Crystallographic analysis reveals the structural basis of the high-affinity binding of iophenoxic acid to human serum albumin

<p>Abstract</p> <p>Background</p> <p>Iophenoxic acid is an iodinated radiocontrast agent that was withdrawn from clinical use because of its exceptionally long half-life in the body, which was due in part to its high-affinity binding to human serum albumin (HSA). It was replaced by Iopanoic acid, which has an amino rather than a hydroxyl group at position 3 on the iodinated benzyl ring and, as a result, binds to albumin with lower affinity and is excreted more rapidly from the body. To understand how iophenoxic acid binds so tightly to albumin, we wanted to examine the structural basis of its interaction with HSA.</p> <p>Results</p> <p>We have determined the co-crystal structure of HSA in complex with iophenoxic acid at 2.75 Å resolution, revealing a total of four binding sites, two of which - in drugs sites 1 and 2 on the protein - are likely to be occupied at clinical doses. High-affinity binding of iophenoxic acid occurs at drug site 1. The structure reveals that polar and apolar groups on the compound are involved in its interactions with drug site 1. In particular, the 3-hydroxyl group makes three hydrogen bonds with the side-chains of Tyr 150 and Arg 257. The mode of binding to drug site 2 is similar except for the absence of a binding partner for the hydroxyl group on the benzyl ring of the compound.</p> <p>Conclusions</p> <p>The HSA-iophenoxic acid structure indicates that high-affinity binding to drug site 1 is likely to be due to extensive desolvation of the compound, coupled with the ability of the binding pocket to provide a full set of salt-bridging or hydrogen bonding partners for its polar groups. Consistent with this interpretation, the structure also suggests that the lower-affinity binding of iopanoic acid arises because replacement of the 3-hydroxyl by an amino group eliminates hydrogen bonding to Arg 257. This finding underscores the importance of polar interactions in high-affinity binding to albumin.</p

Genetic structure of community acquired methicillin-resistant Staphylococcus aureus USA300.

BackgroundCommunity-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) is a significant bacterial pathogen that poses considerable clinical and public health challenges. The majority of the CA-MRSA disease burden consists of skin and soft tissue infections (SSTI) not associated with significant morbidity; however, CA-MRSA also causes severe, invasive infections resulting in significant morbidity and mortality. The broad range of disease severity may be influenced by bacterial genetic variation.ResultsWe sequenced the complete genomes of 36 CA-MRSA clinical isolates from the predominant North American community acquired clonal type USA300 (18 SSTI and 18 severe infection-associated isolates). While all 36 isolates shared remarkable genetic similarity, we found greater overall time-dependent sequence diversity among SSTI isolates. In addition, pathway analysis of non-synonymous variations revealed increased sequence diversity in the putative virulence genes of SSTI isolates.ConclusionsHere we report the first whole genome survey of diverse clinical isolates of the USA300 lineage and describe the evolution of the pathogen over time within a defined geographic area. The results demonstrate the close relatedness of clinically independent CA-MRSA isolates, which carry implications for understanding CA-MRSA epidemiology and combating its spread

Validated Screening Tools for Common Mental Disorders in Low and Middle Income Countries: A Systematic Review.

BACKGROUND: A wide range of screening tools are available to detect common mental disorders (CMDs), but few have been specifically developed for populations in low and middle income countries (LMIC). Cross-cultural application of a screening tool requires that its validity be assessed against a gold standard diagnostic interview. Validation studies of brief CMD screening tools have been conducted in several LMIC, but until now there has been no review of screening tools for all CMDs across all LMIC populations. METHODS: A systematic review with broad inclusion criteria was conducted, producing a comprehensive summary of brief CMD screening tools validated for use in LMIC populations. For each validation, the diagnostic odds ratio (DOR) was calculated as an easily comparable measure of screening tool validity. Average DOR results weighted by sample size were calculated for each screening tool, enabling us to make broad recommendations about best performing screening tools. RESULTS: 153 studies fulfilled our inclusion criteria. Because many studies validated two or more screening tools, this corresponded to 273 separate validations against gold standard diagnostic criteria. We found that the validity of every screening tool tested in multiple settings and populations varied between studies, highlighting the importance of local validation. Many of the best performing tools were purposely developed for a specific population; however, as these tools have only been validated in one study, it is not possible to draw broader conclusions about their applicability in other contexts. CONCLUSIONS: Of the tools that have been validated in multiple settings, the authors broadly recommend using the SRQ-20 to screen for general CMDs, the GHQ-12 for CMDs in populations with physical illness, the HADS-D for depressive disorders, the PHQ-9 for depressive disorders in populations with good literacy levels, the EPDS for perinatal depressive disorders, and the HADS-A for anxiety disorders. We recommend that, wherever possible, a chosen screening tool should be validated against a gold standard diagnostic assessment in the specific context in which it will be employed

Market Segmentation Trees

We seek to provide an interpretable framework for segmenting users in a population for personalized decision-making. The standard approach is to perform market segmentation by clustering users according to similarities in their contextual features, after which a "response model" is fit to each segment to model how users respond to personalized decisions. However, this methodology is not ideal for personalization, since two users could in theory have similar features but different response behaviors. We propose a general methodology, Market Segmentation Trees (MSTs), for learning interpretable market segmentations explicitly driven by identifying differences in user response patterns. To demonstrate the versatility of our methodology, we design two new, specialized MST algorithms: (i) Choice Model Trees (CMTs) which can be used to predict a user's choice amongst multiple options, and (ii) Isotonic Regression Trees (IRTs) which can be used to solve the bid landscape forecasting problem. We provide a customizable, open-source code base for training MSTs in Python which employs several strategies for scalability, including parallel processing and warm starts. We provide a theoretical analysis of the asymptotic running time of our training method validating its computational tractability on large datasets. We assess the practical performance of MSTs on several synthetic and real world datasets, showing our method reliably finds market segmentations which accurately model response behavior. Further, when applying MSTs to historical bidding data from a leading demand-side platform (DSP), we show that MSTs consistently achieve a 5-29% improvement in bid landscape forecasting accuracy over the DSP's current model. Our findings indicate that integrating market segmentation with response modeling consistently leads to improvements in response prediction accuracy, thereby aiding personalization

All-Optical Delay of Images Using Slow Light

Two-dimensional images carried by optical pulses (2 ns) are delayed by up to 10 ns in a 10 cm cesium vapor cell. By interfering the delayed images with a local oscillator, the transverse phase and amplitude profiles of the images are shown to be preserved. It is further shown that delayed images can be well preserved even at very low light levels, where each pulse contains on average less than one photon

Reporting on data monitoring committees in neonatal randomised controlled trials is inconsistent

Aim: To evaluate the reported use of Data Monitoring Committees (DMCs), the frequency of interim analysis, pre-specified stopping rules and early trial termination in neonatal randomised controlled trials (RCTs). Methods: We reviewed neonatal RCTs published in four high impact general medical journals, specifically looking at safety issues including documented involvement of a DMC, stated interim analysis, stopping rules and early trial termination. We searched all journal issues over an 11-year period (2003-2013) and recorded predefined parameters on each item for RCTs meeting inclusion criteria. Results: Seventy neonatal trials were identified in four general medical journals: Lancet, New England Journal of Medicine (NEJM), British Medical Journal and Journal of American Medical Association (JAMA). 43 (61.4%) studies reported the presence of a DMC, 36 (51.4%) explicitly mentioned interim analysis; stopping rules were reported in 15 (21.4%) RCTs and 7 (10%) trials were terminated early. The NEJM most frequently reported these parameters compared to the other three journals reviewed. Conclusion: While the majority of neonatal RCTs report on DMC involvement and interim analysis there is still scope for improvement. Clear documentation of safety related issues should be a central component of reporting in neonatal trials involving newborn infants