457-3582), median Hb level 14.0 g/dl (range 6-20.5), JAK-2V617F positivity 59.7% with a median allele burden of 19,6% (range 0.2- 99.9), spleen enlargement in 18.7% of pts, previous thrombosis223/1239 evaluable pts (17.9%) [arterial 176/223 (14.1%), venous 47/223 (3.8%)]. Baseline features of PV pts: 289F/334M, median age 63.0yrs (range 21-91), median WBC count 10.1 x 109/L (range 3.5-37.6), median PLT count 457 x 109/L (range 169-1790), median Hb level 18.2 g/dl (range 10.5-24.8), JAK-2V617F positivity 94.3% with a median allele burden of 59.1% (range 0.3-99.9), spleen enlargement in 42% of patients, previous thrombosis 146/617 evaluable pts (23.7%)[arterial 114/617 (18.5%), venous 32/617 (5,2%)].in the ET cohort, after a median follow-up of 7.7 yrs, thrombotic complications were seen in 107/1141 evaluable pts (9.4%) [arterial60 (5.25%), venous 47 (4.11%)]; in the PV cohort, after a median follow-up of 8.5 yrs, thrombotic complications were seen in 107/623pts (17.2%) [arterial 67 (10.8%),venous 40 (6.4%)].All common risk factors for thrombosis were evaluated in multivariate analysis, searching the cut-off number for continuous variables with ROC curves. The significant variables at multivariate analysis for ET and PV pts are shown in the table; age, previous thromboses and spleen enlargement were risk factors in ET pts, while previous thromboses and JAK-2V617F allele burden were risk factors in PV pts. PLT count above ROC value seemed to be a protective factor in both cohorts. In conclusion, in contrast with the tendency to evaluate in a similar manner the thrombotic risk of PV and ET, data from our retrospective database showed that these 2 groups should be considered populations with different risk factors for thrombosis. Table 1Putative prognostic factorsPolycythemia VeraEssential ThrombocythemiaHR95% C.I.pHR95% C.I .pPrevious thromboses2,311,13 - 4,740,021,871,08 -3,230,026Age ≥ 60 y1,540,79 - 2,990,211,901,18 - 3,060,009JAK2V617FPV: allelic burden ≥ 81% ET: pos1,951,03 - 3,710,040,760,48 - 1,210,25Plt countPV ≥ 452.109/L ET ≥ 944.109/L0,490,25 - 0,950,040,520,31 - 0,890,017Spleen enlargement0,670,34 -1,310,241,711,02 - 2,890,04CV risk factors (at least 1)0,920,41 - 2,030,830,870,51 - 1,490,62WBCPV ≥ 10,175.109/L ET ≥ 9,630.109/L1,090,57 - 2,080,801,410,89 -2,260,15 DisclosuresNo relevant conflicts of interest to declare.

PASS Targets: Ligand-based multi-target computational system based on a public data and naïve Bayes approach

Estimation of interactions between drug-like compounds and drug targets is very important for drug discovery and toxicity assessment. Using data extracted from the 19th version of the ChEMBL database (https://www.ebi.ac.uk/chembl) as a training set and a Bayesian-like method realized in PASS software (http://www.way2drug.com/PASSOnline), we developed a computational tool for the prediction of interactions between protein targets and drug-like compounds. After training, PASS Targets became able to predict interactions of drug-like compounds with 2507 protein targets from different organisms based on analysis of structure–activity relationships for 589,107 different chemical compounds. The prediction accuracy, estimated as AUC ROC calculated by the leave-one-out cross-validation and 20-fold cross-validation procedures, was about 96%. Average AUC ROC value was about 90% for the external test set from approximately 700 known drugs interacting with 206 protein targets.

Serum 8-hydroxy-2′-deoxyguanosine (8-oxo-dG) levels are elevated in diabetes patients

The increased oxidative stress in diabetes is known to contribute to the development of diabetes. We investigate whether serum 8-hydro-2′-deoxyguanosine (8-oxo-dG) is associated with diabetes at the time of first diagnosis and evaluate whether it can be used as a reliable biomarker for the oxidative stress in diabetes. The study was designed as a case control study with two groups: patient with diabetes and control. The diabetes group consisted of a total of 28 patients consulting the hospital for the first time and definitely diagnosed for diabetes, and the control group was composed of 65 healthy subjects. Serum 8-oxo-dG was measured by a competitive enzyme-linked immunosorbent assay (ELISA) kit, specially developed to minimize cross-reaction of 8-oxo-dG antibody with serum guanosine. The average serum 8-oxo-dG levels in patients with diabetes and controls were 0.72 ± 0.41 and 0.24 ± 0.14 ng/mL, respectively, statistically significant (p < 0.001). The 8-oxo-dG value was significantly higher in women with diabetes, compared with men with diabetes (p = 0.028). The sensitivity and the specificity of the 8-oxo-dG ELISA assay were 0.80 and 0.96, respectively, and the ROC value was 0.93. This study suggests that increased oxidative stress has an important role in the pathogenesis of diabetes. Serum 8-oxo-dG may be a useful clinical biomarker for the early diagnosis of stress-related diseases, e.g. diabetes and its management.

Evaluation of serum prolidase activity in patients with slow coronary flow.

IntroductionSlow coronary flow (SCF) is described as the slow passage of contrast to distal coronaries despite anatomically normal coronary arteries. It has been shown that increased serum prolidase activity (SPA) correlates with collagen turnover. Increased collagen turnover might be associated with the development of atherosclerotic plaques.AimTo investigate the relationship between serum prolidase activity and slow coronary flow.Material and methodsThis cross-sectional study included 40 SCF patients (mean age: 55.0 ±9.5 years, 20 females) and 40 controls (mean age: 53.9 ±8.2 years, 21 females) with normal coronary anatomy and normal coronary flow. The Thrombolysis in Myocardial Infarction (TIMI) frame-count (TFC) method was used for SCF diagnosis. Serum prolidase activity was measured spectrophotometrically, and the relevant parameters were compared between the groups.ResultsThere were no statistically significant differences between the SCF and control groups in terms of basic demographic, clinical, and laboratory data. However, the SPA was significantly higher in the SCF group compared to the control (702.7 ±13.8 and 683.9 ±13.2 respectively, p<0.001). Serum prolidase activity was significantly correlated with the mean TFC (r=0.463, p<0.001). The overall findings of this study support the predictive accuracy of the serum prolidase activity in our cohort, with a statistically significant ROC value of 681.3.ConclusionsOur study showed that SPA was increased in SCF patients. The activity of this enzyme was significantly correlated with the mean TFC.

Arginase-1 Is Increased in Hodgkin Lymphoma, Associated to Poor Outcome and Positively Correlated to Semiquantitative PET Parameters

Background:In Hodgkin Lymphoma (HL) elevated neutrophil (HL-N) count is a well-known negative prognostic factor but its biological meaning is not elucidated. Our previous work showed that HL-N are dysfunctional and can suppress T-cell activation in vitro, as consequence of increased amount of Arginase-1 (Arg-1).Aim:Investigating clinical meaning of arginase increase in HL, correlating its amount to features at diagnosis, including some semiquantitative parameters of 18-FDG- PositronEmission Tomography (PET) acquired with a novel operator-independent algorithm.Material and methods: We prospectively measured soluble Arg-1 (s-Arg-1) in 135 sera obtained from 90 patients with HL, distinguished in a training set (N=35) and a validation set (N=55) and 20 healthy participants. In the training set, blood was taken at three fixed time-points prior, during, and after first-line therapy. In the first ten patients, a correlation between s-Arg-1 and semiquantitative parameters of PET at diagnosis was explored, including the Metabolic Tumor Volume (MTV) and the Total Lesion Glycolysis (TLG). Briefly, our group developed ad hoc tool independent from the operator: PET images are represented as a graph in which the voxels are its nodes and the edges are defined by a cost function which maps a change in image intensity to edge weights. This approach is an efficient and accurate method to segment lesion in low contrast images characterized by noise and weak edges as metabolic images (Stefano, 2013).Results:s-Arg-1 was increased in HL patients compared to healthy subjects, reduced after therapy in responders and increased in relapsed patients (p<0.0001). s-Arg-1 was positively correlated to the amount of Neutrophils and Arg-1 in N detected by RT-PCR. A cut-off level of 205 ng/mL for Arg-1 was chosen (equal to 2 times the 95th percentile in controls and ROC value with sensitivity and specificity of at least 80%) to predict response status at 24 months.In the training set, 32% patients had high s-Arg-1, 24% had positive PET-2 and were addressed to an early salvage therapy accordingly to BEACOPP scheme. A level of 205 ng/mL s-Arg-1 resulted in 83% (95% C.I. 58-96) sensitivity and 81% (95% C.I. 42-96) specificity in predicting response status in the training set (area under curve, AUC, 0.81, p=0.02).In the validation set, baseline levels of s-Arg-1>205 ng/mL resulted in 83% (C.I. 95% 62-95) sensitivity and 87% (C.I. 95% 47-99) specificity in predicting response status. Patients with s-Arg-1 ≥ 205 ng/mL had shorter PFS than patients carrying Arg-1 < 205 ng/mL (despite both groups did not reach the median, because of the short follow-up, p=0.005).In first 10 patients enrolled in the study, semiquantitative parameters of PET at diagnosis were explored: SUVmax was 12.7 (range 5.9-14.2), MTV median was45.5 (range 8.9-308.7), TLG mean was 43.7 (range 25.2-2475.3). MTV and TLG, but not SUVmax, were positively correlated to s-Arg-1 (respectively, r=0.68, p=0.003 and r=0.59, p=0.002). ConclusionS-Arg-1 is a predictor of PFS in HL and it is positively correlated with MTV in PET scans at baseline calculated with a novel operator-independent tool for imaging analysis. An update will be provided at the conference. DisclosuresNo relevant conflicts of interest to declare.

Risk factors of non-sentinel lymph node metastasis in breast cancer patients with positive sentinel lymph node ≤ 2

To explore retrospectively the risk factors of non-sentinel lymph node (NSLN) metastasis in breast cancer patients with sentinel lymph node metastasis ≤ 2 and examine the likelihood of non-sentinel lymph node prediction. A sentinel lymph node biopsy database containing 455 breast cancer patients admitted between July 2005 and February 2012 at Beijing Cancer Hospital was analyzed retrospectively. The patients had ≤ 2 positive sentinel lymph node and complete axillary lymph node dissection. The SLNS⁺/SLNS ratio (P = 0.001), histological grade (P = 0.075), size of mass (P = 0.023) and onset age (P = 0.074) were correlated with NSLN metastases. Only SLNS⁺/SLNS (OR 0.502 95% CI 0.322-0.7844) , histological grade ratio (histological grade ratio II, III and others vs grade I OR 2.696, 2.102, 3.662) were significant independent predictors for NSLN metastases . The ROC value was 0.62 (0.56, 0.68). For ≤ 2 positive sentinel lymph nodes of breast cancer, ratio of SLNS⁺/SLNS and histological grading are independent factors affecting NSLN metastases. However, the results remain unsatisfactory for predicting the status of NSLN.

A RULE-BASED APPROACH FOR DISCOVERING EFFECTIVESOFTWARE TEAM COMPOSITION

Human aspects in software engineering play a key role in composing effective team members. However, to date there is no general consensus on the effective personality types and diversity based on software team roles. Thus, this paper aims to discover the effective personality types and diversity based on two software team roles – team leader and programmer by using a rule-based approach. The rule-based approach by employing the rough set technique was used to discover patterns of the data selected. In this study, four main steps were involved to discover the patterns – reduct generation rules, rules generation, rules fi ltering, and rules evaluation. The results show that the rules generated achieved acceptable prediction accuracy with more than 70 per cent accuracy. In addition, the ROC value achieved 0.65, which indicates the rule-based model is valid and useful. The results reveal that the extrovert personality type is dominant for both software team roles and a homogeneous or heterogeneous team plays an equal role to determine an effective team. This study provides useful rules for decision makers to understand and get insight into selecting effective team members that lead to producing high quality software.

Classification of Human Faces and Non Faces Using Machine Learning Techniques

Face detection technique is used for face authentication and verification and face detection is a front part of face recognition. It is used in many fields such as authentication security, video surveillance and human interaction system. In this paper we have collected data of 400 faces from school students in Muzaffarabad, Azad Kashmir. Besides, 50 non-faces are also collected. Both faces and non-faces are preprocessed using Background Elimination, Noise Reduction, Width Normalization and Thinning. After the preprocessing, we have extracted features from 400 faces and 50 non-faces including Geometric Features such as Image Cropping, Vertical/Horizontal Projection, Global Features such as Aspect Ratio, Normalized Area of Faces and Non-faces, Center of Gravity, Slope of Line joining the center of Gravity and texture features. Finally, we have applied Machine Learning Methods such as Bayes, Function, Lazy, Meta, Misc, Rules and Tree to classify the faces and non- faces using 10 fold cross validation. HyperPipes gives an overall higher accuracy of 99.8%, while ADTree, LWL and LogiBoost gives accuracy of more than 99%. The average AUC of ROC value was calculated as 96.08%. 

Severity of Hepatitis C Virus (Genotype-3) Infection Positively Correlates with Circulating MicroRNA-122 in Patients Sera

Introduction. Hepatitis C virus (genotype-3) causes acute and chronic hepatitis infection predomination in India. The infectious phase of the virus requires various host factors for its survival and subsequent viral particle production. Small RNA molecules like microRNA-122 (miR-122) are one such factor mostly present in the liver and play a supportive role in viral replication. Objective. In this study, diagnostic potential of miR-122 is evaluated in the sera of chronic hepatitis C patients. Methods. miRNAs were isolated from the sera samples of patients as well as controls and miR-122 expression was quantified by real-time PCR. Results. A significant augmentation was observed in the level of circulating miR-122 (median level, 0.66 versus 0.29, P = 0.001) in patients compared to controls with ROC value of 0.929 ± 0.034 (P < 0.001). Interestingly, miR-122 level also depicted a significant positive correlation with serum ALT (r = 0.53), AST (r = 0.44), and viral load (r = 0.52). Conclusion. The study thus unveiled the role of miR-122 as a plausible diagnostic biomarker during HCV genotype-3 infection in India.

Effects of distance and duration on vertical dynamic visual acuity in screening healthy adults and people with vestibular disorders

Dynamic visual acuity (DVA) testing may be a useful, indirect indicator of vestibulo-ocular reflex function. Previous evidence shows that acuity for 2 m targets differs little between patients and normals using a 75 ms display duration and that healthy subjects do not differ in acuity when standing and walking while viewing a far target but they do differ when viewing a near target. Improve the protocol of a screening tool by testing the hypothesis that healthy control subjects and patients and with unilateral peripheral vestibular weakness differ on DVA when viewing far targets while seated. Controls and patients were tested while they were seated in a chair that oscillated vertically at 2 Hz. They viewed a computer screen 4 m away, while stationary and while moving, with viewing times of either 75 ms or 500 ms. The amount of change between static and dynamic conditions did not differ significantly between patients and controls for the 75 ms condition but controls had lower difference scores than patients when using the 500 ms duration. The ROC value was low, 0.68. Compared to historical data using the 75 ms duration at a distance of 2 m, subjects in both diagnostic groups had better visual acuity at the 75 ms/4 m distance. These results suggest that using the longer duration is better for differentiating patients from healthy controls and they support previous evidence showing that near target viewing is more challenging.

Comparison of NN and LR classifiers in the context of screening native American elders with diabetes

Classification is a frequently used decision making tool, however there are many classification methods and these seldom provide adequate and consistent results. In this paper we compare the classification efficiency of neural networks (NN) to more traditional methods such as LR (LR), in the context of identifying American Indian/Alaskan Native (AI/AN) elders who are at risk of developing diabetes. Feature selection is an important first step in building these classification models. We used both stepwise selection and genetic algorithm (GA) to identify features related to diabetes. Each LR and NN models were built twice, once based features identified by stepwise regression and second using features identified using genetic algorithm. Analysis of results from this approach lead to several conclusions: (a) although both LR and NN models exhibit similar classification ability, NN models were marginally better compared to LR models. (b) While the ROC value of these two models were the same (ROC=1), the type of feature selection methodology had no impact on the sensitivity and specificity of these models. In conclusion results from our study shows that although both these models are equally capable of identifying AI/AN elders at risk of developing diabetes, NN models are marginally better.

Early Inflammatory Predictors of Cardiac Allograft Vasculopathy

<h3>Purpose</h3> To determine whether prediction of cardiac allograft vasculopathy (CAV) in heart transplant patients improves by adding inflammatory markers to an atherothrombotic (AT) risk-prediction model. <h3>Methods and Materials</h3> AT markers (myocardial fibrin, loss of vascular antithrombin and tissue plasminogen activator, arterial endothelial intercellular adhesion molecule-1 expression) and inflammatory markers interleukin-6 (IL-6) and C-reactive protein (CRP) were measured in serial heart biopsy and serum samples of 172 patients followed prospectively for 8.9±5.0 years for CAV. First stepwise logistic regression models were estimated using only AT markers obtained from (1) the first post-transplant biopsy only (median: 9 days), or (2) all biopsies obtained within 3 months. Then model improvement was evaluated after adding IL-6 and CRP to the AT models. Final multivariate models were adjusted for other covariates and cross-validated in 200 bootstrapped samples. <h3>Results</h3> Most AT markers univariately predicted CAV at 5- and 10-years. In multivariate AT models, antithrombin and fibrin were the only significant predictors of CAV. Adding the inflammatory marker CRP, but not IL-6, to the best AT models (Table 1) improved discriminatory power as indicated by the increase in ROC value. <h3>Conclusions</h3> AT markers significantly predict future CAV. Adding the inflammatory marker CRP, but not IL-6, improves the predictive power of the AT model. [figure 1] [figure 2]

Early Inflammatory Predictors of Post-Transplant Major Adverse Cardiac Events

<h3>Purpose</h3> To determine whether adding inflammatory markers to an established atherothrombotic (AT) model improves prediction of major adverse cardiac events (MACE) following heart transplantation. <h3>Methods and Materials</h3> Inflammatory markers interleukin-6 (IL-6) and C-reactive protein (CRP) and AT markers (myocardial fibrin, loss of vascular antithrombin and tissue plasminogen activator, arterial endothelial intercellular adhesion molecule-1 expression) were measured in serial serum and heart biopsy samples of 172 patients followed prospectively for 8.9±5.0 years for MACE (myocardial infarction, percutaneous cardiac interventions, coronary artery bypass graft, implantable cardiac defibrillator, cardiac death/retransplantation). Multivariate stepwise logistic regression models were estimated using AT markers obtained from (1) the first post-transplant biopsy only (median: 9 days), or (2) all biopsies obtained within 3 months. The additional predictive value of adding inflammatory markers (IL-6 and CRP) was evaluated by inclusion into the established AT model. Final models were adjusted for covariates (eg., recipient sex) and cross-validated in 200 bootstrapped samples drawn with replacement from the original sample. <h3>Results</h3> Most AT markers univariately predicted MACE at 5- and 10-years; however, in multivariate models (Table 1), antithrombin and fibrin were the only significant (p<0.005) predictors. Table 1 shows that adding IL-6, but not CRP, to the best AT model improves discriminatory power (as indicated by the change in the ROC value). <h3>Conclusions</h3> Loss of antithrombin and presence of fibrin as early as 9 days post-transplant are significant predictors of future MACE. Adding IL-6, but not CRP, improves the AT model.

Abstract P5-10-02: High serum levels of miR-19a are associated with poor outcome in metastatic inflammatory breast cancer

Abstract Background: Increased expression of miR-19a plays an important role in tumor progression by regulating angiogenesis and apoptosis. As miRs can be a valuable diagnostic and prognostic serum biomarker, we assessed if high serum levels of miR-19a were associated with the shorter overall survival (OS) of patients with metastatic inflammatory breast cancer (MIBC) and metastatic non-inflammatory breast cancer (MNIBC). Methods: We analyzed 27 MNIBC (10 HER2 normal [HER2−] and 17 HER2 amplified [HER2+]), 37 MIBC (18 HER2− and 19 HER2+) patients, and 30 healthy donors (HDs). Patients' sera were collected before starting a new line of treatment. MiR-19a levels were measured in patients' sera and cell lines (MCF-7, SKBR3, MA-231, KPL-4, SUM-149) by qRT-PCR to assess differences in expression levels between IBC and non-IBC. MiR-192 and U6 snRNA were used to normalize miR-19a expression levels in serum and cell lines, respectively. Fold-changes in expression of miR-19a were calculated using the 2−DCt method. Mann-Whitney U test, ROC curve analysis, Kaplan-Meier, and Student's t-test were used for statistical analysis. Results: Median levels of miR-19a were higher in sera of both MNIBC and MIBC patients than in HDs (p = .001, p &amp;lt; 001, respectively). Median serum level of miR-19a was higher in MIBC than in MNIBC (p = .013). ROC curve analysis revealed that miR-19a could differentiate MNIBC from MIBC (AUC = 0.683; p = 0.013). With a cutoff value of 1.62 set by ROC, the sensitivity, specificity and positive predictive value (PPV) were 56.7%, 85.2%, and 84.0%, respectively. The triple receptor negative (TN) IBC cell line SUM-149 had the highest level of miR-19a and it was higher than the non-IBC: MDA-231 (TN; p = .031); SKBR3 (HER2+; p = .021); and MCF-7 (ER/PR+; p = .003). KPL-4 (IBC, HER2+) had the second highest miR-19a levels after SUM-149. MIBC patients had a shorter median OS than MNIBC patients (18.8 vs 27.8 months; p = .041; range, 0.7–40.3 months; median follow-up 19.5 months). MIBC patients with HER2− tumor (n = 11) and high serum levels of miR-19a (&amp;gt;1.62) had a shorter median OS than MNIBC patients with HER2− tumors (n = 9) and low serum level of miR-19a (&amp;lt;1.62) (16.15 vs 33.45 months; p = .025). There was no difference in the OS between HER2+ MIBC patients with high miR-19a (&amp;gt; 1.62) and HER2+ MNIBC patients with low miR-19a (&amp;lt;1.62). Discussion: In this pilot study, miR-19a may be a potential biomarker for inflammatory breast cancer, as significantly higher miR-19a levels were present in the sera of MIBC compared with MNIBC as well as in IBC compared with non-IBC cell lines. Furthermore, miR-19a may be predictive of OS, as HER2− MIBC patients with high serum levels of miR-19a had shorter OS than HER2− MNIBC patients with low serum levels of miR-19a. Our data suggest that high expression of miR-19a: is a feature of IBC tumor cells; can be detected in the serum; may favor tumor progression and predict poor outcome of HER2− MNIBC patient. A larger cohort of patients is required to confirm these observations and examine predictive potential. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P5-10-02.

Angiogenic factors and acute-phase proteins in serum samples of preeclampsia and HELLP patients: a matched-pair analysis

Objectives: To investigate the serum level distribution of angiogenic markers (PlGF, endoglin, sFlt-1) and acute-phase proteins (SAA, CRP) in patients with HELLP syndrome or preeclampsia (PE) including matched controls. Methods: The matching procedure revealed 46 controls for 23 HELLP cases, and 81 controls for 42 preeclamptic patients. Maternal serum concentrations were determined by immunoassays. Results: SAA and CRP levels were significantly higher in HELLP patients compared with controls. This finding was not observed in preeclamptic subgroup. Pro-angiogenic PlGF is significantly lower in PE and HELLP syndrome. Anti-angiogenic endoglin is significantly higher in PE and HELLP syndrome. The sFlt-1 analysis supports the anti-angiogenic shift in HELLP and preeclamptic patients, but with smaller differences between subgroups. The SAA/PlGF ratio showed the highest ROC value of all tested parameters in discrimination between HELLP and HELLP controls. Conclusions: These findings support the concept that patients with HELLP syndrome have both an anti-angiogenic state and a pronounced inflammatory response, while patients with PE are characterized only by an anti-angiogenic shift.

Risk-Adjusted Intraventricular Hemorrhage Rates in Very Premature Infants

The incidence of intraventricular hemorrhage (IVH) in very low birth weight infants can be used as an index of the quality of care in neonatal intensive care units as long as it is adjusted to reflect the infants' risk profiles on admission to the unit, which may vary systematically from one institution to another. Adjustment for gestational, birth-related, and neonatological risk factors enables a fair comparison of IVH rates across neonatal intensive care units. Data on 1782 neonates born at less than 32 weeks of gestation or weighing less than 1500 g at birth were retrieved from the 26 744 anonymous data sets collected in the Peri- and Neonatal Survey of the German state of Saxony in the years 2001-2005. An analysis of 30 putative risk factors with stepwise logistic regression analysis enabled the construction of a specific risk predictor for severe (grade 3-4) IVH. Risk-adjusted institutional incidence rates were then calculated. Five independent risk factors (low gestational age, low Apgar scores at 1 min, early infection, absence of pathological Doppler findings during pregnancy, and the use of tocolytic agents) were found to be relevant to the prediction of IVH. A risk predictor incorporating them was found to have a correct prediction rate (ROC(AUC) value) of 87.7%. The crude incidence of severe IVH in different institutions ranged from 1.92% to 15.02% (mean, 8.55%); after adjustment, the range was 5.14% to 11.58%. When the institutions studied were ranked in order of their incidence of IVH before and after adjustment for risk factors, individual institutions rose or fell by as many as 4 places in the ranking because of the adjustment. These findings reveal the importance of adjusting the incidence of IVH in very low birth weight infants by the patients' risk profiles to enable valid comparisons between institutions for the purpose of quality surveillance.

Differential representation of auditory categories between cell classes in primate auditory cortex

A comprehensive understanding of the neural mechanisms of cognitive function requires an understanding of how neural representations are transformed across different scales of neural organization: from within local microcircuits to across different brain areas. However, the neural transformations within the local microcircuits are poorly understood. Particularly, the role that two main cell classes of neurons in cortical microcircuits (i.e. pyramidal neurons and interneurons) have in auditory behaviour and cognition remains unknown. In this study, we tested the hypothesis that pyramidal cells and interneurons in the auditory cortex play a differential role in auditory categorization. To test this hypothesis, we recorded single-unit activity from the auditory cortex of rhesus monkeys while they categorized speech sounds. Based on the spike-waveform shape, a neuron was classified as either a narrow-spiking putative interneuron or a broad-spiking putative pyramidal neuron. We found that putative interneurons and pyramidal neurons in the auditory cortex differentially coded category information: interneurons were more selective for auditory categories than pyramidal neurons. These differences between cell classes may be an essential property of the neural computations underlying auditory categorization within the microcircuitry of the auditory cortex.