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Biomedical subjects

Ersin Bayram

Publications and source records attributed to Ersin Bayram.

9 recordsLinked to original sources
PubMed2006

Leg flow-mediated arterial dilation in elderly patients with heart failure and normal left ventricular ejection fraction.

BACKGROUND: flow-mediated arterial dilation (FMAD), an indicator of endothelial function, is reduced in patients with heart failure and reduced left ventricular ejection fraction (HFREF). Many elderly patients with heart failure exhibit a normal left ventricular ejection fraction (HFNEF). It is unknown whether FMAD is severely reduced in the elderly with HFNEF. METHODS AND RESULTS: 30 participants >60 yr of age, 11 healthy, 9 with HFNEF, and 10 with HFREF, underwent a cardiovascular magnetic resonance (CMR) assessment of FMAD in the superficial femoral artery followed within 48 h by symptom-limited exercise with expired gas analysis. Elderly patients with HFREF and HFNEF had severely reduced peak oxygen consumption (Vo(2 peak); 12 +/- 2 and 13 +/- 1 ml.kg(-1).min(-1), respectively) vs. their healthy age-matched contemporaries (20 +/- 3 ml.kg(-1).min(-1)). FMAD was 3.8 +/- 1.3% (0.85 +/- 0.22 mm(2)) in patients with HFREF; it was 12.1 +/- 3.6% (3.1 +/- 1.2 mm(2)) and 13.7 +/- 5.9% (3.9 +/- 1.7 mm(2)), respectively, in patients with HFNEF and age-matched healthy older individuals. After adjustment for age and gender, the association of FMAD with Vo(2) was high in healthy and HFREF subjects (P = 0.05 and 0.02, respectively) but less so in HFNEF participants (P = 0.58). CONCLUSIONS: elderly patients with HFNEF do not exhibit marked reduction in leg FMAD. These data suggest that mechanisms other than impaired femoral arterial endothelial function contribute to the severe exercise intolerance experienced by these individuals.

Aged↗
PubMed2006

QSAR modeling of mono- and bis-quaternary ammonium salts that act as antagonists at neuronal nicotinic acetylcholine receptors mediating dopamine release.

Back-propagation artificial neural networks (ANNs) were trained on a dataset of 42 molecules with quantitative IC50 values to model structure-activity relationships of mono- and bis-quaternary ammonium salts as antagonists at neuronal nicotinic acetylcholine receptors (nAChR) mediating nicotine-evoked dopamine release. The ANN QSAR models produced a reasonable level of correlation between experimental and calculated log(1/IC50) (r2=0.76, r(cv)2=0.64). An external test for the models was performed on a dataset of 18 molecules with IC50 values >1 microM. Fourteen of these were correctly classified. Classification ability of various models, including self-organizing maps (SOM), for all 60 molecules was also evaluated. A detailed analysis of the modeling results revealed the following relative contributions of the used descriptors to the trained ANN QSAR model: approximately 44.0% from the length of the N-alkyl chain attached to the quaternary ammonium head group, approximately 20.0% from Moriguchi octanol-water partition coefficient of the molecule, approximately 13.0% from molecular surface area, approximately 12.6% from the first component shape directional WHIM index/unweighted, approximately 7.8% from Ghose-Crippen molar refractivity, and 2.6% from the lowest unoccupied molecular orbital energy. The ANN QSAR models were also evaluated using a set of 13 newly synthesized compounds (11 biologically active antagonists and two biologically inactive compounds) whose structures had not been previously utilized in the training set. Twelve among 13 compounds were predicted to be active which further supports the robustness of the trained models. Other insights from modeling include a structural modification in the bis-quinolinium series that involved replacing the 5 and/or 8 as well as the 5' and/or 8' carbon atoms with nitrogen atoms, predicting inactive compounds. Such data can be effectively used to reduce synthetic and in vitro screening activities by eliminating compounds of predicted low activity from the pool of candidate molecules for synthesis. The application of the ANN QSAR model has led to the successful discovery of six new compounds in this study with experimental IC50 values of less than 0.1 microM at nAChR subtypes responsible for mediating nicotine-evoked dopamine release, demonstrating that the ANN QSAR model is a valuable aid to drug discovery.

Dopamine↗
PubMed2006

Minimum reliable scale selection in 3D.

Multiscale analysis is often required in image processing applications because image features are optimally detected at different levels of resolution. With the advance of high-resolution 3D imaging, the extension of multiscale analysis to higher dimensions is necessary. This paper extends an existing 2D scale selection method, known as the minimum reliable scale, to 3D volumetric images. The method is applied to 3D boundary detection and is illustrated in examples from biomedical imaging. The experimental results show that the 3D scale selection improves the detection of edges over single scale operators using as few as three different scales.

Algorithms↗
PubMed2005

Chemical and structural diversity in cyclooxygenase protein active sites.

A major pharmaceutical problem is designing diverse and selective lead compounds. The human genome sequence provides opportunities to discover compounds that are protein selective if we can develop methods to identify specificity determinants from sequence alone. We have analyzed sequence and structural diversity of sheep COX-1 and mouse COX-2 proteins by Active Site Profiling (ASP). Eleven residues that should serve as specificity determinants between COX-1 and COX-2 were identified; however, the literature suggests that only one has been utilized in structure-based discovery. ASP was used to create a position-specific scoring matrix, which was used to identify possible cross-reacting proteins from the human sequences. This method proved selective for cyclooxygenases, comparing well with results using BLAST. The methods identify a probable misannotation of a cyclooxygenase in which there is high sequence similarity scores using BLAST, but ASP shows it does not contain the residues necessary for cyclooxygenase function. ASP Analysis of human COX proteins suggests that some specificity determinants that distinguish COX-1 and COX-2 proteins are similar between sheep COX-1/mouse COX-2 and human COX-1/COX2; however, residue identities at those positions are not necessarily conserved. Our results lay groundwork for development of family-specific pattern recognition methods to selectively match compounds with proteins.

Amino Acid Sequence↗
PubMed2005

An improved analytical model for CT dose simulation with a new look at the theory of CT dose.

Gagne [Med. Phys. 16, 29-37 (1989)] has previously described a model for predicting the sensitivity and dose profiles in the slice-width (z) direction for CT scanners. The model, developed prior to the advent of multidetector CT scanners, is still widely used; however, it does not account for the effect of anode tilt on the penumbra or include the heel effect, both of which are increasingly important for the wider beams (up to 40 mm) of contemporary, multidetector scanners. Additionally, it applied only on (or near) the axis of rotation, and did not incorporate the photon energy spectrum. The improved model described herein transcends all of the aforementioned limitations of the Gagne model, including extension to the peripheral phantom axes. Comparison of simulated and measured dose data provides experimental validation of the model, including verification of the superior match to the penumbra provided by the tilted-anode model, as well as the observable effects on the cumulative dose distribution. The initial motivation for the model was to simulate the quasiperiodic dose distribution on the peripheral, phantom axes resulting from a helical scan series in order to facilitate the implementation of an improved method of CT dose measurement utilizing a short ion chamber, as proposed by Dixon [Med. Phys. 30, 1272-1280 (2003)]. A more detailed set of guidelines for implementing such measurements is also presented in this paper. In addition, some fundamental principles governing CT dose which have not previously been clearly enunciated follow from the model, and a fundamental (energy-based) quantity dubbed "CTDI-aperture" is introduced.

Biophysical Phenomena↗
PubMedDate not supplied

Genetic algorithms and self-organizing maps: a powerful combination for modeling complex QSAR and QSPR problems.

Modeling non-linear descriptor-target activity/property relationships with many dependent descriptors has been a long-standing challenge in the design of biologically active molecules. In an effort to address this problem, we couple the supervised self-organizing map with the genetic algorithm. Although self-organizing maps are non-linear and topology-preserving techniques that hold great potential for modeling and decoding relationships, the large number of descriptors in typical quantitative structure-activity relationship or quantitative structure-property relationship analysis may lead to spurious correlation(s) and/or difficulty in the interpretation of resulting models. To reduce the number of descriptors to a manageable size, we chose the genetic algorithm for descriptor selection because of its flexibility and efficiency in solving complex problems. Feasibility studies were conducted using six different datasets, of moderate-to-large size and moderate-to-great diversity; each with a different biological endpoint. Since favorable training set statistics do not necessarily indicate a highly predictive model, the quality of all models was confirmed by withholding a portion of each dataset for external validation. We also address the variability introduced onto modeling through dataset partitioning and through the stochastic nature of the combined genetic algorithm supervised self-organizing map method using the z-score and other tests. Experiments show that the combined method provides comparable accuracy to the supervised self-organizing map alone, but using significantly fewer descriptors in the models generated. We observed consistently better results than partial least squares models. We conclude that the combination of genetic algorithms with the supervised self-organizing map shows great potential as a quantitative structure-activity/property relationship modeling tool.

Algorithms↗
PubMedDate not supplied

Supervised self-organizing maps in drug discovery. 1. Robust behavior with overdetermined data sets.

The utility of the supervised Kohonen self-organizing map was assessed and compared to several statistical methods used in QSAR analysis. The self-organizing map (SOM) describes a family of nonlinear, topology preserving mapping methods with attributes of both vector quantization and clustering that provides visualization options unavailable with other nonlinear methods. In contrast to most chemometric methods, the supervised SOM (sSOM) is shown to be relatively insensitive to noise and feature redundancy. Additionally, sSOMs can make use of descriptors having only nominal linear correlation with the target property. Results herein are contrasted to partial least squares, stepwise multiple linear regression, the genetic functional algorithm, and genetic partial least squares, collectively referred to throughout as the "standard methods". The k-nearest neighbor (kNN) classification method was also performed to provide a direct comparison with a different classification method. The widely studied dihydrofolate reductase (DHFR) inhibition data set of Hansch and Silipo is used to evaluate the ability of sSOMs to classify unknowns as a function of increasing class resolution. The contribution of the sSOM neighborhood kernel to its predictive ability is assessed in two experiments: (1) training with the k-means clustering limit, where the neighborhood radius is zero throughout the training regimen, and (2) training the sSOM until the neighborhood radius is reduced to zero. Results demonstrate that sSOMs provide more accurate predictions than standard linear QSAR methods.

Algorithms↗
PubMedDate not supplied

Supervised self-organizing maps in drug discovery. 2. Improvements in descriptor selection and model validation.

The modeling of nonlinear descriptor-target relationships is a topic of considerable interest in drug discovery. We, herein, continue reporting the use of the self-organizing map-a nonlinear, topology-preserving pattern recognition technique that exhibits considerable promise in modeling and decoding these relationships. Since simulated annealing is an efficient tool for solving optimization problems, we combined the supervised self-organizing map with simulated annealing to build high-quality, highly predictive quantitative structure-activity/property relationship models. This technique was applied to six data sets representing a variety of biological endpoints. Since a high statistical correlation in the training set does not indicate a highly predictive model, the quality of all the models was confirmed by withholding a portion of each data set for external validation. Finally, we introduce new cross-validation and dynamic partitioning techniques to address model overfitting and assessment.

Algorithms↗