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Primary graft dysfunction (PGD) is a common complication after lung transplantation. A plethora of contributing factors are known and assessment of donor lung function prior to organ retrieval is mandatory for determination of lung quality. Specialized centers increasingly perform ex vivo lung perfusion (EVLP) to further assess lung functionality and improve and extend lung preservation with the aim to increase lung utilization. EVLP can be performed following different protocols. The impact of the individual EVLP parameters on PGD development, organ function and postoperative outcome remains to be fully investigated. The variables relate to the engineering and function of the respective perfusion devices, such as the type of pump used, functional, like ventilation modes or physiological (e.g. perfusion solutions). This review reflects on the individual technical and fluid components relevant to EVLP and their respective impact on inflammatory response and outcome. We discuss key components of EVLP protocols and options for further improvement of EVLP in regard to PGD. This review offers an overview of available options for centers establishing an EVLP program and for researchers looking for ways to adapt existing protocols.

Abstract Virtual patients (VPs) are widely used within quantitative systems pharmacology (QSP) modeling to explore the impact of variability and uncertainty on clinical responses. In one method of generating VPs, parameters are sampled randomly from a distribution, and possible VPs are accepted or rejected based on constraints on model output behavior. This approach works but can be inefficient (i.e., the vast majority of model runs typically do not result in valid VPs). Machine learning surrogate models offer an opportunity to improve the efficiency of VP creation significantly. In this approach, surrogate models are trained using the full QSP model and subsequently used to rapidly pre‐screen for parameter combinations that result in feasible VPs. The overwhelming majority of parameter combinations pre‐vetted using the surrogate models result in valid VPs when tested in the original QSP model. This tutorial presents this novel workflow and demonstrates how a surrogate model software application can be used to select and optimize the surrogate models in a case study. We then discuss the relative efficiency of the methods and scalability of the proposed method.

ABSTRACTCancer associated fibroblasts (CAF) are known to orchestrate multiple components of the tumor microenvironment, whereas the influence of the whole stromal-fibroblast compartment is less understood. Here, an extended stromal fibroblast signature was investigated to define its impact on immune cell infiltration. The lung cancer adenocarcinoma (LUAD) data set of the cancer genome atlas (TCGA) was used to test whole stroma signatures and cancer-associated fibroblast signatures for their impact on prognosis. 3D cell cultures of the NSCLC cancer cell line A549 together with the fibroblast cell line SV80 were used in combination with infiltrating peripheral blood mononuclear cells (PBMC) for in-vitro investigations. Immune cell infiltration was assessed via flow cytometry, chemokines were analyzed by immunoassays and RNA microarrays. Results were confirmed in specimens from NSCLC patients by flow cytometry or immunohistochemistry as well as in the TCGA data set. The TCGA analyses correlated the whole stromal-fibroblast signature with an improved outcome, whereas no effect was found for the CAF signatures. In 3D microtumors, the presence of fibroblasts induced infiltration of B cells and CD69+CD4+ T cells, which was linked to an increased expression of CCL13 and CXCL16. The stroma/lymphocyte interaction was confirmed in NSCLC patients, as stroma-rich tumors displayed an elevated B cell count and survival in the local cohort and the TCGA data set. A whole stromal fibroblast signature was associated with an improved clinical outcome in lung adenocarcinoma and in vitro and in vivo experiments suggest that this signature increases B and T cell recruitment via induction of chemokines.

Abstract Patient pathways from first suspicious imaging until final surgical treatment vary and in some instances cause considerable delay. This study aims to investigate the impact of this delay on survival of lung cancer patients. The institutional database was queried to identify patients with primary lung cancer who were treated with primary surgery. Time intervals were defined as date of first suspicious medical images until date of surgical treatment. All patients received PET-CT staging and tissue confirmation prior to treatment planning in a multidisciplinary tumor board. Patients with unknown date of first contact, follow-up CT-scans of pulmonary nodules, or neoadjuvant therapy were excluded. In total, 287 patients treated between 2009 and 2017 were included for further analysis. Median time between first suspicious medical imaging and surgical therapy was 62 (range 23–120) days and did not differ between male and female patients. Patients were then classified into two groups according to the duration of the medical work-up: group A up to 60 days, and group B from 61 to 120 days. Clinical T and N stages were comparable between the groups. There was no difference in overall survival between the two groups. In the subgroup of cT2 tumors (87 patients), there was a significant survival benefit for patients in group A (p = 0.043), while nodal stages, stage migration, lymphatic vessel invasion, grading and other potentially survival-influencing clinical parameters were comparable between the groups. Delay between diagnosis and treatment of lung cancer may result in dismal outcome. Efforts need to focus on improving and streamlining patient pathways to shorten the delay until surgical treatment to a minimum. Process improvement might be achieved by stringent interdisciplinary work-up and a patient-centered approach.

It is proved that the equation tan ( kπ/ m)=k tan π/ m has no solution in integers k and m with k≧2, m≧3. This answers a question concerning the problem of approximating a convex disc by polygons. [ABSTRACT FROM AUTHOR]

Neutrophil gelatinase-associated lipocalin (NGAL) has emerged as an early marker protein for kidney dysfunction in various clinical settings. In this prospective study we evaluated serial changes of serum and urinary NGAL within the first 7 days after kidney transplantation in 170 consecutive recipients. The main focus of this study was to assess the performance of serum and urinary NGAL in the prediction of delayed graft function (DGF) and two-year graft and patient survival. Serum and urine samples of 170 patients undergoing primary kidney transplantation from October 2010 to December 2012 were prospectively collected from day 0 to 7. NGAL was analyzed by ELISA. Multivariate regression models, receiver-operating characteristics (ROC), and areas under ROC curves (AUC) were used to identify predictors of DGF. DGF occurred in 52 patients (30.6%). Serum (AUC = 0.869) and urinary NGAL (AUC = 0.872) on postoperative day (POD) 2 could accurately predict DGF compared to serum creatinine (AUC = 0.619). Multivariate analyses revealed donor age, serum and urinary NGAL significantly associated with DGF (p