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Beverly Hills, CA, United States

Unkel B.,Justus Liebig University | Hoegner K.,Justus Liebig University | Clausen B.E.,Erasmus Medical Center | Lewe-Schlosser P.,Center for Radiation Therapy | And 6 more authors.
Journal of Clinical Investigation | Year: 2012

Influenza viruses (IVs) cause pneumonia in humans with progression to lung failure. Pulmonary DCs are key players in the antiviral immune response, which is crucial to restore alveolar barrier function. The mechanisms of expansion and activation of pulmonary DC populations in lung infection remain widely elusive. Using mouse BM chimeric and cell-specific depletion approaches, we demonstrated that alveolar epithelial cell (AEC) GM-CSF mediates recovery from IV-induced injury by affecting lung DC function. Epithelial GM-CSF induced the recruitment of CD11b+ and monocyte-derived DCs. GM-CSF was also required for the presence of CD103+ DCs in the lung parenchyma at baseline and for their sufficient activation and migration to the draining mediastinal lymph nodes (MLNs) during IV infection. These activated CD103+ DCs were indispensable for sufficient clearance of IVs by CD8+ T cells and for recovery from IV-induced lung injury. Moreover, GM-CSF applied intratracheally activated CD103+ DCs, inducing increased migration to MLNs, enhanced viral clearance, and attenuated lung injury. Together, our data reveal that GM-CSF-dependent cross-talk between IV-infected AECs and CD103+ DCs is crucial for effective viral clearance and recovery from injury, which has potential implications for GM-CSF treatment in severe IV pneumonia.

Owen J.B.,Boston University | Owen J.B.,University of Houston | Owen J.B.,Center for Radiation Therapy | Owen J.B.,Medical College of Wisconsin | And 28 more authors.
Journal of Oncology Practice | Year: 2014

Purpose: Patient comorbidities may affect the applicability of performance measures that are inherent in multidisciplinary cancer treatment guidelines. This article describes the distribution of common comorbid conditions by disease site and by patient and facility characteristics in patients who received radiation therapy as part of treatment for cancer of the breast, cervix, lung, prostate, and stomach, and investigates the association of comorbidities with treatment decisions.Results: Multivariable logistic regression models predicted the dependent variable "treatment changed or contraindicated due to comorbidities." The final model showed that ACE-27 was highly associated with change in treatment for patients with severe or moderate index values compared to those with none or mild (P < . 001). Two other covariates, age and medical coverage, had no (age) or little (medical coverage) significant contribution to predicting treatment change in the multivariable model. Disease site was associated with treatment change after adjusting for other covariates in the model. Conclusions: ACE-27 is highly predictive of treatment modifications for patients treated for these cancers who receive radiation as part of their care. A standardized tool identifying patients who should be excluded from clinical performance measures allows more accurate use of these measures. © 2014 by American Society of Clinical Oncology.Materials and Methods: Stratified two-stage cluster sampling provided a random sample of radiation oncology facilities. Eligible patients were randomly sampled from each participating facility for each disease site, and data were abstracted from medical records. The Adult Comorbidity Evaluation Index (ACE-27) was used to measure comorbid conditions and their severity. National estimates were calculated using SUDAAN statistical software.

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