New Insights Relating Gasdermin B to the Onset of Childhood Asthma.

Chromosome 17q12-q21 is the most replicated genetic locus for childhood-onset asthma. Polymorphisms in this locus containing ∼10 genes interact with a variety of environmental exposures in the home and outdoors to modify asthma risk. However, the functional basis for these associations and their linkages to the environment have remained enigmatic. Within this extended region, regulation of GSDMB (gasdermin B) expression in airway epithelial cells has emerged as the primary mechanism underlying the 17q12-q21 genome-wide association study signal. Asthma-associated SNPs influence the abundance of GSDMB transcripts as well as the functional properties of GSDMB protein in airway epithelial cells. GSDMB is a member of the gasdermin family of proteins, which regulate pyroptosis and inflammatory responses to microbial infections. The aims of this review are to synthesize recent studies on the relationship of 17q12-q21 SNPs to childhood asthma and the evidence pointing to GSDMB gene expression or protein function as the underlying mechanism and to explore the potential functions of GSDMB that may influence the risk of developing asthma during childhood.

Multiple postoperative complications: Making sense of the trajectories.

BACKGROUND: Many studies have evaluated predictors of postoperative complications, yet little is known about the development of multiple complications. The goal of this study was to assess complication timing in cascades of multiple complications and the risk of future complications given a patient's first complication. METHODS: This study includes 30-day, postoperative complications from the American College of Surgeons National Surgical Quality Improvement Program for all patients who underwent major inpatient and outpatient operative procedures from 2005-2013. The timing and sequencing of complications were evaluated using χ2 analysis and pairwise comparisons. RESULTS: More severe postoperative complications (cardiac arrest or myocardial infarction, renal insufficiency or failure, stroke, intubation, septic shock, coma) had the greatest impact on the risk for developing further complications, increasing the relative risk of developing future, specific, severe complications by more than 40-fold. These more severe complications occur within a few days of other complications (whether as a preceding factor or an outcome), while less severe complications, such as surgical site infection and urinary tract infection, are linked less tightly to complication cascades. CONCLUSION: This analysis highlights both the risk for secondary complications after an initial complication and when those future complications are likely to occur. Physicians can use this information to target interventions to prevent high-risk complications.

Implications of Multiple Complications on the Postoperative Recovery of General Surgery Patients.

OBJECTIVES: To evaluate the association between multiple complications and postoperative outcomes and to assess which complications occur together in patients with multiple complications. BACKGROUND: Patients who suffer multiple complications have increased risk of prolonged hospital stay and mortality. However, little is known about what places patients at risk for multiple complications or which complications tend to occur in these patients. METHODS: Surgical patients were identified from the American College of Surgeons National Quality Improvement Program (ACS NSQIP) database from 2005 to 2011. The frequency of postoperative complications was assessed. Patients with less than two complications were compared with patients who had multiple complications using χ and logistic regression analysis. Relationships among postoperative complications were explored by learning a Bayesian network model. RESULTS: The study population consisted of 470,108 general surgery patients. The overall complication rate was 15% with multiple complications in 27,032 (6%) patients. Patients with multiple complications had worse postoperative outcomes (P < 0.001). The strongest predictors for developing multiple complications were admission from chronic care facility or nursing home, dependent functional status, and higher American Society of Anesthesiologist Physical Status classification. In patients with multiple complications, the most common complication was sepsis (42%), followed by failure to wean ventilator (31%), and organ space surgical site infection (27%). We found that severe complications were most strongly associated with development of multiple complications. Using a Bayesian network, we were able to identify how strongly associated specific complications were in patients who developed multiple complications. CONCLUSIONS: Almost half (40%) of patients with complications suffer multiple complications. Patient factors such as frailty and comorbidity strongly predict the development of multiple complications. The results of our Bayesian analysis identify targets for interventions aimed at disrupting the cascade of multiple complications in high-risk patients.

Modeling the Temporal Evolution of Postoperative Complications.

Post-operative complications have a significant impact on patient morbidity and mortality; these impacts are exacerbated when patients experience multiple complications. However, the task of modeling the temporal sequencing of complications has not been previously addressed. We present an approach based on Markov chain models for characterizing the temporal evolution of post-operative complications represented in the American College of Surgeons National Surgery Quality Improvement Program database. Our work demonstrates that the models have significant predictive value. In particular, an inhomogenous Markov chain model effectively predicts the development of serious complications (coma longer than a day, cardiac arrest, myocardial infarction, septic shock, renal failure, pneumonia) and interventional complications (unplanned re-intubation, longer than 2 days on a ventilator and bleeding transfusion).

Quantifying technical skills during open operations using video-based motion analysis.

INTRODUCTION: Objective quantification of technical operative skills in surgery remains poorly defined, although the delivery of and training in these skills is essential to the profession of surgery. Attempts to measure hand kinematics to quantify operative performance primarily have relied on electromagnetic sensors attached to the surgeon's hand or instrument. We sought to determine whether a similar motion analysis could be performed with a marker-less, video-based review, allowing for a scalable approach to performance evaluation. METHODS: We recorded six reduction mammoplasty operations-a plastic surgery procedure in which the attending and resident surgeons operate in parallel. Segments representative of surgical tasks were identified with Multimedia Video Task Analysis software. Video digital processing was used to extract and analyze the spatiotemporal characteristics of hand movement. RESULTS: Attending plastic surgeons appear to use their nondominant hand more than residents when cutting with the scalpel, suggesting more use of countertraction. While suturing, attendings were more ambidextrous, with smaller differences in movement between their dominant and nondominant hands than residents. Attendings also seem to have more conservation of movement when performing instrument tying than residents, as demonstrated by less nondominant hand displacement. These observations were consistent within procedures and between the different attending plastic surgeons evaluated in this fashion. CONCLUSION: Video motion analysis can be used to provide objective measurement of technical skills without the need for sensors or markers. Such data could be valuable in better understanding the acquisition and degradation of operative skills, providing enhanced feedback to shorten the learning curve.

SIRT3 substrate specificity determined by peptide arrays and machine learning.

Accumulating evidence suggests that reversible protein acetylation may be a major regulatory mechanism that rivals phosphorylation. With the recent cataloging of thousands of acetylation sites on hundreds of proteins comes the challenge of identifying the acetyltransferases and deacetylases that regulate acetylation levels. Sirtuins are a conserved family of NAD(+)-dependent protein deacetylases that are implicated in genome maintenance, metabolism, cell survival, and lifespan. SIRT3 is the dominant protein deacetylase in mitochondria, and emerging evidence suggests that SIRT3 may control major pathways by deacetylation of central metabolic enzymes. Here, to identify potential SIRT3 substrates, we have developed an unbiased screening strategy that involves a novel acetyl-lysine analogue (thiotrifluoroacetyl-lysine), SPOT-peptide libraries, machine learning, and kinetic validation. SPOT peptide libraries based on known and potential mitochondrial acetyl-lysine sites were screened for SIRT3 binding and then analyzed using machine learning to establish binding trends. These trends were then applied to the mitochondrial proteome as a whole to predict binding affinity of all lysine sites within human mitochondria. Machine learning prediction of SIRT3 binding correlated with steady-state kinetic k(cat)/K(m) values for 24 acetyl-lysine peptides that possessed a broad range of predicted binding. Thus, SPOT peptide-binding screens and machine learning prediction provides an accurate and efficient method to evaluate sirtuin substrate specificity from a relatively small learning set. These analyses suggest potential SIRT3 substrates involved in several metabolic pathways such as the urea cycle, ATP synthesis, and fatty acid oxidation.