Generically partisan: Polarization in political communication.

American political parties continue to grow more polarized, but the extent of ideological polarization among the public is much less than the extent of perceived polarization (what the ideological gap is believed to be). Perceived polarization is concerning because of its link to interparty hostility, but it remains unclear what drives this phenomenon. We propose that a tendency for individuals to form broad generalizations about groups on the basis of inconsistent evidence may be partly responsible. We study this tendency by measuring the interpretation, endorsement, and recall of category-referring statements, also known as generics (e.g., "Democrats favor affirmative action"). In study 1 (n = 417), perceived polarization was substantially greater than actual polarization. Further, participants endorsed generics as long as they were true more often of the target party (e.g., Democrats favor affirmative action) than of the opposing party (e.g., Republicans favor affirmative action), even when they believed such statements to be true for well below 50% of the relevant party. Study 2 (n = 928) found that upon receiving information from political elites, people tended to recall these statements as generic, regardless of whether the original statement was generic or not. Study 3 (n = 422) found that generic statements regarding new political information led to polarized judgments and did so more than nongeneric statements. Altogether, the data indicate a tendency toward holding mental representations of political claims that exaggerate party differences. These findings suggest that the use of generic language, common in everyday speech, enables inferential errors that exacerbate perceived polarization.

Resolvin D1 prevents injurious neutrophil swarming in transplanted lungs.

Neutrophils are the primary cell type involved in lung ischemia-reperfusion injury (IRI), which remains a frequent and morbid complication after organ transplantation. Endogenous lipid mediators that become activated during acute inflammation-resolution have gained increasing recognition for their protective role(s) in promoting the restoration of homeostasis, but their influence on early immune responses following transplantation remains to be uncovered. Resolvin D1, 7S,8R,17S-trihydroxy-4Z,9E,11E,13Z,15E,19Z-docosahexaenoic acid (RvD1), is a potent stereoselective mediator that exhibits proresolving and anti-inflammatory actions in the setting of tissue injury. Here, using metabololipidomics, we demonstrate that endogenous proresolving mediators including RvD1 are increased in human and murine lung grafts immediately following transplantation. In mouse grafts, we observe lipid mediator class switching early after reperfusion. We use intravital two-photon microscopy to reveal that RvD1 treatment significantly limits early neutrophil infiltration and swarming, thereby ameliorating early graft dysfunction in transplanted syngeneic lungs subjected to severe IRI. Through integrated analysis of single-cell RNA sequencing data of donor and recipient immune cells from lung grafts, we identify transcriptomic changes induced by RvD1. These results support a role for RvD1 as a potent modality for preventing early neutrophil-mediated tissue damage after lung IRI that may be therapeutic in the clinics.

Club Cell Secretory Protein in Lung Disease: Emerging Concepts and Potential Therapeutics.

Club cell secretory protein (CCSP), also known as secretoglobin 1A1 (gene name SCGB1A1), is one of the most abundant proteins in the lung, primarily produced by club cells of the distal airway epithelium. At baseline, CCSP is found in large concentrations in lung fluid specimens and can also be detected in the blood and urine. Obstructive lung diseases are generally associated with reduced CCSP levels, thought to be due to decreased CCSP production or club cell depletion. Conversely, several restrictive lung diseases have been found to have increased CCSP levels both in the lung and in the circulation, likely related to club cell dysregulation as well as increasedlung permeability. Recent studies demonstrate multiple mechanisms by which CCSP dampens acute and chronic lung inflammation. Given these anti-inflammatory effects, CCSP represents a novel potential therapeutic modality in lung disease.

Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models.

Compartmental models that describe infectious disease transmission across subpopulations are central for assessing the impact of non-pharmaceutical interventions, behavioral changes and seasonal effects on the spread of respiratory infections. We present a Bayesian workflow for such models, including four features: (1) an adjustment for incomplete case ascertainment, (2) an adequate sampling distribution of laboratory-confirmed cases, (3) a flexible, time-varying transmission rate, and (4) a stratification by age group. Within the workflow, we benchmarked the performance of various implementations of two of these features (2 and 3). For the second feature, we used SARS-CoV-2 data from the canton of Geneva (Switzerland) and found that a quasi-Poisson distribution is the most suitable sampling distribution for describing the overdispersion in the observed laboratory-confirmed cases. For the third feature, we implemented three methods: Brownian motion, B-splines, and approximate Gaussian processes (aGP). We compared their performance in terms of the number of effective samples per second, and the error and sharpness in estimating the time-varying transmission rate over a selection of ordinary differential equation solvers and tuning parameters, using simulated seroprevalence and laboratory-confirmed case data. Even though all methods could recover the time-varying dynamics in the transmission rate accurately, we found that B-splines perform up to four and ten times faster than Brownian motion and aGPs, respectively. We validated the B-spline model with simulated age-stratified data. We applied this model to 2020 laboratory-confirmed SARS-CoV-2 cases and two seroprevalence studies from the canton of Geneva. This resulted in detailed estimates of the transmission rate over time and the case ascertainment. Our results illustrate the potential of the presented workflow including stratified transmission to estimate age-specific epidemiological parameters. The workflow is freely available in the R package HETTMO, and can be easily adapted and applied to other infectious diseases.

Necroptosis triggers spatially restricted neutrophil-mediated vascular damage during lung ischemia reperfusion injury.

Ischemia reperfusion injury represents a common pathological condition that is triggered by the release of endogenous ligands. While neutrophils are known to play a critical role in its pathogenesis, the tissue-specific spatiotemporal regulation of ischemia-reperfusion injury is not understood. Here, using oxidative lipidomics and intravital imaging of transplanted mouse lungs that are subjected to severe ischemia reperfusion injury, we discovered that necroptosis, a nonapoptotic form of cell death, triggers the recruitment of neutrophils. During the initial stages of inflammation, neutrophils traffic predominantly to subpleural vessels, where their aggregation is directed by chemoattractants produced by nonclassical monocytes that are spatially restricted in this vascular compartment. Subsequent neutrophilic disruption of capillaries resulting in vascular leakage is associated with impaired graft function. We found that TLR4 signaling in vascular endothelial cells and downstream NADPH oxidase 4 expression mediate the arrest of neutrophils, a step upstream of their extravasation. Neutrophil extracellular traps formed in injured lungs and their disruption with DNase prevented vascular leakage and ameliorated primary graft dysfunction. Thus, we have uncovered mechanisms that regulate the initial recruitment of neutrophils to injured lungs, which result in selective damage to subpleural pulmonary vessels and primary graft dysfunction. Our findings could lead to the development of new therapeutics that protect lungs from ischemia reperfusion injury.

Developing machine learning models to predict primary graft dysfunction after lung transplantation.

Primary graft dysfunction (PGD) is the leading cause of morbidity and mortality in the first 30 days after lung transplantation. Risk factors for the development of PGD include donor and recipient characteristics, but how multiple variables interact to impact the development of PGD and how clinicians should consider these in making decisions about donor acceptance remain unclear. This was a single-center retrospective cohort study to develop and evaluate machine learning pipelines to predict the development of PGD grade 3 within the first 72 hours of transplantation using donor and recipient variables that are known at the time of donor offer acceptance. Among 576 bilateral lung recipients, 173 (30%) developed PGD grade 3. The cohort underwent a 75% to 25% train-test split, and lasso regression was used to identify 11 variables for model development. A K-nearest neighbor's model showing the best calibration and performance with relatively small confidence intervals was selected as the final predictive model with an area under the receiver operating characteristics curve of 0.65. Machine learning models can predict the risk for development of PGD grade 3 based on data available at the time of donor offer acceptance. This may improve donor-recipient matching and donor utilization in the future.

Smoking exposure-induced bronchus-associated lymphoid tissue in donor lungs does not prevent tolerance induction after transplantation.

The presence of bronchus-associated lymphoid tissue (BALT) in donor lungs has been suggested to accelerate graft rejection after lung transplantation. Although chronic smoke exposure can induce BALT formation, the impact of donor cigarette use on alloimmune responses after lung transplantation is not well understood. Here, we show that smoking-induced BALT in mouse donor lungs contains Foxp3+ T cells and undergoes dynamic restructuring after transplantation, including recruitment of recipient-derived leukocytes to areas of pre-existing lymphoid follicles and replacement of graft-resident donor cells. Our findings from mouse and human lung transplant data support the notion that a donor's smoking history does not predispose to acute cellular rejection or prevent the establishment of allograft acceptance with comparable outcomes to nonsmoking donors. Thus, our work indicates that BALT in donor lungs is plastic in nature and may have important implications for modulating proinflammatory or tolerogenic immune responses following transplantation.

Nanoparticle targeting of neutrophil glycolysis prevents lung ischemia-reperfusion injury.

Neutrophils exacerbate pulmonary ischemia-reperfusion injury (IRI) resulting in poor short and long-term outcomes for lung transplant recipients. Glycolysis powers neutrophil activation, but it remains unclear if neutrophil-specific targeting of this pathway will inhibit IRI. Lipid nanoparticles containing the glycolysis flux inhibitor 2-deoxyglucose (2-DG) were conjugated to neutrophil-specific Ly6G antibodies (NP-Ly6G[2-DG]). Intravenously administered NP-Ly6G(2-DG) to mice exhibited high specificity for circulating neutrophils. NP-Ly6G(2-DG)-treated neutrophils were unable to adapt to hypoglycemic conditions of the lung airspace environment as evident by the loss of demand-induced glycolysis, reductions in glycogen and ATP content, and an increased vulnerability to apoptosis. NP-Ly6G(2-DG) treatment inhibited pulmonary IRI following hilar occlusion and orthotopic lung transplantation. IRI protection was associated with less airspace neutrophil extracellular trap generation, reduced intragraft neutrophilia, and enhanced alveolar macrophage efferocytotic clearance of neutrophils. Collectively, our data show that pharmacologically targeting glycolysis in neutrophils inhibits their activation and survival leading to reduced pulmonary IRI.

Bayesian spatial modelling of localised SARS-CoV-2 transmission through mobility networks across England.

In the early phases of growth, resurgent epidemic waves of SARS-CoV-2 incidence have been characterised by localised outbreaks. Therefore, understanding the geographic dispersion of emerging variants at the start of an outbreak is key for situational public health awareness. Using telecoms data, we derived mobility networks describing the movement patterns between local authorities in England, which we have used to inform the spatial structure of a Bayesian BYM2 model. Surge testing interventions can result in spatio-temporal sampling bias, and we account for this by extending the BYM2 model to include a random effect for each timepoint in a given area. Simulated-scenario modelling and real-world analyses of each variant that became dominant in England were conducted using our BYM2 model at local authority level in England. Simulated datasets were created using a stochastic metapopulation model, with the transmission rates between different areas parameterised using telecoms mobility data. Different scenarios were constructed to reproduce real-world spatial dispersion patterns that could prove challenging to inference, and we used these scenarios to understand the performance characteristics of the BYM2 model. The model performed better than unadjusted test positivity in all the simulation-scenarios, and in particular when sample sizes were small, or data was missing for geographical areas. Through the analyses of emerging variant transmission across England, we found a reduction in the early growth phase geographic clustering of later dominant variants as England became more interconnected from early 2022 and public health interventions were reduced. We have also shown the recent increased geographic spread and dominance of variants with similar mutations in the receptor binding domain, which may be indicative of convergent evolution of SARS-CoV-2 variants.

Using leave-one-out cross validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A cautionary tale.

In recent decades, multilevel regression and poststratification (MRP) has surged in popularity for population inference. However, the validity of the estimates can depend on details of the model, and there is currently little research on validation. We explore how leave-one-out cross validation (LOO) can be used to compare Bayesian models for MRP. We investigate two approximate calculations of LOO: Pareto smoothed importance sampling (PSIS-LOO) and a survey-weighted alternative (WTD-PSIS-LOO). Using two simulation designs, we examine how accurately these two criteria recover the correct ordering of model goodness at predicting population and small-area estimands. Focusing first on variable selection, we find that neither PSIS-LOO nor WTD-PSIS-LOO correctly recovers the models' order for an MRP population estimand, although both criteria correctly identify the best and worst model. When considering small-area estimation, the best model differs for different small areas, highlighting the complexity of MRP validation. When considering different priors, the models' order seems slightly better at smaller-area levels. These findings suggest that, while not terrible, PSIS-LOO-based ranking techniques may not be suitable to evaluate MRP as a method. We suggest this is due to the aggregation stage of MRP, where individual-level prediction errors average out. We validate these results by applying to the real world National Health and Nutrition Examination Survey (NHANES) data in the United States. Altogether, these results show that PSIS-LOO-based model validation tools need to be used with caution and might not convey the full story when validating MRP as a method.

Social penumbras predict political attitudes.

To explain the political clout of different social groups, traditional accounts typically focus on the group's size, resources, or commonality and intensity of its members' interests. We contend that a group's penumbra-the set of individuals who are personally familiar with people in that group-is another important explanatory factor that merits systematic analysis. To this end, we designed a panel study that allows us to learn about the characteristics of the penumbras of politically relevant groups such as gay people, the unemployed, or recent immigrants. Our study reveals major and systematic differences in the penumbras of various social groups, even ones of similar size. Moreover, we find evidence that entering a group's penumbra is associated with a change in attitude on group-related policy questions. Taken together, our findings suggest that penumbras are pertinent for understanding variation in the political standing of different groups in society.

Role of tertiary lymphoid organs in the regulation of immune responses in the periphery.

Tertiary lymphoid organs (TLOs) are collections of immune cells resembling secondary lymphoid organs (SLOs) that form in peripheral, non-lymphoid tissues in response to local chronic inflammation. While their formation mimics embryologic lymphoid organogenesis, TLOs form after birth at ectopic sites in response to local inflammation resulting in their ability to mount diverse immune responses. The structure of TLOs can vary from clusters of B and T lymphocytes to highly organized structures with B and T lymphocyte compartments, germinal centers, and lymphatic vessels (LVs) and high endothelial venules (HEVs), allowing them to generate robust immune responses at sites of tissue injury. Although our understanding of the formation and function of these structures has improved greatly over the last 30 years, their role as mediators of protective or pathologic immune responses in certain chronic inflammatory diseases remains enigmatic and may differ based on the local tissue microenvironment in which they form. In this review, we highlight the role of TLOs in the regulation of immune responses in chronic infection, chronic inflammatory and autoimmune diseases, cancer, and solid organ transplantation.

The emerging role of regulatory T cells following lung transplantation.

Regulatory T cells (Treg) have proven to be a powerful immunologic force in nearly every organ system and hold therapeutic potential for a wide range of diseases. Insights gained from non-transplant pathologies, such as infection, cancer, and autoimmunity, are now being translated to the field of solid organ transplantation, particularly for livers and kidneys. Recent insights from animal models of lung transplantation have established that Tregs play a vital role in suppressing rejection and facilitating tolerance of lung allografts, and such discoveries are being validated in human studies and preclinical trials. Given that long-term outcomes following lung transplantation remain profoundly limited by chronic rejection, Treg therapy holds the potential to significantly improve patient outcomes and should be aggressively investigated.

Update on the Features and Measurements of Experimental Acute Lung Injury in Animals: An Official American Thoracic Society Workshop Report.

Advancements in methods, technology, and our understanding of the pathobiology of lung injury have created the need to update the definition of experimental acute lung injury (ALI). We queried 50 participants with expertise in ALI and acute respiratory distress syndrome using a Delphi method composed of a series of electronic surveys and a virtual workshop. We propose that ALI presents as a "multidimensional entity" characterized by four "domains" that reflect the key pathophysiologic features and underlying biology of human acute respiratory distress syndrome. These domains are 1) histological evidence of tissue injury, 2) alteration of the alveolar-capillary barrier, 3) presence of an inflammatory response, and 4) physiologic dysfunction. For each domain, we present "relevant measurements," defined as those proposed by at least 30% of respondents. We propose that experimental ALI encompasses a continuum of models ranging from those focusing on gaining specific mechanistic insights to those primarily concerned with preclinical testing of novel therapeutics or interventions. We suggest that mechanistic studies may justifiably focus on a single domain of lung injury, but models must document alterations of at least three of the four domains to qualify as "experimental ALI." Finally, we propose that a time criterion defining "acute" in ALI remains relevant, but the actual time may vary based on the specific model and the aspect of injury being modeled. The continuum concept of ALI increases the flexibility and applicability of the definition to multiple models while increasing the likelihood of translating preclinical findings to critically ill patients.

Ischemia reperfusion injury facilitates lung allograft acceptance through IL-33-mediated activation of donor-derived IL-5 producing group 2 innate lymphoid cells.

Pathways regulating lung alloimmune responses differ from most other solid organs and remain poorly explored. Based on our recent work identifying the unique role of eosinophils in downregulating lung alloimmunity, we sought to define pathways contributing to eosinophil migration and homeostasis. Using a murine lung transplant model, we have uncovered that immunosuppression increases eosinophil infiltration into the allograft in an IL-5-dependent manner. IL-5 production depends on immunosuppression-mediated preservation of donor-derived group 2 innate lymphoid cells (ILC2). We further describe that ischemia reperfusion injury upregulates the expression of IL-33, which functions as the dominant and nonredundant mediator of IL-5 production by graft-resident ILC2. Our work thus identifies unique cellular mechanisms that contribute to lung allograft acceptance. Notably, ischemia reperfusion injury, widely considered to be solely deleterious to allograft survival, can also downregulate alloimmune responses by initiating unique pathways that promote IL-33/IL-5/eosinophil-mediated tolerance.

A pilot randomized controlled trial of de novo belatacept-based immunosuppression following anti-thymocyte globulin induction in lung transplantation.

The development of donor-specific antibodies (DSA) after lung transplantation is common and results in adverse outcomes. In kidney transplantation, Belatacept has been associated with a lower incidence of DSA, but experience with Belatacept in lung transplantation is limited. We conducted a two-center pilot randomized controlled trial of de novo immunosuppression with Belatacept after lung transplantation to assess the feasibility of conducting a pivotal trial. Twenty-seven participants were randomized to Control (Tacrolimus, Mycophenolate Mofetil, and prednisone, n = 14) or Belatacept-based immunosuppression (Tacrolimus, Belatacept, and prednisone until day 89 followed by Belatacept, Mycophenolate Mofetil, and prednisone, n = 13). All participants were treated with rabbit anti-thymocyte globulin for induction immunosuppression. We permanently stopped randomization and treatment with Belatacept after three participants in the Belatacept arm died compared to none in the Control arm. Subsequently, two additional participants in the Belatacept arm died for a total of five deaths compared to none in the Control arm (log rank p = .016). We did not detect a significant difference in DSA development, acute cellular rejection, or infection between the two groups. We conclude that the investigational regimen used in this study is associated with increased mortality after lung transplantation.

Beyond Vaccination Rates: A Synthetic Random Proxy Metric of Total SARS-CoV-2 Immunity Seroprevalence in the Community.

BACKGROUND: Explicit knowledge of total community-level immune seroprevalence is critical to developing policies to mitigate the social and clinical impact of SARS-CoV-2. Publicly available vaccination data are frequently cited as a proxy for population immunity, but this metric ignores the effects of naturally acquired immunity, which varies broadly throughout the country and world. Without broad or random sampling of the population, accurate measurement of persistent immunity post-natural infection is generally unavailable. METHODS: To enable tracking of both naturally acquired and vaccine-induced immunity, we set up a synthetic random proxy based on routine hospital testing for estimating total immunoglobulin G (IgG) prevalence in the sampled community. Our approach analyzed viral IgG testing data of asymptomatic patients who presented for elective procedures within a hospital system. We applied multilevel regression and poststratification to adjust for demographic and geographic discrepancies between the sample and the community population. We then applied state-based vaccination data to categorize immune status as driven by natural infection or by vaccine. RESULTS: We validated the model using verified clinical metrics of viral and symptomatic disease incidence to show the expected biologic correlation of these entities with the timing, rate, and magnitude of seroprevalence. In mid-July 2021, the estimated immunity level was 74% with the administered vaccination rate of 45% in the two counties. CONCLUSIONS: Our metric improves real-time understanding of immunity to COVID-19 as it evolves and the coordination of policy responses to the disease, toward an inexpensive and easily operational surveillance system that transcends the limits of vaccination datasets alone.

Mismatch between scientific theories and statistical models.

Yarkoni recommends that psychology researchers should take care to align their statistical models to the verbal theories they are studying and testing. This principle applies not just to qualitative theories in psychology but also to more quantitative sciences: there, too, mismatch between open-ended theories and specific statistical models have led to confusion.

Bacterial products in donor airways prevent the induction of lung transplant tolerance.

Although postoperative bacterial infections can trigger rejection of pulmonary allografts, the impact of bacterial colonization of donor grafts on alloimmune responses to transplanted lungs remains unknown. Here, we tested the hypothesis that bacterial products present within donor grafts at the time of implantation promote lung allograft rejection. Administration of the toll-like receptor 2 (TLR2) agonist Pam3 Cys4 to Balb/c wild-type grafts triggered acute cellular rejection after transplantation into B6 wild-type recipients that received perioperative costimulatory blockade. Pam3 Cys4 -triggered rejection was associated with an expansion of CD8+ T lymphocytes and CD11c+ CD11bhi MHC (major histocompatibility complex) class II+ antigen-presenting cells within the transplanted lungs. Rejection was prevented when lungs were transplanted into TLR2-deficient recipients but not when MyD88-deficient donors were used. Adoptive transfer of B6 wild-type monocytes, but not T cells, following transplantation into B6 TLR2-deficient recipients restored the ability of Pam3 Cys4 to trigger acute cellular rejection. Thus, we have demonstrated that activation of TLR2 by a bacterial lipopeptide within the donor airways prevents the induction of lung allograft tolerance through a process mediated by recipient-derived monocytes. Our work suggests that donor lungs harboring bacteria may precipitate an inflammatory response that can facilitate allograft rejection.

Routine Hospital-based SARS-CoV-2 Testing Outperforms State-based Data in Predicting Clinical Burden.

Throughout the coronavirus disease 2019 (COVID-19) pandemic, government policy and healthcare implementation responses have been guided by reported positivity rates and counts of positive cases in the community. The selection bias of these data calls into question their validity as measures of the actual viral incidence in the community and as predictors of clinical burden. In the absence of any successful public or academic campaign for comprehensive or random testing, we have developed a proxy method for synthetic random sampling, based on viral RNA testing of patients who present for elective procedures within a hospital system. We present here an approach under multilevel regression and poststratification to collecting and analyzing data on viral exposure among patients in a hospital system and performing statistical adjustment that has been made publicly available to estimate true viral incidence and trends in the community. We apply our approach to tracking viral behavior in a mixed urban-suburban-rural setting in Indiana. This method can be easily implemented in a wide variety of hospital settings. Finally, we provide evidence that this model predicts the clinical burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) earlier and more accurately than currently accepted metrics. See video abstract at, http://links.lww.com/EDE/B859.