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 4,687 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Multivariate methods based on principal components (PCA and PLS) have been used to reduce NMR spectral information, to predict NMR parameters of complicated structures, and to relate shift data sets to dependent descriptors of biological significance. Noise reduction and elimination of instrumental artifacts are easily performed on 2D NMR data. Configurational classification of triterpenes and shift predictions in disubstituted benzenes can be obtained using PCA and PLS analysis. Finally, the shift predictions of tripeptides from descriptors of amino acids open the possibility of automatic analysis of multidimensional data of complex structures.
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PMID:Multivariate data analysis of NMR data. 179 Jan 85

A series of non-peptide angiotensin II receptor antagonists was investigated with the aim of developing a 3D QSAR model using comparative molecular field analysis descriptors and approaches. The main goals of the study were dictated by an interest in methodologies and an understanding of the binding requirements to the AT1 receptor. Consistency with the previously derived activity models was always checked to contemporarily test the validity of the various hypotheses. The specific conformations chosen for the study, the procedures invoked to superimpose all structures, the conditions employed to generate steric and electrostatic field values and the various PCA/PLS runs are discussed in detail. The effect of experimental design techniques to select objects (molecules) and variables (descriptors) with respect to the predictive power of the QSAR models derived was especially analysed.
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PMID:A 3D QSAR CoMFA study of non-peptide angiotensin II receptor antagonists. 900 90

Recently a new class of molecular descriptors has been proposed and used in QSAR with simulated data and with regression performed by neural networks. In the present paper these descriptors (Zups, from the name of their author, Juri Zupan) have been slightly modified and then applied to a real data set with the aim of studying the structure-activity relationships of a new class of cardiotonics. Forty-one molecules (thirty-seven milrinone analogues, the two lead compounds amrinone and milrinone, and two commercial products) have been studied using classical chemometrical techniques such as PCA (Principal Components Analysis) and PLS (Partial Least Squares regression). Zups describe essentially the local geometry of the molecules. They show promising performances, as compared with other classical geometrical descriptors (as molecular volume, etc.), both in that regards the overall performances, measured by the C.V. Explained variance and in the interpretability of the regression equation. However they have not all the requirements of a good structure representation. Moreover some selectable parameters seem to have a great importance, so that the refinement of the regression model requires time and the evaluation step must be performed in condition of full-validation, because predictive optimisation is used in the selection of parameters, and the final model must be checked on molecules never used to refine the model or, in this case, the parameters of the structure representation.
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PMID:Zupan's descriptors in QSAR applied to the study of a new class of cardiotonic agents. 937 92

Multivariate projective statistical methods (PCA, PLS-DA) and logistic regression analysis were used to create models to make predictions regarding whether a certain fatality shows similarities to homicide or suicide. The 'model set' consisted of 174 deaths due to sharp force injuries that in previous medicolegal investigation had been judged as homicides and 105 as suicides. The models were then validated on a new set of 40 homicides and 27 suicides that had not been used to create the models (test set validation). The model based on the PLS-DA technique had regarding its ability to identify homicides a sensitivity of 40/40 = 100% and a specificity of 25/27 = 93%. The model's predictions agreed with previously performed medicolegal investigations except in two suicides which according to the model were likely to be homicides. The reliability of this model was somewhat better than predictions achieved by means of logistic regression analysis, where six otherwise proven homicides were wrongly classified as suicides and two actual suicides were misclassified as homicides. The technique not only identifies variables but also ranks their importance. Ranked according to falling positive correlation (falling 'importance' of a finding) to the dependent variable 'death caused by homicide', the predictors were: Injuries to clothing, blood alcohol level, presence of defence injuries, injuries due to other type of violence than sharp force, chest stabs with vertical axis of the entrance wound, sharp force injuries to the upper extremity (except wrist and crook of the arm), sharp force injuries to the head and back. Ranked in increasing positive correlation to 'death caused by suicide' were the predictors: sharp force injuries to the crook of the arm, venue being the victim's home, presence of farewell letter, victim's age, sharp force injuries to the wrist, known suicidal ideation and presence of tentative injuries.
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PMID:Multivariate analysis ('forensiometrics')--a new tool in forensic medicine. Differentiation between sharp force homicide and suicide. 971 72

Since the mid-1980s a previously undescribed disease has affected moose in south-western Sweden. Investigations made to reveal evidence of a viral aetiology have proved unsuccessful. Trace element studies in apparently healthy moose shot during regular hunting suggested a trace element imbalance, with excessive molybdenum uptake causing secondary copper deficiency. The results also indicated a possible chromium deficiency. To verify this hypothesis, an experimental study was performed in male goats fed a semi-synthetic diet for 1.5 years. The animals were kept and treated in four groups: Controls, Cu-deficient group (group 1), Cr-deficient group (group 2), and combined Cu- and Cr-deficient group with additional supplementation of tetrathiomolybdate for 10 weeks at the end of the study (group 3). The present paper presents tissue contents of trace and minor elements, haematology and clinical chemical parameters. Feed consumption and weight development, as well as pathological and histopathological investigations, were also performed in this study, but these results are presented elsewhere. Changes in trace element concentrations were determined by comparing groups 1, 2 and 3 with the control group. Increased concentrations were observed for Al, Ca, Co, Fe, Mo, Pb, Se in the liver; for Al, Cd, Co, Cr, Mo in the kidneys; and for Mn and Mo in the ribs. Considerable accumulation of Mn in ribs seems to be a useful way to determine oxidative stress. Decreases in Mg and P in all organs and blood serum is characteristic of Cu deficiency and molybdenosis. Also the ratio of Ca/Mg was increased as the result of tissue lesions causing an intracellular increase in Ca and decrease in Mg. The trace element changes observed in group 1 were enhanced by the Mo supplementation in group 3, resulting in characteristic patterns, 'spectra' of changes. The alterations were not as remarkable in group 2 as in the two other groups. However, Cr deficiency considerably influenced Al, Co, V and to a smaller extent also Mn in ribs. In groups 1 and 2, only a few minor changes were detected in the haematological parameters, probably caused by increased adrenal activity after transportation of the animals. In group 3, severe anaemia was present but also a leukopenia. For the different clinical chemical parameters measured, all three groups showed changes, explained mainly by the altered activity of enzymes induced by trace element deficiencies and imbalance. Impaired carbohydrate and lipid metabolism was seen in groups 1 and 3, with increased concentrations of glucose, lactate and triglycerides in serum. Increased concentrations of total bilirubin were measured in all three groups (bile stasis was also seen post mortem). A considerably increased concentration of serum urea was found in group 3, although there were no indications of renal insufficiency or dehydration. Regarding hormones, a substantial decrease was seen in thyroxine (T4) in group 3 as a result of the molybdenosis, but a minor decrease was also seen in group 1. Insulin on the other hand showed increased levels in group 3--and especially in group 2 due to the Cr deficiency but also affected by the molybdenosis. As could be expected, Cu deficiency (groups 1 and 3) caused low levels of caeruloplasmin, secondarily affecting the Fe metabolism in these animals. Protein abnormalities, detected as altered electrophoretic patterns of serum proteins, were also seen mainly in group 3. The findings were also confirmed by multivariate data analysis, where PCA revealed the overall impact of the deficiencies, and PLS regression coefficients indicated the influence on the various analytes.
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PMID:Experimental copper and chromium deficiency and additional molybdenum supplementation in goats. II. Concentrations of trace and minor elements in liver, kidneys and ribs: haematology and clinical chemistry. 1081 54

Antiviral quinolones are promising compounds in the search for new therapeutically effective agents for the treatment of AIDS. To rationalize the SAR for this new interesting class of anti-HIV derivatives, we performed a 3D-QSAR study on a library of 101 6-fluoro and 6-desfluoroquinolones, taken either from the literature or synthesized by us. The chemometric procedure involved a fully semiempirical minimization of the molecular structures by the AMSOL program, which takes into account the solvatation effect, and their 3D characterization by the VolSurf/GRID program. The QSAR analysis, based on PCA and PLS methods, shows the key structural features responsible for the antiviral activity.
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PMID:QSAR study and VolSurf characterization of anti-HIV quinolone library. 1128 75

The development of Quantitative Structure Activity Relationships (QSAR's) often relies heavily on the application of statistical methods such as multi-linear regression (MLR) or principal component analysis/partial least square (PCA/PLS). Partial order ranking (POR), which from a mathematical point of view is based on elementary methods of Discrete Mathematics, appears as an attractive and operationally simple and more general alternative since the method does not require specific functional relationships between the single descriptors or the end-points. The POR method allows ranking of a series of compounds, based on selected descriptors characterizing their structural and/or electronic nature (model diagram). The ranking of the compounds based on their end-points (experimental ranking) can then be compared to the model diagram. If the model diagram resembles the experimental ranking of the end-points under investigation, other compounds, not being experimentally investigated, can be assigned a rank in the model and hereby obtain an identity based on the known compounds. The present study elucidates the applicability of POR as a simple tool for QSAR modeling. Based on illustrative examples the POR approach to QSAR modeling will be presented with special focus on the precision and the uncertainties of the method, which will be discussed in terms of the number of descriptors and compounds involved. The advantageous interplay between POR and PCA, the latter being applied in order to reduce a possible large number of descriptors into a limited number of latent descriptors will be discussed.
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PMID:QSAR's based on partial order ranking. 1207 84

Multivariate PCA- and PLS-models involving many variables are often difficult to interpret, because plots and lists of loadings, coefficients, VIPs, etc, rapidly become messy and hard to overview. There may then be a strong temptation to eliminate variables to obtain a smaller data set. Such a reduction of variables, however, often removes information and makes the modelling efforts less reliable. Model interpretation may be misleading and predictive power may deteriorate. A better alternative is usually to partition the variables into blocks of logically related variables and apply hierarchical data analysis. Such blocked data may be analyzed by PCA and PLS. This modelling forms the base-level of the hierarchical modelling set-up. On the base-level in-depth information is extracted for the different blocks. The score vectors formed on the base-level, here called 'super variables', may be linked together in new matrices on the top-level. On the top-level superficial relationships between the X- and the Y-data are investigated. In this paper the basic principles of hierarchical modelling by means of PCA and PLS are reviewed. One objective of the paper is to disseminate this concept to a broader QSAR audience. The hierarchical methods are used to analyze a set of 10 haloalkanes for which K = 30 chemical descriptors and M = 255 biological responses have been gathered. Due to the complexity of the biological data, they are sub-divided in four blocks. All the modelling steps on the base-level and the top-level are reported and the final QSAR model is interpreted thoroughly.
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PMID:Megavariate analysis of hierarchical QSAR data. 1265 May 89

Air-filled polymeric microcapsules have been prepared by freeze-drying of emulsions containing the wall-forming polymer in the organic phase of oil in water emulsions. Echogenic air-filled microcapsules were prepared from emulsions containing either (-)-camphene, cyclohexane or cyclooctane as the solvent in the organic phase. Formulation studies have been performed to improve the yield and acoustic quality of the microcapsule suspensions. The yield was measured as particle concentration or efficacy, i.e. normalised attenuation at 3.5 MHz, related to the amount of polymer used. No overall conclusion could be made for all the variables when visually comparing the results from the different investigations. Multivariate analyses (PCA and PLS) were therefore necessary to be able to reveal any relevant systematic information from all the investigations. Different parameters describing the formulation, the production process and parameters describing the characterisation of the intermediates and the final product were set as independent X-variables. Three to four percent (w/v) of polymer was found to be the appropriate concentration of wall forming polymer. Including PEG 3000 resulted in improved freeze-dried product and suspension. Quenching of the emulsions by freezing in dry ice/methanol prior to freeze-drying was not necessary. Process parameters for homogenising and freeze-drying should be optimised with regard to the single systems, due to the different physico-chemical properties of the different solvents, especially melting point and vapour pressure.
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PMID:Evaluation of different formulation studies on air-filled polymeric microcapsules by multivariate analysis. 1271 Nov 56

The use of numerous descriptors that are indicative of molecular structure and topology is becoming more common in quantitative structure-activity relationship (QSAR). How to choose the adequate descriptors for QSAR studies is important but difficult because there are no absolute rules to govern this choice. A variety of variable selection techniques including stepwise, partial least squares/principal component analysis (PLS/PCA), neural network, and evolutionary algorithm such as genetic algorithm have been applied to this common problem. All-subsets regression (ASR) is capable of finding out the best variable subset from among a large pool. In this paper, a novel variable selection and modeling method based on the prediction, for short VSMP, has been developed. Here two controllable parameters, the interrelation coefficient between the pairs of the independent variables (r(int)) and the correlation coefficient (q(2)) obtained using the leave-one-out (LOO) cross-validation technique, are introduced into the ASR to improve its performances. This technique differs from the other variable selection procedures related to the ASR by two main features: (1) The search of various optimal subset search is controlled by the statistic q(2) or root-mean-square error (RMSEP) in the LOO cross-validation step rather than the correlation coefficient obtained in the modeling step (r(2)). (2) The searching speed of all optimal subsets is expedited by the statistic r(int) together with q(2). A comparison of the results of the VSMP applied to the Selwood data set (n = 31 compounds, m = 53 descriptors) with those obtained from alternative algorithms shows the good performance of the technique.
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PMID:VSMP: a novel variable selection and modeling method based on the prediction. 1276 55


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