Surveillance of medication use: early identification of poor adherence

Background: We sought to measure population-level adherence to antihyperlipidemics, antihypertensives, and oral hypoglycemics, and to develop a model for early identification of subjects at high risk of long-term poor adherence. Methods Prescription-filling data for 2 million subjects derived from a payor's insurance claims were used to evaluate adherence to three chronic drugs over 1 year. We relied on patterns of prescription fills, including the length of gaps in medication possession, to measure adherence among subjects and to build models for predicting poor long-term adherence. Results: All prescription fills for a specific drug were sequenced chronologically into drug eras. 61.3% to 66.5% of the prescription patterns contained medication gaps >30 days during the first year of drug use. These interrupted drug eras include long-term discontinuations, where the subject never again filled a prescription for any drug in that category in the dataset, which represent 23.7% to 29.1% of all drug eras. Among the prescription-filling patterns without large medication gaps, 0.8% to 1.3% exhibited long-term poor adherence. Our models identified these subjects as early as 60 days after the first prescription fill, with an area under the curve (AUC) of 0.81. Model performance improved as the predictions were made at later time-points, with AUC values increasing to 0.93 at the 120-day time-point. Conclusions: Dispensed medication histories (widely available in real time) are useful for alerting providers about poorly adherent patients and those who will be non-adherent several months later. Efforts to use these data in point of care and decision support facilitating patient are warranted

Magic numbers in polymer phase separation -- the importance of being rigid

Cells possess non-membrane-bound bodies, many of which are now understood as phase-separated condensates. One class of such condensates is composed of two polymer species, where each consists of repeated binding sites that interact in a one-to-one fashion with the binding sites of the other polymer. Previous biologically-motivated modeling of such a two-component system surprisingly revealed that phase separation is suppressed for certain combinations of numbers of binding sites. This phenomenon, dubbed the "magic-number effect", occurs if the two polymers can form fully-bonded small oligomers by virtue of the number of binding sites in one polymer being an integer multiple of the number of binding sites of the other. Here we use lattice-model simulations and analytical calculations to show that this magic-number effect can be greatly enhanced if one of the polymer species has a rigid shape that allows for multiple distinct bonding conformations. Moreover, if one species is rigid, the effect is robust over a much greater range of relative concentrations of the two species. Our findings advance our understanding of the fundamental physics of two-component polymer-based phase-separation and suggest implications for biological and synthetic systems.Comment: 8 pages + 15 pages S

ĮMONIŲ SOCIALINĖS ATSAKOMYBĖS PAGRINDU SUKURTOS MAINOMOSIOS VERTĖS MATAVIMAS VERTĖS KŪRIMO GRANDINĖJE

The paperwork provides formulas for measuring value (created through CSR) gained by company, its partners (members of VCC) and employees. As far as expert survey confirmed, customers gained value generally might be evaluated by more favourable purchase decision and justification of higher price. Therefore it is suggested to pay main attention on customer’s gained use value – the value which is expressed through more favourable purchase decision and justification of higher price is already calculated as value gained by company.KEYWORDS: corporate social responsibility, value creation chain, value measurement

Modeling RNA loops using sequence homology and geometric constraints

Summary: RNA loop regions are essential structural elements of RNA molecules influencing both their structural and functional properties. We developed RLooM, a web application for homology-based modeling of RNA loops utilizing template structures extracted from the PDB. RLooM allows the insertion and replacement of loop structures of a desired sequence into an existing RNA structure. Furthermore, a comprehensive database of loops in RNA structures can be accessed through the web interface

Scalable Decision Support at the Point of Care: A Substitutable Electronic Health Record App for Monitoring Medication Adherence

Background: Non-adherence to prescribed medications is a serious health problem in the United States, costing an estimated $100 billion per year. While poor adherence should be addressable with point of care health information technology, integrating new solutions with existing electronic health records (EHR) systems require customization within each organization, which is difficult because of the monolithic software design of most EHR products. Objective: The objective of this study was to create a published algorithm for predicting medication adherence problems easily accessible at the point of care through a Web application that runs on the Substitutable Medical Apps, Reusuable Technologies (SMART) platform. The SMART platform is an emerging framework that enables EHR systems to behave as “iPhone like platforms” by exhibiting an application programming interface for easy addition and deletion of third party apps. The app is presented as a point of care solution to monitoring medication adherence as well as a sufficiently general, modular application that may serve as an example and template for other SMART apps. Methods: The widely used, open source Django framework was used together with the SMART platform to create the interoperable components of this app. Django uses Python as its core programming language. This allows statistical and mathematical modules to be created from a large array of Python numerical libraries and assembled together with the core app to create flexible and sophisticated EHR functionality. Algorithms that predict individual adherence are derived from a retrospective study of dispensed medication claims from a large private insurance plan. Patients’ prescription fill information is accessed through the SMART framework and the embedded algorithms compute adherence information, including predicted adherence one year after the first prescription fill. Open source graphing software is used to display patient medication information and the results of statistical prediction of future adherence on a clinician-facing Web interface. Results: The user interface allows the physician to quickly review all medications in a patient record for potential non-adherence problems. A gap-check and current medication possession ratio (MPR) threshold test are applied to all medications in the record to test for current non-adherence. Predictions of 1-year non-adherence are made for certain drug classes for which external data was available. Information is presented graphically to indicate present non-adherence, or predicted non-adherence at one year, based on early prescription fulfillment patterns. The MPR Monitor app is installed in the SMART reference container as the “MPR Monitor”, where it is publically available for use and testing. MPR is an acronym for Medication Possession Ratio, a commonly used measure of adherence to a prescribed medication regime. This app may be used as an example for creating additional functionality by replacing statistical and display algorithms with new code in a cycle of rapid prototyping and implementation or as a framework for a new SMART app. Conclusions: The MPR Monitor app is a useful pilot project for monitoring medication adherence. It also provides an example that integrates several open source software components, including the Python-based Django Web framework and python-based graphics, to build a SMART app that allows complex decision support methods to be encapsulated to enhance EHR functionality

Automated identification of pathways from quantitative genetic interaction data

We present a novel Bayesian learning method that reconstructs large detailed gene networks from quantitative genetic interaction (GI) data.The method uses global reasoning to handle missing and ambiguous measurements, and provide confidence estimates for each prediction.Applied to a recent data set over genes relevant to protein folding, the learned networks reflect known biological pathways, including details such as pathway ordering and directionality of relationships.The reconstructed networks also suggest novel relationships, including the placement of SGT2 in the tail-anchored biogenesis pathway, a finding that we experimentally validated

Model for eukaryotic tail-anchored protein binding based on the structure of Get3

The Get3 ATPase directs the delivery of tail-anchored (TA) proteins to the endoplasmic reticulum (ER). TA-proteins are characterized by having a single transmembrane helix (TM) at their extreme C terminus and include many essential proteins, such as SNAREs, apoptosis factors, and protein translocation components. These proteins cannot follow the SRP-dependent co-translational pathway that typifies most integral membrane proteins; instead, post-translationally, these proteins are recognized and bound by Get3 then delivered to the ER in the ATP dependent Get pathway. To elucidate a molecular mechanism for TA protein binding by Get3 we have determined three crystal structures in apo and ADP forms from Saccharomyces cerevisae (ScGet3-apo) and Aspergillus fumigatus (AfGet3-apo and AfGet3-ADP). Using structural information, we generated mutants to confirm important interfaces and essential residues. These results point to a model of how Get3 couples ATP hydrolysis to the binding and release of TA-proteins

Knowledge-based instantiation of full atomic detail into coarse-grain RNA 3D structural models

Motivation: The recent development of methods for modeling RNA 3D structures using coarse-grain approaches creates a need to bridge low- and high-resolution modeling methods. Although they contain topological information, coarse-grain models lack atomic detail, which limits their utility for some applications

Ion antiport accelerates photosynthetic acclimation in fluctuating light environments

Many photosynthetic organisms globally, including crops, forests and algae, must grow in environments where the availability of light energy fluctuates dramatically. How photosynthesis maintains high efficiency despite such fluctuations in its energy source remains poorly understood. Here we show that Arabidopsis thaliana ​K+ efflux antiporter (​KEA3) is critical for high photosynthetic efficiency under fluctuating light. On a shift from dark to low light, or high to low light, ​kea3 mutants show prolonged dissipation of absorbed light energy as heat. ​KEA3 localizes to the thylakoid membrane, and allows proton efflux from the thylakoid lumen by proton/potassium antiport. ​KEA3’s activity accelerates the downregulation of pH-dependent energy dissipation after transitions to low light, leading to faster recovery of high photosystem II quantum efficiency and increased ​CO2 assimilation. Our results reveal a mechanism that increases the efficiency of photosynthesis under fluctuating light. [EN]This project was funded by the Carnegie Institution for Science, by ERDF-cofinanced grants from the Ministry of Economy and Competitiveness (BIO2012-33655) and Junta de Andalucia (CVI-7558) to K.V., the Natural Sciences and Engineering Research Council of Canada (NSERC) PGS-D3 scholarship to L.P. and Deutsche Forschungsgemeinschaft grants (JA 665/10-1 and GRK 1525 to P.J.; AR 808/1-1 to U.A.).Peer reviewe

An Atlas of human kinase regulation

The coordinated regulation of protein kinases is a rapid mechanism that integrates diverse cues and swiftly determines appropriate cellular responses. However, our understanding of cellular decision-making has been limited by the small number of simultaneously monitored phospho- regulatory events. Here, we have estimated changes in activity in 215 human kinases in 399 condi- tions from a compilation of nearly 3 million phosphopeptide quantifications. This atlas identifies commonly regulated kinases as those that are central in the signaling network and defines the logic relationships between kinase pairs. Co-regulation along the conditions predicts kinase-complex and kinase-substrate associations. Additionally, the kinase regulation profile acts as a molecular fingerprint to identify related and opposing signaling states. Using this atlas, we identified essen- tial mediators of stem cell differentiation, modulators of Salmonella infection and new targets of AKT1. This provides a global view of human phosphorylation-based signaling and the necessary context to better understand kinase driven decision-making