Treating Depression and other Serotonin Deficiency Brain Disorders with Tryptophan

: Deficits of brain serotonin (5-hydroxytryptamine; 5-HT) are implicated in a number of psychiatric illnesses including depression. Treatment efficacy of this highly prevalent brain disorder is not adequate largely because serotonin stores are depleted. Tryptophan an essential amino acid is the sole precursor of serotonin; its systemic or oral administration increases serotonin synthesis because tryptophan hydroxylase, the rate limiting enzyme of 5-HT biosynthesis, is physiologically unsaturated with its substrate. The present article targets importance of tryptophan supplementation in treating serotonin deficiency and improving therapeutic intervention in depression and other serotonin deficiency brain disorders.

Retaining Relative Height Information: An Enhanced Technique for Depression Treatment in Digital Elevation Models

This study presents an enhanced variant of the priority-flood based algorithm proposed by Wang and Liu for treating depressions in digital elevation models (DEMs). The enhanced variant redefines spill elevation, the key concept of the original algorithm, as the lowest elevation that a pixel needs to have to ensure a non-ascending path toward the border of the DEM, plus the larger of a small number (~0.001) and the difference between the unaltered elevation values of the focal pixel and its immediate downhill neighbor. This redefinition is adopted to obtain an intermediate elevation surface to direct flow and ultimately to carve the original DEM. Each carving starts from a depression bottom and propagates downstream until a downhill cell is guaranteed in the original DEM. Tests of these algorithms on a complex terrain of the 260,000 km2 Sichuan structural basin in China shows that the enhanced algorithm maximally preserves the original flow directions and extracts realistic drainage networks. Retaining the relative heights, and therefore flow directions, of cells within depressions allows the new algorithm to offer a depressionless DEM with small modification of its origin for further hydrologic applications. The enhanced depression treatment algorithm is provided as the freely available tool BNUSinkRemv.

Treatment selection using prototyping in latent-space with application to depression treatment

Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. While both paradigms have shown promising results, each of them suffers from important limitations. In this article, we propose a novel deep learning-based treatment selection approach that is shown to strike a balance between the two paradigms using latent-space prototyping. Our approach is specifically tailored for domains in which effective prototypes and sub-groups of patients are assumed to exist, but groupings relevant to the training objective are not observable in the non-latent space. In an extensive evaluation, using both synthetic and Major Depressive Disorder (MDD) real-world clinical data describing 4754 MDD patients from clinical trials for depression treatment, we show that our approach favorably compares with state-of-the-art approaches. Specifically, the model produced an 8% absolute and 23% relative improvement over random treatment allocation. This is potentially clinically significant, given the large number of patients with MDD. Therefore, the model can bring about a much desired leap forward in the way depression is treated today.