Dominik Heider

Impact of Working Memory Load on fMRI Resting State Pattern in Subsequent Resting Phases

Background The default-mode network (DMN) is a functional network with increasing relevance for psychiatric research, characterized by increased activation at rest and decreased activation during task performance. The degree of DMN deactivation during a cognitively demanding task depends on its difficulty. However, the relation of hemodynamic responses in the resting phase after a preceding cognitive challenge remains relatively unexplored. We test the hypothesis that the degree of activation of the DMN following cognitive challenge is influenced by the cognitive load of a preceding working-memory task. Methodology/Principal Findings Twenty-five healthy subjects were investigated with functional MRI at 3 Tesla while performing a working-memory task with embedded short resting phases. Data were decomposed into statistically independent spatio-temporal components using Tensor Independent Component Analysis (TICA). The DMN was selected using a template-matching procedure. The spatial map contained rest-related activations in the medial frontal cortex, ventral anterior and posterior cingulate cortex. The time course of the DMN revealed increased activation at rest after 1-back and 2-back blocks compared to the activation after a 0-back block. Conclusion/Significance We present evidence that a cognitively challenging working-memory task is followed by greater activation of the DMN than a simple letter-matching task. This might be interpreted as a functional correlate of self-evaluation and reflection of the preceding task or as relocation of cerebral resources representing recovery from high cognitive demands. This finding is highly relevant for neuroimaging studies which include resting phases in cognitive tasks as stable baseline conditions. Further studies investigating the DMN should take possible interactions of tasks and subsequent resting phases into account.

A Combination of α-Fetoprotein and Des-γ-Carboxy Prothrombin Is Superior in Detection of Hepatocellular Carcinoma

Background and Aim: The incidence of hepatocellular carcinoma (HCC) is increasing in western countries. Despite its low sensitivity, the diagnosis of HCC still depends on detection of α-fetoprotein (AFP). Therefore, the aim of this study was to evaluate the combined analysis of AFP and des-γ-carboxy prothrombin (DCP) in a European cohort. Methods: We performed a single-center study (164 HCC/422 controls), in which the serum concentrations of AFP and DCP were determined. Results: AFP and DCP were elevated in HCC patients compared to controls (p < 0.0001). By combination of AFP and DCP, the sensitivity was improved from 28.7% for AFP (cutoff 400 ng/ml; AFP at cutoff 10 ng/ml: 54.9%) to 78.0% using cutoffs of 10 ng/ml for AFP and 5 ng/ml for DCP (DCP alone, cutoff 5 ng/ml: 63.4%). Among early-stage patients, the sensitivity increased from 20% for AFP (cutoff 400 ng/ml; AFP at cutoff 10 ng/ml: 38%) to 55% in combination (DCP alone, cutoff 5 ng/ml: 47%). The area under the curve (AUC) for AFP and DCP was similar (AFP: 0.88; DCP: 0.87; combined: 0.91). Among non-cirrhotic patients, DCP (AUC: 0.93) showed a better performance than AFP (AUC: 0.84). Especially patients with non-alcoholic steatohepatitis had a high percentage of DCP-positive tumors. Conclusion: The data suggest that AFP alone is not sufficient for the serological diagnosis of HCC in European patients, while a combination of AFP and DCP can increase the sensitivity even in early-stage patients.

Prediction of Co-Receptor Usage of HIV-1 from Genotype

Human Immunodeficiency Virus 1 uses for entry into host cells a receptor (CD4) and one of two co-receptors (CCR5 or CXCR4). Recently, a new class of antiretroviral drugs has entered clinical practice that specifically bind to the co-receptor CCR5, and thus inhibit virus entry. Accurate prediction of the co-receptor used by the virus in the patient is important as it allows for personalized selection of effective drugs and prognosis of disease progression. We have investigated whether it is possible to predict co-receptor usage accurately by analyzing the amino acid sequence of the main determinant of co-receptor usage, i.e., the third variable loop V3 of the gp120 protein. We developed a two-level machine learning approach that in the first level considers two different properties important for protein-protein binding derived from structural models of V3 and V3 sequences. The second level combines the two predictions of the first level. The two-level method predicts usage of CXCR4 co-receptor for new V3 sequences within seconds, with an area under the ROC curve of 0.937±0.004. Moreover, it is relatively robust against insertions and deletions, which frequently occur in V3. The approach could help clinicians to find optimal personalized treatments, and it offers new insights into the molecular basis of co-receptor usage. For instance, it quantifies the importance for co-receptor usage of a pocket that probably is responsible for binding sulfated tyrosine.

Protistan community analysis: key findings of a large-scale molecular sampling

Protists are perhaps the most lineage-rich of microbial lifeforms, but remain largely unknown. High-throughput sequencing technologies provide opportunities to screen whole habitats in depth and enable detailed comparisons of different habitats to measure, compare and map protistan diversity. Such comparisons are often limited by low sample numbers within single studies and a lack of standardisation between studies. Here, we analysed 232 samples from 10 sampling campaigns using a standardised PCR protocol and bioinformatics pipeline. We show that protistan community patterns are highly consistent within habitat types and geographic regions, provided that sample processing is standardised. Community profiles are only weakly affected by fluctuations of the abundances of the most abundant taxa and, therefore, provide a sound basis for habitat comparison beyond random short-term fluctuations in the community composition. Further, we provide evidence that distribution patterns are not solely resulting from random processes. Distinct habitat types and distinct taxonomic groups are dominated by taxa with distinct distribution patterns that reflect their ecology with respect to dispersal and habitat colonisation. However, there is no systematic shift of the distribution pattern with taxon abundance.

Predicting Bevirimat resistance of HIV-1 from genotype

BackgroundMaturation inhibitors are a new class of antiretroviral drugs. Bevirimat (BVM) was the first substance in this class of inhibitors entering clinical trials. While the inhibitory function of BVM is well established, the molecular mechanisms of action and resistance are not well understood. It is known that mutations in the regions CS p24/p2 and p2 can cause phenotypic resistance to BVM. We have investigated a set of p24/p2 sequences of HIV-1 of known phenotypic resistance to BVM to test whether BVM resistance can be predicted from sequence, and to identify possible molecular mechanisms of BVM resistance in HIV-1.ResultsWe used artificial neural networks and random forests with different descriptors for the prediction of BVM resistance. Random forests with hydrophobicity as descriptor performed best and classified the sequences with an area under the Receiver Operating Characteristics (ROC) curve of 0.93 ± 0.001. For the collected data we find that p2 sequence positions 369 to 376 have the highest impact on resistance, with positions 370 and 372 being particularly important. These findings are in partial agreement with other recent studies. Apart from the complex machine learning models we derived a number of simple rules that predict BVM resistance from sequence with surprising accuracy. According to computational predictions based on the data set used, cleavage sites are usually not shifted by resistance mutations. However, we found that resistance mutations could shorten and weaken the α-helix in p2, which hints at a possible resistance mechanism.ConclusionsWe found that BVM resistance of HIV-1 can be predicted well from the sequence of the p2 peptide, which may prove useful for personalized therapy if maturation inhibitors reach clinical practice. Results of secondary structure analysis are compatible with a possible route to BVM resistance in which mutations weaken a six-helix bundle discovered in recent experiments, and thus ease Gag cleavage by the retroviral protease.

Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistance prediction

MOTIVATION Antiretroviral treatment regimens can sufficiently suppress viral replication in human immunodeficiency virus (HIV)-infected patients and prevent the progression of the disease. However, one of the factors contributing to the progression of the disease despite ongoing antiretroviral treatment is the emergence of drug resistance. The high mutation rate of HIV can lead to a fast adaptation of the virus under drug pressure, thus to failure of antiretroviral treatment due to the evolution of drug-resistant variants. Moreover, cross-resistance phenomena have been frequently found in HIV-1, leading to resistance not only against a drug from the current treatment, but also to other not yet applied drugs. Automatic classification and prediction of drug resistance is increasingly important in HIV research as well as in clinical settings, and to this end, machine learning techniques have been widely applied. Nevertheless, cross-resistance information was not taken explicitly into account, yet. RESULTS In our study, we demonstrated the use of cross-resistance information to predict drug resistance in HIV-1. We tested a set of more than 600 reverse transcriptase sequences and corresponding resistance information for six nucleoside analogues. Based on multilabel classification models and cross-resistance information, we were able to significantly improve overall prediction accuracy for all drugs, compared with single binary classifiers without any additional information. Moreover, we identified drug-specific patterns within the reverse transcriptase sequences that can be used to determine an optimal order of the classifiers within the classifier chains. These patterns are in good agreement with known resistance mutations and support the use of cross-resistance information in such prediction models. CONTACT dominik.heider@uni-due.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Normal liver enzymes are correlated with severity of metabolic syndrome in a large population based cohort.

Key features of the metabolic syndrome are insulin resistance and diabetes. The liver as central metabolic organ is not only affected by the metabolic syndrome as non-alcoholic fatty liver disease (NAFLD), but may contribute to insulin resistance and metabolic alterations. We aimed to identify potential associations between liver injury markers and diabetes in the population-based Heinz Nixdorf RECALL Study. Demographic and laboratory data were analyzed in participants (n = 4814, age 45 to 75y). ALT and AST values were significantly higher in males than in females. Mean BMI was 27.9 kg/m2 and type-2-diabetes (known and unkown) was present in 656 participants (13.7%). Adiponectin and vitamin D both correlated inversely with BMI. ALT, AST, and GGT correlated with BMI, CRP and HbA1c and inversely correlated with adiponectin levels. Logistic regression models using HbA1c and adiponectin or HbA1c and BMI were able to predict diabetes with high accuracy. Transaminase levels within normal ranges were closely associated with the BMI and diabetes risk. Transaminase levels and adiponectin were inversely associated. Re-assessment of current normal range limits should be considered, to provide a more exact indicator for chronic metabolic liver injury, in particular to reflect the situation in diabetic or obese individuals.

Novel Algorithm for Non-Invasive Assessment of Fibrosis in NAFLD

Introduction Various conditions of liver disease and the downsides of liver biopsy call for a non-invasive option to assess liver fibrosis. A non-invasive score would be especially useful to identify patients with slow advancing fibrotic processes, as in Non-Alcoholic Fatty Liver Disease (NAFLD), which should undergo histological examination for fibrosis. Patients/Methods Classic liver serum parameters, hyaluronic acid (HA) and cell death markers of 126 patients undergoing bariatric surgery for morbid obesity were analyzed by machine learning techniques (logistic regression, k-nearest neighbors, linear support vector machines, rule-based systems, decision trees and random forest (RF)). Specificity, sensitivity and accuracy of the evaluated datasets to predict fibrosis were assessed. Results None of the single parameters (ALT, AST, M30, M60, HA) did differ significantly between patients with a fibrosis score 1 or 2. However, combining these parameters using RFs reached 79% accuracy in fibrosis prediction with a sensitivity of more than 60% and specificity of 77%. Moreover, RFs identified the cell death markers M30 and M65 as more important for the decision than the classic liver parameters. Conclusion On the basis of serum parameters the generation of a fibrosis scoring system seems feasible, even when only marginally fibrotic tissue is available. Prospective evaluation of novel markers, i.e. cell death parameters, should be performed to identify an optimal set of fibrosis predictors.

Endoscopic management is the treatment of choice for bile leaks after liver resection

BACKGROUND Despite improvements in surgical techniques and postoperative patient care, bile leaks still occur postoperatively in as many as 15% of liver resections (LRs) and are associated with high mortality. There is a paucity of outcome data on endoscopic treatment of complex bile leaks. OBJECTIVE The aim of this retrospective study was to evaluate the efficacy of interventional endoscopy in the treatment of bile leaks after LR. DESIGN Retrospective interventional study. SETTING, PATIENTS, AND INTERVENTIONS Sixty patients with bile leaks after LR were treated endoscopically with or without implantation of endoprostheses by using ERCP. The characteristics of LR, effects of surgical and other nonendoscopic treatment measures, clinical and endoscopic presentation of bile leaks, and outcomes after stent placement were recorded. MAIN OUTCOME MEASURE Main outcome measure was resolution of leakage or termination of unsuccessful endoscopic leakage therapy. RESULTS The median age of the observed cohort was 58 years. Sixty-five percent of patients had central and 35% peripheral bile leaks; 55% had resection of an entire hepatic lobe, and 45% underwent segmental resection. The overall success rate of endoscopic therapy was 77%. Although endoscopic therapy was performed in all patients with a mean of 2.6 interventions, 28% underwent additional percutaneous drainage. Success of endoscopic treatment was related to stent implantation. Thirteen patients with unsuccessful endoscopic treatment underwent surgical reintervention, and 1 patient died before surgical intervention. LIMITATIONS No standardized protocol for stent placement due to retrospective nature of the study. Small sample number with uneven distribution of outcome. CONCLUSIONS Endoscopic therapy with sphincterotomy and insertion of endoprostheses is effective, even in large postoperative bile leaks and particularly for leaks proximal to the common hepatic duct. Complete resolution of the leakage often necessitates multiple treatment sessions.

DNA watermarks in non-coding regulatory sequences

BackgroundDNA watermarks can be applied to identify the unauthorized use of genetically modified organisms. It has been shown that coding regions can be used to encrypt information into living organisms by using the DNA-Crypt algorithm. Yet, if the sequence of interest presents a non-coding DNA sequence, either the function of a resulting functional RNA molecule or a regulatory sequence, such as a promoter, could be affected. For our studies we used the small cytoplasmic RNA 1 in yeast and the lac promoter region of Escherichia coli.FindingsThe lac promoter was deactivated by the integrated watermark. In addition, the RNA molecules displayed altered configurations after introducing a watermark, but surprisingly were functionally intact, which has been verified by analyzing the growth characteristics of both wild type and watermarked scR1 transformed yeast cells. In a third approach we introduced a second overlapping watermark into the lac promoter, which did not affect the promoter activity.ConclusionEven though the watermarked RNA and one of the watermarked promoters did not show any significant differences compared to the wild type RNA and wild type promoter region, respectively, it cannot be generalized that other RNA molecules or regulatory sequences behave accordingly. Therefore, we do not recommend integrating watermark sequences into regulatory regions.