Two Multiâ Center Studies Evaluating Locally Delivered Doxycycline Hyclate, Placebo Control, Oral Hygiene, and Scaling and Root Planing in the Treatment of Periodontitis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142011/1/jper0490.pd

Structural Analysis of Prolyl Oligopeptidases Using Molecular Docking and Dynamics: Insights into Conformational Changes and Ligand Binding

Prolyl oligopeptidase (POP) is considered as an important pharmaceutical target for the treatment of numerous diseases. Despite enormous studies on various aspects of POPs structure and function still some of the questions are intriguing like conformational dynamics of the protein and interplay between ligand entry/egress. Here, we have used molecular modeling and docking based approaches to unravel questions like differences in ligand binding affinities in three POP species (porcine, human and A. thaliana). Despite high sequence and structural similarity, they possess different affinities for the ligands. Interestingly, human POP was found to be more specific, selective and incapable of binding to a few planar ligands which showed extrapolation of porcine POP in human context is more complicated. Possible routes for substrate entry and product egress were also investigated by detailed analyses of molecular dynamics (MD) simulations for the three proteins. Trajectory analysis of bound and unbound forms of three species showed differences in conformational dynamics, especially variations in β-propeller pore size, which was found to be hidden by five lysine residues present on blades one and seven. During simulation, β-propeller pore size was increased by ∼2 Å in porcine ligand-bound form which might act as a passage for smaller product movement as free energy barrier was reduced, while there were no significant changes in human and A. thaliana POPs. We also suggest that these differences in pore size could lead to fundamental differences in mode of product egress among three species. This analysis also showed some functionally important residues which can be used further for in vitro mutagenesis and inhibitor design. This study can help us in better understanding of the etiology of POPs in several neurodegenerative diseases

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Advancing schizophrenia drug discovery : optimizing rodent models to bridge the translational gap

Although our knowledge of the pathophysiology of schizophrenia has increased, treatments for this devastating illness remain inadequate. Here, we critically assess rodent models and behavioural end points used in schizophrenia drug discovery and discuss why these have not led to improved treatments. We provide a perspective on how new models, based on recent advances in the understanding of the genetics and neural circuitry underlying schizophrenia, can bridge the translational gap and lead to the development of more effective drugs. We conclude that previous serendipitous approaches should be replaced with rational strategies for drug discovery in integrated preclinical and clinical programmes. Validation of drug targets in disease-based models that are integrated with translationally relevant end point assessments will reduce the current attrition rate in schizophrenia drug discovery and ultimately lead to therapies that tackle the disease process