Anti-NMDA Receptor Encephalitis: A Challenge in Psychiatric Settings.

Anti-N-methyl-D-aspartate (anti-NMDA) receptor encephalitis is a condition that was only identified relatively recently. It often presents in psychiatric settings, with clinical presentations that may overlap with those of other psychiatric disorders such as psychoses secondary to schizophrenia, substance use, or brief delusional disorder. It often presents in women of child bearing age and has a relatively high mortality rate. The treatment approach for anti-NMDA receptor encephalitis is considerably different from that used for other psychiatric and neurological conditions. Early recognition, correct diagnosis, and appropriate management of the condition are of vital importance to the prognosis, including reducing mortality rate, admissions to intensive care units, recurrence, complications of the disease and in some cases, irreversible hippocampal damage. We present a case that highlights the typical presentation of anti-NMDA receptor encephalitis in a young woman and discuss management and outcome.

Novel chemosensor for the visual detection of copper(II) in aqueous solution at the ppm level.

A new water-soluble, multisite-coordinating ligand LH(7) was prepared by the condensation of tris(hydroxymethyl)aminomethane with 2,6-diformyl-p-cresol. LH(7) is a selective chemosensor for Cu(2+), under physiological conditions, with visual detection limits of 20 ppm (ambient light conditions) and 4 ppm (UV light conditions). LH(7) can also be used in biological cell lines for the detection of Cu(2+).

Protein bioinformatics and mixtures of bivariate von Mises distributions for angular data.

A fundamental problem in bioinformatics is to characterize the secondary structure of a protein, which has traditionally been carried out by examining a scatterplot (Ramachandran plot) of the conformational angles. We examine two natural bivariate von Mises distributions--referred to as Sine and Cosine models--which have five parameters and, for concentrated data, tend to a bivariate normal distribution. These are analyzed and their main properties derived. Conditions on the parameters are established which result in bimodal behavior for the joint density and the marginal distribution, and we note an interesting situation in which the joint density is bimodal but the marginal distributions are unimodal. We carry out comparisons of the two models, and it is seen that the Cosine model may be preferred. Mixture distributions of the Cosine model are fitted to two representative protein datasets using the expectation maximization algorithm, which results in an objective partition of the scatterplot into a number of components. Our results are consistent with empirical observations; new insights are discussed.