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IntroductionInfluenza virus infections are a major global health problem. Influenza can result in mild/moderate disease or progress to more severe disease, leading to high morbidity and mortality. Severity is thought to be primarily driven by immunopathology, but predicting which individuals are at a higher risk of being hospitalized warrants investigation into host genetics and the molecular signatures of the host response during influenza infections.MethodsHere, we performed transcriptome and genotype analysis in healthy controls and patients exhibiting mild/moderate or severe influenza (ICU patients). A unique aspect of our study was the genotyping of all participants, which allowed us to assign ethnicities based on genetic variation and assess whether the variation was correlated with expression levels. ResultsWe identified 169 differentially expressed genes and related molecular pathways between patients in the ICU and those who were not in the ICU. The transcriptome/genotype association analysis identified 871 genes associated to a genetic variant and 39 genes distinct between African-Americans and Caucasians. We also investigated the effects of age and sex and found only a few discernible gene effects in our cohort. DiscussionTogether, our results highlight select risk factors that may contribute to an increased risk of ICU admission for influenza-infected patients. This should help to develop better diagnostic tools based on molecular signatures, in addition to a better understanding of the biological processes in the host response to influenza.

IntroductionAllergic asthma has been mainly attributed to T helper type 2 (Th2) and proinflammatory responses but many cellular processes remain elusive. There is increasing evidence for distinct roles for macrophage and dendritic cell (DC) subsets in allergic airway inflammation (AAI). At the same time, there are various mouse models for allergic asthma that have been of utmost importance in identifying key inflammatory pathways in AAI but that differ in the allergen and/or route of sensitization. It is unclear whether and how the accumulation and activation of specialized macrophage and DC subsets depend on the experimental model chosen for analyses.MethodsIn our study, we employed high-parameter spectral flow cytometry to comprehensively assess the accumulation and phenotypic alterations of different macrophage- and DC-subsets in the lung in an OVA- and an HDM-mediated mouse model of AAI.ResultsWe observed subset-specific as well as model-specific characteristics with respect to cell numbers and functional marker expression. Generally, alveolar as opposed to interstitial macrophages showed increased MHCII surface expression in AAI. Between the models, we observed significantly increased numbers of alveolar macrophages, CD103+ DC and CD11b+ DC in HDM-mediated AAI, concurrent with significantly increased airway interleukin-4 but decreased total serum IgE levels. Further, increased expression of CD80 and CD86 on DC was exclusively detected in HDM-mediated AAI.DiscussionOur study demonstrates a model-specific involvement of macrophage and DC subsets in AAI. It further highlights spectral flow cytometry as a valuable tool for their comprehensive analysis under inflammatory conditions in the lung.

There is recent interest in using model hubs – a collection of pre-trained models – in computer vision tasks. To employ a model hub, we first select a source model and then adapt the model for the target to compensate for differences. There still needs to be more research on model selection and adaption for renewable power forecasts. In particular, none of the related work examines different model selection and adaptation strategies for neural network architectures. Also, none of the current studies investigates the influence of available training samples and considers seasonality in the evaluation. We close these gaps by conducting the first thorough experiment for model selection and adaptation for transfer learning in renewable power forecast, adopting recent developments from the field of computer vision on 667 wind and photovoltaic parks from six datasets. We simulate different amounts of training samples for each season to calculate informative forecast errors. We examine the marginal likelihood and forecast error for model selection for those amounts. Furthermore, we study four adaption strategies. As an extension of the current state of the art, we utilize a Bayesian linear regression for forecasting the response based on features extracted from a neural network. This approach outperforms the baseline with only seven days of training data and shows that fine-tuning is not beneficial with less than three months of data. We further show how combining multiple models through ensembles can significantly improve the model selection and adaptation approach such that we have a similar mean error with only 30 days of training data which is otherwise only possible with an entire year of training data. We achieve a mean error of 9.8 and 14 percent for the most realistic dataset for PV and wind with only seven days of training data.

Abstract T helper (Th) cells provide immunity to pathogens but also contribute to detrimental immune responses during allergy and autoimmunity. Th2 cells mediate asthmatic airway inflammation and Th1 cells are involved in the pathogenesis of multiple sclerosis. T cell activation involves complex transcriptional networks and metabolic reprogramming, which enable proliferation and differentiation into Th1 and Th2 cells. The essential trace element zinc has reported immunomodulatory capacity and high zinc concentrations interfere with T cell function. However, how high doses of zinc affect T cell gene networks and metabolism remained so far elusive. Herein, we demonstrate by means of transcriptomic analysis that zinc aspartate (UNIZINK), a registered pharmaceutical infusion solution with high bioavailability, negatively regulates gene networks controlling DNA replication and the energy metabolism of murine CD3/CD28-activated CD4+ T cells. Specifically, in the presence of zinc, CD4+ T cells show impaired expression of cell cycle, glycolytic and tricarboxylic acid cycle genes, which functionally cumulates in reduced glycolysis, oxidative phosphorylation, metabolic fitness and viability. Moreover, high zinc concentrations impaired nuclear expression of the metabolic transcription factor MYC, prevented Th1 and Th2 differentiation in vitro and reduced Th1 autoimmune central nervous system (CNS) inflammation and Th2 asthmatic airway inflammation induced by house dust mites in vivo. Together, we find that higher zinc doses impair the metabolic fitness of CD4+ T cells and prevent Th1 CNS autoimmunity and Th2 allergy.

ABSTRACT The tracking of pathogen burden and host responses with minimally invasive methods during respiratory infections is central for monitoring disease development and guiding treatment decisions. Utilizing a standardized murine model of respiratory influenza A virus (IAV) infection, we developed and tested different supervised machine learning models to predict viral burden and immune response markers, i.e., cytokines and leukocytes in the lung, from hematological data. We performed independently in vivo infection experiments to acquire extensive data for training and testing of the models. We show here that lung viral load, neutrophil counts, cytokines (such as gamma interferon [IFN-γ] and interleukin 6 [IL-6]), and other lung infection markers can be predicted from hematological data. Furthermore, feature analysis of the models showed that blood granulocytes and platelets play a crucial role in prediction and are highly involved in the immune response against IAV. The proposed in silico tools pave the path toward improved tracking and monitoring of influenza virus infections and possibly other respiratory infections based on minimally invasively obtained hematological parameters. IMPORTANCE During the course of respiratory infections such as influenza, we do have a very limited view of immunological indicators to objectively and quantitatively evaluate the outcome of a host. Methods for monitoring immunological markers in a host’s lungs are invasive and expensive, and some of them are not feasible to perform. Using machine learning algorithms, we show for the first time that minimally invasively acquired hematological parameters can be used to infer lung viral burden, leukocytes, and cytokines following influenza virus infection in mice. The potential of the framework proposed here consists of a new qualitative vision of the disease processes in the lung compartment as a noninvasive tool.

Abstract Background This differential diagnosis of allergic vs non-allergic asthma is typically made on the basis of sensitization to allergens, such that absence of sensitization could result in a patient being managed as having non-allergic asthma. In Germany, the number of specific allergen tests is limited and non-standardized (across clinicians and laboratories), with the potential for false negative diagnoses. IDENTIFY aimed to gain data on sensitizations toward aeroallergens in patients with severe asthma who had tested negative to perennial aeroallergens in previous tests. Methods This was a single visit, non-randomized, non-interventional study conducted in 87 centers across Germany. The only inclusion criteria were that patients had to be adults (at least 18 years of age) with a diagnosis of severe asthma (receiving at least Global Initiative for Asthma Step IV therapy), and who had previously tested negative to perennial aeroallergens. Patients were then tested for sensitization to a panel of 35 perennial aeroallergens, with positive sensitization indicated by CAP ≥ 0.35 kU/L. Results Of 588 patients recruited, 454 had complete and valid data, and had previously tested negative to perennial aeroallergens. Overall, 43.6% of the analyzed patients tested positive for at least one of the included aeroallergens, with 18.7% testing positive for three or more, and 4.2% positive for more than ten. The five most common sensitizations were to Staphylococcus aureus enterotoxin B, Aspergillus fumigatus, Candida albicans, Dermatophagoides farinae, and Rhizopus nigricans, each of which tested positive in at least 9.7% of the population. Conclusions In this group of patients being managed as having non-allergic asthma (and who had all previously tested negative to perennial aeroallergens), a high proportion tested positive to a broad panel of aeroallergens. A diagnosis of allergic asthma therefore cannot be excluded purely on the basis of standard aeroallergen panels.

Abstract Background Inhalation therapy is the backbone of asthma and COPD control. However, inhaler adherence and device mishandling continue to be a problem in real life. Some studies have shown that using a patient-preferred inhaler may reduce device handling errors and improve adherence to prescribed chronic inhaler drug therapy. The aim of this study was to compare the preferences for commonly used inhaler devices in Germany in patients with chronic obstructive respiratory disease. We also pursued the question which properties of an inhaler device are particularly important to the user and what effects age, gender and type of disease (asthma or COPD) may have on device preference and handling errors. Methods Prospective, open-label cross-sectional study in which 105 patients with asthma (58%) or COPD (42%) participated. Validated checklists were used to objectively assess inhaler technique and errors with 10 different placebo devices. For each device, patients were asked to test the handling, to assess the device properties and to name the device that they would most or least prefer. Results Across the 10 placebo inhaler devices tested, patients needed an average of 1.22 attempts to error-free use. The device with the lowest mean number of attempts was the Turbohaler® (1.02), followed by the Nexthaler® (1.04), the Diskus® (1.07) and the Spiromax® (1.10). Patients over 60 years vs. younger age (p = 0.002) and COPD vs. asthma patients (p = 0.016) required more attempts to ensure correct use. 41% of the study participants chose one of the devices they already used as the most preferred inhaler. Overall, 20% opted for the Spiromax®, 15% for the Nexthaler® and 14% for the Turbohaler® or a pMDI. The least preferred device was the Elpenhaler® (0%). From a selection of 7 predefined inhaler attributes, patients stated easy handling as the most important for them. This was followed by short inhalation time and low inhalation resistance. Conclusions Patient preference may vary between inhaler devices. The lowest number of attempts to error-free use was reported for the Turbohaler® and the Nexthaler®. The Spiromax® and the Nexthaler® achieved the best overall ratings and were the devices most preferred by patients.

Purpose: Pre-operative assessment of thoracic lymphonodal (LN) involvement in patients with lung cancer (LC) is crucial when choosing the treatment modality. Visual assessment of F-18-FDG-PET/CT (PET/CT) is well established, however, there is still a need for prospective quantitative data to differentiate benign from malignant lesions which would simplify staging and guide the further implementation of computer-aided diagnosis (CAD). Methods: In this prospective study, 37 patients with confirmed lung cancer (m/f = 24/13; age: 70 [52–83] years) were analyzed. All patients underwent PET/CT and quantitative data (standardized uptake values) were obtained. Histological results were available for 101 thoracic lymph nodes. Quantitative data were matched to determine cut-off values for delineation between benign vs. malignant lymph nodes. Furthermore, a scoring system derived from these cut-off values was established. Statistical analyses were performed through ROC analysis. Results: Quantitative analysis revealed the optimal cut-off values (p < 0.01) for the differentiation between benign and malignant thoracic lymph nodes in patients suffering from lung cancer. The respective areas under the curve (AUC) ranged from 0.86 to 0.94. The highest AUC for a ratio of lymph node to healthy lung tissue was 0.94. The resulting accuracy ranged from 78.2% to 89.1%. A dedicated scoring system led to an AUC of 0.93 with a negative predictive value of 95.4%. Conclusion: Quantitative analysis of F-18-FDG-PET/CT data provides reliable results for delineation between benign and malignant thoracic lymph nodes. Thus, quantitative parameters can improve diagnostic accuracy and reliability and can also facilitate the handling of the steadily increasing number of clinical examinations.

Abstract Background Aspirin exacerbated respiratory disease (AERD) is a disease of the upper and lower airways. It is characterized by severe asthma, chronic sinusitis with nasal polyps (CRSwNP) and intolerance towards nonsteroidal analgesics (NSAR). Arachidonic acid (AA) metabolites play an important role in the pathogenesis of AERD. It is still unknown, whether metabolism of AA is comparable between the upper and lower airways as well as between patients with and without NSAR intolerance. Objective We sought to analyze differences in the expression of cyclooxygenases type 1 and 2 (COX-1, COX-2), arachidonate 5-lipoxygenase (5-LOX) and cysteinyl leukotriene receptor type 2 ($$\hbox {CysLT}_2$$ CysLT2 ) in nasal polyps and the bronchial mucosa of patients with aspirin intolerant asthma (AIA, $$n=23$$ n=23 ) as compared to patients with aspirin tolerant asthma (ATA, $$n=17$$ n=17 ) and a control group with nasal polyps, but without asthma (NPwA, $$n=15$$ n=15 ). Methods Tissue biopsies from nasal polyps and bronchial mucosa were obtained during surgical treatment of nasal polyps by endonasal functional endoscopic sinus surgery (FESS) under general anesthesia from intubated patients. Immunohistochemistry was used to analyze the expression of COX-1, COX-2, 5-LOX and $$\hbox {CysLT}_2$$ CysLT2 in nasal and bronchial mucosa. Categorization into the different patient groups was performed according to the patient history, clinical and laboratory data, pulmonary function and provocation tests, as well as allergy testing. Results We observed a stronger expression of 5-LOX and $$\hbox {CysLT}_2$$ CysLT2 in submucosal glands of nasal and bronchial tissue compared to epithelial expression. The expression of COX-1 and COX-2 was stronger in epithelia compared to submucosal glands. There was a similar expression of the enzymes and $$\hbox {CysLT}_2$$ CysLT2 between upper and lower airways in all patient groups. We did not detect any significant differences between the patient groups. Conclusions The AA-metabolizing enzymes and the $$\hbox {CysLT}_2$$ CysLT2 were expressed in a very similar way in different microscopic structures in samples of the upper and lower airways of individual patients. We did not detect differences between the patient groups indicating the pathogenetic role of AA metabolism in these disorders is independent of the presence of NSAR-intolerance.

Abstract Background There continues to be a great need for better biomarkers and host-directed treatment targets for community-acquired pneumonia (CAP). Alterations in phospholipid metabolism may constitute a source of small molecule biomarkers for acute infections including CAP. Evidence from animal models of pulmonary infections and sepsis suggests that inhibiting acid sphingomyelinase (which releases ceramides from sphingomyelins) may reduce end-organ damage. Methods We measured concentrations of 105 phospholipids, 40 acylcarnitines, and 4 ceramides, as well as acid sphingomyelinase activity, in plasma from patients with CAP (n = 29, sampled on admission and 4 subsequent time points), chronic obstructive pulmonary disease exacerbation with infection (COPD, n = 13) as a clinically important disease control, and 33 age- and sex-matched controls. Results Phospholipid concentrations were greatly decreased in CAP and normalized along clinical improvement. Greatest changes were seen in phosphatidylcholines, followed by lysophosphatidylcholines, sphingomyelins and ceramides (three of which were upregulated), and were least in acylcarnitines. Changes in COPD were less pronounced, but also differed qualitatively, e.g. by increases in selected sphingomyelins. We identified highly accurate biomarkers for CAP (AUC ≤ 0.97) and COPD (AUC ≤ 0.93) vs. Controls, and moderately accurate biomarkers for CAP vs. COPD (AUC ≤ 0.83), all of which were phospholipids. Phosphatidylcholines, lysophosphatidylcholines, and sphingomyelins were also markedly decreased in S. aureus-infected human A549 and differentiated THP1 cells. Correlations with C-reactive protein and procalcitonin were predominantly negative but only of mild-to-moderate extent, suggesting that these markers reflect more than merely inflammation. Consistent with the increased ceramide concentrations, increased acid sphingomyelinase activity accurately distinguished CAP (fold change = 2.8, AUC = 0.94) and COPD (1.75, 0.88) from Controls and normalized with clinical resolution. Conclusions The results underscore the high potential of plasma phospholipids as biomarkers for CAP, begin to reveal differences in lipid dysregulation between CAP and infection-associated COPD exacerbation, and suggest that the decreases in plasma concentrations are at least partially determined by changes in host target cells. Furthermore, they provide validation in clinical blood samples of acid sphingomyelinase as a potential treatment target to improve clinical outcome of CAP.

Identification of host factors associated with severe influenza infection could provide insights into treatment options. Here, the authors provide transcriptomic analyses of blood from >100 influenza infected patients and show that changes in circulating neutrophils are associated with severe influenza infection.

Integrating new renewable energy resources requires robust and reliable forecasts to ensure a stable electrical grid and avoid blackouts. Sophisticated representation learning techniques, such as autoencoders, play an essential role, as they allow for the extraction of latent features to forecast the expected generated wind and photovoltaic power for the next seconds up to days. Thereby, autoencoders reduce the required training time and the time spent in manual feature engineering and often improve the forecast error. However, most current renewable energy forecasting research on autoencoders focuses on smaller forecast horizons for the following seconds and hours based on meteorological measurements. At the same time, larger forecast horizons, such as day-ahead power forecasts based on numerical weather predictions, are crucial for planning loads and demands within the electrical grid to prevent power failures. There is little evidence on the ability of autoencoders and their respective forecasting models to improve through multi-task learning and time series autoencoders for day-ahead power forecasts. We can close these gaps by proposing a multi-task learning autoencoder based on the recently introduced temporal convolution network. This approach reduces the number of trainable parameters by 38 for photovoltaic data and 202 for wind data while having the best reconstruction error compared to nine other representation learning techniques. At the same time, this model decreases the day-ahead forecast error up to 18.3% for photovoltaic parks and 1.5% for wind parks. We round off these results by analyzing the influences of the latent size and the number of layers to fine-tune the encoder for wind and photovoltaic power forecasts.

COPD and asthma are two distinct but sometimes overlapping diseases exhibiting varying degrees and types of inflammation on different stages of the disease. Although several biomarkers are defined to estimate the inflammatory endotype and stages in these diseases, there is still a need for new markers and potential therapeutic targets. We investigated the levels of a phytohormone, abscisic acid (ABA) and its receptor, LANCL2, in COPD patients and asthmatics. In addition, PPAR-γ that is activated by ABA in a ligand-binding domain-independent manner was also included in the study. In this study, we correlated ABA with COPD-propagating factors to define the possible role of ABA, in terms of immune regulation, inflammation, and disease stages. We collected blood from 101 COPD patients, 52 asthmatics, and 57 controls. Bronchoscopy was performed on five COPD patients and 29 controls. We employed (i) liquid chromatography–tandem mass spectrometry and HPLC to determine the ABA and indoleamine 2,3-dioxygenase levels, respectively; (ii) real-time PCR to quantify the gene expression of LANCL2 and PPAR-γ; (iii) Flow cytometry to quantify adipocytokines; and (iv) immunoturbidimetry and ELISA to measure CRP and cytokines, respectively. Finally, a multinomial regression model was used to predict the probability of using ABA as a biomarker. Blood ABA levels were significantly reduced in COPD patients and asthmatics compared to age- and gender-matched normal controls. However, PPAR-γ was elevated in COPD patients. Intriguingly, ABA was positively correlated with immune-regulatory factors and was negatively correlated with inflammatory markers, in COPD. Of note, ABA was increased in advanced COPD stages. We thereby conclude that ABA might be involved in regulation of COPD pathogenesis and might be regarded as a potential biomarker for COPD stages.

Amino acids and their metabolites are key regulators of immune responses, and plasma levels may change profoundly during acute disease states. Using targeted metabolomics, we evaluated concentration changes in plasma amino acids and related metabolites in community-acquired pneumonia (CAP, n = 29; compared against healthy controls, n = 33) from presentation to hospital through convalescence. We further aimed to identify biomarkers for acute CAP vs. the clinically potentially similar infection-triggered COPD exacerbation (n = 13). Amino acid metabolism was globally dysregulated in both CAP and COPD. Levels of most amino acids were markedly depressed in acute CAP, and total amino acid concentrations on admission were an accurate biomarker for the differentiation from COPD (AUC = 0.93), as were reduced asparagine and threonine levels (both AUC = 0.92). Reduced tryptophan and histidine levels constituted the most accurate biomarkers for acute CAP vs. controls (AUC = 0.96, 0.94). Only kynurenine, symmetric dimethyl arginine, and phenylalanine levels were increased in acute CAP, and the kynurenine/tryptophan ratio correlated best with clinical recovery and resolution of inflammation. Several amino acids did not reach normal levels by the 6-week follow-up. Glutamate levels were reduced on admission but rose during convalescence to 1.7-fold above levels measured in healthy control. Our data suggest that dysregulated amino acid metabolism in CAP partially persists through clinical recovery and that amino acid metabolism constitutes a source of promising biomarkers for CAP. In particular, total amino acids, asparagine, and threonine may constitute plasma biomarker candidates for the differentiation between CAP and infection-triggered COPD exacerbation and, perhaps, the detection of pneumonia in COPD.

The superantigen Staphylococcus aureus (S. aureus) enterotoxin B (SEB) has been proposed a central player in the associations between S. aureus nasal colonization and the development of allergic asthma. Previously, SEB has been shown to aggravate allergic sensitization and allergic airway inflammation (AAI) in experimental mouse models. Aiming at understanding the underlying immunological mechanisms, we tested the hypothesis that intranasal (i.n.) SEB-treatment divergently modulates AAI depending on the timing and intensity of the SEB-encounter. In an ovalbumin-mediated mouse model of AAI, we treated mice i.n. with 50 ng or 500 ng SEB either together with the allergic challenge or prior to the peripheral sensitization. We observed SEB to affect different hallmark parameters of AAI depending on the timing and the dose of treatment. SEB administered i.n. together with the allergic challenge significantly modulated respiratory leukocyte accumulation, intensified lymphocyte activation and, at the higher dose, induced a strong type-1 and pro-inflammatory cytokine response and alleviated airway hyperreactivity in AAI. SEB administered i.n. prior to the allergic sensitization at the lower dose significantly boosted the specific IgE response while administration of the higher dose led to a significantly reduced recruitment of immune cells, including eosinophils, to the respiratory tract and to a significantly dampened Th-2 cytokine response without inducing a Th-1 or pro-inflammatory response. We show a remarkably versatile potential for SEB to either aggravate or alleviate different parameters of allergic sensitization and AAI. Our study thereby not only highlights the complexity of the associations between S. aureus and allergic asthma but possibly even points at prophylactic and therapeutic pathways.

The COVID-19 pandemics has caused the death of almost six million people worldwide. In order to establish collective immunity, the first vaccines that were approved in Germany were the vector virus-based vaccine Vaxzevria and the mRNA vaccines Comirnaty and Spikevax, respectively. As it was reported that SARS-CoV-2 can trigger autoimmunity, it is of significant interest to investigate whether COVID-19 vaccines evoke the formation of autoantibodies and subsequent autoimmunity. Here, we analyzed immune responses after different vaccination regimens (mRNA/mRNA, Vector/Vector or Vector/mRNA) with respect to anti-SARS-CoV-2-specific immunity and the development of autoantibodies well known for their appearance in distinct autoimmune diseases. We found that anti-SARS-CoV-2 antibody levels were 90% lower after Vector/Vector vaccination compared to the other vaccinations and that Vector/mRNA vaccination was more effective than mRNA/mRNA vaccination in terms of IgM and IgA responses. However, until 4 months after booster vaccination we only detected increases in autoantibodies in participants with already pre-existing autoantibodies whereas vaccinees showing no autoantibody formation before vaccination did not respond with sustained autoantibody production. Taken together, our study suggests that all used COVID-19 vaccines do not significantly foster the appearance of autoantibodies commonly associated with lupus erythematodes, rheumatoid arthritis, Celiac disease and antiphospholipid-syndrome but provide immunity to SARS-CoV-2.

An overt pro-inflammatory immune response is a key factor contributing to lethal pneumococcal infection in an influenza pre-infected host and represents a potential target for therapeutic intervention. However, there is a paucity of knowledge about the level of contribution of individual cytokines. Based on the predictions of our previous mathematical modeling approach, the potential benefit of IFN-γ- and/or IL-6-specific antibody-mediated cytokine neutralization was explored in C57BL/6 mice infected with the influenza A/PR/8/34 strain, which were subsequently infected with the Streptococcus pneumoniae strain TIGR4 on day 7 post influenza. While single IL-6 neutralization had no effect on respiratory bacterial clearance, single IFN-γ neutralization enhanced local bacterial clearance in the lungs. Concomitant neutralization of IFN-γ and IL-6 significantly reduced the degree of pneumonia as well as bacteremia compared to the control group, indicating a positive effect for the host during secondary bacterial infection. The results of our model-driven experimental study reveal that the predicted therapeutic value of IFN-γ and IL-6 neutralization in secondary pneumococcal infection following influenza infection is tightly dependent on the experimental protocol while at the same time paving the way toward the development of effective immune therapies.

Allergic airway inflammation (AAI) involves T helper cell type 2 (Th2) and pro-inflammatory responses to aeroallergens and many predisposing factors remain elusive. Influenza A virus (IAV) is a major human pathogen that causes acute respiratory infections and induces specific immune responses essential for viral clearance and resolution of the infection. Beyond acute infection, IAV has been shown to persistently affect lung homeostasis and respiratory immunity. Here we asked how resolved IAV infection affects subsequently induced AAI. Mice infected with a sublethal dose of IAV were sensitized and challenged in an ovalbumin mediated mouse model for AAI after resolution of the acute viral infection. Histological changes, respiratory leukocytes, cytokines and airway hyperreactivity were analyzed in resolved IAV infection alone and in AAI with and without previous IAV infection. More than five weeks after infection, we detected persistent pneumonia with increased activated CD4+ and CD8+ lymphocytes as well as dendritic cells and MHCII expressing macrophages in the lung. Resolved IAV infection significantly affected subsequently induced AAI on different levels including morphological changes, respiratory leukocytes and lymphocytes as well as the pro-inflammatory cytokine responses, which was clearly diminished. We conclude that IAV has exceptional persisting effects on respiratory immunity with substantial consequences for subsequently induced AAI.

Real-world studies include a broader patient population for a longer duration than randomised controlled trials (RCTs) and can provide relevant insights for clinical practice. PASSPORT was a multicentre, prospective, post-authorisation study of patients who were newly prescribed pirfenidone and followed for 2 years after initiating treatment. Physicians collected data on adverse drug reactions (ADRs), serious ADRs (SADRs) and ADRs of special interest (ADRSI) at baseline and then every 3 months. Post hoc stepwise logistic regression models were used to identify baseline characteristics associated with discontinuing treatment due to an ADR. Patients (n=1009, 99.7% with idiopathic pulmonary fibrosis) had a median pirfenidone exposure of 442.0 days. Overall, 741 (73.4%) patients experienced ADRs, most commonly nausea (20.6%) and fatigue (18.5%). ADRs led to treatment discontinuation in 290 (28.7%) patients after a median of 99.5 days. Overall, 55 (5.5%) patients experienced SADRs, with a fatal outcome in six patients. ADRSI were reported in 693 patients, most commonly gastrointestinal symptoms (38.3%) and photosensitivity reactions/skin rashes (29.0%). Older age and female sex were associated with early treatment discontinuation due to an ADR. Findings were consistent with the known safety profile of pirfenidone, based on RCT data and other post-marketing experience, with no new safety signals observed.

The data of a corresponding animal experiment demonstrates that nebulized methacholine (MCh) induced severe bronchoconstriction and significant inhomogeneous ventilation and pulmonary perfusion (V̇A/Q̇) distribution in pigs, which is similar to findings in human asthma. The inhalation of MCh induced bronchoconstriction and delayed both uptake and elimination of desflurane (Kretzschmar et al., 2015) [1]. The objective of the present data is to determine V̇A/Q̇ matching by Multiple Inert Gas Elimination Technique (MIGET) in piglets before and during methacholine- (MCh-) induced bronchoconstriction, induced by MCh infusion, and to assess the blood concentration profiles for desflurane (DES) by Micropore Membrane Inlet Mass Spectrometry (MMIMS). Healthy piglets (n=4) under general anesthesia were instrumented with arterial, central venous, and pulmonary artery lines. The airway was secured via median tracheostomy with an endotracheal tube, and animals were mechanically ventilated with intermittent positive pressure ventilation (IPPV) with a FiO2 of 0.4, tidal volume (VT)=10 ml/kg and PEEP of 5cmH2O using an open system. The determination of V.A/Q. was done by MIGET: before desflurane application and at plateau in both healthy state and during MCh infusion. Arterial blood was sampled at 0, 1, 2, 5, 10, 20, and 30 min during wash-in and washout, respectively. Bronchoconstriction was established by MCH infusion aiming at doubling the peak airway pressure, after which wash-in and washout of the anesthetic gas was repeated. Anesthesia gas concentrations were measured by MMIMS. Data were analyzed by ANOVA, paired t-test, and by nonparametric Friedman׳s test and Wilcoxon׳s matched pairs test. We measured airway pressures, pulmonary resistance, and mean paO2 as well as hemodynamic variables in all pigs before desflurane application and at plateau in both healthy state and during methacholine administration by infusion. By MIGET, fractional alveolar ventilation and pulmonary perfusion in relation to the V.A/Q. compartments, data of logSDQ̇ and logSDV̇ (the second moments describing global dispersion, i.e. heterogeneity of distribution) were estimated prior to and after MCh infusion. The uptake and elimination of desflurane was determined by MMIMS.

The innate immune system senses influenza A virus (IAV) through different pathogen-recognition receptors including Toll-like receptor 7 (TLR7). Downstream of viral recognition natural killer (NK) cells are activated as part of the anti-IAV immune response. Despite the known decisive role of TLR7 for NK cell activation by therapeutic immunostimulatory RNAs, the contribution of TLR7 to the NK cell response following IAV infection has not been addressed. We have analyzed lung cytokine responses as well as the activation, interferon (IFN)-γ production, and cytotoxicity of lung and splenic NK cells following sublethal respiratory IAV infection in wild-type and TLR7ko mice. Early airway IFN-γ levels as well as the induction of lung NK cell CD69 expression and IFN-γ production in response to IAV infection were significantly attenuated in TLR7-deficient hosts. Strikingly, respiratory IAV infection also primed splenic NK cells for IFN-γ production, degranulation, and target cell lysis, all of which were fully dependent on TLR7. At the same time, lung type I IFN levels were significantly reduced in TLR7ko mice early following IAV infection, displaying a potential upstream mechanism of the attenuated NK cell activation observed. Taken together, our data clearly demonstrate a specific role for TLR7 signaling in local and systemic NK cell activation following respiratory IAV infection despite the presence of redundant innate IAV-recognition pathways.