Prediction of adverse perinatal outcome by fetal biometry: comparison of customized and populationâ based standards

ObjectiveTo compare the predictive performance of estimated fetal weight (EFW) percentiles, according to eight growth standards, to detect fetuses at risk for adverse perinatal outcome.MethodsThis was a retrospective cohort study of 3437 Africanâ American women. Populationâ based (Hadlock, INTERGROWTHâ 21st, World Health Organization (WHO), Fetal Medicine Foundation (FMF)), ethnicityâ specific (Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)), customized (Gestationâ Related Optimal Weight (GROW)) and Africanâ American customized (Perinatology Research Branch (PRB)/NICHD) growth standards were used to calculate EFW percentiles from the last available scan prior to delivery. Prediction performance indices and relative risk (RR) were calculated for EFW â 90th percentiles, according to each standard, for individual and composite adverse perinatal outcomes. Sensitivity at a fixed (10%) falseâ positive rate (FPR) and partial (FPR â 90th percentile were also at risk for any adverse perinatal outcome according to the INTERGROWTHâ 21st (RRâ =â 1.4; 95%â CI, 1.0â 1.9) and Hadlock (RRâ =â 1.7; 95%â CI, 1.1â 2.6) standards, many times fewer cases (2â 5â fold lower sensitivity) were detected by using EFW >â 90th percentile, rather than EFW â 90th percentile were at increased risk of adverse perinatal outcomes according to all or some of the eight growth standards, respectively. The RR of a composite adverse perinatal outcome in pregnancies with EFW <â 10th percentile was higher for the mostâ stringent (NICHD) compared with the leastâ stringent (FMF) standard. The results of the complementary analysis of AUC suggest slightly improved detection of adverse perinatal outcome by more recent populationâ based (INTERGROWTHâ 21st) and customized (PRB/NICHD) standards compared with the Hadlock and FMF standards. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153734/1/uog20299.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153734/2/uog20299_am.pd

Promoter Nucleosome Organization Shapes the Evolution of Gene Expression

Understanding why genes evolve at different rates is fundamental to evolutionary thinking. In species of the budding yeast, the rate at which genes diverge in expression correlates with the organization of their promoter nucleosomes: genes lacking a nucleosome-free region (denoted OPN for “Occupied Proximal Nucleosomes”) vary widely between the species, while the expression of those containing NFR (denoted DPN for “Depleted Proximal Nucleosomes”) remains largely conserved. To examine if early evolutionary dynamics contributes to this difference in divergence, we artificially selected for high expression of GFP–fused proteins. Surprisingly, selection was equally successful for OPN and DPN genes, with ∼80% of genes in each group stably increasing in expression by a similar amount. Notably, the two groups adapted by distinct mechanisms: DPN–selected strains duplicated large genomic regions, while OPN–selected strains favored trans mutations not involving duplications. When selection was removed, DPN (but not OPN) genes reverted rapidly to wild-type expression levels, consistent with their lower diversity between species. Our results suggest that promoter organization constrains the early evolutionary dynamics and in this way biases the path of long-term evolution

The evolutionary dynamics of the Saccharomyces cerevisiae protein interaction network after duplication

Gene duplication is an important mechanism in the evolution of protein interaction networks. Duplications are followed by the gain and loss of interactions, rewiring the network at some unknown rate. Because rewiring is likely to change the distribution of network motifs within the duplicated interaction set, it should be possible to study network rewiring by tracking the evolution of these motifs. We have developed a mathematical framework that, together with duplication data from comparative genomic and proteomic studies, allows us to infer the connectivity of the preduplication network and the changes in connectivity over time. We focused on the whole-genome duplication (WGD) event in Saccharomyces cerevisiae. The model allowed us to predict the frequency of intergene interaction before WGD and the post duplication probabilities of interaction gain and loss. We find that the predicted frequency of self-interactions in the preduplication network is significantly higher than that observed in today's network. This could suggest a structural difference between the modern and ancestral networks, preferential addition or retention of interactions between ohnologs, or selective pressure to preserve duplicates of self-interacting proteins

Development of intuitive rules: Evaluating the application of the dual-system framework to understanding children's intuitive reasoning

This is an author-created version of this article. The original source of publication is Psychon Bull Rev. 2006 Dec;13(6):935-53 The final publication is available at www.springerlink.com Published version: http://dx.doi.org/10.3758/BF0321390

Changes in Triglyceride Levels Over Time and Risk of Type 2 Diabetes in Young Men

OBJECTIVE—The association between changes in triglyceride concentrations over time and diabetes is unknown. We assessed whether two triglyceride determinations obtained 5 years apart can predict incident type 2 diabetes

Management recommendations for pancreatic manifestations of von Hippel–Lindau disease

Von Hippel–Lindau disease (VHL) is a multineoplasm inherited disease manifesting with hemangioblastoma of the central nervous system and retina, adrenal pheochromocytoma, renal cell carcinoma, pancreatic neuroendocrine tumors and cysts, and neoplasms/cysts of the ear, broad ligament, and testicles. During 2018-2020, the VHL Alliance gathered several committees of experts in the various clinical manifestations of VHL to review the literature, gather the available evidence on VHL, and develop recommendations for patient management. The current report details the results of the discussion of a group of experts in the pancreatic manifestations of VHL along with their proposed recommendations for the clinical surveillance and management of patients with VHL. The recommendations subcommittee performed a comprehensive systematic review of the literature and conducted panel discussions to reach the current recommendations. The level of evidence was defined according to the Shekelle variation of the Grading of Recommendations, Assessment, Development, and Evaluation grading system. The National Comprehensive Cancer Network Categories of Evidence and Consensus defined the committee members' interpretation of the evidence and degree of consensus. The recommendations encompass the main aspects of VHL-related pancreatic manifestations and their clinical management. They are presented in a clinical orientation, including general planning of screening and surveillance for pancreatic neuroendocrine tumors, utility of biochemical biomarkers, the optimal choice for imaging modality, indirect risk stratification, indications for tissue sampling of VHL-related pancreatic neuroendocrine tumors, and interventions. These recommendations are designed to serve as the reference for all aspects of the screening, surveillance, and management of VHL-related pancreatic manifestations

Using resource graphs to represent conceptual change

We introduce resource graphs, a representation of linked ideas used when reasoning about specific contexts in physics. Our model is consistent with previous descriptions of resources and coordination classes. It can represent mesoscopic scales that are neither knowledge-in-pieces or large-scale concepts. We use resource graphs to describe several forms of conceptual change: incremental, cascade, wholesale, and dual construction. For each, we give evidence from the physics education research literature to show examples of each form of conceptual change. Where possible, we compare our representation to models used by other researchers. Building on our representation, we introduce a new form of conceptual change, differentiation, and suggest several experimental studies that would help understand the differences between reform-based curricula.Comment: 27 pages, 14 figures, no tables. Submitted for publication to the Physical Review Special Topics Physics Education Research on March 8, 200

Evidence-based decision support for pediatric rheumatology reduces diagnostic errors.

BACKGROUND: The number of trained specialists world-wide is insufficient to serve all children with pediatric rheumatologic disorders, even in the countries with robust medical resources. We evaluated the potential of diagnostic decision support software (DDSS) to alleviate this shortage by assessing the ability of such software to improve the diagnostic accuracy of non-specialists. METHODS: Using vignettes of actual clinical cases, clinician testers generated a differential diagnosis before and after using diagnostic decision support software. The evaluation used the SimulConsult® DDSS tool, based on Bayesian pattern matching with temporal onset of each finding in each disease. The tool covered 5405 diseases (averaging 22 findings per disease). Rheumatology content in the database was developed using both primary references and textbooks. The frequency, timing, age of onset and age of disappearance of findings, as well as their incidence, treatability, and heritability were taken into account in order to guide diagnostic decision making. These capabilities allowed key information such as pertinent negatives and evolution over time to be used in the computations. Efficacy was measured by comparing whether the correct condition was included in the differential diagnosis generated by clinicians before using the software ( unaided ), versus after use of the DDSS ( aided ). RESULTS: The 26 clinicians demonstrated a significant reduction in diagnostic errors following introduction of the software, from 28% errors while unaided to 15% using decision support (p \u3c 0.0001). Improvement was greatest for emergency medicine physicians (p = 0.013) and clinicians in practice for less than 10 years (p = 0.012). This error reduction occurred despite the fact that testers employed an open book approach to generate their initial lists of potential diagnoses, spending an average of 8.6 min using printed and electronic sources of medical information before using the diagnostic software. CONCLUSIONS: These findings suggest that decision support can reduce diagnostic errors and improve use of relevant information by generalists. Such assistance could potentially help relieve the shortage of experts in pediatric rheumatology and similarly underserved specialties by improving generalists\u27 ability to evaluate and diagnose patients presenting with musculoskeletal complaints. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT02205086

The gene SMART study: method, study design, and preliminary findings

The gene SMART (genes and the Skeletal Muscle Adaptive Response to Training) Study aims to identify genetic variants that predict the response to both a single session of High-Intensity Interval Exercise (HIIE) and to four weeks of High-Intensity Interval Training (HIIT). While the training and testing centre is located at Victoria University, Melbourne, three other centres have been launched at Bond University, Queensland University of Technology, Australia, and the University of Brighton, UK. Currently 39 participants have already completed the study and the overall aim is to recruit 200 moderately-trained, healthy Caucasians participants (all males 18–45 y, BMI \u3c 30). Participants will undergo exercise testing and exercise training by an identical exercise program. Dietary habits will be assessed by questionnaire and dietitian consultation. Activity history is assessed by questionnaire and current activity level is assessed by an activity monitor. Skeletal muscle biopsies and blood samples will be collected before, immediately after and 3 h post HIIE, with the fourth resting biopsy and blood sample taken after four weeks of supervised HIIT (3 training sessions per week). Each session consists of eight to fourteen 2-min intervals performed at the pre-training lactate threshold (LT) power plus 40 to 70% of the difference between pre-training lactate threshold (LT) and peak aerobic power (Wpeak). A number of muscle and blood analyses will be performed, including (but not limited to) genotyping, mitochondrial respiration, transcriptomics, protein expression analyses, and enzyme activity. The participants serve as their own controls. Even though the gene SMART study is tightly controlled, our preliminary findings still indicate considerable individual variability in both performance (in-vivo) and muscle (in-situ) adaptations to similar training. More participants are required to allow us to better investigate potential underlying genetic and molecular mechanisms responsible for this individual variability