An assessment of the so called point of reversibility of dementia syndrome in making an effective prognosis for continued use of medication.

BACKGROUND: An effort to share our many years of clinical experience. OBJECTIVE: To predict the effectiveness of medication in advanced phases of dementia in the light of pharmaceutical economics and to find the most suitable alternative treatments. METHODS: Use of 3 commonly available geriatric tests Mini-Mental State Examination (MMSE), Activity of Daily Living (ADL) and Instrumental Activity of Daily Living (IADL). Threshold values of these tests, after mathematical transformation to comparable values, enables calculation of the hypothetical Point of Reversibility Pr found on the range here called MEKO. RESULTS: Estimated values of SD can be used to choose the most effective medical treatment. CONCLUSION: Beyond the upper threshold of S(D), on the basis of the above mentioned method, continued medication is of dubious value.

Glibenclamide-related excess in total and cardiovascular mortality risks: data from large Ukrainian observational cohort study.

OBJECTIVE: To compare mortality risks among type 2 diabetes (T2D) patients being treated with glibenclamide, gliclazide, or glimepiride. METHODS: Retrospective observational cohort studies of primary care-based diabetes register were carried out. Risk of total and cardiovascular (CVD) mortality was evaluated in cohort of T2D patients that were treated with either glibenclamide (n=50,341), glimepiride (n=2479) or gliclazide (n=11,368). Cox regression was used for multifactor evaluation. A cross-sectional evaluation of oral anti-diabetic drug (OAD) structure for 2005 and 2007 was also performed, as well as age at the time of death was compared in the timeframe between 2002 and 2007. RESULTS: Total mortality was lower for gliclazide and glimepiride, vs. glibenclamide cohort: HRs 0.33 (95% CI 0.26-0.41), p<0.001 and 0.605 (95% CI 0.413-0.886), p<0.01 respectively. CVD mortality risk reduction vs. glibenclamide was significant only in gliclazide cohort: 0.29 (95% CI 0.21-0.38), p<0.001. Glibenclamide prescriptions had changed from 64.0% (95% CI 63.5-64.5) to 59.5% (95% CI 9.7-10.4). Age at the time of death for OAD-treated patients increased by 6.27 (95% CI 3.67-8.87)yrs, p<0.001. CONCLUSION: Glibenclamide treatment of T2D is associated with greater risk of all-cause mortality, vs. gliclazide or glimepiride treatment, and CVD mortality, vs. gliclazide treatment.

Microarray learning with ABC.

Standard clustering algorithms when applied to DNA microarray data often tend to produce erroneous clusters. A major contributor to this divergence is the feature characteristic of microarray data sets that the number of predictors (genes) in such data far exceeds the number of samples by many orders of magnitude, with only a small percentage of predictors being truly informative with regards to the clustering while the rest merely add noise. An additional complication is that the predictors exhibit an unknown complex correlational configuration embedded in a small subspace of the entire predictor space. Under these conditions, standard clustering algorithms fail to find the true clusters even when applied in tandem with some sort of gene filtering or dimension reduction to reduce the number of predictors. We propose, as an alternative, a novel method for unsupervised classification of DNA microarray data. The method, which is based on the idea of aggregating results obtained from an ensemble of randomly resampled data (where both samples and genes are resampled), introduces a way of tilting the procedure so that the ensemble includes minimal representation from less important areas of the gene predictor space. The method produces a measure of dissimilarity between each pair of samples that can be used in conjunction with (a) a method like Ward's procedure to generate a cluster analysis and (b) multidimensional scaling to generate useful visualizations of the data. We call the dissimilarity measures ABC dissimilarities since they are obtained by aggregating bundles of clusters. An extensive comparison of several clustering methods using actual DNA microarray data convincingly demonstrates that classification using ABC dissimilarities offers significantly superior performance.