@phdthesis{Bucher2018,
  author    = {Bucher, Hannes},
  title     = {Pre-clinical modeling of viral- and bacterial-induced exacerbations of chronic obstructive pulmonary disease},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-144368},
  school      = {Universit{\"a}t W{\"u}rzburg},
  pages     = {XIII, 105},
  year      = {2018},
  abstract  = {Chronic Obstructive Pulmonary Disease (COPD) exacerbations are a considerable reason for increased morbidity and mortality in patients. Infections with influenza virus (H1N1), respiratory syncytial virus (RSV) or nontypeable Haemophilus influenzae (NTHi) are important triggers of exacerbations. To date, no treatments are available which can stop the progression of COPD. Novel approaches are urgently needed. Pre-clinical models of the disease are crucial for the development of novel therapeutic options. In order to establish pre-clinical models which mimic aspects of human COPD exacerbations, mice were exposed to cigarette smoke (CS) and additionally infected with H1N1, RSV and/or NTHi. Clinically relevant treatments such as the corticosteroids Fluticasone propionate and Dexamethasone, the phosphodiesterase-4 (PDE-4) inhibitor Roflumilast and the long-acting muscarinic receptor antagonist Tiotropium were tested in the established models. Furthermore, a novel treatment approach using antibodies (Abs) directed against IL-1α, IL-1β or IL-1R1 was examined in the established CS/H1N1 model. Levels of IFN-γ, IL-1β, IL-2, IL-6, KC, TNF-α, RANTES, IL-17, MCP-1, MIP 1α and MIP-1β were measured in lung homogenate. Numbers of total cells, neutrophils and macrophages were assessed in bronchoalveolar lavage (BAL) fluid. Hematoxylin- and eosin- (H\&E-) stained lung slices were analyzed to detect pathological changes. Quantitative polymerase-chain-reaction (qPCR) was used to investigate gene expression of ICAM-1 and MUC5 A/C. The viral/bacterial load was investigated in lung homogenate or BAL fluid. In addition to the in vivo studies, the effects of the above mentioned treatments were investigated in vitro in H1N1, RSV or NTHi-infected (primary) human bronchial epithelial cells using submerged or air-liquid-interface (ALI) cell culture systems. Four pre-clinical models (CS/H1N1, CS/RSV, CS/NTHi, CS/H1N1/NTHi) were established depicting clinically relevant aspects of COPD exacerbations such as increased inflammatory cells and cytokines in the airways and impaired lung function. In the CS/H1N1 model, Tiotropium improved lung function and was superior in reducing inflammation in comparison to Fluticasone or Roflumilast. Moreover, Fluticasone increased the loss of body-weight, levels of IL-6, KC and TNF-α and worsened lung function. In CS/RSV-exposed mice Tiotropium but not Fluticasone or Roflumilast treatment reduced neutrophil numbers and IL-6 and TNF α levels in the lung. The viral load of H1N1 and RSV was significantly elevated in CS/virus-exposed mice and NCI-H292 cells after Fluticasone and Dexamethasone treatment. The results from these studies demonstrate that Tiotropium has anti-inflammatory effects on CS/virus-induced inflammation and might help to explain the observed reduction of exacerbation rates in Tiotropium-treated COPD patients. Furthermore, the findings from this work indicate that treatment with Fluticasone or Dexamethasone might not be beneficial to reduce inflammation in the airways of COPD patients and supports clinical studies that link treatment with corticosteroids to an increased risk for pneumonia. Testing of anti-IL-1α, anti-IL-1β or anti-IL-1R1 Abs in the CS/H1N1 model suggests that, in line with clinical data, antagonization of IL-1β is not sufficient to reduce pulmonary inflammation and indicates a predominant role of IL-1α in CS/virus-induced airway inflammation. In line with the in vivo findings, anti-IL-1α but not anti-IL-1β Abs reduced levels of TNF-α and IL-6 in H1N1-infected primary human bronchial epithelial ALI cell culture. Blocking the IL-1R1 provided significant inhibitory effects on inflammatory cells in vivo but was inferior compared to inhibiting both its soluble ligands IL-1α and IL-1β. Concomitant usage of Abs against IL-1α/IL-1β revealed strong effects and reduced total cells, neutrophils and macrophages. Additionally, levels of KC, IL-6, TNF-α, MCP-1, MIP-1α and MIP-1β were significantly reduced and ICAM-1 mRNA expression was attenuated. These results suggest that combined inhibition of IL-1α/IL-1β might be beneficial to reduce inflammation and exacerbations in COPD patients. Moreover, combined targeting of both IL-1α/IL-1β might be more efficient compared to inhibition of the IL-1R1. As in the CS/virus models, corticosteroid treatment failed to reduce inflammatory cells in the CS/NTHi and CS/H1N1/NTHi models, increased the loss of body-weight and the bacterial load. Furthermore, Roflumilast administration had no significant effects on cell counts or cytokines. However, it improved compliance in the CS/NTHi model. Treatment with Azithromycin reduced the bacterial load in the CS/NTHi model and reduced numbers of total cells, neutrophils, macrophages and levels of KC and TNF-α in the CS/H1N1/NTHi model. In conclusion, the established CS/H1N1, CS/RSV, CS/NTHi, CS/H1N1/NTHi models depict clinically relevant aspects of human COPD exacerbations in mice and provide the opportunity to investigate underlying disease mechanisms and to test novel therapies.},
  subject      = {Obstruktive Ventilationsst{\"o}rung},
  language  = {en}
}
@article{AlmadeJongJelusicetal.2016,
  author    = {Alma, Harma and de Jong, Corina and Jelusic, Danijel and Wittmann, Michael and Schuler, Michael and Flokstra-de Blok, Bertine and Kocks, Janwillem and Schultz, Konrad and van der Molen, Thys},
  title     = {Health status instruments for patients with COPD in pulmonary rehabilitation: defining a minimal clinically important difference},
  series = {npj Primary Care Respiration Medicine},
  volume    = {26},
  journal   = {npj Primary Care Respiration Medicine},
  number    = {16041},
  doi       = {10.1038/npjpcrm.2016.41},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-166327},
  year      = {2016},
  abstract  = {The minimal clinically important difference (MCID) defines to what extent change on a health status instrument is clinically relevant, which aids scientists and physicians in measuring therapy effects. This is the first study that aimed to establish the MCID of the Clinical chronic obstructive pulmonary disease (COPD) Questionnaire (CCQ), the COPD Assessment Test (CAT) and the St George's Respiratory Questionnaire (SGRQ) in the same pulmonary rehabilitation population using multiple approaches. In total, 451 COPD patients participated in a 3-week Pulmonary Rehabilitation (PR) programme (58 years, 65\% male, 43 pack-years, GOLD stage II/III/IV 50/39/11\%). Techniques used to assess the MCID were anchor-based approaches, including patient-referencing, criterion-referencing and questionnaire-referencing, and the distribution-based methods standard error of measurement (SEM), 1.96SEM and half standard deviation (0.5s.d.). Patient- and criterion-referencing led to MCID estimates of 0.56 and 0.62 (CCQ); 3.12 and 2.96 (CAT); and 8.40 and 9.28 (SGRQ). Questionnaire-referencing suggested MCID ranges of 0.28-0.61 (CCQ), 1.46-3.08 (CAT) and 6.86-9.47 (SGRQ). The SEM, 1.96SEM and 0.5s.d. were 0.29, 0.56 and 0.46 (CCQ); 3.28, 6.43 and 2.80 (CAT); 5.20, 10.19 and 6.06 (SGRQ). Pooled estimates were 0.52 (CCQ), 3.29 (CAT) and 7.91 (SGRQ) for improvement. MCID estimates differed depending on the method used. Pooled estimates suggest clinically relevant improvements needing to exceed 0.40 on the CCQ, 3.00 on the CAT and 7.00 on the SGRQ for moderate to very severe COPD patients. The MCIDs of the CAT and SGRQ in the literature might be too low, leading to overestimation of treatment effects for patients with COPD.},
  language  = {en}
}
@article{KarnatiSeimetzKleefeldtetal.2021,
  author    = {Karnati, Srikanth and Seimetz, Michael and Kleefeldt, Florian and Sonawane, Avinash and Madhusudhan, Thati and Bachhuka, Akash and Kosanovic, Djuro and Weissmann, Norbert and Kr{\"u}ger, Karsten and Erg{\"u}n, S{\"u}leyman},
  title     = {Chronic Obstructive Pulmonary Disease and the Cardiovascular System: Vascular Repair and Regeneration as a Therapeutic Target},
  series = {Frontiers in Cardiovascular Medicine},
  volume    = {8},
  journal   = {Frontiers in Cardiovascular Medicine},
  issn      = {2297-055X},
  doi       = {10.3389/fcvm.2021.649512},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-235631},
  year      = {2021},
  abstract  = {Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality worldwide and encompasses chronic bronchitis and emphysema. It has been shown that vascular wall remodeling and pulmonary hypertension (PH) can occur not only in patients with COPD but also in smokers with normal lung function, suggesting a causal role for vascular alterations in the development of emphysema. Mechanistically, abnormalities in the vasculature, such as inflammation, endothelial dysfunction, imbalances in cellular apoptosis/proliferation, and increased oxidative/nitrosative stress promote development of PH, cor pulmonale, and most probably pulmonary emphysema. Hypoxemia in the pulmonary chamber modulates the activation of key transcription factors and signaling cascades, which propagates inflammation and infiltration of neutrophils, resulting in vascular remodeling. Endothelial progenitor cells have angiogenesis capabilities, resulting in transdifferentiation of the smooth muscle cells via aberrant activation of several cytokines, growth factors, and chemokines. The vascular endothelium influences the balance between vaso-constriction and -dilation in the heart. Targeting key players affecting the vasculature might help in the development of new treatment strategies for both PH and COPD. The present review aims to summarize current knowledge about vascular alterations and production of reactive oxygen species in COPD. The present review emphasizes on the importance of the vasculature for the usually parenchyma-focused view of the pathobiology of COPD.},
  language  = {en}
}
@article{VollmerVollmerLangetal.2022,
  author    = {Vollmer, Andreas and Vollmer, Michael and Lang, Gernot and Straub, Anton and Shavlokhova, Veronika and K{\"u}bler, Alexander and Gubik, Sebastian and Brands, Roman and Hartmann, Stefan and Saravi, Babak},
  title     = {Associations between periodontitis and COPD: An artificial intelligence-based analysis of NHANES III},
  series = {Journal of Clinical Medicine},
  volume    = {11},
  journal   = {Journal of Clinical Medicine},
  number    = {23},
  issn      = {2077-0383},
  doi       = {10.3390/jcm11237210},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-312713},
  year      = {2022},
  abstract  = {A number of cross-sectional epidemiological studies suggest that poor oral health is associated with respiratory diseases. However, the number of cases within the studies was limited, and the studies had different measurement conditions. By analyzing data from the National Health and Nutrition Examination Survey III (NHANES III), this study aimed to investigate possible associations between chronic obstructive pulmonary disease (COPD) and periodontitis in the general population. COPD was diagnosed in cases where FEV (1)/FVC ratio was below 70\% (non-COPD versus COPD; binary classification task). We used unsupervised learning utilizing k-means clustering to identify clusters in the data. COPD classes were predicted with logistic regression, a random forest classifier, a stochastic gradient descent (SGD) classifier, k-nearest neighbors, a decision tree classifier, Gaussian naive Bayes (GaussianNB), support vector machines (SVM), a custom-made convolutional neural network (CNN), a multilayer perceptron artificial neural network (MLP), and a radial basis function neural network (RBNN) in Python. We calculated the accuracy of the prediction and the area under the curve (AUC). The most important predictors were determined using feature importance analysis. Results: Overall, 15,868 participants and 19 feature variables were included. Based on k-means clustering, the data were separated into two clusters that identified two risk characteristic groups of patients. The algorithms reached AUCs between 0.608 (DTC) and 0.953\% (CNN) for the classification of COPD classes. Feature importance analysis of deep learning algorithms indicated that age and mean attachment loss were the most important features in predicting COPD. Conclusions: Data analysis of a large population showed that machine learning and deep learning algorithms could predict COPD cases based on demographics and oral health feature variables. This study indicates that periodontitis might be an important predictor of COPD. Further prospective studies examining the association between periodontitis and COPD are warranted to validate the present results.},
  language  = {en}
}