Step-in dosing of bosutinib in pts with chronic phase chronic myeloid leukemia (CML) after second-generation tyrosine kinase inhibitor (TKI) therapy: results of the Bosutinib Dose Optimization (BODO) Study.

The approved dose of bosutinib in chronic phase CML is 400 mg QD in first-line and 500 mg QD in later-line treatment. However, given that gastrointestinal (GI) toxicity typically occurs early after treatment initiation, physicians often tend to start therapy with lower doses although this has never been tested systematically in prospective trials in the Western world. The Bosutinib Dose Optimization (BODO) Study, a multicenter phase II study, investigated the tolerability and efficacy of a step-in dosing concept of bosutinib (starting at 300 mg QD) in chronic phase CML patients in 2nd or 3rd line who were intolerant and/or refractory to previous TKI treatment. Of 57 patients included until premature closure of the study due to slow recruitment, 34 (60%) reached the targeted dose level of 500 mg QD following the 2-weekly step-in dosing regimen. While the dosing-in concept failed to reduce GI toxicity (grade II-IV, primary study endpoint) to < 40% (overall rate of 60%; 95% CI: 45-74%), bosutinib treatment (mean dosage: 403 mg/day) showed remarkable efficacy with a cumulative major molecular remission (MMR) rate of 79% (95% CI: 66 to 88%) at month 24. Of thirty patients refractory to previous therapy and not in MMR at baseline, 19 (64%) achieved an MMR during treatment. GI toxicity did not significantly impact on patient-reported outcomes (PRO) and led to treatment discontinuation in only one patient. Overall, the results of our trial support the efficacy and safety of bosutinib after failure of second-generation TKI pre-treatment. Trial registration: NCT02577926.

Influence of molecular subgroups on outcome of acute myeloid leukemia with normal karyotype in 141 patients undergoing salvage allogeneic stem cell transplantation in primary induction failure or beyond first relapse.

Based on molecular aberrations, in particular the NPM1 mutation (NPM1(mut)) and the FLT3 internal tandem duplication (Flt3-ITD), prognostic subgroups have been defined among patients with acute myeloid leukemia with normal karyotype. Whereas these subgroups are known to play an important role in outcome in first complete remission, and also in the indication for allogeneic stem cell transplantation, data are limited on their role after transplantation in advanced disease. To evaluate the role of molecular subgroups of acute myeloid leukemia with normal karyotype after allogeneic stem cell transplantation beyond first complete remission, we analyzed the data from 141 consecutive adults (median age: 51.0 years, range 18.4-69.3 years) who had received an allogeneic transplant either in primary induction failure or beyond first complete remission. A sequential regimen of cytoreductive chemotherapy (fludarabine, high-dose AraC, amsacrine) followed by reduced intensity conditioning (FLAMSA-RIC), was uniformly used for conditioning. After a median follow up of three years, overall survival from transplantation was 64 ± 4%, 53 ± 4% and 44 ± 5% at one, two and four years, respectively. Forty patients transplanted in primary induction failure achieved an encouraging 2-year survival of 69%. Among 101 patients transplanted beyond first complete remission, 2-year survival was 81% among patients with the NPM1(mut)/FLT3(wt) genotype in contrast to 43% in other genotypes. Higher numbers of transfused CD34(+) cells (hazard ratio 2.155, 95% confidence interval 0.263-0.964, P=0.039) and favorable genotype (hazard ratio 0.142, 95% confidence interval: 0.19-0.898, P=0.048) were associated with superior overall survival in multivariate analysis. In conclusion, patients with acute myeloid leukemia with normal karyotype can frequently be rescued after primary induction failure by allogeneic transplantation following FLAMSA-RIC. The prognostic role of NPM1(mut)/FLT3-ITD based subgroups was carried through after allogeneic stem cell transplantation beyond first complete remission.

Pure White Cell Aplasia Associated With Long-Term Unprotected Exposure to High Concentrations of Benzalkonium Chloride and 2-Phenoxyethanol.

Pure white cell aplasia (PWCA) is a very rare hematological disorder with a nearly total absence of granulocytes and their precursor cells. While the disease is rarely diagnosed incidentally in otherwise asymptomatic individuals, most patients suffer from sometimes life-threatening infections. Due to its very low incidence, the precise pathomechanism of PWCA still needs to be elucidated. While most cases reported in the literature have been associated with an underlying thymic or autoimmune disease, some other factors including the intake of certain drugs such as antimicrobial agents or immune checkpoint inhibitors have been identified as potential triggers. Since PWCA is commonly refractory to treatment with granulocyte colony-stimulating factors (G-CSF), the main focus lies in identifying and eliminating the underlying trigger. Here, we report a unique case where the development of PWCA in a 56-year-old man with an upper respiratory tract infection has to be attributed to the long-term unprotected exposure to an industrial detergent containing high concentrations of the preservatives benzalkonium chloride (BAC) and 2-phenoxyethanol (2-PE). As a matter of fact, certain hematotoxic potential has been described in the literature for both BAC and 2-PE.

A novel approach to 2D/3D registration of X-ray images using Grangeat's relation.

Fast and accurate 2D/3D registration plays an important role in many applications, ranging from scientific and engineering domains all the way to medical care. Today's predominant methods are based on computationally expensive approaches, such as virtual forward or back projections, that limit the real-time applicability of the routines. Here, we present a novel concept that makes use of Grangeat's relation to intertwine information from the 3D volume and the 2D projection space in a way that allows pre-computation of all time-intensive steps. The main effort within actual registration tasks is reduced to simple resampling of the pre-calculated values, which can be executed rapidly on modern GPU hardware. We analyze the applicability of the proposed method on simulated data under various conditions and evaluate the findings on real data from a C-arm CT scanner. Our results show high registration quality in both simulated as well as real data scenarios and demonstrate a reduction in computation time for the crucial computation step by a factor of six to eight when compared to state-of-the-art routines. With minor trade-offs in accuracy, this speed-up can even be increased up to a factor of 100 in particular settings. To our knowledge, this is the first application of Grangeat's relation to the topic of 2D/3D registration. Due to its high computational efficiency and broad range of potential applications, we believe it constitutes a highly relevant approach for various problems dealing with cone beam transmission images.

Large Vessel Vasculitis as a Possible Mechanism of Vascular Side Effects of Ponatinib: A Case Report.

Arterial occlusive events (AOEs) such as cerebrovascular, cardiovascular and peripheral arterial events are known side effects of ponatinib, assumed due to the rapid development and increase of arteriosclerosis, while the definitive pathomechanisms therefore are still unclear. We present a case of clinically apparent large vessel vasculitis and discuss this phenomenon as a possible mechanism of AOEs beside arteriosclerosis.

Time separation technique: Accurate solution for 4D C-Arm-CT perfusion imaging using a temporal decomposition model.

PURPOSE: The issue of perfusion imaging using a temporal decomposition model is to enable the reconstruction of undersampled measurements acquired with a slowly rotating x-ray-based imaging system, for example, a C-arm-based cone beam computed tomography (CB-CT). The aim of this work is to integrate prior knowledge into the dynamic CT task in order to reduce the required number of views and the computational effort as well as to save dose. The prior knowledge comprises of a mathematical model and clinical perfusion data. METHODS: In case of model-based perfusion imaging via superposition of specified orthogonal temporal basis functions, a priori knowledge is incorporated into the reconstructions. Instead of estimating the dynamic attenuation of each voxel by a weighting sum, the modeling approach is done as a preprocessing step in the projection space. This point of view provides a method that decomposes the temporal and spatial domain of dynamic CT data. The resulting projection set consists of spatial information that can be treated as individual static CT tasks. Consequently, the high-dimensional model-based CT system can be completely transformed, allowing for the use of an arbitrary reconstruction algorithm. RESULTS: For CT, reconstructions of preprocessed dynamic in silico data are illustrated and evaluated by means of conventional clinical parameters for stroke diagnostics. The time separation technique presented here, provides the expected accuracy of model-based CT perfusion imaging. Consequently, the model-based handled 4D task can be solved approximately as fast as the corresponding static 3D task. CONCLUSION: For C-arm-based CB-CT, the algorithm presented here provides a solution for resorting to model-based perfusion reconstruction without its connected high computational cost. Thus, this algorithm is potentially able to have recourse to the benefit from model-based perfusion imaging for practical application. This study is a proof of concept.

Extracting duration information in a picture category decoding task using hidden Markov Models.

OBJECTIVE: Adapting classifiers for the purpose of brain signal decoding is a major challenge in brain-computer-interface (BCI) research. In a previous study we showed in principle that hidden Markov models (HMM) are a suitable alternative to the well-studied static classifiers. However, since we investigated a rather straightforward task, advantages from modeling of the signal could not be assessed. APPROACH: Here, we investigate a more complex data set in order to find out to what extent HMMs, as a dynamic classifier, can provide useful additional information. We show for a visual decoding problem that besides category information, HMMs can simultaneously decode picture duration without an additional training required. This decoding is based on a strong correlation that we found between picture duration and the behavior of the Viterbi paths. MAIN RESULTS: Decoding accuracies of up to 80% could be obtained for category and duration decoding with a single classifier trained on category information only. SIGNIFICANCE: The extraction of multiple types of information using a single classifier enables the processing of more complex problems, while preserving good training results even on small databases. Therefore, it provides a convenient framework for online real-life BCI utilizations.

The German Research Network on Depression and Suicidality.

Under the common roof of the German Research Network on Depression and Suicidality, scientists, medical doctors, psychotherapists, and several of the most relevant institutions of the German health care system are aligned to proceed against the overlapping health problems of depression and suicidality. The project addresses professionals and scientists, affected patients and their relatives, and the total population at the same time. Comprehensive public relations activities contribute to an overall sensitization concerning the topics of depression and suicidality and inform about diagnosis and treatment options. The network is funded by the German Ministry of Education and Research and was established in 1999. Since then more than 20 individual projects have been initiated. Meanwhile more and more results of these projects become available and increasing efforts are undertaken to transfer these results into elements of standard health care.

How effective is advanced training concerning depression and suicidality among the elderly? Results of a pilot study.

Three Hundred and Seventy Four geriatric caregivers in Nuremberg and Munich participated in a four-hour advanced in-house training addressing the topic. "depression and suicidality in old age." Geriatric caregivers are key persons in early diagnosis of depressive symptoms and suicidality. The training focused on qualifying and sensitizing caregivers for these topics. A 12-item questionnaire was applied to measure the outcome parameters "knowledge and attitudes towards depression and suicidality in old age" within a pre-, post-, Three-months-follow-up design (rate of return = 58%). Short-term effectiveness of the advanced training "depression in old age" could be shown for most of the items. Significant long-term effects could be found concerning increase of knowledge about pharmacotherapy with antidepressants. Attitude towards "suicide in old age" also changed significantly. The training program was effective in improving knowledge and attitudes of geriatric caregivers concerning depression and suicidality.

Hidden Markov model and support vector machine based decoding of finger movements using electrocorticography.

OBJECTIVE: Support vector machines (SVM) have developed into a gold standard for accurate classification in brain-computer interfaces (BCI). The choice of the most appropriate classifier for a particular application depends on several characteristics in addition to decoding accuracy. Here we investigate the implementation of hidden Markov models (HMM) for online BCIs and discuss strategies to improve their performance. APPROACH: We compare the SVM, serving as a reference, and HMMs for classifying discrete finger movements obtained from electrocorticograms of four subjects performing a finger tapping experiment. The classifier decisions are based on a subset of low-frequency time domain and high gamma oscillation features. MAIN RESULTS: We show that decoding optimization between the two approaches is due to the way features are extracted and selected and less dependent on the classifier. An additional gain in HMM performance of up to 6% was obtained by introducing model constraints. Comparable accuracies of up to 90% were achieved with both SVM and HMM with the high gamma cortical response providing the most important decoding information for both techniques. SIGNIFICANCE: We discuss technical HMM characteristics and adaptations in the context of the presented data as well as for general BCI applications. Our findings suggest that HMMs and their characteristics are promising for efficient online BCIs.