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  • 1
    Online Resource
    Online Resource
    Ruxolitinib and interferon-α2 combination therapy for patients with polycythemia vera or myelofibrosis: a phase II study
    Ferrata Storti Foundation (Haematologica) ; 2020
    In:  Haematologica Vol. 105, No. 9 ( 2020-06-05), p. 2262-2272
    Details
    In: Haematologica, Ferrata Storti Foundation (Haematologica), Vol. 105, No. 9 ( 2020-06-05), p. 2262-2272
    Abstract: We report the final 2-year end-of-study results from the first clinical trial investigating combination treatment with ruxolitinib and low-dose pegylated interferon-α2 (PEG-IFNα2). The study included 32 patients with polycythemia vera and 18 with primary or secondary myelofibrosis; 46 patients were previously intolerant of or refractory to PEGIFNα2. The primary outcome was efficacy, based on hematologic parameters, quality of life measurements, and JAK2 V617F allele burden. We used the 2013 European LeukemiaNet and International Working Group- Myeloproliferative Neoplasms Research and Treatment response criteria, including response in symptoms, splenomegaly, peripheral blood counts, and bone marrow. Of 32 patients with polycythemia vera, ten (31%) achieved a remission which was a complete remission in three (9%) cases. Of 18 patients with myelofibrosis, eight (44%) achieved a remission; five (28%) were complete remissions. The cumulative incidence of peripheral blood count remission was 0.85 and 0.75 for patients with polycythemia vera and myelofibrosis, respectively. The Myeloproliferative Neoplasm Symptom Assessment Form total symptom score decreased from 22 [95% confidence interval (95% CI):, 16-29] at baseline to 15 (95% CI: 10-22) after 2 years. The median JAK2 V617F allele burden decreased from 47% (95% CI: 33-61%) to 12% (95% CI: 6-22%), and 41% of patients achieved a molecular response. The drop-out rate was 6% among patients with polycythemia vera and 32% among those with myelofibrosis. Of 36 patients previously intolerant of PEG-IFNα2, 31 (86%) completed the study, and 24 (67%) of these received PEG-IFNα2 throughout the study. In conclusion, combination treatment improved cell counts, reduced bone marrow cellularity and fibrosis, decreased JAK2 V617F burden, and reduced symptom burden with acceptable toxicity in several patients with polycythemia vera or myelofibrosis. #EudraCT2013-003295-12.
    Type of Medium: Online Resource
    ISSN: 1592-8721 , 0390-6078
    URL: Article
    DOI: 10.3324/haematol.2019.235648
    Language: Unknown
    Publisher: Ferrata Storti Foundation (Haematologica)
    Publication Date: 2020
    detail.hit.zdb_id: 2186022-1
    detail.hit.zdb_id: 2030158-3
    detail.hit.zdb_id: 2805244-4
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  • 2
    Online Resource
    Online Resource
    Novel radioisotope-based nanomedical approaches
    Walter de Gruyter GmbH ; 2013
    In:  European Journal of Nanomedicine Vol. 5, No. 4 ( 2013-01-01)
    Details
    In: European Journal of Nanomedicine, Walter de Gruyter GmbH, Vol. 5, No. 4 ( 2013-01-01)
    Abstract: Radioisotope therapy of cancer is on the rise applying mainly β-emitting radionuclides. However, due to exposure of healthy tissues, the maximum achievable radiation dose with these is limited. Auger-electron emitters (AEs) represent a promising alternative because of their mode of decay within a short nanometer range. The challenge is that their therapeutic efficacy relies on a close vicinity to DNA. To overcome this and to minimize toxicity, the construction of smart nanomedical devices is required, which ascertain tumor cell targeting with succeeding cellular uptake and nuclear translocation. In this review we describe the potential of AEs with focus on their delivery down to the DNA level and their cellular effects. Reported efforts comprise different tumor-targeting strategies, including the use of antibodies or peptides with nuclear localizing sequences. Recently, attention has shifted to various nanoparticle formats for overcoming delivery problems. To this end, these approaches have mostly been tested in cell lines in vitro applying AEs more suited for imaging than therapy. This defines a demand for nanomedical formulations with documented in vivo activity, using AEs selected for their therapeutic potential to come closer to real clinical settings.
    Type of Medium: Online Resource
    ISSN: 1662-596X , 1662-5986
    URL: Article
    DOI: 10.1515/ejnm-2013-0025
    Language: Unknown
    Publisher: Walter de Gruyter GmbH
    Publication Date: 2013
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  • 3
    Online Resource
    Online Resource
    Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data
    Walter de Gruyter GmbH ; 2014
    In:  Journal of Integrative Bioinformatics Vol. 11, No. 2 ( 2014-06-1), p. 1-14
    Details
    In: Journal of Integrative Bioinformatics, Walter de Gruyter GmbH, Vol. 11, No. 2 ( 2014-06-1), p. 1-14
    Abstract: Selecting the most promising treatment strategy for breast cancer crucially depends on determining the correct subtype. In recent years, gene expression profiling has been investigated as an alternative to histochemical methods. Since databases like TCGA provide easy and unrestricted access to gene expression data for hundreds of patients, the challenge is to extract a minimal optimal set of genes with good prognostic properties from a large bulk of genes making a moderate contribution to classification. Several studies have successfully applied machine learning algorithms to solve this so-called gene selection problem. However, more diverse data from other OMICS technologies are available, including methylation. We hypothesize that combining methylation and gene expression data could already lead to a largely improved classification model, since the resulting model will reflect differences not only on the transcriptomic, but also on an epigenetic level. We compared so-called random forest derived classification models based on gene expression and methylation data alone, to a model based on the combined features and to a model based on the gold standard PAM50. We obtained bootstrap errors of 10-20% and classification error of 1-50%, depending on breast cancer subtype and model. The gene expression model was clearly superior to the methylation model, which was also reflected in the combined model, which mainly selected features from gene expression data. However, the methylation model was able to identify unique features not considered as relevant by the gene expression model, which might provide deeper insights into breast cancer subtype differentiation on an epigenetic level.
    Type of Medium: Online Resource
    ISSN: 1613-4516
    URL: Article
    DOI: 10.1515/jib-2014-236
    Language: Unknown
    Publisher: Walter de Gruyter GmbH
    Publication Date: 2014
    detail.hit.zdb_id: 2147212-9
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  • 4
    Online Resource
    Online Resource
    Serum peptide/protein profiling by mass spectrometry provides diagnostic information independently of CA125 in women with an ovarian tumor
    IOS Press ; 2010
    In:  Cancer Biomarkers Vol. 6, No. 2 ( 2010-06-18), p. 73-82
    Details
    In: Cancer Biomarkers, IOS Press, Vol. 6, No. 2 ( 2010-06-18), p. 73-82
    Type of Medium: Online Resource
    ISSN: 1875-8592 , 1574-0153
    URL: Article
    DOI: 10.3233/CBM-2009-0120
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2010
    detail.hit.zdb_id: 2525487-X
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