The Role of Neighborhood Air Pollution in Disparate Racial and Ethnic Asthma Acute Care Use.

Rationale: The share of Black or Latinx residents in a census tract remains associated with asthma-related emergency department (ED) visit rates after controlling for socioeconomic factors. The extent to which evident disparities relate to the within-city heterogeneity of long-term air pollution exposure remains unclear. Objectives: To investigate the role of intraurban spatial variability of air pollution in asthma acute care use disparity. Methods: An administrative database was used to define census tract population-based incidence rates of asthma-related ED visits. We estimate the associations between census tract incidence rates and 1) average fine and coarse particulate matter, nitrogen dioxide (NO2), and sulfur dioxide (SO2), and 2) racial and ethnic composition using generalized linear models controlling for socioeconomic and housing covariates. We also examine for the attenuation of incidence risk ratios (IRRs) associated with race/ethnicity when controlling for air pollution exposure. Measurements and Main Results: Fine and coarse particulate matter and SO2 are all associated with census tract-level incidence rates of asthma-related ED visits, and multipollutant models show evidence of independent risk associated with coarse particulate matter and SO2. The association between census tract incidence rate and Black resident share (IRR, 1.51 [credible interval (CI), 1.48-1.54]) is attenuated by 24% when accounting for air pollution (IRR, 1.39 [CI, 1.35-1.42]), and the association with Latinx resident share (IRR, 1.11 [CI, 1.09-1.13]) is attenuated by 32% (IRR, 1.08 [CI, 1.06-1.10]). Conclusions: Neighborhood-level rates of asthma acute care use are associated with local air pollution. Controlling for air pollution attenuates associations with census tract racial/ethnic composition, suggesting that intracity variability in air pollution could contribute to neighborhood-to-neighborhood asthma morbidity disparities.

Pollen and viruses contribute to spatio-temporal variation in asthma-related emergency department visits.

BACKGROUND: Asthma exacerbations are an important cause of emergency department visits but much remains unknown about the role of environmental triggers including viruses and allergenic pollen. A better understanding of spatio-temporal variation in exposure and risk posed by viruses and pollen types could help prioritize public health interventions. OBJECTIVE: Here we quantify the effects of regionally important Cupressaceae pollen, tree pollen, other pollen types, rhinovirus, seasonal coronavirus, respiratory syncytial virus, and influenza on asthma-related emergency department visits for people living near eight pollen monitoring stations in Texas. METHODS: We used age stratified Poisson regression analyses to quantify the effects of allergenic pollen and viruses on asthma-related emergency department visits. RESULTS: Young children (<5 years of age) had high asthma-related emergency department rates (24.1 visits/1,000,000 person-days), which were mainly attributed to viruses (51.2%). School-aged children also had high rates (20.7 visits/1,000,000 person-days), which were attributed to viruses (57.0%), Cupressaceae pollen (0.7%), and tree pollen (2.8%). Adults had lower rates (8.1 visits/1,000,000 person-days) which were attributed to viruses (25.4%), Cupressaceae pollen (0.8%), and tree pollen (2.3%). This risk was spread unevenly across space and time; for example, during peak Cuppressaceae season, this pollen accounted for 8.2% of adult emergency department visits near Austin where these plants are abundant, but 0.4% in cities like Houston where they are not; results for other age groups were similar. CONCLUSIONS: Although viruses are a major contributor to asthma-related emergency department visits, airborne pollen can explain a meaningful portion of visits during peak pollen season and this risk varies over both time and space because of differences in plant composition.

Probing the binding specificities of human Siglecs by cell-based glycan arrays.

Siglecs are a family of sialic acid-binding receptors expressed by cells of the immune system and a few other cell types capable of modulating immune cell functions upon recognition of sialoglycan ligands. While human Siglecs primarily bind to sialic acid residues on diverse types of glycoproteins and glycolipids that constitute the sialome, their fine binding specificities for elaborated complex glycan structures and the contribution of the glycoconjugate and protein context for recognition of sialoglycans at the cell surface are not fully elucidated. Here, we generated a library of isogenic human HEK293 cells with combinatorial loss/gain of individual sialyltransferase genes and the introduction of sulfotransferases for display of the human sialome and to dissect Siglec interactions in the natural context of glycoconjugates at the cell surface. We found that Siglec-4/7/15 all have distinct binding preferences for sialylated GalNAc-type O-glycans but exhibit selectivity for patterns of O-glycans as presented on distinct protein sequences. We discovered that the sulfotransferase CHST1 drives sialoglycan binding of Siglec-3/8/7/15 and that sulfation can impact the preferences for binding to O-glycan patterns. In particular, the branched Neu5Acα2-3(6-O-sulfo)Galß1-4GlcNAc (6'-Su-SLacNAc) epitope was discovered as the binding epitope for Siglec-3 (CD33) implicated in late-onset Alzheimer's disease. The cell-based display of the human sialome provides a versatile discovery platform that enables dissection of the genetic and biosynthetic basis for the Siglec glycan interactome and other sialic acid-binding proteins.

Nanoparticles Displaying Allergen and Siglec-8 Ligands Suppress IgE-FcεRI-Mediated Anaphylaxis and Desensitize Mast Cells to Subsequent Antigen Challenge.

Siglec-8 is an inhibitory receptor expressed on eosinophils and mast cells. In this study, we took advantage of a novel Siglec-8 transgenic mouse model to assess the impact of modulating IgE-dependent mast cell degranulation and anaphylaxis using a liposomal platform to display an allergen with or without a synthetic glycan ligand for Siglec-8 (Sig8L). The hypothesis is that recruitment of Siglec-8 to the IgE-FcεRI receptor complex will inhibit allergen-induced mast cell degranulation. Codisplay of both allergen and Sig8L on liposomes profoundly suppresses IgE-mediated degranulation of mouse bone marrow-derived mast cells or rat basophilic leukemia cells expressing Siglec-8. In contrast, liposomes displaying only Sig8L have no significant suppression of antigenic liposome-induced degranulation, demonstrating that the inhibitory activity by Siglec-8 occurs only when Ag and Sig8L are on the same particle. In mouse models of anaphylaxis, display of Sig8L on antigenic liposomes completely suppresses IgE-mediated anaphylaxis in transgenic mice with mast cells expressing Siglec-8 but has no protection in mice that do not express Siglec-8. Furthermore, mice protected from anaphylaxis remain desensitized to subsequent allergen challenge because of loss of Ag-specific IgE from the cell surface and accelerated clearance of IgE from the blood. Thus, although expression of human Siglec-8 on murine mast cells does not by itself modulate IgE-FcεRI-mediated cell activation, the enforced recruitment of Siglec-8 to the FcεRI receptor by Sig8L-decorated antigenic liposomes results in inhibition of degranulation and desensitization to subsequent Ag exposure.

Cystic adrenal masses: spectrum of multimodality imaging features and pathological correlation.

Adrenal cystic lesions are generally rare and encompass a wide spectrum of benign and malignant entities. Increased utilisation of cross-sectional imaging has led to increased detection of incidentally discovered adrenal lesions. Many of these lesions are cystic or solid with cystic changes, and the majority are benign; however, some may represent malignant lesions and/or even metastases. Therefore, it is vital to characterise these lesions appropriately and follow-up with laboratory tests and imaging if necessary. Key imaging techniques include computed tomography (CT) and magnetic resonance imaging (MRI). Other supplemental imaging tools include metaiodobenzyl-guanidine (MIBG) scintigraphy and 2-[18F]-fluoro-2-deoxy-d-glucose positron-emission tomography (FDG-PET). Accurate diagnosis of adrenal cystic lesions is crucial in guiding appropriate evaluation and management of these patients. This review highlights the clinical presentations, pathological and imaging features, and management of cystic adrenal lesions.

Statistical considerations for the analysis of massively parallel reporter assays data.

Noncoding DNA contains gene regulatory elements that alter gene expression, and the function of these elements can be modified by genetic variation. Massively parallel reporter assays (MPRA) enable high-throughput identification and characterization of functional genetic variants, but the statistical methods to identify allelic effects in MPRA data have not been fully developed. In this study, we demonstrate how the baseline allelic imbalance in MPRA libraries can produce biased results, and we propose a novel, nonparametric, adaptive testing method that is robust to this bias. We compare the performance of this method with other commonly used methods, and we demonstrate that our novel adaptive method controls Type I error in a wide range of scenarios while maintaining excellent power. We have implemented these tests along with routines for simulating MPRA data in the Analysis Toolset for MPRA (@MPRA), an R package for the design and analyses of MPRA experiments. It is publicly available at http://github.com/redaq/atMPRA.

Invited Commentary: The Promise and Pitfalls of Causal Inference With Multivariate Environmental Exposures.

The accompanying article by Keil et al. (Am J Epidemiol. 2021;190(12):2647-2657) deploys Bayesian g-computation to investigate the causal effect of 6 airborne metal exposures linked to power-plant emissions on birth weight. In so doing, it articulates the potential value of framing the analysis of environmental mixtures as an explicit contrast between exposure distributions that might arise in response to a well-defined intervention-here, the decommissioning of coal plants. Framing the mixture analysis as that of an approximate "target trial" is an important approach that deserves incorporation into the already rich literature on the analysis of environmental mixtures. However, its deployment in the power plant example highlights challenges that can arise when the target trial is at odds with the exposure distribution observed in the data, a discordance that seems particularly difficult in studies of environmental mixtures. Bayesian methodology such as model averaging and informative priors can help, but they are ultimately limited for overcoming this salient challenge.

Bipartite Causal Inference with Interference.

Statistical methods to evaluate the effectiveness of interventions are increasingly challenged by the inherent interconnectedness of units. Specifically, a recent flurry of methods research has addressed the problem of interference between observations, which arises when one observational unit's outcome depends not only on its treatment but also the treatment assigned to other units. We introduce the setting of bipartite causal inference with interference, which arises when 1) treatments are defined on observational units that are distinct from those at which outcomes are measured and 2) there is interference between units in the sense that outcomes for some units depend on the treatments assigned to many other units. The focus of this work is to formulate definitions and several possible causal estimands for this setting, highlighting similarities and differences with more commonly considered settings of causal inference with interference. Towards an empirical illustration, an inverse probability of treatment weighted estimator is adapted from existing literature to estimate a subset of simplified, but interesting, estimands. The estimators are deployed to evaluate how interventions to reduce air pollution from 473 power plants in the U.S. causally affect cardiovascular hospitalization among Medicare beneficiaries residing at 18,807 zip code locations.

Four Decades of United States Mobile Source Pollutants: Spatial-Temporal Trends Assessed by Ground-Based Monitors, Air Quality Models, and Satellites.

On-road emissions sources degrade air quality, and these sources have been highly regulated. Epidemiological and environmental justice studies often use road proximity as a proxy for traffic-related air pollution (TRAP) exposure, and other studies employ air quality models or satellite observations. To assess these metrics' abilities to reproduce observed near-road concentration gradients and changes over time, we apply a hierarchical linear regression to ground-based observations, long-term air quality model simulations using Community Multiscale Air Quality (CMAQ), and satellite products. Across 1980-2019, observed TRAP concentrations decreased, and road proximity was positively correlated with TRAP. For all pollutants, concentrations decreased fastest at locations with higher road proximity, resulting in "flatter" concentration fields in recent years. This flattening unfolded at a relatively constant rate for NOx, whereas the flattening of CO concentration fields has slowed. CMAQ largely captures observed spatial-temporal NO2 trends across 2002-2010 but overstates the relationships between CO and elemental carbon fine particulate matter (EC) road proximity. Satellite NOx measures overstate concentration reductions near roads. We show how this perspective provides evidence that California's on-road vehicle regulations led to substantial decreases in NO2, NOx, and EC in California, with other states that adopted California's light-duty automobile standards showing mixed benefits over states that did not adopt these standards.

Adjusting for unmeasured spatial confounding with distance adjusted propensity score matching.

Propensity score matching is a common tool for adjusting for observed confounding in observational studies, but is known to have limitations in the presence of unmeasured confounding. In many settings, researchers are confronted with spatially-indexed data where the relative locations of the observational units may serve as a useful proxy for unmeasured confounding that varies according to a spatial pattern. We develop a new method, termed distance adjusted propensity score matching (DAPSm) that incorporates information on units' spatial proximity into a propensity score matching procedure. We show that DAPSm can adjust for both observed and some forms of unobserved confounding and evaluate its performance relative to several other reasonable alternatives for incorporating spatial information into propensity score adjustment. The method is motivated by and applied to a comparative effectiveness investigation of power plant emission reduction technologies designed to reduce population exposure to ambient ozone pollution. Ultimately, DAPSm provides a framework for augmenting a "standard" propensity score analysis with information on spatial proximity and provides a transparent and principled way to assess the relative trade-offs of prioritizing observed confounding adjustment versus spatial proximity adjustment.

Uncertainty in the design stage of two-stage Bayesian propensity score analysis.

The two-stage process of propensity score analysis (PSA) includes a design stage where propensity scores (PSs) are estimated and implemented to approximate a randomized experiment and an analysis stage where treatment effects are estimated conditional on the design. This article considers how uncertainty associated with the design stage impacts estimation of causal effects in the analysis stage. Such design uncertainty can derive from the fact that the PS itself is an estimated quantity, but also from other features of the design stage tied to choice of PS implementation. This article offers a procedure for obtaining the posterior distribution of causal effects after marginalizing over a distribution of design-stage outputs, lending a degree of formality to Bayesian methods for PSA that have gained attention in recent literature. Formulation of a probability distribution for the design-stage output depends on how the PS is implemented in the design stage, and propagation of uncertainty into causal estimates depends on how the treatment effect is estimated in the analysis stage. We explore these differences within a sample of commonly used PS implementations (quantile stratification, nearest-neighbor matching, caliper matching, inverse probability of treatment weighting, and doubly robust estimation) and investigate in a simulation study the impact of statistician choice in PS model and implementation on the degree of between- and within-design variability in the estimated treatment effect. The methods are then deployed in an investigation of the association between levels of fine particulate air pollution and elevated exposure to emissions from coal-fired power plants.

Murine Red Blood Cells Lack Ligands for B Cell Siglecs, Allowing Strong Activation by Erythrocyte Surface Antigens.

CD22 and sialic acid-binding Ig-like lectin (Siglec)-G are members of the Siglec family of inhibitory coreceptors expressed on B cells that participate in enforcement of peripheral B cell tolerance. We have shown previously that when a BCR engages its cognate Ag on a cell surface that also expresses Siglec ligands, B cell Siglecs are recruited to the immunological synapse, resulting in suppression of BCR signaling and B cell apoptosis. Because all cells display sialic acids, and CD22 and Siglec-G have distinct, yet overlapping, specificities for sialic acid-containing glycan ligands, any cell could, in principle, invoke this tolerogenic mechanism for cell surface Ags. However, we show in this article that C57BL/6J mouse RBCs are essentially devoid of CD22 and Siglec-G ligands. As a consequence, RBCs that display a cell surface Ag, membrane-bound hen egg lysozyme, strongly activate Ag-specific B cells. We reasoned that de novo introduction of CD22 ligands in RBCs should abolish B cell activation toward its cognate Ag on the surface of RBCs. Accordingly, we used a glyco-engineering approach wherein synthetic CD22 ligands linked to lipids are inserted into the membrane of RBCs. Indeed, insertion of CD22 ligands into the RBC cell surface strongly inhibited B cell activation, cytokine secretion, and proliferation. These results demonstrate that the lack of Siglec ligands on the surface of murine RBCs permits B cell responses to erythrocyte Ags and show that Siglec-mediated B cell tolerance is restricted to cell types that express glycan ligands for the B cell Siglecs.

A Sulfonamide Sialoside Analogue for Targeting Siglec-8 and -F on Immune Cells.

The Siglec family of cell surface receptors have emerged as attractive targets for cell-directed therapies due to their restricted expression on immune cells, endocytic properties, and ability to modulate receptor signaling. Human Siglec-8, for instance, has been identified as a therapeutic target for the treatment of eosinophil and mast cell disorders. A promising strategy to target Siglecs involves the use of liposomal nanoparticles with a multivalent display of Siglec ligands. A key challenge for this approach is the identification of a high affinity ligand for the target Siglec. Here, we report the development of a ligand of Siglec-8 and its closest murine functional orthologue Siglec-F that is capable of targeting liposomes to cells expressing Siglec-8 or -F. A glycan microarray library of synthetic 9-N-sulfonyl sialoside analogues was screened to identify potential lead compounds. The best ligand, 9-N-(2-naphthyl-sulfonyl)-Neu5Acα2-3-[6-O-sulfo]-Galß1-4GlcNAc (6'-O-sulfo NSANeu5Ac) combined the lead 2-naphthyl sulfonyl C-9 substituent with the preferred sulfated scaffold. The ligand 6'-O-sulfo NSANeu5Ac was conjugated to lipids for display on liposomes to evaluate targeted delivery to cells. Targeted liposomes showed strong in vitro binding/uptake and selectivity to cells expressing Siglec-8 or -F and, when administered to mice, exhibit in vivo targeting to Siglec-F+ eosinophils.

A structural explanation for the low effectiveness of the seasonal influenza H3N2 vaccine.

The effectiveness of the annual influenza vaccine has declined in recent years, especially for the H3N2 component, and is a concern for global public health. A major cause for this lack in effectiveness has been attributed to the egg-based vaccine production process. Substitutions on the hemagglutinin glycoprotein (HA) often arise during virus passaging that change its antigenicity and hence vaccine effectiveness. Here, we characterize the effect of a prevalent substitution, L194P, in egg-passaged H3N2 viruses. X-ray structural analysis reveals that this substitution surprisingly increases the mobility of the 190-helix and neighboring regions in antigenic site B, which forms one side of the receptor binding site (RBS) and is immunodominant in recent human H3N2 viruses. Importantly, the L194P substitution decreases binding and neutralization by an RBS-targeted broadly neutralizing antibody by three orders of magnitude and significantly changes the HA antigenicity as measured by binding of human serum antibodies. The receptor binding mode and specificity are also altered to adapt to avian receptors during egg passaging. Overall, these findings help explain the low effectiveness of the seasonal vaccine against H3N2 viruses, and suggest that alternative approaches should be accelerated for producing influenza vaccines as well as isolating clinical isolates.

Accountability Assessment of Health Improvements in the United States Associated with Reduced Coal Emissions Between 2005 and 2012.

BACKGROUND: National, state, and local policies contributed to a 65% reduction in sulfur dioxide emissions from coal-fired power plants between 2005 and 2012 in the United States, providing an opportunity to directly quantify public health benefits attributable to these reductions under an air pollution accountability framework. METHODS: We estimate ZIP code-level changes in two different-but related-exposure metrics: total PM2.5 concentrations and exposure to coal-fired power plant emissions. We associate changes in 10 health outcome rates among approximately 30 million US Medicare beneficiaries with exposure changes between 2005 and 2012 using two difference-in-difference regression approaches designed to mitigate observed and unobserved confounding. RESULTS: Rates per 10,000 person-years of six cardiac and respiratory health outcomes-all cardiovascular disease, chronic obstructive pulmonary disorder, cardiovascular stroke, heart failure, ischemic heart disease, and respiratory tract infections-decreased by between 7.89 and 1.95 per (Equation is included in full-text article.)decrease in PM2.5, with comparable decreases in coal exposure leading to slightly larger rate decreases. Results for acute myocardial infarction, heart rhythm disorders, and peripheral vascular disease were near zero and/or mixed between the various exposure metrics and analyses. A secondary analysis found that nonlinearities in relationships between changing health outcome rates and coal exposure may explain differences in their associations. CONCLUSIONS: The direct analyses of emissions reductions estimate substantial health benefits via coal power plant emission and PM2.5 concentration reductions. Differing responses associated with changes in the two exposure metrics underscore the importance of isolating source-specific impacts from those due to total PM2.5 exposure.

Causal inference with interfering units for cluster and population level treatment allocation programs.

Interference arises when an individual's potential outcome depends on the individual treatment level, but also on the treatment level of others. A common assumption in the causal inference literature in the presence of interference is partial interference, implying that the population can be partitioned in clusters of individuals whose potential outcomes only depend on the treatment of units within the same cluster. Previous literature has defined average potential outcomes under counterfactual scenarios where treatments are randomly allocated to units within a cluster. However, within clusters there may be units that are more or less likely to receive treatment based on covariates or neighbors' treatment. We define new estimands that describe average potential outcomes for realistic counterfactual treatment allocation programs, extending existing estimands to take into consideration the units' covariates and dependence between units' treatment assignment. We further propose entirely new estimands for population-level interventions over the collection of clusters, which correspond in the motivating setting to regulations at the federal (vs. cluster or regional) level. We discuss these estimands, propose unbiased estimators and derive asymptotic results as the number of clusters grows. For a small number of observed clusters, a bootstrap approach for confidence intervals is proposed. Finally, we estimate effects in a comparative effectiveness study of power plant emission reduction technologies on ambient ozone pollution.

Approaches to treatment effect heterogeneity in the presence of confounding.

The literature on causal effect estimation tends to focus on the population mean estimand, which is less informative as medical treatments are becoming more personalized and there is increasing awareness that subpopulations of individuals may experience a group-specific effect that differs from the population average. In fact, it is possible that there is underlying systematic effect heterogeneity that is obscured by focusing on the population mean estimand. In this context, understanding which covariates contribute to this treatment effect heterogeneity (TEH) and how these covariates determine the differential treatment effect (TE) is an important consideration. Towards such an understanding, this paper briefly reviews three approaches used in making causal inferences and conducts a simulation study to compare these approaches according to their performance in an exploratory evaluation of TEH when the heterogeneous subgroups are not known a priori. Performance metrics include the detection of any heterogeneity, the identification and characterization of heterogeneous subgroups, and unconfounded estimation of the TE within subgroups. The methods are then deployed in a comparative effectiveness evaluation of drug-eluting versus bare-metal stents among 54 099 Medicare beneficiaries in the continental United States admitted to a hospital with acute myocardial infarction in 2008.

Characterizing population exposure to coal emissions sources in the United States using the HyADS model.

In anticipation of the expanding appreciation for air quality models in health outcomes studies, we develop and evaluate a reduced-complexity model for pollution transport that intentionally sacrifices some of the sophistication of full-scale chemical transport models in order to support applicability to a wider range of health studies. Specifically, we introduce the HYSPLIT average dispersion model, HyADS, which combines the HYSPLIT trajectory dispersion model with modern advances in parallel computing to estimate ZIP code level exposure to emissions from individual coal-powered electricity generating units in the United States. Importantly, the method is not designed to reproduce ambient concentrations of any particular air pollutant; rather, the primary goal is to characterize each ZIP code's exposure to these coal power plants specifically. We show adequate performance towards this goal against observed annual average air pollutant concentrations (nationwide Pearson correlations of 0.88 and 0.73 with SO 4 2 - and PM2.5, respectively) and coal-combustion impacts simulated with a full-scale chemical transport model and adjusted to observations using a hybrid direct sensitivities approach (correlation of 0.90). We proceed to provide multiple examples of HyADS's single-source applicability, including to show that 22% of the population-weighted coal exposure comes from 30 coal-powered electricity generating units.

Quality of Care at Hospitals Identified as Outliers in Publicly Reported Mortality Statistics for Percutaneous Coronary Intervention.

BACKGROUND: Public reporting of percutaneous coronary intervention (PCI) outcomes may create disincentives for physicians to provide care for critically ill patients, particularly at institutions with worse clinical outcomes. We thus sought to evaluate the procedural management and in-hospital outcomes of patients treated for acute myocardial infarction before and after a hospital had been publicly identified as a negative outlier. METHODS: Using state reports, we identified hospitals that were recognized as negative PCI outliers in 2 states (Massachusetts and New York) from 2002 to 2012. State hospitalization files were used to identify all patients with an acute myocardial infarction within these states. Procedural management and in-hospital outcomes were compared among patients treated at outlier hospitals before and after public report of outlier status. Patients at nonoutlier institutions were used to control for temporal trends. RESULTS: Among 86 hospitals, 31 were reported as outliers for excess mortality. Outlier facilities were larger, treating more patients with acute myocardial infarction and performing more PCIs than nonoutlier hospitals (P<0.05 for each). Among 507 672 patients with acute myocardial infarction hospitalized at these institutions, 108 428 (21%) were treated at an outlier hospital after public report. The likelihood of PCI at outlier (relative risk [RR], 1.13; 95% confidence interval [CI], 1.12-1.15) and nonoutlier institutions (RR, 1.13; 95% CI, 1.11-1.14) increased in a similar fashion (interaction P=0.50) after public report of outlier status. The likelihood of in-hospital mortality decreased at outlier institutions (RR, 0.83; 95% CI, 0.81-0.85) after public report, and to a lesser degree at nonoutlier institutions (RR, 0.90; 95% CI, 0.87-0.92; interaction P<0.001). Among patients that underwent PCI, in-hospital mortality decreased at outlier institutions after public recognition of outlier status in comparison with prior (RR, 0.72; 9% CI, 0.66-0.79), a decline that exceeded the reduction at nonoutlier institutions (RR, 0.87; 95% CI, 0.80-0.96; interaction P<0.001). CONCLUSIONS: Large hospitals with higher clinical volume are more likely to be designated as negative outliers. The rates of percutaneous revascularization increased similarly at outlier and nonoutlier institutions after report of outlier status. After outlier designation, in-hospital mortality declined at outlier institutions to a greater extent than was observed at nonoutlier facilities.

Mechanistic Investigation and Multiplexing of Liposome-Assisted Metabolic Glycan Labeling.

Metabolic labeling of glycans with bioorthogonal reporters has been widely used for glycan imaging and glycoproteomic profiling. One of the intrinsic limitations of metabolic glycan labeling is the lack of cell-type selectivity. The recently developed liposome-assisted bioorthogonal reporter (LABOR) strategy provides a promising means to overcome this limitation, but the mechanism of LABOR has not been investigated in detail. In this work, we performed a mechanistic study on LABOR and explored its multiplexing capability. Our studies support an endocytosis-salvage mechanism. The ligand-targeted liposomes encapsulating azidosugars are internalized into the endosome via the receptor-mediated endocytosis. Unlike the conventional drug delivery, LABOR does not rely on the endosomal escape pathways. Rather, the liposomes are allowed to enter the lysosome, inside which the azidosugars are released from the liposomes. The released azidosugars then intercept the salvage pathways of monosaccharides and get transported into the cytosol by lysosomal sugar transporters. Based on this mechanism, we expanded the scope of LABOR by evaluating a series of ligand-receptor pairs for targeting sialoglycans in various cell types. Different ligand types including small molecules, antibodies, aptamers, and peptides could be easily implemented into LABOR. Finally, we demonstrated that the sialoglycans in two distinct cell populations in a co-cultured system could be selectively labeled with two distinct chemical reporters by performing a multiplexed LABOR labeling.