460 research outputs found

    Interdependent interactions between TFIIB, TATA binding protein, and DNA.

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    Temperature-sensitive mutants of TFIIB that are defective for essential interactions were isolated. One mutation (G204D) results in disruption of a protein-protein contact between TFIIB and TATA binding protein (TBP), while the other (K272I) disrupts an interaction between TFIIB and DNA. The TBP gene was mutagenized, and alleles that suppress the slow-growth phenotypes of the TFIIB mutants were isolated. TFIIB with the G204D mutation [TFIIB(G204D)] was suppressed by hydrophobic substitutions at lysine 239 of TBP. These changes led to increased affinity between TBP and TFIIB. TFIIB(K272I) was weakly suppressed by TBP mutants in which K239 was changed to hydrophobic residues. However, this mutant TFIIB was strongly suppressed by conservative substitutions in the DNA binding surface of TBP. Biochemical characterization showed that these TBP mutants had increased affinity for a TATA element. The TBPs with increased affinity could not suppress TFIIB(G204D), leading us to propose a two-step model for the interaction between TFIIB and the TBP-DNA complex

    cAMP Pulsing of Denuded Mouse Oocytes Increases Meiotic Resumption Via Activation of AMP-activated Protein Kinase

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    cAMP plays a critical role in the control of oocyte maturation, as a high level of cAMP maintains oocyte arrest at the first meiotic prophase. Yet this study shows that pulsing meiotically arrested denuded oocytes (DO) with cAMP induces oocyte maturation through the activation of AMP-activated protein kinase (PRKA). Short-term (3 h) pulsing of meiotically arrested oocytes with forskolin, an adenyl cyclase (AC) activator, increased oocyte cAMP, led to elevated AMP, and induced oocyte meiotic resumption compared to oocytes continuously cultured in the control medium with or without forskolin. Western analysis showed that germinal vesicle (GV)-stage oocytes after forskolin pulsing contained increased levels of phospho-acetyl CoA carboxylase (pACACA), a primary substrate of PRKA. Pulsing oocytes with the phosphodiesterase (PDE)-sensitive cAMP analog, 8-bromo-cAMP (8-Br-cAMP), also increased pACACA and pPRKA levels in GV-stage oocytes and induced oocyte meiotic resumption. Moreover, the PRKA inhibitors, compound C and araA, prevented 8-Br-cAMP pulsing-induced maturation. The lack of effect on meiotic induction and PRKA activation when oocytes were pulsed with the PDE-resistant activators of cAMP-dependent protein kinase, Sp-cAMP-AM and Sp-5,6-DCI-cBIMPS, suggests that cAMP degradation is required for pulsing-induced maturation. Pulsing oocytes with the exchange protein directly activated by cAMP (Epac)-specific activator, 8-CPT-2′-O-Me-cAMP, had no stimulatory effect on oocyte maturation, suggesting Epac is not involved in the pulsing-induced maturation. Taken together, these data support the idea that a transient increase in oocyte cAMP can induce meiotic resumption via activation of PRKA

    Medical legal partnership and health informatics impacting child health: Interprofessional innovations

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    Dramatic differences in health are closely related to degrees of social and economic disadvantage. Poverty-induced hardships such as food insecurity, utility shut-offs, and substandard housing, all have the potential to negatively impact the health of families. In an effort to better address social determinants of health in pediatric primary health care settings using the Medical Legal Partnership (MLP) model of health care delivery, an interprofessional team of investigators came together to design an innovative process for using computerized clinical decision support to identify health-harming legal and social needs, improve the delivery of appropriate physician counseling, and streamline access to legal and social service professionals when non-medical remedies are required. This article describes the interprofessional nature of the MLP model itself, illustrates the work that was done to craft this innovative health informatics approach to implementing MLP, and demonstrates how pediatricians, social workers and attorneys may work together to improve child health outcomes

    Effect of a Computer-Based Decision Support Intervention on Autism Spectrum Disorder Screening in Pediatric Primary Care Clinics: A Cluster Randomized Clinical Trial

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    Importance: Universal early screening for autism spectrum disorder (ASD) is recommended but not routinely performed. Objective: To determine whether computer-automated screening and clinical decision support can improve ASD screening rates in pediatric primary care practices. Design, Setting, and Participants: This cluster randomized clinical trial, conducted between November 16, 2010, and November 21, 2012, compared ASD screening rates among a random sample of 274 children aged 18 to 24 months in urban pediatric clinics of an inner-city county hospital system with or without an ASD screening module built into an existing decision support software system. Statistical analyses were conducted from February 6, 2017, to June 1, 2018. Interventions: Four clinics were matched in pairs based on patient volume and race/ethnicity, then randomized within pairs. Decision support with the Child Health Improvement Through Computer Automation system (CHICA) was integrated with workflow and with the electronic health record in intervention clinics. Main Outcomes and Measures: The main outcome was screening rates among children aged 18 to 24 months. Because the intervention was discontinued among children aged 18 months at the request of the participating clinics, only results for those aged 24 months were collected and analyzed. Rates of positive screening results, clinicians' response rates to screening results in the computer system, and new cases of ASD identified were also measured. Main results were controlled for race/ethnicity and intracluster correlation. Results: Two clinics were randomized to receive the intervention, and 2 served as controls. Records from 274 children (101 girls, 162 boys, and 11 missing information on sex; age range, 23-30 months) were reviewed (138 in the intervention clinics and 136 in the control clinics). Of 263 children, 242 (92.0%) were enrolled in Medicaid, 138 (52.5%) were African American, and 96 (36.5%) were Hispanic. Screening rates in the intervention clinics increased from 0% (95% CI, 0%-5.5%) at baseline to 68.4% (13 of 19) (95% CI, 43.4%-87.4%) in 6 months and to 100% (18 of 18) (95% CI, 81.5%-100%) in 24 months. Control clinics had no significant increase in screening rates (baseline, 7 of 64 children [10.9%]; 6-24 months after the intervention, 11 of 72 children [15.3%]; P = .46). Screening results were positive for 265 of 980 children (27.0%) screened by CHICA during the study period. Among the 265 patients with positive screening results, physicians indicated any response in CHICA in 151 (57.0%). Two children in the intervention group received a new diagnosis of ASD within the time frame of the study. Conclusions and Relevance: The findings suggest that computer automation, when integrated with clinical workflow and the electronic health record, increases screening of children for ASD, but follow-up by physicians is still flawed. Automation of the subsequent workup is still needed

    Retrospective Evaluation of the Epidemiology and Practice Variation of Dexmedetomidine Use in Invasively Ventilated Pediatric Intensive Care Admissions, 2007-2013

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    OBJECTIVES: The study assessed dexmedetomidine utilization and practice variation over time in ventilated pediatric intensive care unit (PICU) patients; and evaluated differences in hospital outcomes between high- and low-dexmedetomidine utilization hospitals. STUDY DESIGN: This serial cross-sectional analysis used administrative data from PICU admissions in the pediatric health information system (37 US tertiary care pediatric hospitals). Included admissions from 2007 to 2013 had simultaneous dexmedetomidine and invasive mechanical ventilation charges, <18 years of age, excluding neonates. Patient and hospital characteristics were compared as well as hospital-level severity-adjusted indexed length of stay (LOS), charges, and mortality. RESULTS: The utilization of dexmedetomidine increased from 6.2 to 38.2 per 100 ventilated PICU patients among pediatric hospitals. Utilization ranged from 3.8 to 62.8 per 100 in 2013. Few differences in patient demographics and no differences in hospital-level volume/severity of illness measures between high- and low-utilization hospitals occurred. No differences in hospital-level, severity-adjusted indexed outcomes (LOS, charges, and mortality) were found. CONCLUSION: Wide practice variation in utilization of dexmedetomidine for ventilated PICU patients existed even as use has increased sixfold. Higher utilization was not associated with increased hospital charges or reduced hospital LOS. Further work should define the expected outcome benefits of dexmedetomidine and its appropriate use

    Patient-tailored prioritization for a pediatric care decision support system through machine learning

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    Objective Over 8 years, we have developed an innovative computer decision support system that improves appropriate delivery of pediatric screening and care. This system employs a guidelines evaluation engine using data from the electronic health record (EHR) and input from patients and caregivers. Because guideline recommendations typically exceed the scope of one visit, the engine uses a static prioritization scheme to select recommendations. Here we extend an earlier idea to create patient-tailored prioritization. Materials and methods We used Bayesian structure learning to build networks of association among previously collected data from our decision support system. Using area under the receiver-operating characteristic curve (AUC) as a measure of discriminability (a sine qua non for expected value calculations needed for prioritization), we performed a structural analysis of variables with high AUC on a test set. Our source data included 177 variables for 29 402 patients. Results The method produced a network model containing 78 screening questions and anticipatory guidance (107 variables total). Average AUC was 0.65, which is sufficient for prioritization depending on factors such as population prevalence. Structure analysis of seven highly predictive variables reveals both face-validity (related nodes are connected) and non-intuitive relationships. Discussion We demonstrate the ability of a Bayesian structure learning method to ‘phenotype the population’ seen in our primary care pediatric clinics. The resulting network can be used to produce patient-tailored posterior probabilities that can be used to prioritize content based on the patient's current circumstances. Conclusions This study demonstrates the feasibility of EHR-driven population phenotyping for patient-tailored prioritization of pediatric preventive care services

    Pediatricians’ Responses to Printed Clinical Reminders: Does Highlighting Prompts Improve Responsiveness?

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    Objective Physicians typically respond to roughly half of the clinical decision support prompts they receive. This study was designed to test the hypothesis that selectively highlighting prompts in yellow would improve physicians' responsiveness. Methods We conducted a randomized controlled trial using the Child Health Improvement Through Computer Automation clinical decision support system in 4 urban primary care pediatric clinics. Half of a set of electronic prompts of interest was highlighted in yellow when presented to physicians in 2 clinics. The other half of the prompts was highlighted when presented to physicians in the other 2 clinics. Analyses compared physician responsiveness to the 2 randomized sets of prompts: highlighted versus not highlighted. Additionally, several prompts deemed high priority were highlighted during the entire study period in all clinics. Physician response rates to the high-priority highlighted prompts were compared to response rates for those prompts from the year before the study period, when they were not highlighted. Results Physicians did not respond to prompts that were highlighted at higher rates than prompts that were not highlighted (62% and 61%, respectively; odds ratio 1.056, P = .259, NS). Similarly, physicians were no more likely to respond to high-priority prompts that were highlighted compared to the year before, when the prompts were not highlighted (59% and 59%, respectively, χ2 = 0.067, P = .796, NS). Conclusions Highlighting reminder prompts did not increase physicians' responsiveness. We provide possible explanations why highlighting did not improve responsiveness and offer alternative strategies to increasing physician responsiveness to prompts

    Understanding the acceptability of a computer decision support system in pediatric primary care

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    Objective Individual users' attitudes and opinions help predict successful adoption of health information technology (HIT) into practice; however, little is known about pediatric users' acceptance of HIT for medical decision-making at the point of care. Materials and methods We wished to examine the attitudes and opinions of pediatric users' toward the Child Health Improvement through Computer Automation (CHICA) system, a computer decision support system linked to an electronic health record in four community pediatric clinics. Surveys were administered in 2011 and 2012 to all users to measure CHICA's acceptability and users' satisfaction with it. Free text comments were analyzed for themes to understand areas of potential technical refinement. Results 70 participants completed the survey in 2011 (100% response rate) and 64 of 66 (97% response rate) in 2012. Initially, satisfaction with CHICA was mixed. In general, users felt the system held promise; however various critiques reflected difficulties understanding integrated technical aspects of how CHICA worked, as well as concern with the format and wording on generated forms for families and users. In the subsequent year, users' ratings reflected improved satisfaction and acceptance. Comments also reflected a deeper understanding of the system's logic, often accompanied by suggestions on potential refinements to make CHICA more useful at the point of care. Conclusions Pediatric users appreciate the system's automation and enhancements that allow relevant and meaningful clinical data to be accessible at point of care. Understanding users' acceptability and satisfaction is critical for ongoing refinement of HIT to ensure successful adoption into practice

    Screen Exposure and BMI Status in 2-11 Year Old Children

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    Objective. To measure the relationship between screen exposure and obesity in a large, urban sample of children and to examine whether the relationship is moderated by sociodemographics. Methods. We asked parents of 11 141 children visiting general pediatrics clinics if the child had a television (TV) in the bedroom and/or watched more than 2 hours of TV/computer daily. We measured children’s height and weight, then used logistic regression to determine whether screen exposure indicators predicted obesity (body mass index ≥85th percentile) and interacted with race/ethnicity, sex, age, and health care payer. Results. Having a TV in the bedroom predicted obesity risk (P = .01); however, watching TV/computer for more than 2 hours a day did not (P = 0.54). There were no interactions. Conclusions. Asking whether a child has a TV in the bedroom may be more important than asking about duration of screen exposure to predict risk for obesity

    The HealthPia GlucoPack™ Diabetes Phone: A Usability Study

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    This is a copy of an article published in Diabetes Technology & Therapeutics copyright Mary Ann Liebert, Inc.; Diabetes Technology & Therapeutics is available online at: http://online.liebertpub.com.Background: Type I diabetes is a common chronic disease of childhood. Both the growing influence of peers and the shifting away from parental influence have been implicated as prime elements contributing to poor glycemic outcomes in adolescents. Mobile technology that can be directed towards providing self-management support and modifying potentially negative child parent interaction holds promise to improve control in adolescents with diabetes. Methods: HealthPia, Inc. (Palisades Park, NJ) has developed a prototype system, the HealthPia GlucoPack™ Diabetes Monitoring System, which integrates a small blood glucose monitoring device into the battery pack of a cell phone. A pilot study used mixed quantitative and qualitative methods to evaluate user satisfaction with the integrated system, including the potential of the device to transmit self-monitoring data to a website for review and analysis by clinicians, parents, and patients. Results: Adolescents in our study liked the integration of the two technologies and agreed that the glucometer was easy to use and that the tool was useful in the management of their diabetes. Conclusions: Future work will focus on the utilization of the diabetes phone as a component of a care delivery system for adolescents with diabetes, including involvement of the health care team and enhancement of the web services that support the use of the phone
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