Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

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

<div><p>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.</p></div

Heatmap of matched data set with hierarchical clustering.

<p>A visualization of the scaled covariate values in the matched data set with hierarchical clustering of the individual revealed little clustering of the individuals by site type, indicated in the bar on the left, suggesting that balance in the covariates was achieved. The dendrogram on the right is the result of hierarchical clustering of the individuals based on their covariate values. The color bar on the left indicates the site size where each individual was treated (purple corresponds to small-volume sites and blue to large-volume sites).</p

“Counterfactual” residual plot for binary outcomes.

<p>Residuals (see <i>Diagnostics</i>) for each outcome were plotted against probabilities of overall mortality for large (dotted line) and small-volume (solid line) site patients for binary outcomes using loess smooths.</p

Difference in means, and adjusted ETT estimates (simple substitution, TMLE, and propensity score matching).

<p>95% confidence intervals are included in parentheses after each estimate. Asymptotic (normal-based) confidence intervals were calculated using the standard error of the Unadjusted, Targeted Maximum-Likelihood, and Matching estimators. The nonparametric bootstrap was used to generate 95% confidence intervals in the case of the Simple Substitution estimator.</p

Summary statistics.

<p>^{<b>^</b>}<b>p</b>-values derived from Mann-Whitney U and Z-tests for continuous and binary variables respectively.</p><p>* <b>p</b>-value significant (α = 0.05).</p><p>BMI: body mass index; ISS: injury severity score; BP: blood pressure; BPM: beats per minute; INR: international normalized ratio; RBC: red blood cell; U: units.</p><p>Summaries are presented as percent where indicated, and mean (standard deviation) otherwise.</p