Depression in Primary care: Interpersonal Counseling vs Selective serotonin reuptake inhibitors. The DEPICS Study. A multicenter randomized controlled trial. Rationale and design

<p>Abstract</p> <p>Background</p> <p>Depression is a frequently observed and disabling condition in primary care, mainly treated by Primary Care Physicians with antidepressant drugs. Psychological interventions are recommended as first-line treatment by the most authoritative international guidelines but few evidences are available on their efficacy and effectiveness for mild depression.</p> <p>Methods/Design</p> <p>This multi-center randomized controlled trial was conducted in 9 Italian centres with the aim to compare the efficacy of Inter-Personal Counseling, a brief structured psychological intervention, to that of Selective Serotonin Reuptake Inhibitors. Patients with depressive symptoms referred by Primary Care Physicians to psychiatric consultation-liaison services were eligible for the study if they met the DSM-IV criteria for major depression, had a score ≥13 on the 21-item Hamilton Depression Rating Scale, and were at their first or second depressive episode. The primary outcome was remission of depressive symptoms at 2-months, defined as a HDRS score ≤ 7. Secondary outcome measures were improvement in global functioning and recurrence of depressive symptoms at 12-months. Patients who did not respond to Inter-Personal Counseling or Selective Serotonin Reuptake Inhibitors at 2-months received augmentation with the other treatment.</p> <p>Discussion</p> <p>This trial addresses some of the shortcomings of existing trials targeting major depression in primary care by evaluating the comparative efficacy of a brief psychological intervention that could be easily disseminated, by including a sample of patients with mild/moderate depression and by using different outcome measures.</p> <p>Trial registration</p> <p>Australian New Zealand Clinical Trials Registry ACTRN12608000479303</p

The Princeton Protein Orthology Database (P-POD): A Comparative Genomics Analysis Tool for Biologists

Many biological databases that provide comparative genomics information and tools are now available on the internet. While certainly quite useful, to our knowledge none of the existing databases combine results from multiple comparative genomics methods with manually curated information from the literature. Here we describe the Princeton Protein Orthology Database (P-POD, http://ortholog.princeton.edu), a user-friendly database system that allows users to find and visualize the phylogenetic relationships among predicted orthologs (based on the OrthoMCL method) to a query gene from any of eight eukaryotic organisms, and to see the orthologs in a wider evolutionary context (based on the Jaccard clustering method). In addition to the phylogenetic information, the database contains experimental results manually collected from the literature that can be compared to the computational analyses, as well as links to relevant human disease and gene information via the OMIM, model organism, and sequence databases. Our aim is for the P-POD resource to be extremely useful to typical experimental biologists wanting to learn more about the evolutionary context of their favorite genes. P-POD is based on the commonly used Generic Model Organism Database (GMOD) schema and can be downloaded in its entirety for installation on one's own system. Thus, bioinformaticians and software developers may also find P-POD useful because they can use the P-POD database infrastructure when developing their own comparative genomics resources and database tools

Humeral development from neonatal period to skeletal maturity—application in age and sex assessment

The goal of the present study is to examine cross-sectional information on the growth of the humerus based on the analysis of four measurements, namely, diaphyseal length, transversal diameter of the proximal (metaphyseal) end of the shaft, epicondylar breadth and vertical diameter of the head. This analysis was performed in 181 individuals (90 ♂ and 91 ♀) ranging from birth to 25 years of age and belonging to three documented Western European skeletal collections (Coimbra, Lisbon and St. Bride). After testing the homogeneity of the sample, the existence of sexual differences (Student"s t- and Mann-Whitney U-test) and the growth of the variables (polynomial regression) were evaluated. The results showed the presence of sexual differences in epicondylar breadth above 20 years of age and vertical diameter of the head from 15 years of age, thus indicating that these two variables may be of use in determining sex from that age onward. The growth pattern of the variables showed a continuous increase and followed first- and second-degree polynomials. However, growth of the transversal diameter of the proximal end of the shaft followed a fourth-degree polynomial. Strong correlation coefficients were identified between humeral size and age for each of the four metric variables. These results indicate that any of the humeral measurements studied herein is likely to serve as a useful means of estimating sub-adult age in forensic samples

Nested Machine Learning Facilitates Increased Sequence Content for Large-Scale Automated High Resolution Melt Genotyping

High Resolution Melt (HRM) is a versatile and rapid post-PCR DNA analysis technique primarily used to differentiate sequence variants among only a few short amplicons. We recently developed a one-vs-one support vector machine algorithm (OVO SVM) that enables the use of HRM for identifying numerous short amplicon sequences automatically and reliably. Herein, we set out to maximize the discriminating power of HRM + SVM for a single genetic locus by testing longer amplicons harboring significantly more sequence information. Using universal primers that amplify the hypervariable bacterial 16 S rRNA gene as a model system, we found that long amplicons yield more complex HRM curve shapes. We developed a novel nested OVO SVM approach to take advantage of this feature and achieved 100% accuracy in the identification of 37 clinically relevant bacteria in Leave-One-Out-Cross-Validation. A subset of organisms were independently tested. Those from pure culture were identified with high accuracy, while those tested directly from clinical blood bottles displayed more technical variability and reduced accuracy. Our findings demonstrate that long sequences can be accurately and automatically profiled by HRM with a novel nested SVM approach and suggest that clinical sample testing is feasible with further optimization