IL11-mediated stromal cell activation may not be the master regulator of pro-fibrotic signaling downstream of TGFß.

Fibrotic diseases, such as idiopathic pulmonary fibrosis (IPF) and systemic scleroderma (SSc), are commonly associated with high morbidity and mortality, thereby representing a significant unmet medical need. Interleukin 11 (IL11)-mediated cell activation has been identified as a central mechanism for promoting fibrosis downstream of TGFß. IL11 signaling has recently been reported to promote fibroblast-to-myofibroblast transition, thus leading to various pro-fibrotic phenotypic changes. We confirmed increased mRNA expression of IL11 and IL11Rα in fibrotic diseases by OMICs approaches and in situ hybridization. However, the vital role of IL11 as a driver for fibrosis was not recapitulated. While induction of IL11 secretion was observed downstream of TGFß signaling in human lung fibroblasts and epithelial cells, the cellular responses induced by IL11 was quantitatively and qualitatively inferior to that of TGFß at the transcriptional and translational levels. IL11 blocking antibodies inhibited IL11Rα-proximal STAT3 activation but failed to block TGFß-induced profibrotic signals. In summary, our results challenge the concept of IL11 blockade as a strategy for providing transformative treatment for fibrosis.

Association between Contrast-Enhanced Computed Tomography Radiomic Features, Genomic Alterations and Prognosis in Advanced Lung Adenocarcinoma Patients.

Non-invasive methods to assess mutational status, as well as novel prognostic biomarkers, are warranted to foster therapy personalization of patients with advanced non-small cell lung cancer (NSCLC). This study investigated the association of contrast-enhanced Computed Tomography (CT) radiomic features of lung adenocarcinoma lesions, alone or integrated with clinical parameters, with tumor mutational status (EGFR, KRAS, ALK alterations) and Overall Survival (OS). In total, 261 retrospective and 48 prospective patients were enrolled. A Radiomic Score (RS) was created with LASSO-Logistic regression models to predict mutational status. Radiomic, clinical and clinical-radiomic models were trained on retrospective data and tested (Area Under the Curve, AUC) on prospective data. OS prediction models were trained and tested on retrospective data with internal cross-validation (C-index). RS significantly predicted each alteration at training (radiomic and clinical-radiomic AUC 0.95-0.98); validation performance was good for EGFR (AUC 0.86), moderate for KRAS and ALK (AUC 0.61-0.65). RS was also associated with OS at univariate and multivariable analysis, in the latter with stage and type of treatment. The validation C-index was 0.63, 0.79, and 0.80 for clinical, radiomic, and clinical-radiomic models. The study supports the potential role of CT radiomics for non-invasive identification of gene alterations and prognosis prediction in patients with advanced lung adenocarcinoma, to be confirmed with independent studies.

Quantification of pulmonary involvement in COVID-19 pneumonia: an upgrade of the LungQuant software for lung CT segmentation.

Computed tomography (CT) scans are used to evaluate the severity of lung involvement in patients affected by COVID-19 pneumonia. Here, we present an improved version of the LungQuant automatic segmentation software (LungQuant v2), which implements a cascade of three deep neural networks (DNNs) to segment the lungs and the lung lesions associated with COVID-19 pneumonia. The first network (BB-net) defines a bounding box enclosing the lungs, the second one (U-net 1 ) outputs the mask of the lungs, and the final one (U-net 2 ) generates the mask of the COVID-19 lesions. With respect to the previous version (LungQuant v1), three main improvements are introduced: the BB-net, a new term in the loss function in the U-net for lesion segmentation and a post-processing procedure to separate the right and left lungs. The three DNNs were optimized, trained and tested on publicly available CT scans. We evaluated the system segmentation capability on an independent test set consisting of ten fully annotated CT scans, the COVID-19-CT-Seg benchmark dataset. The test performances are reported by means of the volumetric dice similarity coefficient (vDSC) and the surface dice similarity coefficient (sDSC) between the reference and the segmented objects. LungQuant v2 achieves a vDSC (sDSC) equal to 0.96 ± 0.01 (0.97 ± 0.01) and 0.69 ± 0.08 (0.83 ± 0.07) for the lung and lesion segmentations, respectively. The output of the segmentation software was then used to assess the percentage of infected lungs, obtaining a Mean Absolute Error (MAE) equal to 2%.

Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models.

Radiomics investigates the predictive role of quantitative parameters calculated from radiological images. In oncology, tumour segmentation constitutes a crucial step of the radiomic workflow. Manual segmentation is time-consuming and prone to inter-observer variability. In this study, a state-of-the-art deep-learning network for automatic segmentation (nnU-Net) was applied to computed tomography images of lung tumour patients, and its impact on the performance of survival radiomic models was assessed. In total, 899 patients were included, from two proprietary and one public datasets. Different network architectures (2D, 3D) were trained and tested on different combinations of the datasets. Automatic segmentations were compared to reference manual segmentations performed by physicians using the DICE similarity coefficient. Subsequently, the accuracy of radiomic models for survival classification based on either manual or automatic segmentations were compared, considering both hand-crafted and deep-learning features. The best agreement between automatic and manual contours (DICE = 0.78 ± 0.12) was achieved averaging 2D and 3D predictions and applying customised post-processing. The accuracy of the survival classifier (ranging between 0.65 and 0.78) was not statistically different when using manual versus automatic contours, both with hand-crafted and deep features. These results support the promising role nnU-Net can play in automatic segmentation, accelerating the radiomic workflow without impairing the models' accuracy. Further investigations on different clinical endpoints and populations are encouraged to confirm and generalise these findings.

HeLLePhant: A phantom mimicking non-small cell lung cancer for texture analysis in CT images.

PURPOSE: Phantoms mimicking human tissue heterogeneity and intensity are required to establish radiomic features robustness in Computed Tomography (CT) images. We developed inserts with two different techniques for the radiomic study of Non-Small Cell Lung Cancer (NSCLC) lesions. METHODS: We developed two insert prototypes: two 3D-printed made of glycol-modified polyethylene terephthalate (PET-G), and nine with sodium polyacrylate plus iodinated contrast medium. The inserts were put in a handcraft phantom (HeLLePhant). We also analysed four materials of a commercial homogeneous phantom (Catphan® 424) and collected 29 NSCLC patients for comparison. All the CT acquisitions were performed with the same clinical protocol and scanner at 120kVp. The HeLLePhant phantom was scanned ten times in fixed condition at 120kVp and 100kVp for repeatability investigation. We extracted 153 radiomic features using Pyradiomics. To compare the features between phantoms and patients, we computed how many phantom features fell in the range between 10th and 90th percentile of the corresponding patient values. We deemed repeatable the features with a coefficient of variation (CV) less than or equal to 0.10. RESULTS: The best similarity with the patients was obtained with the polyacrylate inserts (55.6-90.2%), the worst with Catphan (15.7-19.0%). For the PET-G inserts 35.3% and 36.6% of the features match the patient range. We found high repeatability for all the inserts of the HeLLePhant phantom (74.3-100% at 120kVp, 75.7-97.9% at 100kVp), and observed a texture dependency in repeatability. CONCLUSIONS: Our study shows a promising way to construct heterogeneous inserts mimicking a target tissue for radiomic studies.

Reproducibility of radiomic features in CT images of NSCLC patients: an integrative analysis on the impact of acquisition and reconstruction parameters.

BACKGROUND: We investigated to what extent tube voltage, scanner model, and reconstruction algorithm affect radiomic feature reproducibility in a single-institution retrospective database of computed tomography images of non-small-cell lung cancer patients. METHODS: This study was approved by the Institutional Review Board (UID 2412). Images of 103 patients were considered, being acquired on either among two scanners, at 100 or 120 kVp. For each patient, images were reconstructed with six iterative blending levels, and 1414 features were extracted from each reconstruction. At univariate analysis, Wilcoxon-Mann-Whitney test was applied to evaluate feature differences within scanners and voltages, whereas the impact of the reconstruction was established with the overall concordance correlation coefficient (OCCC). A multivariable mixed model was also applied to investigate the independent contribution of each acquisition/reconstruction parameter. Univariate and multivariable analyses were combined to analyse feature behaviour. RESULTS: Scanner model and voltage did not affect features significantly. The reconstruction blending level showed a significant impact at both univariate analysis (154/1414 features yielding an OCCC < 0.85) and multivariable analysis, with most features (1042/1414) revealing a systematic trend with the blending level (multiple comparisons adjusted p < 0.05). Reproducibility increased in association to image processing with smooth filters, nonetheless specific investigation in relation to clinical endpoints should be performed to ensure that textural information is not removed. CONCLUSIONS: Combining univariate and multivariable models is allowed to identify features for which corrections may be applied to reduce the trend with the algorithm and increase reproducibility. Subsequent clustering may be applied to eliminate residual redundancy.

Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade of two U-nets: training and assessment on multiple datasets using different annotation criteria.

PURPOSE: This study aims at exploiting artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. The limited data availability and the annotation quality are relevant factors in training AI-methods. We investigated the effects of using multiple datasets, heterogeneously populated and annotated according to different criteria. METHODS: We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The first one (U-net[Formula: see text]) is devoted to the identification of the lung parenchyma; the second one (U-net[Formula: see text]) acts on a bounding box enclosing the segmented lungs to identify the areas affected by COVID-19 lesions. Different public datasets were used to train the U-nets and to evaluate their segmentation performances, which have been quantified in terms of the Dice Similarity Coefficients. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated. RESULTS: Both the volumetric DSC (vDSC) and the accuracy showed a dependency on the annotation quality of the released data samples. On an independent dataset (COVID-19-CT-Seg), both the vDSC and the surface DSC (sDSC) were measured between the masks predicted by LungQuant system and the reference ones. The vDSC (sDSC) values of 0.95±0.01 and 0.66±0.13 (0.95±0.02 and 0.76±0.18, with 5 mm tolerance) were obtained for the segmentation of lungs and COVID-19 lesions, respectively. The system achieved an accuracy of 90% in CT-SS identification on this benchmark dataset. CONCLUSION: We analysed the impact of using data samples with different annotation criteria in training an AI-based quantification system for pulmonary involvement in COVID-19 pneumonia. In terms of vDSC measures, the U-net segmentation strongly depends on the quality of the lesion annotations. Nevertheless, the CT-SS can be accurately predicted on independent test sets, demonstrating the satisfactory generalization ability of the LungQuant.

The Challenge of Choosing the Best Classification Method in Radiomic Analyses: Recommendations and Applications to Lung Cancer CT Images.

Radiomics uses high-dimensional sets of imaging features to predict biological characteristics of tumors and clinical outcomes. The choice of the algorithm used to analyze radiomic features and perform predictions has a high impact on the results, thus the identification of adequate machine learning methods for radiomic applications is crucial. In this study we aim to identify suitable approaches of analysis for radiomic-based binary predictions, according to sample size, outcome balancing and the features-outcome association strength. Simulated data were obtained reproducing the correlation structure among 168 radiomic features extracted from Computed Tomography images of 270 Non-Small-Cell Lung Cancer (NSCLC) patients and the associated to lymph node status. Performances of six classifiers combined with six feature selection (FS) methods were assessed on the simulated data using AUC (Area Under the Receiver Operating Characteristics Curves), sensitivity, and specificity. For all the FS methods and regardless of the association strength, the tree-based classifiers Random Forest and Extreme Gradient Boosting obtained good performances (AUC ≥ 0.73), showing the best trade-off between sensitivity and specificity. On small samples, performances were generally lower than in large-medium samples and with larger variations. FS methods generally did not improve performances. Thus, in radiomic studies, we suggest evaluating the choice of FS and classifiers, considering specific sample size, balancing, and association strength.

Clinical and volumetric outcomes after vertical ridge augmentation using computer-aided-design/computer-aided manufacturing (CAD/CAM) customized titanium meshes: a pilot study.

BACKGROUND: One of the most recent innovations in bone augmentation surgery is represented by computer-aided-design/computer-aided-manufacturing (CAD/CAM) customized titanium meshes, which can be used to restore vertical bone defects before implant-prosthetic rehabilitations. The aim of this study was to evaluate the effectiveness/reliability of this technique in a consecutive series of cases. METHODS: Ten patients in need of bone augmentation before implant therapy were treated using CAD/CAM customized titanium meshes. A digital workflow was adopted to design virtual meshes on 3D bone models. Then, Direct Metal Laser Sintering (DMLS) technology was used to produce the titanium meshes, and vertical ridge augmentation was performed according to an established surgical protocol. Surgical complications, healing complications, vertical bone gain (VBG), planned bone volume (PBV), lacking bone volume (LBV), regenerated bone volume (RBV), average regeneration rate (RR) and implant success rate were evaluated. RESULTS: All augmented sites were successfully restored with definitive implant-supported fixed partial dentures. Measurements showed an average VBG of 4.5 ± 1.8 mm at surgical re-entry. Surgical and healing complications occurred in 30% and 10% of cases, respectively. Mean values of PBV, LBV, and RBV were 984, 92, and 892 mm3, respectively. The average RR achieved was 89%. All 26 implants were successfully in function after 1 year of follow-up. CONCLUSIONS: The results of this study suggest that the bone augmentation by means of DMLS custom-made titanium meshes can be considered a reliable and effective technique in restoring vertical bone defects.

Association of a CT-Based Clinical and Radiomics Score of Non-Small Cell Lung Cancer (NSCLC) with Lymph Node Status and Overall Survival.

BACKGROUND: To evaluate whether a model based on radiomic and clinical features may be associated with lymph node (LN) status and overall survival (OS) in lung cancer (LC) patients; to evaluate whether CT reconstruction algorithms may influence the model performance. METHODS: patients operated on for LC with a pathological stage up to T3N1 were retrospectively selected and divided into training and validation sets. For the prediction of positive LNs and OS, the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model was used; univariable and multivariable logistic regression analysis assessed the association of clinical-radiomic variables and endpoints. All tests were repeated after dividing the groups according to the CT reconstruction algorithm. p-values < 0.05 were considered significant. RESULTS: 270 patients were included and divided into training (n = 180) and validation sets (n = 90). Transfissural extension was significantly associated with positive LNs. For OS prediction, high- and low-risk groups were different according to the radiomics score, also after dividing the two groups according to reconstruction algorithms. CONCLUSIONS: a combined clinical-radiomics model was not superior to a single clinical or single radiomics model to predict positive LNs. A radiomics model was able to separate high-risk and low-risk patients for OS; CTs reconstructed with Iterative Reconstructions (IR) algorithm showed the best model performance.

IL-36 receptor antagonistic antibodies inhibit inflammatory responses in preclinical models of psoriasiform dermatitis.

Psoriasis vulgaris (PV) results from activation of IL-23/Th17 immune pathway and is further amplified by cytokines/chemokines from skin cells. Among skin-derived pro-inflammatory cytokines, IL-36 family members are highly upregulated in PV patients and play a critical role in general pustular psoriasis. However, there is limited data showing crosstalk between the IL-23 and IL-36 pathways in PV. Herein, potential attenuation of skin inflammation in the IL-23-induced mouse model of psoriasiform dermatitis by functional inhibition of IL-36 receptor (IL-36R) was interrogated. Anti-mouse IL-36R monoclonal antibodies (mAbs) were generated and validated in vitro by inhibiting IL-36α-induced secretion of CXCL1 from NIH 3T3 cells. Antibody target engagement was demonstrated by inhibition of CXCL1 production in a novel acute model of IL-36α systemic injection in mice. In addition, anti-IL-36R mAbs inhibited tissue inflammation and inflammatory gene expression in an IL-36α ear injection model of psoriasiform dermatitis demonstrating engagement of the target in the ear skin. To elucidate the possible role of IL-36 signalling in IL-23/Th17 pathway, the ability of anti-IL-36R mAbs to inhibit skin inflammation in an IL-23 ear injection model was assessed. Inhibiting the IL-36 pathway resulted in significant attenuation of skin thickening and psoriasis-relevant gene expression. Taken together, these data suggest a role for IL-36 signalling in the IL-23/Th17 signalling axis in PV.

Detrimental effects of albuterol on airway responsiveness requires airway inflammation and is independent of ß-receptor affinity in murine models of asthma.

BACKGROUND: Inhaled short acting ß2-agonists (SABA), e.g. albuterol, are used for quick reversal of bronchoconstriction in asthmatics. While SABA are not recommended for maintenance therapy, it is not uncommon to find patients who frequently use SABA over a long period of time and there is a suspicion that long term exposure to SABA could be detrimental to lung function. To test this hypothesis we studied the effect of long-term inhaled albuterol stereoisomers on immediate allergic response (IAR) and airway hyperresponsiveness (AHR) in mouse models of asthma. METHODS: Balb/C mice were sensitized and challenged with ovalbumin (OVA) and then we studied the IAR to inhaled allergen and the AHR to inhaled methacholine. The mice were pretreated with nebulizations of either racemic (RS)-albuterol or the single isomers (S)- and (R)-albuterol twice daily over 7 days prior to harvest. RESULTS: We found that all forms of albuterol produced a significant increase of IAR measured as respiratory elastance. Similarly, we found that AHR was elevated by albuterol. At the same time a mouse strain that is intrinsically hyperresponsive (A/J mouse) was not affected by the albuterol isomers nor was AHR induced by epithelial disruption with Poly-L-lysine affected by albuterol. CONCLUSIONS: We conclude that long term inhalation treatment with either isomer of albuterol is capable of precipitating IAR and AHR in allergically inflamed airways but not in intrinsically hyperresponsive mice or immunologically naïve mice. Because (S)-albuterol, which lacks affinity for the ß2-receptor, did not differ from (R)-albuterol, we speculate that isomer-independent properties of the albuterol molecule, other than ß2-agonism, are responsible for the effect on AHR.

Th2 allergic immune response to inhaled fungal antigens is modulated by TLR-4-independent bacterial products.

Allergic airway disease is characterized by eosinophilic inflammation, mucus hypersecretion and increased airway resistance. Fungal antigens are ubiquitous within the environment and are well known triggers of allergic disease. Bacterial products are also frequently encountered within the environment and may alter the immune response to certain antigens. The consequence of simultaneous exposure to bacterial and fungal products on the lung adaptive immune response has not been explored. Here, we show that oropharyngeal aspiration of fungal lysates (Candida albicans, Aspergillus fumigatus) promotes airway eosinophilia, secretion of Th2 cytokines and mucus cell metaplasia. In contrast, oropharyngeal exposure to bacterial lysates (Pseudomonas aeruginosa) promotes airway inflammation characterized by neutrophils, Th1 cytokine secretion and no mucus production. More importantly, administration of bacterial lysates together with fungal lysates deviates the adaptive immune response to a Th1 type associated with neutrophilia and diminished mucus production. The immunomodulatory effect that bacterial lysates have on the response to fungi is TLR4 independent but MyD88 dependent. Thus, different types of microbial products within the airway can alter the host's adaptive immune response and potentially impact the development of allergic airway disease to environmental fungal antigens.

The synergistic interactions of allergic lung inflammation and intratracheal cationic protein.

RATIONALE: Airways hyperresponsiveness (AHR) is a hallmark feature of asthma, and can be caused by various disparate mechanisms. Mouse models of AHR have been useful for studying these mechanisms in isolation, but such models still typically do not exhibit the same degree of AHR as seen in severe human asthma. We hypothesized that more severe AHR in mice could be achieved by imbuing them with more than one mechanism of AHR. OBJECTIVES: We sought to determine if the airway wall thickening accompanying allergic inflammation and the exaggerated smooth muscle shortening induced by intratracheal cationic protein could act together to produce a severe form of AHR. METHODS: We used the forced oscillation technique to measure methacholine responsiveness in BALB/c mice that had been sensitized and challenged with ovalbumin followed by an intratracheal instillation of poly-l-lysine. MEASUREMENTS AND MAIN RESULTS: We found that both ovalbumin and poly-l-lysine treatment alone caused moderate levels of AHR. When the two treatments were combined, however, they synergized in terms of their effect on lung stiffness to an extent that could even be fatal, reflecting a significantly enhanced level of airway closure. CONCLUSIONS: Our results suggest that mechanistic synergy between airway wall thickening and exaggerated smooth muscle shortening produces a more germane mouse model of asthma that may have particular relevance to the pathophysiology of the acute severe asthma exacerbation.

Transforming growth factor-beta1 suppresses airway hyperresponsiveness in allergic airway disease.

RATIONALE: Asthma is characterized by increases in airway resistance, pulmonary remodeling, and lung inflammation. The cytokine transforming growth factor (TGF)-beta has been shown to have a central role in asthma pathogenesis and in mouse models of allergic airway disease. OBJECTIVES: To determine the contribution of TGF-beta to airway hyperresponsiveness (AHR), we examined the time course, source, and isoform specificity of TGF-beta production in an in vivo mouse asthma model. To then elucidate the function of TGF-beta in AHR, inflammation, and pulmonary fibrosis, we examined the effects of blocking TGF-beta signaling with neutralizing antibody. METHODS: Mice were sensitized and challenged with ovalbumin (OVA) to establish allergic airway disease. TGF-beta activity was neutralized by intranasal administration of monoclonal antibody. MEASUREMENTS AND MAIN RESULTS: TGF-beta1 protein levels were increased in OVA-challenged lungs versus naive controls, and airway epithelial cells were shown to be a likely source of TGF-beta1. In addition, TGF-beta1 levels were elevated in OVA-exposed IL-5-null mice, which fail to recruit eosinophils into the airways. Neutralization of TGF-beta1 with specific antibody had no significant effect on airway inflammation and eosinophilia, although anti-TGF-beta1 antibody enhanced OVA-induced AHR and suppressed pulmonary fibrosis. CONCLUSIONS: These data show that TGF-beta1 is the main TGF-beta isoform produced after OVA challenge, with a likely cellular source being the airway epithelium. The effects of blocking TGF-beta1 signaling had differential effects on AHR, fibrosis, and inflammation. While TGF-beta neutralization may be beneficial to abrogating airway remodeling, it may be detrimental to lung function by increasing AHR.

Airway hyperresponsiveness in allergically inflamed mice: the role of airway closure.

RATIONALE: Allergically inflamed mice exhibit airway hyperresponsiveness to inhaled methacholine, which computer simulations of lung impedance suggest is due to enhanced lung derecruitment and which we sought to verify in the present study. METHODS: BALB/c mice were sensitized and challenged with ovalbumin to induce allergic inflammation; the control mice were sensitized but received no challenge. The mice were then challenged with inhaled methacholine and respiratory system impedance tracked for the following 10 minutes. Respiratory elastance (H) was estimated from each impedance measurement. One group of mice was ventilated with 100% O(2) during this procedure and another group was ventilated with air. After the procedure, the mice were killed and ventilated with pure N(2), after which the trachea was tied off and the lungs were imaged with micro-computed tomography (micro-CT). RESULTS: H was significantly higher in allergic mice than in control animals after methacholine challenge. The ratio of H at the end of the measurement period between allergic and nonallergic mice ventilated with O(2) was 1.36, indicating substantial derecruitment in the allergic animals. The ratio between lung volumes determined by micro-CT in the control and the allergic mice was also 1.36, indicative of a corresponding volume loss due to absorption atelectasis. Micro-CT images and histograms of Hounsfield units from the lungs also showed increased volume loss in the allergic mice compared with control animals after methacholine challenge. CONCLUSIONS: These results support the conclusion that airway closure is a major component of hyperresponsiveness in allergically inflamed mice.

Choosing the frequency of deep inflation in mice: balancing recruitment against ventilator-induced lung injury.

Low tidal volume (Vt) ventilation is protective against ventilator-induced lung injury but can promote development of atelectasis. Periodic deep inflation (DI) can open the lung, but if delivered too frequently may cause damage via repeated overdistention. We therefore examined the effects of varying DI frequency on lung mechanics, gas exchange, and biomarkers of injury in mice. C57BL/6 males were mechanically ventilated with positive end-expiratory pressure (PEEP) of 2 cmH2O for 2 h. One high Vt group received a DI with each breath (HV). Low Vt groups received 2 DIs after each hour of ventilation (LV) or 2 DIs every minute (LVDI). Control groups included a nonventilated surgical sham and a group receiving high Vt with zero PEEP (HVZP). Respiratory impedance was measured every 4 min, from which tissue elastance (H) and damping (G) were derived. G and H rose progressively during LV and HVZP, but returned to baseline after hourly DI during LV. During LVDI and HV, G and H remained low and gas exchange was superior to that of LV. Bronchoalveolar lavage fluid protein was elevated in HV and HVZP but was not different between LV and LVDI. Lung tissue IL-6 and IL-1beta levels were elevated in HVZP and lower in LVDI compared with LV. We conclude that frequent DI can safely improve gas exchange and lung mechanics and may confer protection from biotrauma. Differences between LVDI and HV suggest that an optimal frequency range of DI exists, within which the benefits of maintaining an open lung outweigh injury incurred from overdistention.

Exaggerated airway narrowing in mice treated with intratracheal cationic protein.

Airway hyperresponsiveness in mice with allergic airway inflammation can be attributed entirely to exaggerated closure of peripheral airways (Wagers S, Lundblad LK, Ekman M, Irvin CG, and Bates JHT. J Appl Physiol 96: 2019-2027, 2004). However, clinical asthma can be characterized by hyperresponsiveness of the central airways as well as the lung periphery. We, therefore, sought to establish a complementary model of hyperresponsiveness in the mouse due to excessive narrowing of the airways. We treated mice with a tracheal instillation of the cationic protein poly-l-lysine (PLL), hypothesizing that this would reduce the barrier function of the epithelium and thereby render the underlying airway smooth muscle more accessible to aerosolized methacholine. The PLL-treated animals were hypersensitive to methacholine: they exhibited an exaggerated response to submaximal doses but had a maximal response that was similar to controls. With the aid of a computational model of the mouse lung, we conclude that the methacholine responsiveness of PLL-treated mice is fundamentally different in nature to the hyperresponsiveness that we found previously in mice with allergically inflamed lungs.

Defining a link with asthma in mice congenitally deficient in eosinophils.

Eosinophils are often dominant inflammatory cells present in the lungs of asthma patients. Nonetheless, the role of these leukocytes remains poorly understood. We have created a transgenic line of mice (PHIL) that are specifically devoid of eosinophils, but otherwise have a full complement of hematopoietically derived cells. Allergen challenge of PHIL mice demonstrated that eosinophils were required for pulmonary mucus accumulation and the airway hyperresponsiveness associated with asthma. The development of an eosinophil-less mouse now permits an unambiguous assessment of a number of human diseases that have been linked to this granulocyte, including allergic diseases, parasite infections, and tumorigenesis.

Extravascular fibrin, plasminogen activator, plasminogen activator inhibitors, and airway hyperresponsiveness.

Mechanisms underlying airway hyperresponsiveness are not yet fully elucidated. One of the manifestations of airway inflammation is leakage of diverse plasma proteins into the airway lumen. They include fibrinogen and thrombin. Thrombin cleaves fibrinogen to form fibrin, a major component of thrombi. Fibrin inactivates surfactant. Surfactant on the airway surface maintains airway patency by lowering surface tension. In this study, immunohistochemically detected fibrin was seen along the luminal surface of distal airways in a patient who died of status asthmaticus and in mice with induced allergic airway inflammation. In addition, we observed altered airway fibrinolytic system protein balance consistent with promotion of fibrin deposition in mice with allergic airway inflammation. The airways of mice were exposed to aerosolized fibrinogen, thrombin, or to fibrinogen followed by thrombin. Only fibrinogen followed by thrombin resulted in airway hyperresponsiveness compared with controls. An aerosolized fibrinolytic agent, tissue-type plasminogen activator, significantly diminished airway hyperresponsiveness in mice with allergic airway inflammation. These results are consistent with the hypothesis that leakage of fibrinogen and thrombin and their accumulation on the airway surface can contribute to the pathogenesis of airway hyperresponsiveness.