Acute myeloid leukemias (AMLs) with translocations of the mixed lineage leukemia (MLL/KMT2A) gene are common in young patients and are generally associated with poor clinical outcomes. The molecular biology of MLL fusion genes remains incompletely characterized and is complicated by the fact that more than 100 different partner genes have been identified in fusions with MLL. The continuously growing list of MLL fusions also represents a clinical challenge with respect to identification of novel fusions and tracking of the fusions to monitor progression of the disease after treatment. Recently, we have developed a novel single-donor model leukemia system that permits the development of human AML from normal cord blood cells. Gene expression analysis of this model and of MLL-AML patient samples has identified a number of candidate biomarker genes with highly biased expression on leukemic cells. Here, we present data demonstrating the potential clinical utility of several of these candidate genes for identifying known and novel MLL fusions.
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http://dx.doi.org/10.1016/j.exphem.2017.08.006 | DOI Listing |
Breast Cancer Res Treat
January 2025
Department of Breast Surgery, Thyroid Surgery, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, No.141, Tianjin Road, Huangshi, 435000, Hubei, China.
Background: The heterogeneity of breast cancer (BC) necessitates the identification of novel subtypes and prognostic models to enhance patient stratification and treatment strategies. This study aims to identify novel BC subtypes based on PANoptosis-related genes (PRGs) and construct a robust prognostic model to guide individualized treatment strategies.
Methods: The transcriptome data along with clinical data of BC patients were sourced from the TCGA and GEO databases.
ISA Trans
January 2025
Institute of Artificial Intelligence and Future Networks, Beijing Normal University at Zhuhai, Zhuhai, China; BNU-HKBU United International College Tangjiawan, Rd. JinTong 2000#, Zhuhai, China. Electronic address:
In this paper, a novel recursive hierarchical parametric identification method based on initial value optimization is proposed for Wiener-Hammerstein systems subject to stochastic measurement noise. By transforming the traditional Wiener-Hammerstein system model into a generalized form, the system model parameters are uniquely expressed for estimation. To avoid cross-coupling between estimating block-oriented model parameters, a hierarchical identification algorithm is presented by dividing the parameter vector into two subvectors containing the coupled and uncoupled terms for estimation, respectively.
View Article and Find Full Text PDFAnterior segment dysgenesis (ASD) defines a collection of congenital eye disorders that affect structures within the anterior segment of the eye. Mutations in genes that initiate and regulate the complex pathways involved in eye development can cause a spectrum of disorders such as ASD, congenital cataracts and corneal opacity. In South Africa, causes of ASD are poorly understood with few studies looking at the possible genetic basis for these disorders.
View Article and Find Full Text PDFPlanta Med
January 2025
Instituto de Química, Departamento de Productos Naturales, Universidad Nacional Autónoma de México, Mexico City, Mexico.
An approach combining enzymatic inhibition and untargeted metabolomics through molecular networking was employed to search for human recombinant full-length protein tyrosine phosphatase 1B (PTP1 B) inhibitors from a collection of 66 mangrove-associated fungal taxa. This strategy prioritized two strains (IQ-1612, section , and IQ-1620, section ) for further studies. Chemical investigation of strain IQ-1612 resulted in the isolation of a new nonanolide derivative, roseoglobuloside A (1: ), along with two known metabolites (2: and 3: ), whereas strain IQ-1620 led to the isolation of four known naphtho-γ-pyrones and one known diketopiperazine (4: -8: ).
View Article and Find Full Text PDFComput Biol Med
January 2025
Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124, Cagliari, Italy.
Background: Malaria is a critical and potentially fatal disease caused by the Plasmodium parasite and is responsible for more than 600,000 deaths globally. Early and accurate detection of malaria parasites is crucial for effective treatment, yet conventional microscopy faces limitations in variability and efficiency.
Methods: We propose a novel computer-aided detection framework based on deep learning and attention mechanisms, extending the YOLO-SPAM and YOLO-PAM models.
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