Use of Valproate in Women: An Audit of Prescriptions to 10,001 Psychiatry, Neurology, and Neurosurgery Outpatients.

Gestational exposure to valproate is associated with an unacceptably high risk of major congenital malformations, neurodevelopmental disorders, and other adverse outcomes. Prescription of valproate to reproductive-age women is therefore strongly discouraged in many parts of the world. To our knowledge, there is no pharmacoepidemiologic study of the prescription of valproate to women in a developing country.

Clinical Evolution of Epithelial-Mesenchymal Transition in Human Carcinomas.

The significance of the phenotypic plasticity afforded by epithelial-mesenchymal transition (EMT) for cancer progression and drug resistance remains to be fully elucidated in the clinic. We evaluated epithelial-mesenchymal phenotypic characteristics across a range of tumor histologies using a validated, high-resolution digital microscopic immunofluorescence assay (IFA) that incorporates β-catenin detection and cellular morphology to delineate carcinoma cells from stromal fibroblasts and that quantitates the individual and colocalized expression of the epithelial marker E-cadherin (E) and the mesenchymal marker vimentin (V) at subcellular resolution ("EMT-IFA"). We report the discovery of β-catenin cancer cells that coexpress E-cadherin and vimentin in core-needle biopsies from patients with various advanced metastatic carcinomas, wherein these cells are transitioning between strongly epithelial and strongly mesenchymal-like phenotypes.

Survival outcomes post percutaneous coronary intervention: Why the hype about stent type? Lessons from a healthcare system in India.

A prospective, multicenter study was initiated by the Government of Maharashtra, India, to determine predictors of long-term outcomes of percutaneous coronary intervention (PCI) for coronary artery disease, and to compare the effectiveness of drug-eluting stents (DESs) and bare-metal stents (BMSs) in patients undergoing PCI under government-funded insurance. The present analysis included 4595 patients managed between August 2012 and November 2016 at any of 110 participating centers. Using the classical multivariable regression and propensity-matching approach, we found age to be the most important predictor of 1-year mortality and target lesion revascularization at 1 year post-PCI.

The Bayesian basket design for genomic variant-driven phase II trials.

Basket clinical trials are a new category of early clinical trials in which a treatment is evaluated in a population of patients with tumors of various histologic types and primary sites selected for containing specific genomic abnormalities. The objective of such studies is generally to discover histologic types in which the treatment is active. Basket trials are early discovery trials whose results should be confirmed in expanded histology specific cohorts.

Overfitting in prediction models - is it a problem only in high dimensions?

The growing recognition that human diseases are molecularly heterogeneous has stimulated interest in the development of prognostic and predictive classifiers for patient selection and stratification. In the process of classifier development, it has been repeatedly emphasized that in situations where the number of candidate predictor variables is much larger than the number of observations, the apparent (training set, resubstitution) accuracy of the classifiers can be highly optimistically biased and hence, classification accuracy should be reported based on evaluation of the classifier on a separate test set or using complete cross-validation. Such evaluation methods have however not been the norm in the case of low-dimensional, p View Article and Find Full Text PDF

An evaluation of resampling methods for assessment of survival risk prediction in high-dimensional settings.

Resampling techniques are often used to provide an initial assessment of accuracy for prognostic prediction models developed using high-dimensional genomic data with binary outcomes. Risk prediction is most important, however, in medical applications and frequently the outcome measure is a right-censored time-to-event variable such as survival. Although several methods have been developed for survival risk prediction with high-dimensional genomic data, there has been little evaluation of the use of resampling techniques for the assessment of such models.

Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data.

Developments in whole genome biotechnology have stimulated statistical focus on prediction methods. We review here methodology for classifying patients into survival risk groups and for using cross-validation to evaluate such classifications. Measures of discrimination for survival risk models include separation of survival curves, time-dependent ROC curves and Harrell's concordance index.

What should physicians look for in evaluating prognostic gene-expression signatures?

Most cancer treatments benefit only a minority of patients. This has led to a widespread interest in the identification of gene-expression-based prognostic signatures. Well-developed and validated genomic signatures can lead to personalized treatment decisions resulting in improved patient management.

Gene expression-based prognostic signatures in lung cancer: ready for clinical use?

A substantial number of studies have reported the development of gene expression-based prognostic signatures for lung cancer. The ultimate aim of such studies should be the development of well-validated clinically useful prognostic signatures that improve therapeutic decision making beyond current practice standards. We critically reviewed published studies reporting the development of gene expression-based prognostic signatures for non-small cell lung cancer to assess the progress made toward this objective.

Managing protein flexibility in docking and its applications.

Docking, virtual screening and structure-based drug design are routinely used in modern drug discovery programs. Although current docking methods deal with flexible ligands, managing receptor flexibility has proved to be challenging. In this brief review, we present the current state-of-the-art for computationally handling receptor flexibility, including a novel statistical computational approach published recently.

Modeling and selection of flexible proteins for structure-based drug design: backbone and side chain movements in p38 MAPK.

Receptor rearrangement upon ligand binding (induced fit) is a major stumbling block in docking and virtual screening. Even though numerous studies have stressed the importance of including protein flexibility in ligand docking, currently available methods provide only a partial solution to the problem. Most of these methods, being computer intensive, are often impractical to use in actual drug discovery settings.

A novel computational analysis of ligand-induced conformational changes in the ATP binding sites of cyclin dependent kinases.

Protein kinases in general are known to be very flexible macromolecules. In this article, the conformational plasticity of the ATP binding site in cyclin dependent kinases is analyzed. Movement of the two lysine residues lining the ATP binding site are shown to play a major role in the conformational variability of the site.

Structure prediction of a multi-domain EF-hand Ca2+ binding protein by PROPAINOR.

PROPAINOR is a new algorithm developed for ab initio prediction of the 3D structures of proteins using knowledge-based nonparametric multivariate statistical methods. This algorithm is found to be most efficient in terms of computational simplicity and prediction accuracy for single-domain proteins as compared to other ab initio methods. In this paper, we have used the algorithm for the atomic structure prediction of a multi-domain (two-domain) calcium-binding protein, whose solution structure has been deposited in the PDB recently (PDB ID: 1JFK).

Ab initio structure of human seminal plasma prostatic inhibin gives significant insight into its biological functions.

Human seminal plasma prostatic inhibin (HSPI) is a protein isolated from the human prostate gland. Despite its profound biomedical and biotechnological importance, the 3D structure of this protein of 94 amino acids remains undeciphered. The difficulties in extracting it in pure form and crystallizing it have restrained the determination of its structure experimentally.