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The vast majority of chronic myeloid leukemia patients express a BCR-ABL1 fusion gene mRNA encoding a 210 kDa tyrosine kinase which promotes leukemic transformation. A possible differential impact of the corresponding BCR-ABL1 transcript variants e13a2 ("b2a2") and e14a2 ("b3a2") on disease phenotype and outcome is still a subject of debate. A total of 1105 newly diagnosed imatinib-treated patients were analyzed according to transcript type at diagnosis (e13a2, n=451; e14a2, n=496; e13a2+e14a2, n=158). No differences regarding age, sex, or Euro risk score were observed. A significant difference was found between e13a2 and e14a2 when comparing white blood cells (88 vs. 65 x 10(9)/L, respectively; P<0.001) and platelets (296 vs. 430 x 109/L, respectively; P<0.001) at diagnosis, indicating a distinct disease phenotype. No significant difference was observed regarding other hematologic features, including spleen size and hematologic adverse events, during imatinib-based therapies. Cumulative molecular response was inferior in e13a2 patients (P=0.002 for major molecular response; P<0.001 for MR4). No difference was observed with regard to cytogenetic response and overall survival. In conclusion, e13a2 and e14a2 chronic myeloid leukemia seem to represent distinct biological entities. However, clinical outcome under imatinib treatment was comparable and no risk prediction can be made according to e13a2 versus e14a2 BCR-ABL1 transcript type at diagnosis. (clinicaltrials.gov identifier: 00055874)
Major molecular remission (MMR) is an important therapy goal in chronic myeloid leukemia (CML). So far, MMR is not a failure criterion according to ELN management recommendation leading to uncertainties when to change therapy in CML patients not reaching MMR after 12 months. At monthly landmarks, for different molecular remission status Hazard ratios (HR) were estimated for patients registered to CML study IV who were divided in a learning and a validation sample. The minimum HR for MMR was found at 2.5 years with 0.28 (compared to patients without remission). In the validation sample, a significant advantage for progression-free survival (PFS) for patients in MMR could be detected (p-value 0.007). The optimal time to predict PFS in patients with MMR could be validated in an independent sample at 2.5 years. With our model we provide a suggestion when to define lack of MMR as therapy failure and thus treatment change should be considered. The optimal response time for 1% BCR-ABL at about 12-15 months was confirmed and for deep molecular remission no specific time point was detected. Nevertheless, it was demonstrated that the earlier the MMR is achieved the higher is the chance to attain deep molecular response later.
Background
Oncolytic virotherapy of tumors is an up-coming, promising therapeutic modality of cancer therapy. Unfortunately, non-invasive techniques to evaluate the inflammatory host response to treatment are rare. Here, we evaluate \(^{19}\)F magnetic resonance imaging (MRI) which enables the non-invasive visualization of inflammatory processes in pathological conditions by the use of perfluorocarbon nanoemulsions (PFC) for monitoring of oncolytic virotherapy.
Methodology/Principal Findings
The Vaccinia virus strain GLV-1h68 was used as an oncolytic agent for the treatment of different tumor models. Systemic application of PFC emulsions followed by \(^1H\)/\(^{19}\)F MRI of mock-infected and GLV-1h68-infected tumor-bearing mice revealed a significant accumulation of the \(^{19}\)F signal in the tumor rim of virus-treated mice. Histological examination of tumors confirmed a similar spatial distribution of the \(^{19}\)F signal hot spots and \(CD68^+\)-macrophages. Thereby, the \(CD68^+\)-macrophages encapsulate the GFP-positive viral infection foci. In multiple tumor models, we specifically visualized early inflammatory cell recruitment in Vaccinia virus colonized tumors. Furthermore, we documented that the \(^{19}\)F signal correlated with the extent of viral spreading within tumors.
Conclusions/Significance
These results suggest \(^{19}\)F MRI as a non-invasive methodology to document the tumor-associated host immune response as well as the extent of intratumoral viral replication. Thus, \(^{19}\)F MRI represents a new platform to non-invasively investigate the role of the host immune response for therapeutic outcome of oncolytic virotherapy and individual patient response.
Plattform für das integrierte Management von Kollaborationen in Wertschöpfungsnetzwerken (PIMKoWe)
(2022)
Das Verbundprojekt „Plattform für das integrierte Management von Kollaborationen in Wertschöpfungsnetzwerken“ (PIMKoWe – Förderkennzeichen „02P17D160“) ist ein Forschungsvorhaben im Rahmen des Forschungsprogramms „Innovationen für die Produktion, Dienstleistung und Arbeit von morgen“ der Bekanntmachung „Industrie 4.0 – Intelligente Kollaborationen in dynamischen Wertschöpfungs-netzwerken“ (InKoWe). Das Forschungsvorhaben wurde mit Mitteln des Bundesministeriums für Bildung und Forschung (BMBF) gefördert und durch den Projektträger des Karlsruher Instituts für Technologie (PTKA) betreut.
Ziel des Forschungsprojekts PIMKoWe ist die Entwicklung und Bereitstellung einer Plattformlösung zur Flexibilisierung, Automatisierung und Absicherung von Kooperationen in Wertschöpfungsnetzwerken des industriellen Sektors.
S2k guidelines for the treatment of pemphigus vulgaris/foliaceus and bullous pemphigoid: 2019 update
(2020)
Background
Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes.
Methods
A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported.
Results
1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy.
Conclusions
Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models.
Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
Objectives: Chronic recurrent multifocal osteomyelitis (CRMO), the most severe form of chronic nonbacterial osteomyelitis (CNO), is an autoinflammatory bone disorder. In the absence of diagnostic criteria or biomarkers, CNO/CRMO remains a diagnosis of exclusion. The aim of this study was to identify biomarkers for diagnosing multifocal disease (CRMO).
Study design: Sera from 71 pediatric CRMO patients, 11 patients with osteoarticular infections, 62 patients with juvenile idiopathic arthritis (JIA), 7 patients with para-infectious or reactive arthritis, and 43 patients with acute leukemia or lymphoma, as well as 59 healthy individuals were collected. Multiplex analysis of 18 inflammation- and/or bone remodeling-associated serum proteins was performed. Statistical analysis included univariate ANOVA, discriminant analysis, univariate receiver operating characteristic (ROC) analysis, and logistic regression analyses.
Results: For 14 of 18 blood serum proteins, significant differences were determined between CRMO patients, at least one alternative diagnosis, or healthy controls. Multi-component discriminant analysis delivered five biomarkers (IL-6, CCL11/eotaxin, CCL5/RANTES, collagen Iα, sIL-2R) for the diagnosis of CRMO. ROC analysis allowed further reduction to a core set of 2 biomarkers (CCL11/eotaxin, IL-6) that are sufficient to discern between CRMO, healthy controls, and alternative diagnoses.
Conclusion: Serum biomarkers CCL11/eotaxin and IL-6 differentiate between patients with CRMO, healthy controls, and alternative diagnoses (leukemia and lymphoma, osteoarticular infections, para-infectious arthritis, and JIA). Easily accessible biomarkers may aid in diagnosing CRMO. Further studies testing biomarkers in larger unrelated cohorts are warranted.
The analysis of real data by means of statistical methods with the aid of a software package common in industry and administration usually is not an integral part of mathematics studies, but it will certainly be part of a future professional work. The present book links up elements from time series analysis with a selection of statistical procedures used in general practice including the statistical software package SAS. Consequently this book addresses students of statistics as well as students of other branches such as economics, demography and engineering, where lectures on statistics belong to their academic training. But it is also intended for the practician who, beyond the use of statistical tools, is interested in their mathematical background. Numerous problems illustrate the applicability of the presented statistical procedures, where SAS gives the solutions. The programs used are explicitly listed and explained. No previous experience is expected neither in SAS nor in a special computer system so that a short training period is guaranteed. This book is meant for a two semester course (lecture, seminar or practical training) where the first three chapters can be dealt within the first semester. They provide the principal components of the analysis of a time series in the time domain. Chapters 4, 5 and 6 deal with its analysis in the frequency domain and can be worked through in the second term. In order to understand the mathematical background some terms are useful such as convergence in distribution, stochastic convergence, maximum likelihood estimator as well as a basic knowledge of the test theory, so that work on the book can start after an introductory lecture on stochastics. Each chapter includes exercises. An exhaustive treatment is recommended. Chapter 7 (case study) deals with a practical case and demonstrates the presented methods. It is possible to use this chapter independent in a seminar or practical training course, if the concepts of time series analysis are already well understood. This book is consecutively subdivided in a statistical part and an SAS-specific part. For better clearness the SAS-specific parts are highlighted. This book is an open source project under the GNU Free Documentation License.
The analysis of real data by means of statistical methods with the aid of a software package common in industry and administration usually is not an integral part of mathematics studies, but it will certainly be part of a future professional work. The present book links up elements from time series analysis with a selection of statistical procedures used in general practice including the statistical software package SAS. Consequently this book addresses students of statistics as well as students of other branches such as economics, demography and engineering, where lectures on statistics belong to their academic training. But it is also intended for the practician who, beyond the use of statistical tools, is interested in their mathematical background. Numerous problems illustrate the applicability of the presented statistical procedures, where SAS gives the solutions. The programs used are explicitly listed and explained. No previous experience is expected neither in SAS nor in a special computer system so that a short training period is guaranteed. This book is meant for a two semester course (lecture, seminar or practical training) where the first three chapters can be dealt within the first semester. They provide the principal components of the analysis of a time series in the time domain. Chapters 4, 5 and 6 deal with its analysis in the frequency domain and can be worked through in the second term. In order to understand the mathematical background some terms are useful such as convergence in distribution, stochastic convergence, maximum likelihood estimator as well as a basic knowledge of the test theory, so that work on the book can start after an introductory lecture on stochastics. Each chapter includes exercises. An exhaustive treatment is recommended. Chapter 7 (case study) deals with a practical case and demonstrates the presented methods. It is possible to use this chapter independent in a seminar or practical training course, if the concepts of time series analysis are already well understood. This book is consecutively subdivided in a statistical part and an SAS-specific part. For better clearness the SAS-specific parts are highlighted. This book is an open source project under the GNU Free Documentation License.
The impact of imatinib dose on response rates and survival in older patients with chronic myeloid leukemia in chronic phase has not been studied well. We analyzed data from the German CML-Study IV, a randomized five-arm treatment optimization study in newly diagnosed BCR-ABL-positive chronic myeloid leukemia in chronic phase. Patients randomized to imatinib 400 mg/day (IM400) or imatinib 800 mg/day (IM800) and stratified according to age (≥65 years vs. <65 years) were compared regarding dose, response, adverse events, rates of progression, and survival. The full 800 mg dose was given after a 6-week run-in period with imatinib 400 mg/day. The dose could then be reduced according to tolerability. A total of 828 patients were randomized to IM400 or IM800. Seven hundred eighty-four patients were evaluable (IM400, 382; IM800, 402). One hundred ten patients (29 %) on IM400 and 83 (21 %) on IM800 were ≥65 years. The median dose per day was lower for patients ≥65 years on IM800, with the highest median dose in the first year (466 mg/day for patients ≥65 years vs. 630 mg/day for patients <65 years). Older patients on IM800 achieved major molecular remission and deep molecular remission as fast as younger patients, in contrast to standard dose imatinib with which older patients achieved remissions much later than younger patients. Grades 3 and 4 adverse events were similar in both age groups. Five-year relative survival for older patients was comparable to that of younger patients. We suggest that the optimal dose for older patients is higher than 400 mg/day. ClinicalTrials.gov identifier: NCT00055874