Wensong Wu

Mechanistic insight into the ability of American ginseng to suppress colon cancer associated with colitis

We have recently shown that American ginseng (AG) prevents and treats mouse colitis. Because both mice and humans with chronic colitis have a high colon cancer risk, we tested the hypothesis that AG can be used to prevent colitis-driven colon cancer. Using the azoxymethane (AOM)/dextran sulfate sodium (DSS) mouse model of ulcerative colitis, we show that AG can suppress colon cancer associated with colitis. To explore the molecular mechanisms of the anticancer effects of AG, we also carried out antibody array experiments on colon cells isolated at a precancerous stage. We found there were 82 protein end points that were either significantly higher (41 proteins) or significantly lower (41 proteins) in the AOM + DSS group compared with the AOM-alone (control) group. In contrast, there were only 19 protein end points that were either significantly higher (10 proteins) or significantly lower (9 proteins) in the AOM + DSS + AG group compared with the AOM-alone (control) group. Overall, these results suggest that AG keeps the colon environment in metabolic equilibrium when mice are treated with AOM + DSS and gives insight into the mechanisms by which AG protects from colon cancer associated with colitis.

Classification of FMRI Patterns—A Study of the Language Network Segregation in Pediatric Localization Related Epilepsy

This article describes a pattern classification algorithm for pediatric epilepsy using fMRI language‐related activation maps. 122 fMRI datasets from a control group (64) and localization related epilepsy patients (58) provided by five childrens hospitals were used. Each subject performed an auditory description decision task. Using the artificial data as training data, incremental Principal Component Analysis was used in order to generate the feature space while overcoming memory requirements of large datasets. The nearest‐neighbor classifier (NNC) and the distance‐based fuzzy classifier (DFC) were used to perform group separation into left dominant, right dominant, bilateral, and others. The results show no effect of age, age at seizure onset, seizure duration, or seizure etiology on group separation. Two sets of parameters were significant for group separation, the patient vs. control populations and handedness. Of the 122 real datasets, 90 subjects gave the same classification results across all the methods (three raters, LI, bootstrap LI, NNC, and DFC). For the remaining datasets, 18 cases for the IPCA‐NNC and 21 cases for the IPCA‐DFC agreed with the majority of the five classification results (three visual ratings and two LI results). Kappa values vary from 0.59 to 0.73 for NNC and 0.61 to 0.75 for DFC, which indicate good agreement between NNC or DFC with traditional methods. The proposed method as designed can serve as an alternative method to corroborate existing LI and visual rating classification methods and to resolve some of the cases near the boundaries in between categories. Hum Brain Mapp 35:1446–1460, 2014.

The effects of pediatric epilepsy on a language connectome

This study introduces a new approach for assessing the effects of pediatric epilepsy on a language connectome. Two novel data‐driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI. An auditory word definition decision task paradigm was used to activate the language network for 29 patients and 30 controls. Evaluations illustrated that pediatric epilepsy is associated with a network efficiency reduction. Patients showed a propensity to inefficiently use the whole brain network to perform the language task; whereas, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was performed. The analysis revealed substantial global network feature differences between the patients and controls for the extent of activation network. It also showed that for both subject groups the language network exhibited small‐world characteristics; however, the patients extent of activation network showed a tendency toward randomness. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. We finally showed that a clustering scheme was able to fairly separate the subjects into their respective patient or control groups. The clustering was initiated using local and global nodal measurements. Compared to the intensity of activation network, the extent of activation network clustering demonstrated better precision. This ascertained that the network differences presented by the networks were associated with pediatric epilepsy. Hum Brain Mapp 35:5996–6010, 2014.

Differential immunoglobulin class-mediated responses to components of the U1 small nuclear ribonucleoprotein particle in systemic lupus erythematosus and mixed connective tissue disease.

Objective The objective of this paper is to determine whether patients with systemic lupus erythematosus (SLE) and mixed connective tissue disease (MCTD) possess differential IgM- and IgG-specific reactivity against peptides from the U1 small nuclear ribonucleoprotein particle (U1 snRNP). Methods The IgM- and IgG-mediated responses against 15 peptides from subunits of the U1 snRNP were assessed by indirect enzyme linked immunosorbent assays (ELISAs) in sera from patients with SLE and MCTD and healthy individuals (n = 81, 41, and 31, respectively). Additionally, 42 laboratory tests and 40 clinical symptoms were evaluated to uncover potential differences. Binomial logistic regression analyses (BLR) were performed to construct models to support the independent nature of SLE and MCTD. Receiver operating characteristic (ROC) curves corroborated the classification power of the models. Results We analyzed IgM and IgG anti-U1 snRNP titers to classify SLE and MCTD patients. IgG anti-U1 snRNP reactivity segregates SLE and MCTD from nondisease controls with an accuracy of 94.1% while IgM-specific anti-U1 snRNP responses distinguish SLE from MCTD patients with an accuracy of 71.3%. Comparison of the IgG and IgM anti-U1 snRNP approach with clinical tests used for diagnosing SLE and MCTD revealed that our method is the best classification tool of those analyzed (p ≤ 0.0001). Conclusions Our IgM anti-U1 snRNP system along with lab tests and symptoms provide additional molecular and clinical evidence to support the hypothesis that SLE and MCTD may be distinct syndromes.

Bayes Multiple Decision Functions

This paper deals with the problem of simultaneously making many (M) binary decisions based on one realization of a random data matrix X. M is typically large and X will usually have M rows associated with each of the M decisions to make, but for each row the data may be low dimensional. Such problems arise in many practical areas such as the biological and medical sciences, where the available dataset is from microarrays or other high-throughput technology and with the goal being to decide which among of many genes are relevant with respect to some phenotype of interest; in the engineering and reliability sciences; in astronomy; in education; and in business. A Bayesian decision-theoretic approach to this problem is implemented with the overall loss function being a cost-weighted linear combination of Type I and Type II loss functions. The class of loss functions considered allows for use of the false discovery rate (FDR), false nondiscovery rate (FNR), and missed discovery rate (MDR) in assessing the quality of decision. Through this Bayesian paradigm, the Bayes multiple decision function (BMDF) is derived and an efficient algorithm to obtain the optimal Bayes action is described. In contrast to many works in the literature where the rows of the matrix X are assumed to be stochastically independent, we allow a dependent data structure with the associations obtained through a class of frailty-induced Archimedean copulas. In particular, non-Gaussian dependent data structure, which is typical with failure-time data, can be entertained. The numerical implementation of the determination of the Bayes optimal action is facilitated through sequential Monte Carlo techniques. The theory developed could also be extended to the problem of multiple hypotheses testing, multiple classification and prediction, and high-dimensional variable selection. The proposed procedure is illustrated for the simple versus simple hypotheses setting and for the composite hypotheses setting through simulation studies. The procedure is also applied to a subset of a microarray data set from a colon cancer study.

Can SLE classification rules be effectively applied to diagnose unclear SLE cases

Objective The objective of this paper is to develop novel classification criteria to distinguish between unclear systemic lupus erythematosus (SLE) and mixed connective tissue disease (MCTD) cases. Methods A total of 205 variables from 111 SLE and 55 MCTD patients were evaluated to uncover unique molecular and clinical markers for each disease. Binomial logistic regressions (BLRs) were performed on currently used SLE and MCTD classification criteria sets to obtain six reduced models with power to discriminate between unclear SLE and MCTD patients that were confirmed by receiving operating characteristic (ROC) curve. Decision trees were employed to delineate novel classification rules to discriminate between unclear SLE and MCTD patients. Results SLE and MCTD patients exhibited contrasting molecular markers and clinical manifestations. Furthermore, reduced models highlighted SLE patients exhibiting prevalence of skin rashes and renal disease while MCTD cases show dominance of myositis and muscle weakness. Additionally decision tree analyses revealed a novel classification rule tailored to differentiate unclear SLE and MCTD patients (Lu-vs-M) with an overall accuracy of 88%. Conclusions Validation of our novel proposed classification rule (Lu-vs-M) includes novel contrasting characteristics (calcinosis, CPK elevated and anti-IgM reactivity for U1-70K, U1A and U1C) between SLE and MCTD patients and showed a 33% improvement in distinguishing these disorders when compared to currently used classification criteria sets. Pending additional validation, our novel classification rule is a promising method to distinguish between patients with unclear SLE and MCTD diagnosis.

Classes of Multiple Decision Functions Strongly Controlling FWER and FDR

Two general classes of multiple decision functions, where each member of the first class strongly controls the family-wise error rate (FWER), while each member of the second class strongly controls the false discovery rate (FDR), are described. These classes offer the possibility that optimal multiple decision functions with respect to a pre-specified Type II error criterion, such as the missed discovery rate (MDR), could be found which control the FWER or FDR Type I error rates. The gain in MDR of the associated FDR-controlling procedure relative to the well-known Benjamini–Hochberg procedure is demonstrated via a modest simulation study with gamma-distributed component data. Such multiple decision functions may have the potential of being utilized in multiple testing, specifically in the analysis of high-dimensional data sets.