Detrimental Effect of Type I IFNs During Acute Lung Infection With Pseudomonas aeruginosa Is Mediated Through the Stimulation of Neutrophil NETosis.

Pseudomonas aeruginosa is an opportunistic multidrug-resistant pathogen, able to grow in biofilms. It causes life-threatening complications in diseases characterized by the up-regulation of type I interferon (IFN) signaling, such as cancer or viral infections. Since type I IFNs regulate multiple functions of neutrophils, which constitute the first line of anti-bacterial host defense, in this work we aimed to study how interferon-activated neutrophils influence the course of P. aeruginosa infection of the lung. In lungs of infected IFN-sufficient WT mice, significantly elevated bacteria load was observed, accompanied by the prominent lung tissue damage. At the same time IFN-deficient animals seem to be partly resistant to the infection. Lung neutrophils from such IFN-deficient animals release significantly lower amounts of neutrophil extracellular traps (NETs) and reactive oxygen species (ROS), as compared to WT neutrophils. Of note, such IFN-deficient neutrophils show significantly decreased capacity to stimulate biofilm formation by P. aeruginosa. Reduced biofilm production impairs in turn the survival of bacteria in a lung tissue. In line with that, treatment of neutrophils with recombinant IFN-ß enhances their NETosis and stimulates biofilm formation by Pseudomonas after co-incubation with such neutrophils. Possibly, bacteria utilizes neutrophil-derived NETs as a scaffold for released biofilms. In agreement with this, in vivo treatment with ROS-scavengers, NETs disruption or usage of the bacterial strains unable to bind DNA, suppress neutrophil-mediated biofilm formation in the lungs. Together, our findings indicate that the excessive activation of neutrophils by type I IFNs leads to their boosted NETosis that in turn triggers biofilm formation by P. aeruginosa and supports its persistence in the infected lung. Targeting these mechanisms could offer a new therapeutic approach to prevent persistent bacterial infections in patients with diseases associated with the up-regulation of type I IFNs.

Prognostic Role of Blood NETosis in the Progression of Head and Neck Cancer.

Neutrophil extracellular traps (NETs) represent web-like structures consisting of externalized DNA decorated with granule proteins that are responsible for trapping and killing bacteria. However, undesirable effects of NET formation during carcinogenesis, such as metastasis support, have been described. In the present study, we evaluated the correlation between NETosis and disease progression in head and neck cancer (HNC) patients in order to establish a valid biomarker for an early detection and monitoring of HNC progression. Moreover, factors influencing NET release in HNC patients were revealed. We showed a significantly elevated vital NETosis in neutrophils isolated from early T1-T2 and N0-N2 stage patients, as compared to healthy controls. Additionally, in our experimental setting, we confirmed the involvement of tumor cells in the stimulation of NET formation. Interestingly, in advanced cancer stages (T3-4, N3) NETosis was reduced. This also correlated with the levels of granulocyte colony-stimulating factor (G-CSF) in plasma and tumor tissue. Altogether, we suggest that the elevated NETosis in blood can be used as a biomarker to detect early HNC and to predict patients at risk to develop tumor metastasis. Therapeutic disruption of NET formation may offer new roads for successful treatment of HNC patients in order to prevent metastasis.

It's About Time: Transfusion effects on postinjury platelet aggregation over time.

BACKGROUND: Impaired postinjury platelet aggregation is common, but the effect of transfusion on this remains unclear. Data suggest that following injury platelet transfusion may not correct impaired platelet aggregation, and impaired platelet aggregation may not predict the need for platelet transfusion. We sought to further investigate platelet aggregation responses to transfusions, using regression statistics to isolate the independent effects of transfusions given in discrete time intervals from injury on both immediate and longitudinal platelet aggregation. We hypothesized that platelet aggregation response to platelet transfusion increases over time from injury. METHODS: Serial (0-96 hours) blood samples were collected from 248 trauma patients. Platelet aggregation was assessed in vitro with impedance aggregometry stimulated by adenosine diphosphate, collagen, and thrombin receptor-activating peptide-6. Using regression, transfusion exposure was modeled against platelet aggregation at each subsequent timepoint and adjusted for confounders (Injury Severity Score, international normalized ratio (INR), base deficit, platelet count, and interval transfusions). The expected change in platelet aggregation at each timepoint under the intervention of transfusion exposure was calculated and compared with the observed platelet aggregation. RESULTS: The 248 patients analyzed were severely injured (Injury Severity Score, 21 ± 19), with normal platelet counts (mean, 268 × 10/L ± 90), and 62% were transfused in 24 hours. The independent effect of transfusions on subsequent platelet aggregation over time was modeled with observed platelet aggregation under hypothetical treatment of one unit transfusion of blood, plasma, or platelets. Platelet transfusions had increasing expected effects on subsequent platelet aggregation over time, with the maximal expected effect occurring late (4-5 days from injury). CONCLUSION: Controversy exists on whether transfusions improve impaired postinjury platelet aggregation. Using regression modeling, we identified that expected transfusion effects on subsequent platelet aggregation are maximal with platelet transfusion given late after injury. This is critical for tailored resuscitation, identifying a potential early period of resistance to platelet transfusion that resolves by 96 hours. LEVEL OF EVIDENCE: Therapeutic, level V.

What makes a good teacher? The relative importance of mathematics teachers' cognitive ability, personality, knowledge, beliefs, and motivation for instructional quality.

BACKGROUND: Teachers differ substantially in their instructional performance in the classroom. Thus, researchers and policymakers are interested in how these differences can be explained and how the instruction provided by low-performing teachers can be improved. Previous research has focused either on generic (cognitive ability and personality) or profession-specific (professional knowledge, beliefs, and motivation for teaching) teacher characteristics as predictors of instructional quality but their relative importance has not yet been tested. AIMS: Hardly any studies have combined central generic and profession-specific variables in ascertaining their relative importance for instructional quality. In the present study, we seek to close this research gap. SAMPLES: We investigated 209 German mathematics teachers and their 4,672 students attending grades 7-10 (13- to 16-year-old students). METHODS: Teacher characteristics (cognitive ability, personality, professional knowledge, beliefs about, and enthusiasm for teaching) were assessed using standardized tests and self-report measures. Instructional quality (learning support, classroom disruptions, and cognitive activation) was rated by the students. RESULTS: Using structural equation modelling, we found extraversion, enthusiasm for teaching, and pedagogical/psychological knowledge to be significant predictors of learning support (R2  = .31) and conscientiousness and enthusiasm for teaching to be significant predictors of classroom discipline (R2  = .21). We did not find significant predictors for cognitive activation. CONCLUSIONS: Our results indicate the relative significance of generic and profession-specific teacher variables for instructional quality. Overall, a substantial amount of variance in instructional quality is explained by teacher characteristics.

Statistical Machines for Trauma Hospital Outcomes Research: Application to the PRospective, Observational, Multi-Center Major Trauma Transfusion (PROMMTT) Study.

Improving the treatment of trauma, a leading cause of death worldwide, is of great clinical and public health interest. This analysis introduces flexible statistical methods for estimating center-level effects on individual outcomes in the context of highly variable patient populations, such as those of the PRospective, Observational, Multi-center Major Trauma Transfusion study. Ten US level I trauma centers enrolled a total of 1,245 trauma patients who survived at least 30 minutes after admission and received at least one unit of red blood cells. Outcomes included death, multiple organ failure, substantial bleeding, and transfusion of blood products. The centers involved were classified as either large or small-volume based on the number of massive transfusion patients enrolled during the study period. We focused on estimation of parameters inspired by causal inference, specifically estimated impacts on patient outcomes related to the volume of the trauma hospital that treated them. We defined this association as the change in mean outcomes of interest that would be observed if, contrary to fact, subjects from large-volume sites were treated at small-volume sites (the effect of treatment among the treated). We estimated this parameter using three different methods, some of which use data-adaptive machine learning tools to derive the outcome models, minimizing residual confounding by reducing model misspecification. Differences between unadjusted and adjusted estimators sometimes differed dramatically, demonstrating the need to account for differences in patient characteristics in clinic comparisons. In addition, the estimators based on robust adjustment methods showed potential impacts of hospital volume. For instance, we estimated a survival benefit for patients who were treated at large-volume sites, which was not apparent in simpler, unadjusted comparisons. By removing arbitrary modeling decisions from the estimation process and concentrating on parameters that have more direct policy implications, these potentially automated approaches allow methodological standardization across similar comparativeness effectiveness studies.

Variable importance and prediction methods for longitudinal problems with missing variables.

We present prediction and variable importance (VIM) methods for longitudinal data sets containing continuous and binary exposures subject to missingness. We demonstrate the use of these methods for prognosis of medical outcomes of severe trauma patients, a field in which current medical practice involves rules of thumb and scoring methods that only use a few variables and ignore the dynamic and high-dimensional nature of trauma recovery. Well-principled prediction and VIM methods can provide a tool to make care decisions informed by the high-dimensional patient's physiological and clinical history. Our VIM parameters are analogous to slope coefficients in adjusted regressions, but are not dependent on a specific statistical model, nor require a certain functional form of the prediction regression to be estimated. In addition, they can be causally interpreted under causal and statistical assumptions as the expected outcome under time-specific clinical interventions, related to changes in the mean of the outcome if each individual experiences a specified change in the variable (keeping other variables in the model fixed). Better yet, the targeted MLE used is doubly robust and locally efficient. Because the proposed VIM does not constrain the prediction model fit, we use a very flexible ensemble learner (the SuperLearner), which returns a linear combination of a list of user-given algorithms. Not only is such a prediction algorithm intuitive appealing, it has theoretical justification as being asymptotically equivalent to the oracle selector. The results of the analysis show effects whose size and significance would have been not been found using a parametric approach (such as stepwise regression or LASSO). In addition, the procedure is even more compelling as the predictor on which it is based showed significant improvements in cross-validated fit, for instance area under the curve (AUC) for a receiver-operator curve (ROC). Thus, given that 1) our VIM applies to any model fitting procedure, 2) under assumptions has meaningful clinical (causal) interpretations and 3) has asymptotic (influence-curve) based robust inference, it provides a compelling alternative to existing methods for estimating variable importance in high-dimensional clinical (or other) data.

Evolving beyond the vicious triad: Differential mediation of traumatic coagulopathy by injury, shock, and resuscitation.

BACKGROUND: A subset of trauma patients with critical injury present with coagulopathy, portending markedly worse outcomes. Clinical practice is evolving to treat the classical risk factors of hypothermia, hemodilution, and acidosis; however, coagulopathy persists even in the absence of these factors. We sought to determine the relative importance of injury- and shock-specific factors compared with resuscitation-associated factors in coagulopathy after trauma. METHODS: Comprehensive demographic data, laboratory data, and outcomes data were prospectively collected from seven trauma centers over 8 years (November 2003 to August 2011) as part of the Inflammation and the Host Response to Injury Large-Scale Collaborative Program. A total of 1,537 critically injured patients with blunt trauma and hemorrhagic shock were analyzed to evaluate predictors of admission coagulopathy (international normalized ratio [INR] ≥ 1.3), multiorgan failure, and mortality. RESULTS: Of 1,537 patients, 578 (37.6%) had admission INR of 1.3 or greater. Coagulopathic patients had more severe injury, more severe base deficit and lactate levels, as well as lower admission temperature, lower pH, and higher prehospital crystalloid volume (all p < 0.001). Coagulopathic patients required more blood products and mechanical ventilation and had higher rates of nosocomial infection, multiorgan failure, and mortality (all p < 0.02). Injury severity, temperature, and acidosis (all p < 0.02) independently predicted coagulopathy in multivariate analysis, with a significant interaction between lactate and prehospital crystalloid. In Cox regression models, however, coagulopathy itself remained an independent predictor of both multiorgan failure and mortality (p < 0.02) even when adjusted for injury severity, shock, and elements of the vicious triad. CONCLUSION: Most patients with coagulopathy after trauma have mixed risk factors; however, coagulopathy has deleterious effects independent of injury severity, shock, and the vicious triad. Better understanding of the biochemical mechanisms of acute traumatic coagulopathy may facilitate biochemically targeted resuscitation strategies and improve outcomes. LEVEL OF EVIDENCE: Prognostic and epidemiologic study, level II.

Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package.

While child and adolescent obesity is a serious public health concern, few studies have utilized parameters based on the causal inference literature to examine the potential impacts of early intervention. The purpose of this analysis was to estimate the causal effects of early interventions to improve physical activity and diet during adolescence on body mass index (BMI), a measure of adiposity, using improved techniques. The most widespread statistical method in studies of child and adolescent obesity is multi-variable regression, with the parameter of interest being the coefficient on the variable of interest. This approach does not appropriately adjust for time-dependent confounding, and the modeling assumptions may not always be met. An alternative parameter to estimate is one motivated by the causal inference literature, which can be interpreted as the mean change in the outcome under interventions to set the exposure of interest. The underlying data-generating distribution, upon which the estimator is based, can be estimated via a parametric or semi-parametric approach. Using data from the National Heart, Lung, and Blood Institute Growth and Health Study, a 10-year prospective cohort study of adolescent girls, we estimated the longitudinal impact of physical activity and diet interventions on 10-year BMI z-scores via a parameter motivated by the causal inference literature, using both parametric and semi-parametric estimation approaches. The parameters of interest were estimated with a recently released R package, ltmle, for estimating means based upon general longitudinal treatment regimes. We found that early, sustained intervention on total calories had a greater impact than a physical activity intervention or non-sustained interventions. Multivariable linear regression yielded inflated effect estimates compared to estimates based on targeted maximum-likelihood estimation and data-adaptive super learning. Our analysis demonstrates that sophisticated, optimal semiparametric estimation of longitudinal treatment-specific means via ltmle provides an incredibly powerful, yet easy-to-use tool, removing impediments for putting theory into practice.

Considerations for normalization of DNA methylation data by Illumina 450K BeadChip assay in population studies.

Analysis of epigenetic mechanisms, particularly DNA methylation, is of increasing interest for epidemiologic studies examining disease etiology and impacts of environmental exposures. The Infinium HumanMethylation450 BeadChip(®) (450K), which interrogates over 480,000 CpG sites and is relatively cost effective, has become a popular tool to characterize the DNA methylome. For large-scale studies, minimizing technical variability and potential bias is paramount. The goal of this paper was to evaluate the performance of several existing and novel color channel normalizations designed to reduce technical variability and batch effects in 450K analysis from a large population study. Comparative assessment of 10 normalization procedures included the GenomeStudio(®) Illumina procedure, the lumi smooth quantile approach, and the newly proposed All Sample Mean Normalization (ASMN). We also examined the performance of normalizations in combination with correction for the two types of Infinium chemistry utilized on the 450K array. We observed that the performance of the GenomeStudio(®) normalization procedure was highly variable and dependent on the quality of the first sample analyzed in an experiment, which is used as a reference in this procedure. While the lumi normalization was able to decrease batch variability, it increased variation among technical replicates, potentially reducing biologically meaningful findings. The proposed ASMN procedure performed consistently well, both at reducing batch effects and improving replicate comparability. In summary, the ASMN procedure can improve existing color channel normalization, especially for large epidemiologic studies, and can be successfully implemented to enhance a 450K DNA methylation data pipeline.

Time-dependent prediction and evaluation of variable importance using superlearning in high-dimensional clinical data.

BACKGROUND: Prediction of outcome after injury is fraught with uncertainty and statistically beset by misspecified models. Single-time point regression only gives prediction and inference at one time, of dubious value for continuous prediction of ongoing bleeding. New statistical machine learning techniques such as SuperLearner (SL) exist to make superior prediction at iterative time points while evaluating the changing relative importance of each measured variable on an outcome. This then can provide continuously changing prediction of outcome and evaluation of which clinical variables likely drive a particular outcome. METHODS: PROMMTT data were evaluated using both naive (standard stepwise logistic regression) and SL techniques to develop a time-dependent prediction of future mortality within discrete time intervals. We avoided both underfitting and overfitting using cross validation to select an optimal combination of predictors among candidate predictors/machine learning algorithms. SL was also used to produce interval-specific robust measures of variable importance measures (VIM resulting in an ordered list of variables, by time point) that have the strongest impact on future mortality. RESULTS: Nine hundred eighty patients had complete clinical and outcome data and were included in the analysis. The prediction of ongoing transfusion with SL was superior to the naive approach for all time intervals (correlations of cross-validated predictions with the outcome were 0.819, 0.789, 0.792 for time intervals 30-90, 90-180, 180-360, >360 minutes). The estimated VIM of mortality also changed significantly at each time point. CONCLUSION: The SL technique for prediction of outcome from a complex dynamic multivariate data set is superior at each time interval to standard models. In addition, the SL VIM at each time point provides insight into the time-specific drivers of future outcome, patient trajectory, and targets for clinical intervention. Thus, this automated approach mimics clinical practice, changing form and content through time to optimize the accuracy of the prognosis based on the evolving trajectory of the patient.