Acute efficacy of ECT in the treatment of major depression in the old-old.

OBJECTIVE: There are few data addressing the outcome of ECT for persons over 75 years of age. In a prospective, multisite study, the authors compared characteristics and treatment outcomes of adult (59 and younger), young-old (60 to 74 years), and old-old (75 and older) patients treated with ECT for major depression. METHOD: At four hospitals, 268 patients with primary unipolar major depression and scores of at least 20 on the 24-item Hamilton Depression Rating Scale were treated with suprathreshold right unilateral or bilateral ECT in a standardized manner. Demographic variables, clinical characteristics, and short-term outcomes of the three groups were compared. RESULTS: The demographic and clinical characteristics of the old-old patients were similar to those of the young-old patients, whereas both groups differed from the adult patients on these variables. Both older groups had significantly greater burdens from physical illness and global cognitive impairment at baseline than the adult subjects. Both older groups had shorter index depressive episodes and were less likely to have had inadequate responses to adequate medication trials before ECT. The older groups had higher seizure thresholds, but the three groups received similar courses of treatment. The adult patients experienced a significantly lower rate of ECT response (54%) than the young-old patients (73%), while the old-old patients had an intermediate rate of response (67%). CONCLUSIONS: Despite a higher level of physical illness and cognitive impairment, even the oldest patients with severe major depression tolerate ECT in a manner similar to that for younger patients and demonstrate similar or better acute response.

The use of VRML in chemical education.

The Virtual Reality Modelling Language can be used for molecular modelling. The language provides some significant advantages in the area of chemical education; it can be used to communicate 3D concepts not normally covered by existing modelling packages, the data can be distributed to a large number of students over the web, and the viewers are free to students. The strengths and weakness of VRML in various aspects of molecular modelling are discussed.

Computational studies of sialyllactones: methods and uses.

N-Acetylneuraminic acid (1) is a common sugar in many biological recognition processes. Neuraminidase enzymes recognize and cleave terminal sialic acids from cell surfaces. Viral entry into host cells requires neuraminidase activity, thus inhibition of neuraminidase is a useful strategy for development of drugs for viral infections. A recent crystal structure for influenza viral neuraminidase with sialic acid bound shows that the sialic acid is in a boat conformation [Prot Struct Funct Genet 14: 327 (1992)]. Our studies seek to determine if structural pre-organization can be achieved through the use of sialyllactones. Determination of whether siallylactones are pre-organized in a binding conformation requires conformational analysis. Our inability to find a systematic study comparing the results obtained by various computational methods for carbohydrate modeling led us to compare two different conformational analysis techniques, four different force fields, and three different solvent models. The computational models were compared based on their ability to reproduce experimental coupling constants for sialic acid, sialyl-1,4-lactone, and sialyl-1,7-lactone derivatives. This study has shown that the MM3 forcefield using the implicit solvent model for water implemented in Macromodel best reproduces the experimental coupling constants. The low-energy conformations generated by this combination of computational methods are pre-organized toward conformations which fit well into the active site of neuraminidase.

MOUSE-III: learning rules of conformational analysis from X-ray data.

MOUSE-III is learning program that finds rules of conformational analysis from raw crystallographic data. The program perceives molecular features, finds conformational classes in the data and then learns rules that link features to classes. The rules that MOUSE learns are capable of correctly assigning conformations to ring systems that were not used for training with greater than 95% accuracy, when MOUSE was presented with sufficient data. The rules also show a compression of as much as 99% when compared to the raw data. This is accomplished through abstraction and generalization. The algorithm is presented along with a carefully worked example. An example of a learned rule is also presented and analyzed. Some conclusions about the scope and limitations of the learning process are presented.

Tylophora compounds as Na+/K(+)-ATPase inhibitors.

1. Acetyltylophoroside (AcT) and tylogenin inhibit Na+/K(+)-ATPase in spite of having structures very different from cardiac glycosides (CGs). 2. Calculation of the lowest energy conformations of AcT and tylogenin and superpositions of these with the X-ray conformations of CGs and chlormadinone acetate led to a model for the interaction of these different types of Na+/K(+)-ATPase inhibitors with the receptor.

MOUSE: a teachable program for learning in conformational analysis.

MOUSE is a teachable program which learns concepts of conformational analysis from examples obtained from WIZARD. The algorithms are presented, and a fully worked example is used to demonstrate how MOUSE learns about the so-called "pentane rule."

Short-term learning in conformational analysis.

A method for learning short-term rules of conformational analysis is introduced. The technique works by discovering problems during the building of a conformation in Cartesian coordinate space, and builds an abstract critic suitable for reasoning in abstract symbolic space. The methods not only afford speed increases ranging from 1.0- to 2.3-fold in WIZARD (analysis completed in 100% to 43% of original run time), but can be modified to provide similar increases in other programs that use "template joining" and distance geometry. These methods also provide the basis for a long-term learning project.

Conformations of large cycloalkanes: cyclooctadecane, cyclononadecane and cycloicosane.

The conformations of cyclooctadecane, cyclononadecane, and cycloicosane were generated by a stochastic program that works in conjunction with MM2. The shapes of the rings are analyzed in terms of previous work by Dale and others, and in terms of distributions of energies, torsion angle distributions, and torsion angle sequences. A new shape element called the 'nick' has been discovered, and it seems to be increasingly important with 18-membered and larger rings. Previously suggested relationships between ring size and energy distribution were observed, and a geometrical explanation is provided for the relative distributions of stable conformations in 16-, 18-, and 20-membered rings.

An easily fixed error in the Nyburg algorithm for discovering the best fit between molecules.

The extremely popular Nyburg algorithm for discovering the best fit between two molecules or fragments has an error that can give a false best fit under some circumstances. This error is described, and a simple fix is provided. The original Nyburg program (BMFIT) is compared to Sippl's program of 1991.

Automated conformational analysis: algorithms for the efficient construction of low-energy conformations.

The method of constructing low-energy conformations using template joining can provide an efficient means of searching the conformational space of molecules. The simplest algorithm to perform this task would construct each potential conformation from scratch. However, new algorithms, some of which use techniques from Artificial Intelligence, have been developed which can greatly improve the efficiency of this approach.

Automated conformational analysis and structure generation: algorithms for molecular perception.

Many methodologies for performing automated conformational analysis require some means of "perceiving" a molecule to determine features of interest. Algorithms for finding rings, bond orders, and stereocenters and detecting the presence of substructural fragments have been developed. These algorithms are described, emphasizing their importance in conformational analysis.

An investigation into the construction of molecular models by the template joining method.

The results of a wide-ranging investigation into some of the different methods available for performing the 'joining' of templates to build molecular models show that the choice of algorithm can significantly affect the quality of the results obtained, and different algorithms are most suited to particular categories of join.

WIZARD: AI in conformational analysis.

A program which utilizes the techniques of Artificial Intelligence and Expert Systems to solve problems in the area of Conformational Analysis is described. The program searches conformational space in a systematic fashion, based on the technique known as heuristic state-space search. The program proceeds by recognizing conformational units, assigning one or more conformational templates to each unit, and joining them to form conformational suggestions. These suggestions are criticized to discover logical inconsistencies, and any resulting stresses are resolved. The resulting conformational suggestions are sometimes accurate enough for immediate use, or may be further refined by a numerical program. The latter combination is shown to be quite efficient compared to purely numerical conformational search techniques.