Testing the Prognostic Accuracy of the Updated Pediatric Sepsis Biomarker Risk Model

Background We previously derived and validated a risk model to estimate mortality probability in children with septic shock (PERSEVERE; PEdiatRic SEpsis biomarkEr Risk modEl). PERSEVERE uses five biomarkers and age to estimate mortality probability. After the initial derivation and validation of PERSEVERE, we combined the derivation and validation cohorts (n = 355) and updated PERSEVERE. An important step in the development of updated risk models is to test their accuracy using an independent test cohort. Objective To test the prognostic accuracy of the updated version PERSEVERE in an independent test cohort. Methods Study subjects were recruited from multiple pediatric intensive care units in the United States. Biomarkers were measured in 182 pediatric subjects with septic shock using serum samples obtained during the first 24 hours of presentation. The accuracy of PERSEVERE 28-day mortality risk estimate was tested using diagnostic test statistics, and the net reclassification improvement (NRI) was used to test whether PERSEVERE adds information to a physiology-based scoring system. Results Mortality in the test cohort was 13.2%. Using a risk cut-off of 2.5%, the sensitivity of PERSEVERE for mortality was 83% (95% CI 62–95), specificity was 75% (68–82), positive predictive value was 34% (22–47), and negative predictive value was 97% (91–99). The area under the receiver operating characteristic curve was 0.81 (0.70–0.92). The false positive subjects had a greater degree of organ failure burden and longer intensive care unit length of stay, compared to the true negative subjects. When adding PERSEVERE to a physiology-based scoring system, the net reclassification improvement was 0.91 (0.47–1.35; p<0.001). Conclusions The updated version of PERSEVERE estimates mortality probability reliably in a heterogeneous test cohort of children with septic shock and provides information over and above a physiology-based scoring system

Automated High-Content Live Animal Drug Screening Using C. elegans Expressing the Aggregation Prone Serpin α1-antitrypsin Z

The development of preclinical models amenable to live animal bioactive compound screening is an attractive approach to discovering effective pharmacological therapies for disorders caused by misfolded and aggregation-prone proteins. In general, however, live animal drug screening is labor and resource intensive, and has been hampered by the lack of robust assay designs and high throughput work-flows. Based on their small size, tissue transparency and ease of cultivation, the use of C. elegans should obviate many of the technical impediments associated with live animal drug screening. Moreover, their genetic tractability and accomplished record for providing insights into the molecular and cellular basis of human disease, should make C. elegans an ideal model system for in vivo drug discovery campaigns. The goal of this study was to determine whether C. elegans could be adapted to high-throughput and high-content drug screening strategies analogous to those developed for cell-based systems. Using transgenic animals expressing fluorescently-tagged proteins, we first developed a high-quality, high-throughput work-flow utilizing an automated fluorescence microscopy platform with integrated image acquisition and data analysis modules to qualitatively assess different biological processes including, growth, tissue development, cell viability and autophagy. We next adapted this technology to conduct a small molecule screen and identified compounds that altered the intracellular accumulation of the human aggregation prone mutant that causes liver disease in α1-antitrypsin deficiency. This study provides powerful validation for advancement in preclinical drug discovery campaigns by screening live C. elegans modeling α1-antitrypsin deficiency and other complex disease phenotypes on high-content imaging platforms

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

Human Genetic Diversity and Comparative Economic Development

This research contributes to the understanding of human genetic diversity within a society as a significant determinant of its economic development. The hypothesis advanced and empirically examined in this paper suggests that there are socioeconomic trade-offs associated with genetic diversity within a given society. The investigation exploits an exogenous source of cross-country variation in genetic diversity by appealing to the 'out of Africa' hypothesis of human origins to empirically establish a highly statistically significant and robust non-monotonic effect of genetic diversity on development outcomes in the pre-colonial era. Contrary to theories that reject a possible role for human genetics in influencing economic development, this study demonstrates the economic significance of diversity in genetic traits, while abstaining entirely from conceptual frameworks that posit a hierarchy of such traits in terms of their conduciveness to the process of economic development

Testing the prognostic accuracy of the updated pediatric sepsis biomarker risk model

Background We previously derived and validated a risk model to estimate mortality probability in children with septic shock (PERSEVERE; PEdiatRic SEpsis biomarkEr Risk modEl). PERSEVERE uses five biomarkers and age to estimate mortality probability. After the initial derivation and validation of PERSEVERE, we combined the derivation and validation cohorts (n = 355) and updated PERSEVERE. An important step in the development of updated risk models is to test their accuracy using an independent test cohort. Objective To test the prognostic accuracy of the updated version PERSEVERE in an independent test cohort. Methods Study subjects were recruited from multiple pediatric intensive care units in the United States. Biomarkers were measured in 182 pediatric subjects with septic shock using serum samples obtained during the first 24 hours of presentation. The accuracy of PERSEVERE 28-day mortality risk estimate was tested using diagnostic test statistics, and the net reclassification improvement (NRI) was used to test whether PERSEVERE adds information to a physiology-based scoring system. Results Mortality in the test cohort was 13.2%. Using a risk cut-off of 2.5%, the sensitivity of PERSEVERE for mortality was 83% (95% CI 62–95), specificity was 75% (68–82), positive predictive value was 34% (22–47), and negative predictive value was 97% (91–99). The area under the receiver operating characteristic curve was 0.81 (0.70–0.92). The false positive subjects had a greater degree of organ failure burden and longer intensive care unit length of stay, compared to the true negative subjects. When adding PERSEVERE to a physiology-based scoring system, the net reclassification improvement was 0.91 (0.47–1.35; p\u3c0.001). Conclusions The updated version of PERSEVERE estimates mortality probability reliably in a heterogeneous test cohort of children with septic shock and provides information over and above a physiology-based scoring system

The temporal version of the pediatric sepsis biomarker risk model

Background PERSEVERE is a risk model for estimating mortality probability in pediatric septic shock, using five biomarkers measured within 24 hours of clinical presentation. Objective Here, we derive and test a temporal version of PERSEVERE (tPERSEVERE) that considers biomarker values at the first and third day following presentation to estimate the probability of a “complicated course”, defined as persistence of ≥2 organ failures at seven days after meeting criteria for septic shock, or death within 28 days. Methods Biomarkers were measured in the derivation cohort (n = 225) using serum samples obtained during days 1 and 3 of septic shock. Classification and Regression Tree (CART) analysis was used to derive a model to estimate the risk of a complicated course. The derived model was validated in the test cohort (n = 74), and subsequently updated using the combined derivation and test cohorts. Results A complicated course occurred in 23% of the derivation cohort subjects. The derived model had a sensitivity for a complicated course of 90% (95% CI 78–96), specificity was 70% (62–77), positive predictive value was 47% (37–58), and negative predictive value was 96% (91–99). The area under the receiver operating characteristic curve was 0.85 (0.79–0.90). Similar test characteristics were observed in the test cohort. The updated model had a sensitivity of 91% (81–96), a specificity of 70% (64–76), a positive predictive value of 47% (39–56), and a negative predictive value of 96% (92–99). Conclusions tPERSEVERE reasonably estimates the probability of a complicated course in children with septic shock. tPERSEVERE could potentially serve as an adjunct to physiological assessments for monitoring how risk for poor outcomes changes during early interventions in pediatric septic shock

Angiopoietin-1, Angiopoietin-2 and Bicarbonate as Diagnostic Biomarkers in Children with Severe Sepsis

<div><p>Severe pediatric sepsis continues to be associated with high mortality rates in children. Thus, an important area of biomedical research is to identify biomarkers that can classify sepsis severity and outcomes. The complex and heterogeneous nature of sepsis makes the prospect of the classification of sepsis severity using a single biomarker less likely. Instead, we employ machine learning techniques to validate the use of a multiple biomarkers scoring system to determine the severity of sepsis in critically ill children. The study was based on clinical data and plasma samples provided by a tertiary care center's Pediatric Intensive Care Unit (PICU) from a group of 45 patients with varying sepsis severity at the time of admission. Canonical Correlation Analysis with the Forward Selection and Random Forests methods identified a particular set of biomarkers that included Angiopoietin-1 (Ang-1), Angiopoietin-2 (Ang-2), and Bicarbonate (HCO) as having the strongest correlations with sepsis severity. The robustness and effectiveness of these biomarkers for classifying sepsis severity were validated by constructing a linear Support Vector Machine diagnostic classifier. We also show that the concentrations of Ang-1, Ang-2, and HCO enable predictions of the time dependence of sepsis severity in children.</p></div

Prediction measures obtained from the Support Vector Machine (SVM) using the -combinations selected by the Canonical Correlation Analysis (CCA) and Random Forests (RF) methods.

<p>The prediction measures (A) true positive rate (TPR), (B) true negative rate (TNR), (C) positive predictive value (PPV), and (D) negative predictive value (NPV) are shown for each step . For each , a SVM ensemble with bagging is constructed based on the CCA- and RF-selected subset of biomarkers.</p

Prediction measures for single biomarker from Support Vector Machine.

<p>True positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) are shown for each single variable.</p><p>Prediction measures for single biomarker from Support Vector Machine.</p