Psychoeducational and Skill-building Interventions for Emotion Dysregulation.

Family psychoeducation plus skill building is a class of interventions considered to be well-established for youth with mood disorders or emotion dysregulation. Psychoeducational psychotherapy (PEP) is an example of this class of interventions. PEP provides psychoeducation for parents and children, skill building to help children better regulate emotions and behaviors, and strategies for parents to better facilitate school-based interventions, develop specific symptom management techniques, and generate coping strategies for the entire family. Evidence is summarized supporting the efficacy of PEP for reducing rage, overall mood symptom severity, disruptive behavior, and executive functioning deficits in youth. Long-term benefits of PEP are discussed.

Cladograms with Path to Event (ClaPTE): a novel algorithm to detect associations between genotypes or phenotypes using phylogenies.

BACKGROUND: Associations between genotype and phenotype provide insight into the evolution of pathogenesis, drug resistance, and the spread of pathogens between hosts. However, common ancestry can lead to apparent associations between biologically unrelated features. The novel method Cladograms with Path to Event (ClaPTE) detects associations between character-pairs (either a pair of mutations or a mutation paired with a phenotype) while adjusting for common ancestry, using phylogenetic trees. METHODS: ClaPTE tests for character-pairs changing close together on the phylogenetic tree, consistent with an associated character-pair. ClaPTE is compared to three existing methods (independent contrasts, mixed model, and likelihood ratio) to detect character-pair associations adjusted for common ancestry. Comparisons utilize simulations on gene trees for: HIV Env, HIV promoter, and bacterial DnaJ and GuaB; and case studies for Oseltamavir resistance in Influenza, and for DnaJ and GuaB. Simulated data include both true-positive/associated character-pairs, and true-negative/not-associated character-pairs, used to assess type I (frequency of p-values in true-negatives) and type II (sensitivity to true-positives) error control. RESULTS AND CONCLUSIONS: ClaPTE has competitive sensitivity and better type I error control than existing methods. In the Influenza/Oseltamavir case study, ClaPTE reports no new permissive mutations but detects associations between adjacent (in primary sequence) amino acid positions which other methods miss. In the DnaJ and GuaB case study, ClaPTE reports more frequent associations between positions both from the same protein family than between positions from different families, in contrast to other methods. In both case studies, the results from ClaPTE are biologically plausible.

An Application of Endpoint Detection to Bivariate Data in Tau-Path Order.

The Fligner and Verducci (1988) multistage model for rankings is modified to create the moving average maximum likelihood estimator (MAMLE), a locally smooth estimator that measures stage-wise agreement between two long ranked lists, and provides a stopping rule for the detection of the endpoint of agreement. An application of this MAMLE stopping rule to bivariate data set in tau-path order (Yu, Verducci and Blower (2011)) is discussed. Data from the National Cancer Institute measuring associations between gene expression and compound potency are studied using this application, providing insights into the length of the relationship between the variables.

MICE models: superior to the HERG model in predicting Torsade de Pointes.

Drug-induced block of the cardiac hERG (human Ether-à-go-go-Related Gene) potassium channel delays cardiac repolarization and increases the risk of Torsade de Pointes (TdP), a potentially lethal arrhythmia. A positive hERG assay has been embraced by regulators as a non-clinical predictor of TdP despite a discordance of about 30%. To test whether assaying concomitant block of multiple ion channels (Multiple Ion Channel Effects or MICE) improves predictivity we measured the concentration-responses of hERG, Nav1.5 and Cav1.2 currents for 32 torsadogenic and 23 non-torsadogenic drugs from multiple classes. We used automated gigaseal patch clamp instruments to provide higher throughput along with accuracy and reproducibility. Logistic regression models using the MICE assay showed a significant reduction in false positives (Type 1 errors) and false negatives (Type 2 errors) when compared to the hERG assay. The best MICE model only required a comparison of the blocking potencies between hERG and Cav1.2.

How Stable Are Top Choices Over Time? An Investigation Into Preferences Among Popular Baby Names In The United States.

For the problem of assessing initial agreement between two rankings of long lists, inference in the Fligner and Verducci (1988) multistage model for rankings is modified to provide a locally smooth estimator of stage-wise agreement. An extension to the case of overlapping but different sets of items in the two lists, and a stopping rule to identify the endpoint of agreement, are also provided. Simulations show that this approach performs very well under several conditions. The methodology is applied to a database of popular names for newborns in the United States and provides insights into trends as well as differences in naming conventions between the two sexes.

Estrogen receptor mRNA expression patterns in the liver and ovary of female rainbow trout over a complete reproductive cycle.

Estrogens are critical hormones involved in reproduction and need to bind to estrogen receptors in target organs for biological activity. Fishes have two distinct estrogen receptor subtypes, alpha (α) and beta (ß), with variable combinations of additional isoforms of each subtype dependent on the history of genome duplication within a taxon. The comparative expression patterns of estrogen receptor isoforms during the female reproductive cycle will provide important insights into the unique function and importance of each. The purpose of this study was to measure the mRNAs for the four estrogen receptor isoforms (erα1, erα2, erß1, erß2) in the liver and ovary of adult, female rainbow trout over the course of an annual reproductive cycle. The expression of estrogen receptor mRNA isoforms was measured by quantitative real-time RT-PCR. Several reproductive indices (gonadosomatic index, maximum oocyte diameter, plasma estradiol-17ß, plasma vitellogenin, and ovulation) were also quantified for comparison and used in a correlation analysis to examine any inter-relationships. Of the four isoforms, the expression of erα1 was highest in the liver, and had a significant positive correlation with liver erß1 expression. Liver expression of erα2 mRNA was the lowest, but showed a significant positive correlation with maximum oocyte diameter in the ovary. The pattern of the erß isoforms in liver was one of initially elevated mRNA expression followed by a gradual decrease as reproductive development proceeded. In the ovary the erß1 isoform had the highest mRNA expression of all estrogen receptor isoforms, at the beginning of the reproductive cycle, but then decreased afterward. Both ovarian erß isoforms had a significant positive correlation with one another. In contrast, erα2 mRNA expression showed a high maximum level in the ovary near the end of the cycle along with a significant positive correlation with plasma estradiol-17ß levels; the highest gonadosomatic indices, maximum oocyte diameter, and vitellogenin levels occurred then too.

Is There a Partial Consensus Ordering Between Rankings?

We propose an innovative approach to the problem recently posed by Hall and Schimek (2012): determining at what point the agreement between two rankings of a long list of items degenerates into noise. We modify the method of estimation in Fligner and Verducci's (1988) multistage model for rankings, from maximum likelihood of conditional agreement over a sample of rankings to a locally smooth estimator of agreement. Through simulations we show that this innovation performs very well under several conditions. Some ramifications are discussed as planned extensions.

Impact of multifamily psychoeducational psychotherapy in treating children aged 8 to 12 years with mood disorders.

CONTEXT: Childhood mood disorders lack sufficient evidence-based treatments. While psychosocial treatments are recommended for both childhood depression and bipolar disorder, empirical support is scarce. OBJECTIVE: To determine whether adjunctive multifamily psychoeducational psychotherapy would improve outcome for children aged 8 to 12 years with depression or bipolar disorder. DESIGN: One hundred sixty-five children were studied in a randomized controlled trial of multifamily psychoeducational psychotherapy plus treatment as usual (n = 78) compared with a wait-list control (WLC) condition plus treatment as usual (n = 87). Assessments occurred at baseline and at 6, 12, and 18 months. Intervention occurred between baseline and 6 months for the immediate treatment group and between 12 and 18 months for the WLC group. SETTING: University medical center. PARTICIPANTS: Children were recruited from mental health and physical health care providers, media contacts, and word of mouth. All had a major mood disorder (major depressive disorder or dysthymic disorder, 30%; bipolar disorder type I, type II, or not otherwise specified, 70%). Intervention Children and 1 or more parents participated in eight 90-minute multifamily psychoeducational psychotherapy sessions. Parent and child groups met separately but began and ended sessions together. MAIN OUTCOME MEASURES: The Mood Severity Index (MSI) combines Mania Rating Scale and Children's Depression Rating Scale-Revised scores. RESULTS: Multifamily psychoeducational psychotherapy plus treatment as usual was associated with lower MSI scores at follow-up in intent-to-treat analyses compared with WLC plus treatment as usual (MSI: chi(2)(1) = 4.55; P = .03). The WLC group showed a similar decrease in MSI scores 1 year later, when also following their treatment (MSI decrease = 3.24 units per 6 months in the immediate treatment group and 3.50 units per 6 months in the WLC group). CONCLUSION: Brief, adjunctive psychoeducational group psychotherapy is associated with improved outcome for children aged 8 to 12 years with major mood disorders. Trial Registration clinicaltrials.gov Identifier: NCT00050557.

MicroRNAs modulate the chemosensitivity of tumor cells.

MicroRNAs are strongly implicated in such processes as development, carcinogenesis, cell survival, and apoptosis. It is likely, therefore, that they can also modulate sensitivity and resistance to anticancer drugs in substantial ways. To test this hypothesis, we studied the pharmacologic roles of three microRNAs previously implicated in cancer biology (let-7i, mir-16, and mir-21) and also used in silico methods to test pharmacologic microRNA effects more broadly. In the experimental system, we increased the expression of individual microRNAs by transfecting their precursors (which are active) or suppressed the expression by transfection of antisense oligomers. In three NCI-60 human cancer cell lines, a panel of 60 lines used for anticancer drug discovery, we assessed the growth-inhibitory potencies of 14 structurally diverse compounds with known anticancer activities. Changing the cellular levels of let-7i, mir-16, and mir-21 affected the potencies of a number of the anticancer agents by up to 4-fold. The effect was most prominent with mir-21, with 10 of 28 cell-compound pairs showing significant shifts in growth-inhibitory activity. Varying mir-21 levels changed potencies in opposite directions depending on compound class; indicating that different mechanisms determine toxic and protective effects. In silico comparison of drug potencies with microRNA expression profiles across the entire NCI-60 panel revealed that approximately 30 microRNAs, including mir-21, show highly significant correlations with numerous anticancer agents. Ten of those microRNAs have already been implicated in cancer biology. Our results support a substantial role for microRNAs in anticancer drug response, suggesting novel potential approaches to the improvement of chemotherapy.

MicroRNA expression profiles for the NCI-60 cancer cell panel.

Advances in the understanding of cancer cell biology and response to drug treatment have benefited from new molecular technologies and methods for integrating information from multiple sources. The NCI-60, a panel of 60 diverse human cancer cell lines, has been used by the National Cancer Institute to screen >100,000 chemical compounds and natural product extracts for anticancer activity. The NCI-60 has also been profiled for mRNA and protein expression, mutational status, chromosomal aberrations, and DNA copy number, generating an unparalleled public resource for integrated chemogenomic studies. Recently, microRNAs have been shown to target particular sets of mRNAs, thereby preventing translation or accelerating mRNA turnover. To complement the existing NCI-60 data sets, we have measured expression levels of microRNAs in the NCI-60 and incorporated the resulting data into the CellMiner program package for integrative analysis. Cell line groupings based on microRNA expression were generally consistent with tissue type and with cell line clustering based on mRNA expression. However, mRNA expression seemed to be somewhat more informative for discriminating among tissue types than was microRNA expression. In addition, we found that there does not seem to be a significant correlation between microRNA expression patterns and those of known target transcripts. Comparison of microRNA expression patterns and compound potency patterns showed significant correlations, suggesting that microRNAs may play a role in chemoresistance. Combined with gene expression and other biological data using multivariate analysis, microRNA expression profiles may provide a critical link for understanding mechanisms involved in chemosensitivity and chemoresistance.

50-year appraisal of gastrinoma: recommendations for staging and treatment.

BACKGROUND: Gastrinoma is a rare neuroendocrine tumor associated with ulcerogenic syndrome. The purpose of this study was to provide information on current controversies related to treatment, including staging, patient selection, and outcomes for surgical resection. STUDY DESIGN: A retrospective review of 106 patients with gastrinoma. Patients were classified as sporadic gastrinoma (SG) or MEN. End points of analysis included disease-free and disease-specific survival. Kaplan-Meier survival analysis was performed and significance (p < 0.05) was determined by Mantel-Haenszel log-rank test. RESULTS: Gastrinoma can be staged by TNM criteria into four groups (stage 0, I, II, and III), which had notably different survival curves, dependent on tumor size and distant metastases (p < 0.0001), but independent of lymph node metastases (p = 0.324). Surgical resection was possible in 72 patients (SG, n = 50; MEN, n = 22). Durable cure rate for SG was 26%, compared with 4% for MEN-1. Surgical resection achieving gross removal of all tumor resulted in improved survival in both SG and MEN patients (p < 0.0001). Improved survival was independent of a normal postoperative serum gastrin. Stage III was highly predictive of incomplete resection and the associated failure to improve survival (p = 0.0001). CONCLUSIONS: Staging provides a reliable method for the clinician to select patients for operation and to provide a prognosis, and should permit better comparisons of treatment between institutions. In the management of gastrinoma, it is recommended that SG and MEN patients with clinical stage I and II disease have surgical exploration, patients with stage III disease not have mandatory surgical treatment, and some stage 0 patients might not need routine surgical exploration.

Microarray analysis of gene expression: considerations in data mining and statistical treatment.

DNA microarray represents a powerful tool in biomedical discoveries. Harnessing the potential of this technology depends on the development and appropriate use of data mining and statistical tools. Significant current advances have made microarray data mining more versatile. Researchers are no longer limited to default choices that generate suboptimal results. Conflicting results in repeated experiments can be resolved through attention to the statistical details. In the current dynamic environment, there are many choices and potential pitfalls for researchers who intend to incorporate microarrays as a research tool. This review is intended to provide a simple framework to understand the choices and identify the pitfalls. Specifically, this review article discusses the choice of microarray platform, preprocessing raw data, differential expression and validation, clustering, annotation and functional characterization of genes, and pathway construction in light of emergent concepts and tools.

Systematic analysis of large screening sets in drug discovery.

Each year large pharmaceutical companies produce massive amounts of primary screening data for lead discovery. To make better use of the vast amount of information in pharmaceutical databases, companies have begun to scrutinize the lead generation stage to ensure that more and better qualified lead series enter the downstream optimization and development stages. This article describes computational techniques for end to end analysis of large drug discovery screening sets. The analysis proceeds in three stages: In stage 1 the initial screening set is filtered to remove compounds that are unsuitable as lead compounds. In stage 2 local structural neighborhoods around active compound classes are identified, including similar but inactive compounds. In stage 3 the structure-activity relationships within local structural neighborhoods are analyzed. These processes are illustrated by analyzing two large, publicly available databases.

Finding discriminating structural features by reassembling common building blocks.

We present a new method for constructing discriminating substructures by reassembling common medicinal chemistry building blocks. The algorithm can be parametrized to meet differing objectives: (1) to build features that discriminate for biological activity in a local structural neighborhood, (2) to build scaffolds for R-group analysis, (3) to construct cluster signatures that discriminate for membership in the cluster and provide a graphical representation for its members, and (4) to identify substructures that characterize major classes in a heterogeneous compound set. We illustrated the results of the algorithm on a literature dataset is of 118 compounds with in vitro inhibition data against recombinant human protein tyrosine phosphatase 1B (PTP-1B).