Lung eosinophil recruitment in response to Aspergillus fumigatus is correlated with fungal cell wall composition and requires γδ T cells

The differential recognition of fungal cell wall polysaccharides that program innate and adaptive immunity to the human opportunistic fungal pathogen Aspergillus fumigatus has been a focus of considerable interest. In a mouse model of fungal conidia aspiration, decreased relative levels of cell wall core carbohydrates β-1,3-glucan to chitin in A. fumigatus isolates and mutant strains were correlated with increased airway eosinophil recruitment. In addition, an increase in fungal surface chitin exposure induced by the β-1,3-glucan synthesis-targeting drug caspofungin was associated with increased murine airway eosinophil recruitment after a single challenge of conidia. The response to increased A. fumigatus chitin was associated with increased transcription of IL-17A after a single aspiration, although this cytokine was not required for eosinophil recruitment. Rather, both RAG1 and γδ T cells were required, suggesting that this subset of innate-like lymphocytes may be an important regulator of potentially detrimental type 2 immune responses to fungal inhalation and infection

Eosinophils Are Recruited in Response to Chitin Exposure and Enhance Th2-Mediated Immune Pathology in Aspergillus fumigatus Infection

In patients infected with the fungus Aspergillus fumigatus, Th1 responses are considered protective, while Th2 responses are associated with increased morbidity and mortality. How host-pathogen interactions influence the development of these protective or detrimental immune responses is not clear. We compared lung immune responses to conidia from two fungal isolates that expressed different levels of the fungal cell wall component chitin. We observed that repeated aspirations of the high-chitin-expressing isolate Af5517 induced increased airway eosinophilia in the lungs of recipient mice compared to the level of eosinophilia induced by isolate Af293. CD4+ T cells in the bronchoalveolar lavage fluid (BALF) of Af5517-aspirated mice displayed decreased gamma interferon secretion and increased interleukin-4 transcription. In addition, repeated aspirations of Af5517 induced lung transcription of the Th2-associated chemokines CCL11 (eotaxin-1) and CCL22 (macrophage-derived chemokine). Eosinophil recruitment in response to conidial aspiration was correlated with the level of chitin exposure during germination and was decreased by constitutive lung chitinase expression. Moreover, eosinophil-deficient mice subjected to multiple aspirations of Af5517 prior to neutrophil depletion and infection exhibited decreased morbidity and fungal burden compared to the levels of morbidity and fungal burden found in wild-type mice. These results suggest that exposure of chitin in germinating conidia promotes eosinophil recruitment and ultimately induces Th2-skewed immune responses after repeated aspiration. Furthermore, our results suggest that eosinophils should be examined as a potential therapeutic target in patients that mount poorly protective Th2 responses to A. fumigatus infection

Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease

BACKGROUND: Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization. RESULTS: During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events. CONCLUSIONS: Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)

Challenges of COVID-19 Case Forecasting in the US, 2020-2021.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making

COS Ambassadors

A collection of materials and resources for COS ambassadors

First narrow-band search for continuous gravitational waves from known pulsars in advanced detector data

International audienceSpinning neutron stars asymmetric with respect to their rotation axis are potential sources of continuous gravitational waves for ground-based interferometric detectors. In the case of known pulsars a fully coherent search, based on matched filtering, which uses the position and rotational parameters obtained from electromagnetic observations, can be carried out. Matched filtering maximizes the signal-to-noise (SNR) ratio, but a large sensitivity loss is expected in case of even a very small mismatch between the assumed and the true signal parameters. For this reason, narrow-band analysis methods have been developed, allowing a fully coherent search for gravitational waves from known pulsars over a fraction of a hertz and several spin-down values. In this paper we describe a narrow-band search of 11 pulsars using data from Advanced LIGO’s first observing run. Although we have found several initial outliers, further studies show no significant evidence for the presence of a gravitational wave signal. Finally, we have placed upper limits on the signal strain amplitude lower than the spin-down limit for 5 of the 11 targets over the bands searched; in the case of J1813-1749 the spin-down limit has been beaten for the first time. For an additional 3 targets, the median upper limit across the search bands is below the spin-down limit. This is the most sensitive narrow-band search for continuous gravitational waves carried out so far

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks