Case study in six sigma methadology : manufacturing quality improvement and guidence for managers

This article discusses the successful implementation of Six Sigma methodology in a high precision and critical process in the manufacture of automotive products. The Six Sigma define–measure–analyse–improve–control approach resulted in a reduction of tolerance-related problems and improved the first pass yield from 85% to 99.4%. Data were collected on all possible causes and regression analysis, hypothesis testing, Taguchi methods, classification and regression tree, etc. were used to analyse the data and draw conclusions. Implementation of Six Sigma methodology had a significant financial impact on the profitability of the company. An approximate saving of US$70,000 per annum was reported, which is in addition to the customer-facing benefits of improved quality on returns and sales. The project also had the benefit of allowing the company to learn useful messages that will guide future Six Sigma activities

Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells

<p>Abstract</p> <p>Background</p> <p>The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable physicians to decide when to intervene more aggressively and to plan clinical trials more accurately.</p> <p>Methods</p> <p>In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray.</p> <p>Results</p> <p>We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p < 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used to give a more accurate estimation of the time till the next relapse (in resolution of 50 days). The error rate of the second stage predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p < 0.001). The predictors were further evaluated and found effective both for untreated MS patients and for MS patients that subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p < 0.001 for all the patient groups).</p> <p>Conclusion</p> <p>We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature.</p

Individual and combined soy isoflavones exert differential effects on metastatic cancer progression

To investigate the effects soy isoflavones in established cancers, the role of genistein, daidzein, and combined soy isoflavones was studied on progression of subcutaneous tumors in nude mice created from green fluorescent protein (GFP) tagged-MDA-MB-435 cells. Following tumor establishment, mice were gavaged with vehicle or genistein or daidzein at 10 mg/kg body weight (BW) or a combination of genistein (10 mg/kg BW), daidzein (9 mg/kg BW), and glycitein (1 mg/kg BW) three times per week. Tumor progression was quantified by whole body fluorescence image analysis followed by microscopic image analysis of excised organs for metastases. Results show that daidzein increased while genistein decreased mammary tumor growth by 38 and 33% respectively, compared to vehicle. Daidzein increased lung and heart metastases while genistein decreased bone and liver metastases. Combined soy isoflavones did not affect primary tumor growth but increased metastasis to all organs tested, which include lung, liver, heart, kidney, and bones. Phosphoinositide-3-kinase (PI3-K) pathway real time PCR array analysis and western blotting of excised tumors demonstrate that genistein significantly downregulated 10/84 genes, including the Rho GTPases RHOA, RAC1, and CDC42 and their effector PAK1. Daidzein significantly upregulated 9/84 genes that regulate proliferation and protein synthesis including EIF4G1, eIF4E, and survivin protein levels. Combined soy treatment significantly increased gene and protein levels of EIF4E and decreased TIRAP gene expression. Differential regulation of Rho GTPases, initiation factors, and survivin may account for the disparate responses of breast cancers to genistein and daidzein diets. This study indicates that consumption of soy foods may increase metastasis