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The rate of manifestation of depressive episodes can vary appreciably. The complete development of a depressive episode may be very rapid, taking less than one hour or be very slow, taking up to one month. Altough this clinical observation suggests different neurobiological pathomechanisms, the onset speed of depressive episodes in different affective disorders has not been investigated systematically up to now. The objective of this study was to establish the onset speed of depressive episodes in patients with a history of at least one depressive episode and to compare the onset speed in unipolar with that in bipolar affective disorders.
A group of 96 inpatients was investigated consecutively using the structured patient interview “Onset of Depression Inventory” (ODI). In 76 patients, there was a unipolar depressive disorder and 20 patients suffered from a bipolar depressive disorder.
The onset speed of the current depressive episode in patients with effective disorders correlated significantly with the onset speed of the previous depressive episodes (p < 0.001). Furthermore, there was a significant difference in the onset of the depressive episodes between unipolar and bipolar affective disorders (p < 0.001). In 55% of patients with a bipolar disorder, the depressive episode was manifested within one week whereas this was the case in only 22,37% of the patients with a unipolar affective disorder.
The rate of manifestation of depressive episodes differs between unipolar and bipolar disorders. The clinical observation reported here can support the diagnostic appraisal of depressive episodes.
Patients with impulse control deficits often show cognitive abnormalities especially in executive abilities. One possibility to examine the underlying neurophysiological mechanisms is to assess evoked potentials. In the present study an adapted go/nogo-paradigm was used to investigate electrophysiological correlates of voluntary selection and behaviour control processes in patients suffering from alcohol dependence and attention deficit hyperactivity disorder (ADHD).
15 patients with alcohol dependence, 15 adult patients with ADHD and 15 control persons were included into the study. Patients with alcoholism were examined twice: before and after an inpatient detoxification.
The participants performed a go/nogo task, comprising three different conditions:
Apart from the go-condition (button press required) and the nogo-task (inhibition of a behavioural response), a voluntary selection task was included in which participants were allowed to freely decide, whether to press the response button or not.
Results and discussion
Response inhibition and voluntary selection processes were related to a fronto-central negativity after 200 ms (N2) and a positivity after 300 ms (P3) in healthy subjects. In patients, the P3 amplitude was reduced compared to the controls. In addition, alcohol dependent patients did not show a N2 potential.
The results indicate fronto-central dysfunctions dysfunctions in either patient group. However, different neuronal processes seemed to be affected in patients with ADHD and patients with alcoholism.
Polycrystalline samples of the single-layered cobaltate La2-xCaxCoO4 were prepared in a wide doping range of 0 ≤ x ≤ 1.5. Structural properties were characterized at room temperature. The orthorhombic distorted structure of the mother compound La2CoO4 changes to a tetragonal structure for x = 0.5 and then becomes orthorhombic again for x > 0.5. The magnetic properties were investigated in the temperature range from 5 K ≤ T ≤ 300 K. With increasing hole-doping a successive decrease of antiferromagnetic exchange is observed for x ≤ 0.5 whereas an increase of ferromagnetic exchange evolves for x ≥ 0.5.
Wire shading during thin film deposition is a promising approach to low-cost, high volume manufacturing of flexible thin film photovoltaic modules. This contribution demonstrates successful patterning of a transparent conducting oxide layer by wire shading during dynamic web coating. Continuous sputter deposition of Al-doped ZnO on a 30 cm wide polymer foil and simultaneous wire shading form 1 cm wide and 300 cm long front contact stripes for thin film photovoltaic modules. Analysing the distribution of lateral shunt resistances after separating the initial 28 stripes into 1323 pieces, yields a patterning success of 97.3 %. Thus the technique seems well suited for flexible modules from organic solar cells.
We revisit the stochastic model of Alai et al. (2009) for the Bornhuetter-Ferguson claims reserving method, Bornhuetter & Ferguson (1972). We derive an estimator of its conditional mean square error of prediction (MSEP) using an approach that is based on generalized linear models and maximum likelihood estimators for the model parameters. This approach leads to simple formulas, which can easily be implemented in a spreadsheet.
The routine use of quantum mechanics (QM) in all phases of in silico drug design is the logical next step in the evolution of this field. The first principles nature of QM allows it to systematically improve the accuracy of the description of the nature of the interactions between molecules. Moreover, the systematic way in which one can approach the use of QM methods to solve chemical and biological problems is quite appealing, but the practical use of many of the appealing features of QM in in silico drug design applications is still to be realized in large part because of computational limitations. In recent years it has become clear that classical potential functions are being pushed to their limits and as many pitfalls of using them are coming to light, one is tempted to explore the use of QM procedures. This is a somewhat naïve view, however, because one of the main observations of a large body of computational work has shown that sampling of relevant conformational states can be as important as providing an accurate representation of an inter-or intramolecular interaction. Hence, even as QM becomes a routine tool used to calculate the energy of individual states of a biological system, one still faces the daunting task of sampling relevant conformational space, which, in our view, will for the near term be largely confined to classical models.
Our goal in producing this book is to provide a broad overview of the most important approaches used in protein- and ligand-structure-based drug design. Beyond this we aim to illustrate how these approaches are currently being applied in drug discovery efforts. We hope this book will be a useful resource to practitioners in the field, as well as a good introduction for researchers or students who are new to the field. We believe it provides a snapshot of the most important trends and capabilities in the application of modeling and structural data in drug discovery.
Since the 1990s the role of structure and modeling in drug discovery has grown enormously. There have been remarkable scientific advances in both the experimental and computational fields that are the underpinnings of modern drug design. For example, x-ray capabilities have improved to the point that protein structures are now routinely available for a wide range of protein targets. One only need look at the exponential growth of the Protein Databank (RCSB) for evidence. Tremendous strides have been made in all aspects of protein structure determination, including crystallization, data acquisition, and structure refinement. Modeling has made similar gains. Recent years have brought more realistic force fields, new and more robust free-energy methods, computational models for absorption/distribution/metabolism/excretion (ADME)-toxicity, faster and better docking algorithms, automated 3D pharmacophore detection and searching, and very-large-scale quantum calculations.
Structure-based (SBDD) and ligand-based (LBDD) drug design are extremely important and active areas of research in both the academic and commercial realms. This book provides a complete snapshot of the field of computer-aided drug design and associated experimental approaches. Topics covered include X-ray crystallography, NMR, fragment-based drug design, free energy methods, docking and scoring, linear-scaling quantum calculations, QSAR, pharmacophore methods, computational ADME-Tox, and drug discovery case studies. A variety of authors from academic and commercial institutions all over the world have contributed to this book, which is illustrated with more than 200 images. This is the only book to cover the subject of structure and ligand-based drug design, and it provides the most up-to-date information on a wide range of topics for the practising computational chemist, medicinal chemist, or structural biologist. Professor Kenneth Merz has been selected as the recipient of the 2010 ACS Award for Computers in Chemical & Pharmaceutical Research that recognizes the advances he has made in the use of quantum mechanics to solve biological and drug discovery problems.