Cross-validated Score Research Articles

NMR-based metabonomic study of Chinese medicine Gegen Qinlian Decoction as an effective treatment for type 2 diabetes in rats

Metabonomic profiles of the type 2 diabetic rats induced by streptozotocin and high-sugar, high fat diet on the treatment of Gegen Qinlian Decoction (GQD) for 9 weeks were investigated. Rats were randomly divided into five groups: normal control (NC), type 2 diabetes (DM), metformin hydrochloric, GQD in high and low dosages. Plasma samples for 1H NMR-based metabolomic research, serum samples for clinical biochemistry, and liver and pancreas tissues for histopathology test were collected. Compared with NC rats, metabolic pathways of DM rats were revealed to be altered by pattern analyses of plasma NMR data, which was further correlated with serum biochemistry. Cross-validated scores mean trajectory derived from PLS-DA of NMR spectra demonstrated that GQD significantly restored the abnormal metabolic state in the long run, more potent than metformin hydrochloric. Detailed analysis of the altered metabolite levels indicated that GQD significantly ameliorated the disturbance in glucose metabolism, tricarboxylic acid cycle, lipid metabolism, amino acid metabolism and gut microbial metabolism and N-acetyl group metabolism. The results confirmed the hypoglycemic efficacy of GQD and its ability to ameliorate the diabetic symptoms in a global scale. NMR-based metabonomics approach is helpful for the further understanding of diabetes-related mechanisms.

Gene expression profiles in esophageal adenocarcinoma predict survival after resection

The incidence of esophageal adenocarcinoma is rapidly increasing in the western population. Despite aggressive treatment, survival after esophagectomy is suboptimal. The main objective of the present study was to evaluate the gene expression profiles in esophageal adenocarcinoma and determine their association with survival after resection. We conducted a prospective National Institutes of Health/National Cancer Institute funded study to evaluate the prognostic significance of gene expression in patients with esophageal adenocarcinoma undergoing esophagectomy. Gene expression in tumor tissue was analyzed using high-throughput oligonucleotide arrays. The association of gene expression and overall survival was analyzed using the tail-strength statistic and Cox regression analysis. Gene signatures were constructed with semisupervised methods using principal components. A cross-validated risk score was devised by conducting 10-fold cross-validation, 100 times. We evaluated the gene expression in 64 patients with esophageal adenocarcinoma who underwent esophagectomy. The median overall survival was 27 months (95% confidence interval 22 to not reached). After filtering, 10,214 probe sets were used for survival analysis. The tail-strength statistic for these probe sets (0.318) indicated a significant association with overall survival. Patients were classified into high- and low-risk groups, according to the gene signature. High-risk patients had a predicted median survival of 19 months, but the median was not reached for the low-risk group (P < .05). On multivariate analysis, the gene signature was independently associated with survival (hazard ratio, 2.22; P = .04). Global gene expression levels were significantly associated with overall survival after esophagectomy. Furthermore, individual genes could be successfully combined into a strongly predictive, internally cross-validated gene signature. If validated further, these results could help direct additional clinical trials of neoadjuvant and adjuvant therapies for esophageal adenocarcinoma.

A classification tool for N-way array based on SIMCA methodology

In the literature there are only few papers concerned with classification methods for multi-way arrays. The most common procedure, by far, is to unfold the multi-way data array into an ordinary matrix and then to apply the traditional multivariate tools for classification. As opposed to unfolding the data several possibilities exist for building classification models more directly based on the multi-way structure of the data. As an example, multi-way partial least squares discriminant analysis has been used as a supervised classification method, another alternative that has been investigated is to perform classification using Fisher's LDA or SIMCA on the score matrix from e.g. a PARAFAC or a Tucker model. Despite a few attempts of applying such multi-way classification approaches, no-one has looked into how such models are best built and implemented. In this work, the SIMCA method is extended to three-way arrays. Included in this work is also actual code that will work on general multi-way arrays rather than just three-way arrays. In analogy with two-way SIMCA, a decomposition model is separately built for the multi-way data for each class, using multi-way decomposition method such as PARAFAC or Tucker3. In the choice of the best class dimensionality, i.e. number of latent factors, both the results of cross-validation but mainly the sensitivity/specificity values are evaluated. In order to estimate the class limits for each class model, orthogonal and score distances are considered, and different statistics are implemented and tested to set confidence limits for these two parameters. Classification performance using different definitions of class boundaries and classification rules, including the use of cross-validated residuals and scores is compared. The proposed N-SIMCA methodology and code, besides simulated data sets of varying dimensionality, has been tested on two case studies, concerning food authentication tasks for typical food products.

Changes in the Spinal Cord Proteome of an Amyotrophic Lateral Sclerosis Murine Model Determined by Differential In-gel Electrophoresis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by loss of motor neurons resulting in progressive paralysis. To date, more than 140 different mutations in the gene encoding CuZn-superoxide dismutase (SOD1) have been associated with ALS. Several transgenic murine models exist in which various mutant SOD1s are expressed. We used DIGE to analyze the changes in the spinal cord proteome induced by expression of the unstable SOD1 truncation mutant G127insTGGG (G127X) in mice. Unlike mutants used in most other models, G127X lacks SOD activity and is present at low levels, thus reducing the risk of overexpression artifacts. The mice were analyzed at their peak body weights just before onset of symptoms. Variable importance plot analysis showed that 420 of 1,800 detected protein spots contributed significantly to the differences between the groups. By MALDI-TOF MS analysis, 54 differentially regulated proteins were identified. One spot was found to be a covalently linked mutant SOD1 dimer, apparently analogous to SOD1-immunoreactive bands migrating at double the molecular weight of SOD1 monomers previously detected in humans and mice carrying mutant SOD1s and in sporadic ALS cases. Analyses of affected functional pathways and the subcellular representation of alterations suggest that the toxicity exerted by mutant SODs induces oxidative stress and affects mitochondria, cellular assembly/organization, and protein degradation.

Changes in the Expression of Proteasome Genes in Tumor Cells Following Short-Term Proteasome Inhibitor Therapy Predicts Survival in Multiple Myeloma Treated with Bortezomib-Containing Multi-Agent Chemotherapy

Introduction: The mechanism of action and resistance to chemotherapy is poorly understood and measures of efficacy typically rely on clinical outcome data. Recent advances suggest that prospective gene expression profiling (GEP) can be used to more accurately define not only short-term but lasting treatment benefits. We recently reported that baseline tumor cell gene signatures, encompassing 70 and as few as 17 genes, can discriminate risk groups of myeloma patients both in the untreated and previously treated settings. However, a subset of predicted low-risk cases followed an aggressive clinical course accompanied by a shift from 70-gene-defined low-to high-risk over time, either reflecting clonal evolution or outgrowth of aggressive clones present, but undetectable, at diagnosis. Accurately identifying this patient population is a first step in preempting such transformation. We hypothesized that changes in gene expression patterns of tumor cells following a short term in-vivo challenge with a single agent chemotherapeutic might expose these latently aggressive cells. Unlike in-vitro testing, clinical drug administration also allows for assessing tumor cell perturbation in the context of host interactions. Building on recent observations that clinical outcome in myeloma patients could be correlated with 48hr GEP changes induced in vivo following single agent administration of thalidomide, lenalidomide, and dexamethasone, we now examined whether such short-term tumor-cell GEP and proteomic alterations could fine-tune clinical outcome prediction beyond the well established 70-gene-based baseline prediction model.Methods: Affymetrix U133Plus2.0 microarray analysis and mass spectrometry were performed on CD138-enriched tumor cells prior to and 48hr following a single test-dose application of bortezomib at 1.0mg/m2 in 142 newly diagnosed patients with MM. A total of 1051 genes (P < .005) were differentially expressed at 48 hours. Both change in expression, adjusting for baseline expression, and post-drug expression levels of each gene were examined for correlations with event-free survival in a Cox proportional hazards model. Post-drug expression was chosen and 113 genes were retained (p <= .05). The difference of the mean log2 expression of genes with hazard ratios (HR) of < 1.0 (favorable) and genes with HR >=1 (unfavorable) was used to create a score which, in the context of running log rank statistics, was used to classify patients into high- and low-risk groups. The independent prognostic power of the score for event-free and overall survival was investigated, together with baseline prognostic variables, by multivariate analysis. This method was tested in a 10-fold cross-validation procedure using the same data set. The model is currently being validated in an independent set of 100 cases and results will be reported.Results: Changes in the expression of proteasome genes, and their related proteins, predominated a list of 113 outcome-related genes. A high-risk score, associated with upregulation of proteasome genes, seen in 24% of cases, was associated with median survival of less than 24 months, dramatically contrasting with a 3-year survival estimate of greater than 80% in the 76% in whom proteasome genes were not activated (p<0.0001). The cross-validated post-bortezomib score was an independent predictor of outcome in multivariate analysis of standard and genetic variables, including the well established and validated baseline 70-gene risk score. Importantly, 12% of patients in the 70-gene model-defined low-risk category were upgraded to high-risk by the 113-gene post-bortezomib model, with poor outcomes resembling those in the 70-gene-defined high-risk baseline model. Moreover, the 113-gene post-bortezomib score alone accounted for an unprecedented 57% of outcome variability by R2 statistics, with hazard ratio values of 5.45 for overall and 7.84 for event-free survival.Conclusions: The rapid activation of proteasome genes and their corresponding proteins in MM cells within 48hr of a single bortezomib test-dose exposure as an indicator of poor clinical outcome suggests a novel and perhaps central mechanism of resistance to this new class of cancer therapeutics and perhaps standard genotoxic agents, which were part of the overall treatment. We are now testing the hypothesis whether the high-risk associated with the post-bortezomib proteasome activation can be overcome by higher doses of bortezomib or the addition of agents targeting other critical pathways.

Improved Combination of Multiple Atmospheric GCM Ensembles for Seasonal Prediction

Abstract An improved Bayesian optimal weighting scheme is developed and used to combine six atmospheric general circulation model (GCM) seasonal hindcast ensembles. The approach is based on the prior belief that the forecast probabilities of tercile-category precipitation and near-surface temperature are equal to the climatological ones. The six GCMs are integrated over the 1950–97 period with observed monthly SST prescribed at the lower boundary, with 9–24 ensemble members. The weights of the individual models are determined by maximizing the log likelihood of the combination by season over the integration period. A key ingredient of the scheme is the climatological equal-odds forecast, which is included as one of the “models” in the multimodel combination. Simulation skill is quantified in terms of the cross-validated ranked probability skill score (RPSS) for the three-category probabilistic hindcasts. The individual GCM ensembles, simple poolings of three and six models, and the optimally combined mult...

Statistical Prediction of Seasonal East Coast Winter Storm Frequency

A statistical method for predicting seasonal winter storm activity is developed using discriminant analysis. A large pool of plausible predictors is initially considered, which includes sea surface temperature anomalies from the Niño-3.4 region, the tropical Atlantic, and adjacent to the southeastern U.S. coast; land temperature over the southeastern United States; and values of the Southern, North Atlantic, and quasi-biennial oscillation indices. These values are screened using a chi-squared procedure to isolate the most relevant variables and averaging periods. A reduced pool of predictors is then used to develop a series of discriminant functions relating the variables to East Coast winter storm activity categories (e.g., above or below normal). The selection and addition of variables to the discriminant functions is based upon the cross-validated skill score. In all cases, the discriminant forecasting procedure exhibits a high degree of skill relative to a random reference forecast that is constrained to follow the unconditional climatological distribution of storms. For December through February (DJF) storm frequency, only 4 out of 46 seasons are misclassified among three overlapping activity categories (i.e., ≤5, 5–8, and ≥8 storms). For discrete boundaries (≤5, 6–7, and ≥8 storms), about a third of the seasons are misclassified with average seasons being the most problematic. In both cases, two-category errors occur only twice. Gulf sea surface temperatures during the previous storm season is the strongest discriminator of DJF storm activity, with warmer than normal Gulf temperatures associated with active seasons. It is shown that this variable is related to both El Niño and the North Atlantic Oscillation (NAO) phase, as heightened storm activity tends to occur during the positive phase of the NAO and El Niño conditions. Over the period from October–April, forecast skill is weaker, but still high. Using a two-category forecast with overlapping boundaries (≤12 and ≥11 storms), 84% of the active seasons and 71% of the nonactive seasons are predicted. For this longer season, sea surface temperature off the southeast U.S. coast during the preceding summer is the best discriminator. Again warmer than normal temperatures are associated with active storm seasons.

An investigation of the relationship between psychopathic traits and malingering on the psychopathic personality inventory.

This study employed a repeated-measures simulation design to examine (a) the specific effects of malingering on a recently developed measure of psychopathy, the Psychopathic Personality Inventory (PPI), and (b) the broader association between psychopathic traits and dissimulation. One hundred and forty-three participants completed the PPI twice (both under standard instructions and with instructions to feign psychosis), and also completed post-test questionnaires assessing their attitudes toward engaging in malingering across several hypothetical settings. When attempting to feign psychosis, participants produced elevated scores on a validity scale designed to identify deviant responding, and use of a cross-validated cutoff score with this scale produced high sensitivity and specificity rates across the honest and malingering conditions. Furthermore, PPI scores (in the honest condition) were significantly correlated with a willingness to engage in dissimulation across various hypothetical forensic/correctional scenarios. Results are discussed in terms of the "fakability" of the PPI, as well as the relationship between psychopathic personality features and malingering more generally.

Evaluation of the EVA descriptor for QSAR studies: 3. The use of a genetic algorithm to search for models with enhanced predictive properties (EVA_GA).

The EVA structural descriptor, based upon calculated fundamental molecular vibrational frequencies, has proved to be an effective descriptor for both QSAR and database similarity calculations. The descriptor is sensitive to 3D structure but has an advantage over field-based 3D-QSAR methods inasmuch as structural superposition is not required. The original technique involves a standardisation method wherein uniform Gaussians of fixed standard deviation (sigma) are used to smear out frequencies projected onto a linear scale. The smearing function permits the overlap of proximal frequencies and thence the extraction of a fixed dimensional descriptor regardless of the number and precise values of the frequencies. It is proposed here that there exist optimal localised values of sigma in different spectral regions; that is, the overlap of frequencies using uniform Gaussians may, at certain points in the spectrum, either be insufficient to pick up relationships where they exist or mix up information to such an extent that significant correlations are obscured by noise. A genetic algorithm is used to search for optimal localised sigma values using crossvalidated PLS regression scores as the fitness score to be optimised. The resultant models were then validated against a previously unseen test set of compounds and through data scrambling. The performance of EVA_GA is compared to that of EVA and analogous CoMFA studies; in the latter case a brief evaluation is made of the effect of grid resolution upon the stability of CoMFA PLS scores particularly in relation to test set predictions.