100,472 research outputs found
High conservation in mammals of a genomic region containing two homologous of acute myeloid leukemia-associated genes PLZF and MLL.
No full textDisruption of PLZP in mice leads to increased T-lymphocytes proliferation, cytokine production and altered hematopoietic stem cell homeostasis
No full textDeregulated function of members of the POK (POZ and Kruppel) family of transcriptional repressors, such as promyelocytic leukemia zinc finger (PLZF) and B-cell lymphoma 6 (BCL-6), plays a critical role in the pathogenesis of acute promyelocytic leukemia (APL) and non-Hodgkin's lymphoma, respectively. PLZP, also known as TZFP, FAZF, or ROG, is a novel POK protein that displays strong homology with PLZF and has been implicated in the pathogenesis of the cancer-predisposing syndrome, Fanconi's anemia, and of APL, in view of its ability to heterodimerize with the FANC-C and PLZF proteins, respectively. Here we report the generation and characterization of mice in which we have specifically inactivated the PLZP gene through in-frame insertion of a lacZ reporter and without perturbing the expression of the neighboring MLL2 gene. We show that PLZP-deficient mice display defects in cell cycle control and cytokine production in the T-cell compartment. Importantly, PLZP inactivation perturbs the homeostasis of the hematopoietic stem and/or progenitor cell. On the basis of our data, a deregulation of PLZP function in Fanconi's anemia and APL may affect the biology of the hematopoietic stem cell, in turn contributing to the pathogenesis of these disorders
Modeling acute promyelocytic leukemia in the mouse: new insights in the pathogenesis of human leukemias.
No full textLetter, [Author unclear] to Paulina T. Merritt
No full textHandwritten letter to Paulina Merritt from an unknown author, October 1, 1876.
Using LIBS to diagnose melanoma in biomedical fluids deposited on solid substrates: Limits of direct spectral analysis and capability of machine learning
No full textDiagnosis is crucial to increase the success rate of cancer treatments as well as the survival rate and life quality of patients, in particular for forms of cancer that remain largely asymptomatic until metastasis. Methodologies that allow the diagnosis of early-stage tumors as well as the detection of residual disease have the potential to improve cancer control and help monitor therapeutic outcomes. In this work, we report a Laser-Induced Breakdown Spectroscopy (LIBS) approach to early diagnosis of a form of skin cancer, melanoma, based on the analysis of biological fluids (blood and tissue homogenates) harvested from diseased mice and healthy controls. We acquired femtosecond LIBS spectra and used two different approaches for the analysis: through comparison of the emission intensity of selected analytes in healthy and diseased samples; and by using machine learning classification algorithms (LDA, Linear Discriminant Analysis; FDA, Fisher Discriminant Analysis; SVM, Support Vector Machines; and Gradient Boosting). We also addressed the effect of substrates on the analysis of liquid samples, by using four different substrates (PVDF, Cu, Al, Si) and comparing their performance. We show that with a combination of the most appropriate substrate and algorithm, we are able to discriminate between healthy and diseased mice with accuracy up to 96% while direct analysis of LIBS spectra did not provide any conclusive results. These series of results demonstrate that carefully designed LIBS measurements combined with machine learning can be a powerful and practical approach for the diagnosis of cancer
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