[Clinical characteristics and prognosis of long-term glucocorticoid users with community-acquired pneumonia]

Objective: To explore the clinical features, etiological features and prognostic risk factors of long-term glucocorticoid users with community-acquired pneumonia (CAP). Methods: A retrospective study included 100 long-term glucocorticoid users with CAP (G-CAP group) from 11 hospitals of China between January 2014 and December 2014, while 100 non-immunocompromised patients with community-acquired pneumonia were enrolled as controls (nICH-CAP group). Characteristics including age, gender, underlying diseases, corticosteroids, symptoms, disease severity, imaging manifestations, etiology, respiratory failure, mechanical ventilation, whether the application of vasoactive drugs, antibiotics application, hospital mortality rate between the two groups were compared, and the prognostic factors of G-CAP were investigated using Logistic regression. Results: The peripheral blood lymphocytes[1.06(0.70, 1.68) vs 1.44 (0.87, 1.98)]in G-CAP group was less than nICH-CAP group (P<0.05). CT with pulmonary interstitial change (28.6% vs 9.9%), the proportion of patients with respiratory failure (25.0% vs 7.0%), mechanical ventilation (9.0% vs 2.0%), noninvasive mechanical ventilation (12.0% vs 2.0%), septic shock (9.0% vs 2.0%), and the hospital mortality rate (13.0% vs 3.0%) in G-CAP group were significantly higher than in nICH-CAP group (all P<0.05). Bacterial infection accounted for the highest proportion of infection (61.3%) in G-CAP group, but also virus infection (19.4%) and mixed infection (16.1%). Pseudomonas accounted for the highest proportion (47.4%) in bacterial infection of G-CAP. Logistic regression analysis showed that peripheral blood lymphocytes (OR=0.004, 95% CI: 0.000-0.234; P<0.05) and respiratory failure (OR=17.766, 95% CI: 4.933-131.0; P<0.05) were independent predictors of death in G-CAP group. Conclusions: The proportion of severe pneumonia and the mortality rate of patients with G-CAP are higher than the patients with nICH-CAP. Lymphopenia and respiratory failure are associated with poor outcome of patients with G-CAP.

Support vector machines-based quantitative structure-property relationship for the prediction of heat capacity

The support vector machine (SVM), as a novel type of learning machine, for the first time, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the heat capacity of a diverse set of 182 compounds based on the molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function networks (RBFNNs) were also utilized to construct quantitative linear and nonlinear models to compare with the results obtained by SVM. The root-mean-square (rms) errors in heat capacity predictions for the whole data set given by MLR, RBFNNs, and SVM were 4.648, 4.337, and 2.931 heat capacity units, respectively. The prediction results are in good agreement with the experimental value of heat capacity; also, the results reveal the superiority of the SVM over MLR and RBFNNs models.

Diagnosing anorexia based on partial least squares, back propagation neural network, and support vector machines

Support vector machine (SVM), as a novel type of learning machine, for the first time, was used to develop a predictive model for early diagnosis of anorexia. It was based on the concentration of six elements (Zn, Fe, Mg, Cu, Ca, and Mn) and the age extracted from 90 cases. Compared with the results obtained from two other classifiers, partial least squares (PLS) and back-propagation neural network (BPNN), the SVM method exhibited the best whole performance. The accuracies for the test set by PLS, BPNN, and SVM methods were 52%, 65%, and 87%, respectively. Moreover, the models we proposed could also provide some insight into what factors were related to anorexia.

QSAR study of natural, synthetic and environmental endocrine disrupting compounds for binding to the androgen receptor

A large data set of 146 natural, synthetic and environmental chemicals belonging to a broad range of structural classes have been tested for their relative binding affinity (expressed as log (RBA)) to the androgen receptor (AR). These chemicals commonly termed endocrine disrupting compounds (EDCs) present a variety of adverse effects in humans and animals. As assays for binding affinity remains a time-consuming task, it is important to develop predictive methods. In this work, quantitative structure-activity relationships (QSARs) were determined using three methods, multiple linear regression (MLR), radical basis function neural network (RBFNN) and support vector machine (SVM). Five descriptors, accounting for hydrogen-bonding interaction, distribution of atomic charges and molecular branching degree, were selected from a heuristic method to build predictive QSAR models. Comparison of the results obtained from three models showed that the SVM method exhibited the best overall performances, with a RMS error of 0.54 log (RBA) units for the training set, 0.59 for the test set, and 0.55 for the whole set. Moreover, six linear QSAR models were constructed for some specific families based on their chemical structures. These predictive toxicology models, should be useful to rapidly identify potential androgenic endocrine disrupting compounds.

Study of probabilistic neural networks to classify the active compounds in medicinal plants

Probabilistic neural networks (PNNs) were utilized for the classifications of 102 active compounds from diverse medicinal plants with anticancer activity against human rhinopharyngocele cell line KB. Molecular descriptors calculated from structure alone were used to represent molecular structures. A subset of the calculated descriptors selected using factor correlation analysis and forward stepwise regression was used to construct the prediction models. Linear discriminant analysis (LDA) was also utilized to construct the classification model to compare the results with those obtained by PNNs. The accuracy of the training set, the cross-validation set, and the test set given by PNNs and LDA were 100, 92.3, 90.9% and 71.8, 92.3, 54.5%, respectively, which indicated that the results obtained by PNNs agree well with the experimental values of these compounds and also revealed the superiority of PNNs over LDA approach for the classification of anticancer activities of compounds. The models built in this work would be of potential help in the design of novel and more potent anticancer agents.

3D QSAR studies on antimalarial alkoxylated and hydroxylated chalcones by CoMFA and CoMSIA

The 3D QSAR analyses of antimalarial alkoxylated and hydroxylated chalcones were first conducted by Comparative molecular field analysis (CoMFA) and Comparative similarity indices analysis (CoMSIA) to determine the factors required for the activity of these compounds. Satisfactory results were obtained after performing a leave-one-out (LOO) cross-validation study with cross-validation q(2) and conventional r(2) values of 0.740 and 0.972 by the CoMFA model, 0.714 and 0.976 by the CoMSIA model, respectively. The results provided the tools for predicting the affinity of related compounds, and for guiding the design and synthesis of novel and more potent antimalarial agents.

Quantitative Prediction of logk of Peptides in High-Performance Liquid Chromatography Based on Molecular Descriptors by Using the Heuristic Method and Support Vector Machine

A new method support vector machine (SVM) and the heuristic method (HM) were used to develop the nonlinear and linear models between the capacity factor (logk) and seven molecular descriptors of 75 peptides for the first time. The molecular descriptors representing the structural features of the compounds only included the constitutional and topological descriptors, which can be obtained easily without optimizing the structure of the molecule. The seven molecular descriptors selected by the heuristic method in CODESSA were used as inputs for SVM. The results obtained by SVM were compared with those obtained by the heuristic method. The prediction result of the SVM model is better than that of heuristic method. For the test set, a predictive correlation coefficient R = 0.9801 and root-mean-square error of 0.1523 were obtained. The prediction results are in very good agreement with the experimental values. But the linear model of the heuristic method is easier to understand and ready to use for a chemist. This paper provided a new and effective method for predicting the chromatography retention of peptides and some insight into the structural features which are related to the capacity factor of peptides.

QSAR Models for the Prediction of Binding Affinities to Human Serum Albumin Using the Heuristic Method and a Support Vector Machine

The binding affinities to human serum albumin for 94 diverse drugs and drug-like compounds were modeled with the descriptors calculated from the molecular structure alone using a quantitative structure-activity relationship (QSAR) technique. The heuristic method (HM) and support vector machine (SVM) were utilized to construct the linear and nonlinear prediction models, leading to a good correlation coefficient (R2) of 0.86 and 0.94 and root-mean-square errors (rms) of 0.212 and 0.134 albumin drug binding affinity units, respectively. Furthermore, the models were evaluated by a 10 compound external test set, yielding R2 of 0.71 and 0.89 and rms error of 0.430 and 0.222. The specific information described by the heuristic linear model could give some insights into the factors that are likely to govern the binding affinity of the compounds and be used as an aid to the drug design process; however, the prediction results of the nonlinear SVM model seem to be better than that of the HM.

Study of the Quantitative Structure-Mobility Relationship of Carboxylic Acids in Capillary Electrophoresis Based on Support Vector Machines

The support vector machines (SVM), as a novel type of learning machine, were used to develop a quantitative structure-mobility relationship (QSMR) model of 58 aliphatic and aromatic carboxylic acids based on molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function neural networks (RBFNNs) were also utilized to construct the linear and the nonlinear model to compare with the results obtained by SVM. The root-mean-square errors in absolute mobility predictions for the whole data set given by MLR, RBFNNs, and SVM were 1.530, 1.373, and 0.888 mobility units (10(-5) cm(2) S(-1) V(-1)), respectively, which indicated that the prediction result agrees well with the experimental values of these compounds and also revealed the superiority of SVM over MLR and RBFNNs models for the prediction of the absolute mobility of carboxylic acids. Moreover, the models we proposed could also provide some insight into what structural features are related to the absolute mobility of aliphatic and aromatic carboxylic acids.

An Accurate QSPR Study of O−H Bond Dissociation Energy in Substituted Phenols Based on Support Vector Machines

The support vector machine (SVM), as a novel type of learning machine, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the O-H bond dissociation energy (BDE) of 78 substituted phenols. The six descriptors calculated solely from the molecular structures of compounds selected by forward stepwise regression were used as inputs for the SVM model. The root-mean-square (rms) errors in BDE predictions for the training, test, and overall data sets were 3.808, 3.320, and 3.713 BDE units (kJ mol(-1)), respectively. The results obtained by Gaussian-kernel SVM were much better than those obtained by multiple linear regression, radial basis function neural networks, linear-kernel SVM, and other QSPR approaches.