A 6-month open-label study of the effectiveness and tolerability of galantamine in patients with Alzheimer's disease

The objective of this study was to assess the effectiveness and tolerability of galantamine in patients with mild-to-moderate Alzheimer's disease (AD) in everyday clinical practice. Patient selection was made on 36 sequential patients attending Belfast City Hospital Memory Clinic between December 2000 and June 2001. Patients were treated with galantamine for 6 months, starting from 4 mg twice daily increasing to 8 mg twice daily and then to 12 mg twice daily at 4-weekly intervals. Patients (25 females, 11 males), mean age 78 years (59-90), were diagnosed with probable AD and had a mini-mental state examination (MMSE) score of 10-26. Efficacy was assessed using the MMSE, neuropsychiatric inventory (NPI), neuropsychiatric inventory caregiver distress (NPI-D) scale and the Bristol activities of daily living (B-ADL) scale at baseline and after 3 and 6 months of treatment. Mean improvements were noted on all four measures of efficacy at 3 and 6 months; improvements were significant on the MMSE, NPI and NPI-D at 3 months and on the NPI-D at 6 months. Galantamine was overall well tolerated. The most common adverse events were gastrointestinal, particularly nausea. Four patients stopped treatment due to adverse events, and seven were stabilised on 8 mg twice daily as they were unable to tolerate the target dose. This naturalistic study confirms clinical trial data, which shows galantamine improves cognition and behavioural symptoms and is overall well tolerated. © 2004 Blackwell Publishing Ltd.

Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier

Feature selection and feature weighting are useful techniques for improving the classification accuracy of K-nearest-neighbor (K-NN) rule. The term feature selection refers to algorithms that select the best subset of the input feature set. In feature weighting, each feature is multiplied by a weight value proportional to the ability of the feature to distinguish pattern classes. In this paper, a novel hybrid approach is proposed for simultaneous feature selection and feature weighting of K-NN rule based on Tabu Search (TS) heuristic. The proposed TS heuristic in combination with K-NN classifier is compared with several classifiers on various available data sets. The results have indicated a significant improvement in the performance in classification accuracy. The proposed TS heuristic is also compared with various feature selection algorithms. Experiments performed revealed that the proposed hybrid TS heuristic is superior to both simple TS and sequential search algorithms. We also present results for the classification of prostate cancer using multispectral images, an important problem in biomedicine.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Gene selection by cooperative competition clustering

Clustering analysis of data from DNA microarray hybridization studies is an essential task for identifying biologically relevant groups of genes. Attribute cluster algorithm (ACA) has provided an attractive way to group and select meaningful genes. However, ACA needs much prior knowledge about the genes to set the number of clusters. In practical applications, if the number of clusters is misspecified, the performance of the ACA will deteriorate rapidly. In fact, it is a very demanding to do that because of our little knowledge. We propose the Cooperative Competition Cluster Algorithm (CCCA) in this paper. In the algorithm, we assume that both cooperation and competition exist simultaneously between clusters in the process of clustering. By using this principle of Cooperative Competition, the number of clusters can be found in the process of clustering. Experimental results on a synthetic and gene expression data are demonstrated. The results show that CCCA can choose the number of clusters automatically and get excellent performance with respect to other competing methods.

Selection with melphalan or paclitaxel (Taxol) yields variants with different patterns of multidrug resistance, integrin expression and in vitro invasiveness

A melphalan-resistant variant (Roswell Park Memorial Institute (RPMI)-2650M1) and a paclitaxel-resistant variant (RPMI-1650Tx) of the drug-sensitive human nasal carcinoma cell line, RPMI-2650. were established. The multidrug resistance (MDR) phenotype in the RPMI-2650Tx appeared to be P-glycoprotein (PgP)-mediated. Overexpression of multidrug resistant protein (MRP) family members was observed in the RPMI-2650M1 cells, which were also much more invasive in vitro than the parental cell line or the paclitaxel-resistant variant. Increased expression of alpha (2), alpha (5), alpha (6), beta (1) and beta (4) integrin subunits, decreased expression of alpha (4) integrin subunit, stronger adhesion to collagen type IV, laminin, fibronectin and matrigel, increased expression of MMP-2 and MMP-9 and significant motility compared with the parental cells were observed, along with a high invasiveness in the RPMI-7650M1 cells. Decreased expression of the alpha (2) integrin subunit, decreased attachment to collagen type IV, absence of cytokeratin 18 expression, no detectable expression of gelatin-degrading proteases and poor motility may be associated with the non-invasiveness of the RPMI-2650Tx variant. These results suggest that melphalan exposure can result in not only a MDR phenotype. but could also make cancer cells more invasive, whereas paclitaxel exposure resulted in MDR without increasing the in vitro invasiveness in the RPMI-2650 cells. (C) 2001 Elsevier Science Ltd. All rights reserved.