Is there Progress? An Overview of Select Biomarker Candidates for Major Depressive Disorder

Major Depressive Disorder (MDD) contributes to a significant worldwide disease burden, expected to be second only to heart disease by 2050. However, accurate diagnosis has been a historical weakness in clinical psychiatry. As a result, there is a demand for diagnostic modalities with greater objectivity that could improve on current psychiatric practice that relies mainly on self-reporting of symptoms and clinical interviews. Over the past two decades, literature on a growing number of putative biomarkers for MDD increasingly suggests that MDD patients have significantly different biological profiles compared to healthy controls. However, difficulty in elucidating their exact relationships within depression pathology renders individual markers inconsistent diagnostic tools. Consequently, further biomarker research could potentially improve our understanding of MDD pathophysiology as well as aid in interpreting response to treatment, narrow differential diagnoses, and help refine current MDD criteria. Representative of this, multiplex assays using multiple sources of biomarkers are reported to be more accurate options in comparison to individual markers that exhibit lower specificity and sensitivity, and are more prone to confounding factors. In the future, more sophisticated multiplex assays may hold promise for use in screening and diagnosing depression and determining clinical severity as an advance over relying solely on current subjective diagnostic criteria. A pervasive limitation in existing research is heterogeneity inherent in MDD studies, which impacts the validity of biomarker data. Additionally, small sample sizes of most studies limit statistical power. Yet, as the RDoC project evolves to decrease these limitations, and stronger studies with more generalizable data are developed, significant advances in the next decade are expected to yield important information in the development of MDD biomarkers for use in clinical settings

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Divide-and-Conquer Multiple Sequence Alignment

Contents 1 Introduction 1 1.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 Analysis of Differences: Sequence Alignment 7 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Global Sequence Alignment . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Pairwise Sequence Alignment . . . . . . . . . . . . . . . . . . 9 2.2.2 Multiple Sequence Alignment . . . . . . . . . . . . . . . . . . 11 2.3 Alignment Scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.1 Single Letter Substitutions . . . . . . . . . . . . . . . . . . . . 13 2.3.2 Pairwise Alignment Score . . . . . . . . . . . . . . . . . . . . 15 2.3.3 Multiple Sequence Alignment Score . . . . . . . . . . . . . . . 18 2.4 The Problem . . . . . . . . .