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AbstractDuring the immune response there is a significant increase in the number of protrusions of the chloroplast stroma known as stromules. These stromules were reported over 100 years ago; however, their function has remained elusive. Recently, it was shown that chloroplast stromules are induced during the plant innate immune response and serve as positive regulators of the immune response. Toward this, we wanted to identify genetic components required for stromule formation. Using a candidate approach, we found a kinesin containing a calponin homology domain, Kinesin required for Inducing Stromules 1 (KIS1) which induces constitutive stromule formation when overexpressed and is required for N-TNL mediated stromule formation and immunity to Tobacco mosaic virus (TMV). Further, we used pathogen effectors as a probe to identify proteins which are required for stromule formation and subsequent perinuclear chloroplast clustering during the N-mediated immune response. Using TurboID-based proximity labeling we identified four proteins which interact with the Type 3 secretion system (T3SS) effector SH9 and are required for immunity to TMV. Toward this, we showed that one SH9 interactor, Sh9 interactor 1 is required for perinuclear chloroplast clustering during the N-mediated immune response. Our results with KIS1 represent the first known component required for stromule formation. Further, our initial characterization of Sh9 interactor 1 suggests that perinuclear chloroplast clustering is required for immunity to TMV and is specifically inhibited by the effector Sh9.

While the discourses of the physical and social sciences gave literary naturalists a useful framework for understanding the world (how environments determined human action), those same discourses ended up calling into question the freedom—and thus the value—of artists who were themselves understood to be determined by natural and social forces. How, over time, literary naturalists frame and navigate both this issue and the larger issue of the relationship between the aesthetic and determinism furnishes the subject matter for this dissertation.

The pediatric early warning score (PEWS) is a tool used to predict clinical deterioration. Referenced vital sign parameters are based on expert opinion but heart rate and respiratory rate percentiles in hospitalized children have been published. This retrospective case-control study of unplanned intensive care unit (ICU) transfers compares evidence-based vital signs (EBVS) effect on PEWS sensitivity and specificity, determines the impact of age categories on PEWS deterioration prediction, and evaluates whether EBVS PEWS is associated with need for invasive ICU supports. EBVS PEWS improved sensitivity (43%-71% vs 30%-63%) for unplanned transfers with slightly decreased specificity (88%-98% vs 93%-99%). Logistic regression analysis and odds ratios (ORs) demonstrated EBVS PEWS was associated with increased risk for ICU-specific supports (OR = 1.16, 95% confidence interval [CI] = 1.0-1.34, P = .0498). Evidence-based vital signs can improve PEWS sensitivity to identify unplanned ICU transfers and identify patients requiring ICU-specific interventions.

Diversion of cerebrospinal fluid (CSF) has been used for decades as a treatment for children with severe traumatic brain injury (TBI) and is recommended by evidenced-based guidelines. However, these recommendations are based on limited studies.To determine whether CSF diversion is associated with improved Glasgow Outcome Score-Extended for Pediatrics (GOS-EP) and decreased intracranial pressure (ICP) in children with severe TBI.This observational comparative effectiveness study was performed at 51 clinical centers that routinely care for children with severe TBI in 8 countries (US, United Kingdom, Spain, the Netherlands, Australia, New Zealand, South Africa, and India) from February 2014 to September 2017, with follow-up at 6 months after injury (final follow-up, October 22, 2021). Children with severe TBI were included if they had Glasgow Coma Scale (GCS) scores of 8 or lower, had intracranial pressure (ICP) monitor placed on-site, and were aged younger than 18 years. Children were excluded if they were pregnant or an ICP monitor was not placed at the study site. Consecutive children were screened and enrolled, data regarding treatments were collected, and at discharge, consent was obtained for outcomes testing. Propensity matching for pretreatment characteristics was performed to develop matched pairs for primary analysis. Data analyses were completed on April 18, 2022.Clinical care followed local standards, including the use of CSF diversion (or not), with patients stratified at the time of ICP monitor placement (CSF group vs no CSF group).The primary outcome was GOS-EP at 6 months, while ICP was considered as a secondary outcome. CSF vs no CSF was treated as an intention-to-treat analysis, and a sensitivity analysis was performed for children who received delayed CSF diversion.A total of 1000 children with TBI were enrolled, including 314 who received CSF diversion (mean [SD] age, 7.18 [5.45] years; 208 [66.2%] boys) and 686 who did not (mean [SD] age, 7.79 [5.33] years; 437 [63.7%] boys). The propensity-matched analysis included 98 pairs. In propensity score-matched analyses, there was no difference between groups in GOS-EP (median [IQR] difference, 0 [-3 to 1]; P = .08), but there was a decrease in overall ICP in the CSF group (mean [SD] difference, 3.97 [0.12] mm Hg; P .001).In this comparative effectiveness study, CSF diversion was not associated with improved outcome at 6 months after TBI, but a decrease in ICP was observed. Given the higher quality of evidence generated by this study, current evidence-based guidelines related to CSF diversion should be reconsidered.

Background: Early warning scores aid in the detection of pediatric clinical deteriorations but include limited data inputs, rarely include data trends over time, and have limited validation. Objective: Machine learning methods that make use of large numbers of predictor variables are now commonplace. This work examines how different types of predictor variables derived from the electronic health record affect the performance of predicting unplanned transfers to the intensive care unit (ICU) at three large children’s hospitals. Methods: We trained separate models with data from three different institutions from 2011 through 2013 and evaluated models with 2014 data. Cases consisted of patients who transferred from the floor to the ICU and met one or more of 5 different priori defined criteria for suspected unplanned transfers. Controls were patients who were never transferred to the ICU. Predictor variables for the models were derived from vitals, labs, acuity scores, and nursing assessments. Classification models consisted of L1 and L2 regularized logistic regression and neural network models. We evaluated model performance over prediction horizons ranging from 1 to 16 hours. Results: Across the three institutions, the c-statistic values for our best models were 0.892 (95% CI 0.875-0.904), 0.902 (95% CI 0.880-0.923), and 0.899 (95% CI 0.879-0.919) for the task of identifying unplanned ICU transfer 6 hours before its occurrence and achieved 0.871 (95% CI 0.855-0.888), 0.872 (95% CI 0.850-0.895), and 0.850 (95% CI 0.825-0.875) for a prediction horizon of 16 hours. For our first model at 80% sensitivity, this resulted in a specificity of 80.5% (95% CI 77.4-83.7) and a positive predictive value of 5.2% (95% CI 4.5-6.2). Conclusions: Feature-rich models with many predictor variables allow for patient deterioration to be predicted accurately, even up to 16 hours in advance.

Parapneumonic effusions complicating pneumonia are associated with increased morbidity and mortality. Along with increased mortality, complicated parapneumonic effusion and empyema often necessitate prolonged treatment, longer hospital stay and interventions. Parapneumonic effusions arise from inflammation in the lungs and pleural space from direct invasion of bacteria, cascade of inflammatory events and bacteriologic virulence features. Patient factors and comorbidities also contribute to the pathophysiology of parapneumonic effusion development. The evolution of parapneumonic effusion can be divided into three progressive stages: (I) exudative stage; (II) fibrinopurulent stage; and (III) organizing stage with pleural peel formation. These stages can help categorize effusions into groups in order to evaluate the risk of a complicated course requiring intervention. We recommend that clinical data be evaluated and a stepwise approach be taken in management of these patients. This review article discusses current understanding of the development and relationship of parapneumonic effusions with pneumonia.

Assessment of oxygenation in patients with community-acquired pneumonia is critical for treatment. The accuracy of percutaneous oxygen saturation (SpO2 ) determined by pulse oximetry is uncertain, and it has limited value in patients receiving supplemental oxygen. We hypothesized that calculation of partial arterial oxygen concentration/inspired oxygen faction (PaO2 /FiO2 ) from SpO2 by the Ellis or Rice equations might adequately correlate with PaO2 /FiO2 measured by arterial blood gases.We studied 1004 patients with pneumonia in the emergency department with simultaneous measurement of SpO2 and PaO2 from two cohorts from Valencia, Spain and Utah, USA. We compared SpO2 with measured SaO2 , compared the equations' accuracy in calculating PaO2 /FiO2 and determined how often patients would be misclassified at clinically important thresholds. We compared estimated PaO2 /FiO2 to measured PaO2 /FiO2 using the Spearman correlation.Pairwise correlation of SpO2 with SaO2 was moderate (rho = 0.66; P 0.01). Both equations performed similarly among patients with lower PaO2 /FiO2 ratios. The Ellis equation estimated PaO2 /FiO2 from SpO2 more accurately than the Rice equation in patients with PaO2 /FiO2 ≥200. Simple agreement between calculated and measured P/F was 91% and 92%, respectively.The Ellis equation was more accurate than the Rice equation for estimating PaO2 /FiO2 , especially at higher levels of P/F ratio. Estimation of PaO2 /FiO2 from SpO2 is accurate enough for initial oxygenation assessment. Ellis and Rice equations could misclassify 20% and 30% of patients, respectively, at higher levels of PaO2 /FiO2 . For patients with abnormal oxygenation falling near thresholds for clinical decision making, arterial blood gas measurement preferably on room air is more accurate.