Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Does high activity after total and unicompartmental knee arthroplasty increase the risk for aseptic revision?

INTRODUCTION It has been suggested that high activity might negatively impact implant survival following total and unicompartmental knee arthroplasty (TKA/UKA) and many surgeons advise their patients to only participate in moderate level sport activities. To date, it remains unclear whether such restraints are necessary to assure longevity of the implants. MATERIALS AND METHODS We conducted a retrospective study on 1906 knees (1745 TKA, 161 UKA) in 1636 patients aged 45-75 years who underwent primary arthroplasty for primary osteoarthritis. Lower extremity activity scale (LEAS) at a two year follow-up was assessed to define the activity level. Cases were grouped in low (LEAS ≤ 6), moderate (LEAS 7-13) and high activity (LEAS ≥ 14). Cohorts were compared with Kruskal-Wallis- or Pearson-Chi2-Test. Univariate logistic regression was conducted to test for association between activity level at two years and later revisions. Odds ratio was reported and converted to predicted probability. A Kaplan-Meier curve was plotted to predict implant survival. RESULTS The predicted implant survival for UKA was 100.0% at two years and 98.1% at five years. The predicted implant survival for TKA was 99.8% at two years, 98.1% at five years. The difference was not significant (p = 0.410). 2.5% of the UKA underwent revision, one knee in the low and three knees in the moderate activity group, differences between the moderate and high activity group were not significant (p = 0.292). The revision rate in the high activity TKA group was lower than in the low and moderate activity groups (p = 0.008). A higher LEAS two years after surgery was associated with a lower risk for future revision (p = 0.001). A one-point increase in LEAS two years after surgery lowered the odds for undergoing revision surgery by 19%. CONCLUSIONS The study suggests that participating in sports activity following both UKA and TKA is safe and not a risk factor for revision surgery at a mid-term follow-up. Patients should not be prevented from an active lifestyle following knee replacement

Timing and volume of crystalloid and blood products in pediatric trauma: An Eastern Association for the Surgery of Trauma multicenter prospective observational study

Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved. BACKGROUND The purpose of this study was to determine the relationship between timing and volume of crystalloid before blood products and mortality, hypothesizing that earlier transfusion and decreased crystalloid before transfusion would be associated with improved outcomes. METHODS A multi-institutional prospective observational study of pediatric trauma patients younger than 18 years, transported from the scene of injury with elevated age-adjusted shock index on arrival, was performed from April 2018 to September 2019. Volume and timing of prehospital, emergency department, and initial admission resuscitation were assessed including calculation of 20 ± 10 mL/kg crystalloid boluses overall and before transfusion. Multivariable Cox proportional hazards and logistic regression models identified factors associated with mortality and extended intensive care, ventilator, and hospital days. RESULTS In 712 children at 24 trauma centers, mean age was 7.6 years, median (interquartile range) Injury Severity Score was 9 (2-20), and in-hospital mortality was 5.3% (n = 38). There were 311 patients(43.7%) who received at least one crystalloid bolus and 149 (20.9%) who received blood including 65 (9.6%) with massive transfusion activation. Half (53.3%) of patients who received greater than one crystalloid bolus required transfusion. Patients who received blood first (n = 41) had shorter median time to transfusion (19.8 vs. 78.0 minutes, p = 0.005) and less total fluid volume (50.4 vs. 86.6 mL/kg, p = 0.033) than those who received crystalloid first despite similar Injury Severity Score (median, 22 vs. 27, p = 0.40). On multivariable analysis, there was no association with mortality (p = 0.51); however, each crystalloid bolus after the first was incrementally associated with increased odds of extended ventilator, intensive care unit, and hospital days (all p \u3c 0.05). Longer time to transfusion was associated with extended ventilator duration (odds ratio, 1.11; p = 0.04). CONCLUSION Resuscitation with greater than one crystalloid bolus was associated with increased need for transfusion and worse outcomes including extended duration of mechanical ventilation and hospitalization in this prospective study. These data support a crystalloid-sparing, early transfusion approach for resuscitation of injured children. LEVEL OF EVIDENCE Therapeutic, level IV

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense