David Anderson

Hidden specificity in an apparently nonspecific RNA-binding protein

Nucleic-acid-binding proteins are generally viewed as either specific or nonspecific, depending on characteristics of their binding sites in DNA or RNA. Most studies have focused on specific proteins, which identify cognate sites by binding with highest affinities to regions with defined signatures in sequence, structure or both. Proteins that bind to sites devoid of defined sequence or structure signatures are considered nonspecific. Substrate binding by these proteins is poorly understood, and it is not known to what extent seemingly nonspecific proteins discriminate between different binding sites, aside from those sequestered by nucleic acid structures. Here we systematically examine substrate binding by the apparently nonspecific RNA-binding protein C5, and find clear discrimination between different binding site variants. C5 is the protein subunit of the transfer RNA processing ribonucleoprotein enzyme RNase P from Escherichia coli. The protein binds 5′ leaders of precursor tRNAs at a site without sequence or structure signatures. We measure functional binding of C5 to all possible sequence variants in its substrate binding site, using a high-throughput sequencing kinetics approach (HITS-KIN) that simultaneously follows processing of thousands of RNA species. C5 binds different substrate variants with affinities varying by orders of magnitude. The distribution of functional affinities of C5 for all substrate variants resembles affinity distributions of highly specific nucleic acid binding proteins. Unlike these specific proteins, C5 does not bind its physiological RNA targets with the highest affinity, but with affinities near the median of the distribution, a region that is not associated with a sequence signature. We delineate defined rules governing substrate recognition by C5, which reveal specificity that is hidden in cellular substrates for RNase P. Our findings suggest that apparently nonspecific and specific RNA-binding modes may not differ fundamentally, but represent distinct parts of common affinity distributions.

Drivers of ED efficiency: a statistical and cluster analysis of volume, staffing, and operations

STUDY OBJECTIVEnThe percentage of patients leaving before treatment is completed (LBTC) is an important indicator of emergency department performance. The objective of this study is to identify characteristics of hospital operations that correlate with LBTC rates.nnnMETHODSnThe Emergency Department Benchmarking Alliance 2012 and 2013 cross-sectional national data sets were analyzed using multiple regression and k-means clustering. Significant operational variables affecting LBTC including annual patient volume, percentage of high-acuity patients, percentage of patients admitted to the hospital, number of beds, academic status, waiting times to see a physician, length of stay (LOS), registered nurse (RN) staffing, and physician staffing were identified. LBTC was regressed onto these variables. Because of the strong correlation between waiting times measured as door to first provider (DTFP), we regressed DTFP onto the remaining predictors. Cluster analysis was applied to the data sets to further analyze the impact of individual predictors on LBTC and DTFP.nnnRESULTSnLOS and the time from DTFP were both strongly associated with LBTC rate (P<.001). Patient volume is not significantly associated with LBTC rate (P=.16). Cluster analysis demonstrates that physician and RN staffing ratios correlate with shorter DTFP and lower LBTC.nnnCONCLUSIONnVolume is not the main driver of LBTC. DTFP and LOS are much more strongly associated. We show that operational factors including LOS and physician and RN staffing decisions, factors under the control of hospital and physician executives, correlate with waiting time and, thus, in determining the LBTC rate.

Determination of the Specificity Landscape for Ribonuclease P Processing of Precursor tRNA 5′ Leader Sequences

Maturation of tRNA depends on a single endonuclease, ribonuclease P (RNase P), to remove highly variable 5 leader sequences from precursor tRNA transcripts. Here, we use high-throughput enzymology to report multiple-turnover and single-turnover kinetics for Escherichia coli RNase P processing of all possible 5 leader sequences, including nucleotides contacting both the RNA and protein subunits of RNase P. The results reveal that the identity of N(-2) and N(-3) relative to the cleavage site at N(1) primarily control alternative substrate selection and act at the level of association not the cleavage step. As a consequence, the specificity for N(-1), which contacts the active site and contributes to catalysis, is suppressed. This study demonstrates high-throughput RNA enzymology as a means to globally determine RNA specificity landscapes and reveals the mechanism of substrate discrimination by a widespread and essential RNA-processing enzyme.

Predicting prostate cancer risk using magnetic resonance imaging data

AbstractWe propose a method of diagnosing prostate cancer using magnetic resonance imaging data. Logistic regression and nearest neighbor classification are combined to identify the risk of cancer. Our method performs well, having 79xa0% predictive accuracy, and an area under the ROC curve of 0.85. It identifies the most aggressive cancers with 82xa0% accuracy.n

The impact of electronic health record implementation on emergency physician efficiency and patient throughput

BACKGROUNDnIn emergency departments (EDs), the implementation of electronic health records (EHRs) has the potential to impact the rapid assessment and management of life threatening conditions. In order to quantify this impact, we studied the implementation of EHRs in the EDs of a two hospital system.nnnMETHODSnusing a prospective pre-post study design, patient processing metrics were collected for each ED physician at two hospitals for 7 months prior and 10 months post-EHR implementation. Metrics included median patient workup time, median length of stay, and the composite outcome indicator processing time.nnnRESULTSnmedian processing time increased immediately post-implementation and then returned to, and surpassed, the baseline level over 10 months. Overall, we see significant decreases in processing time as the number of patients treated increases.nnnCONCLUSIONSnimplementation of new EHRs into the ED setting can be expected to cause an initial decrease in efficiency. With adaptation, efficiency should return to baseline levels and may eventually surpass them.nnnIMPLICATIONSnwhile EDs can expect long term gains from the implementation of EHRs, they should be prepared for initial decreases in efficiency and take preparatory measures to avert adverse effects on the quality of patient care.

Geographic disparities in donor lung supply and lung transplant waitlist outcomes: A cohort study

Despite the Final Rule mandate for equitable organ allocation in the United States, geographic disparities exist in donor lung allocation, with the majority of donor lungs being allocated locally to lower‐priority candidates. We conducted a retrospective cohort study of 19 622 lung transplant candidates waitlisted between 2006 and 2015. We used multivariable adjusted competing risk survival models to examine the relationship between local lung availability and waitlist outcomes. The primary outcome was a composite of death and removal from the waitlist for clinical deterioration. Waitlist candidates in the lowest quartile of local lung availability had an 84% increased risk of death or removal compared with candidates in the highest (subdistribution hazard ratio [SHR]: 1.84, 95% confidence interval [CI]: 1.51‐2.24, P < .001). The transplantation rate was 57% lower in the lowest quartile compared with the highest (SHR: 0.43, 95% CI: 0.39‐0.47). The adjusted death or removal rate decreased by 11% with a 50% increase in local lung availability (SHR: 0.89, 95% CI: 0.85‐0.93, P < .001) and the adjusted transplantation rate increased by 19% (SHR: 1.19, 95% CI: 1.17‐1.22, P < .001). There are geographically disparate waitlist outcomes in the current lung allocation system. Candidates listed in areas of low local lung availability have worse waitlist outcomes.

The contribution of the C5 protein subunit of Escherichia coli ribonuclease P to specificity for precursor tRNA is modulated by proximal 5’ leader sequences

Recognition of RNA by RNA processing enzymes and RNA binding proteins often involves cooperation between multiple subunits. However, the interdependent contributions of RNA and protein subunits to molecular recognition by ribonucleoproteins are relatively unexplored. RNase P is an endonuclease that removes 5 leaders from precursor tRNAs and functions in bacteria as a dimer formed by a catalytic RNA subunit (P RNA) and a protein subunit (C5 in E. coli). The P RNA subunit contacts the tRNA body and proximal 5 leader sequences [N(-1) and N(-2)] while C5 binds distal 5 leader sequences [N(-3) to N(-6)]. To determine whether the contacts formed by P RNA and C5 contribute independently to specificity or exhibit cooperativity or anti-cooperativity, we compared the relative kcat/Km values for all possible combinations of the six proximal 5 leader nucleotides (n = 4096) for processing by the E. coli P RNA subunit alone and by the RNase P holoenzyme. We observed that while the P RNA subunit shows specificity for 5 leader nucleotides N(-2) and N(-1), the presence of the C5 protein reduces the contribution of P RNA to specificity, but changes specificity at N(-2) and N(-3). The results reveal that the contribution of C5 protein to RNase P processing is controlled by the identity of N(-2) in the pre-tRNA 5 leader. The data also clearly show that pairing of the 5 leader with the 3 ACCA of tRNA acts as an anti-determinant for RNase P cleavage. Comparative analysis of genomically encoded E. coli tRNAs reveals that both anti-determinants are subject to negative selection in vivo.

When is an ounce of prevention worth a pound of cure? Identifying high-risk candidates for case management

ABSTRACT Case management is a

Accelerating the adoption of bundled payment reimbursement systems: A data-driven approach utilizing claims data

6 billion industry that employs over 34,000 people in the United States alone. Traditionally, case management has been utilized to help patients navigate the health care system and to coordinate care in the hope of lowering costs and achieving better health outcomes. However, since enrollment into these programs is typically either universal or limited to very sick patients, studies on the cost-effectiveness of case management programs find that their performance is mixed, at best. In this article we posit an opportunity to improve outcomes and lower costs by targeting certain patients for case management and early intervention. Utilizing modern data mining methods, we develop a methodology to identify these patients, who we describe as “jumpers” because their costs are currently low but will increase significantly in the near future. Given the performance of the prediction models, we also show that unless case management can prevent over 7.5% of health care cost increases, it may benefit enrolled members but will not reduce overall costs. The article then introduces a performance bounding methodology that characterizes the best obtainable prediction accuracy on a given data set. The derived upper bound demonstrates that searching for jumpers presents a far more challenging prediction problem than identifying future high-cost members, which is the traditional approach to selecting case management candidates.

Life Is All about Timing: : An Examination of Differences in Treatment Quality for Trauma Patients Based on Hospital Arrival Time

ABSTRACT Bundled payments as a reimbursement mechanism have the potential to reduce health care expenditures and improve the quality of care by aligning the incentives of payers, providers and, most importantly, patients. The Centers for Medicare and Medicaid Services (CMS) launched the Bundled Payments for Care Improvement (BPCI) program in April 2013 and has set ambitious goals for adopting alternative payment models on a large scale. One of the crucial components for successful implementation of a bundled payment system is the identification of procedural homogeneous groups within an episode of care (a set of services needed to treat a medical condition), to which a flat reimbursement rate can be applied. In this study, we propose a data-driven clustering approach to automatically detect and explicitly represent homogeneous sub-groups of services for a given condition. Manual detection is slow and relies on consensus decisions, but automatic detection can serve as an important foundational input for bundle building. We explore the results from analyzing two conditions, one with a low and the other with a high degree of treatment complexity. Resulting clusters characterize episodes of care by specifying included services. The automatically extracted clusters of services have different cost patterns and highlight the payers expenditure and providers financial risk under bundled payments. Such a data-driven approach could be used by payers (e.g., CMS) to facilitate the adoption of bundled payments by different providers. To demonstrate, we use the clusters identified to model a payment scheme that minimizes providers’ financial risk.