BACKGROUND:Early recognition and intervention of hemorrhage are associated with decreased morbidity in children. Triage models have been developed to aid in the recognition of hemorrhagic shock after injury but require complete data and have limited accuracy. To address these limitations, we developed a Bayesian belief network, a machine learning model that represents the joint probability distribution for a set of observed or unobserved independent variables, to predict blood transfusion after injury in children and adolescents. METHODS:We abstracted patient, injury, and resuscitation characteristics of injured children and adolescents (age 1 to 18 years) from the 2017 to 2019 Trauma Quality Improvement Project database. We trained a Bayesian belief network to predict blood transfusion within 4 hours after arrival to the hospital following injury using data from 2017 and recalibrated the model using data from 2018. We validated our model on a subset of patients from the 2019 Trauma Quality Improvement Project. We evaluated model performance using the area under the receiver operating characteristic curve and calibration curves and compared performance with pediatric age-adjusted shock index (SIPA) and reverse shock index with Glasgow Coma Scale (rSIG) using sensitivity, specificity, accuracy, and Matthew's correlation coefficient (MCC). RESULTS:The final model included 14 predictor variables and had excellent discrimination and calibration. The model achieved an area under the receiver operating characteristic curve of 0.92 using emergency department data. When used as a binary predictor at an optimal threshold probability, the model had similar sensitivity, specificity, accuracy, and MCC compared with SIPA when only age, systolic blood pressure, and heart rate were observed. With the addition of the Glasgow Coma Scale score, the model has a higher accuracy and MCC than SIPA and rSIG. CONCLUSION:A Bayesian belief network predicted blood transfusion after injury in children and adolescents better than SIPA and rSIG. This probabilistic model may allow clinicians to stratify hemorrhagic control interventions based upon risk.

The R-type voltage-gated Ca2+ (Cav) channels Cav2.3, widely expressed in neuronal and neuroendocrine cells, represent potential drug targets for pain, seizures, epilepsy, and Parkinson’s disease. Despite their physiological importance, there have lacked selective small-molecule inhibitors targeting these channels. High-resolution structures may aid rational drug design. Here, we report the cryo-EM structure of human Cav2.3 in complex with α2δ−1 and β3 subunits at an overall resolution of 3.1 Å. The structure is nearly identical to that of Cav2.2, with VSDII in the down state and the other three VSDs up. A phosphatidylinositol 4,5-bisphosphate (PIP2) molecule binds to the interface of VSDII and the tightly closed pore domain. We also determined the cryo-EM structure of a Cav2.3 mutant in which a Cav2-unique cytosolic helix in repeat II (designated the CH2II helix) is deleted. This mutant, named ΔCH2, still reserves a down VSDII, but PIP2 is invisible and the juxtamembrane region on the cytosolic side is barely discernible. Our structural and electrophysiological characterizations of the wild type and ΔCH2 Cav2.3 show that the CH2II helix stabilizes the inactivated conformation of the channel by tightening the cytosolic juxtamembrane segments, while CH2II helix is not necessary for locking the down state of VSDII.

The Klebsiella pneumoniae phages SopranoGao, MezzoGao, and AltoGao were isolated from the Seneca Wastewater Treatment Plant in Germantown, MD. The following reports the complete genome sequence of these bacteriophages and describes their major features.