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Sökning: WFRF:(Javeed A)

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  • Jain, Neeraj, et al. (författare)
  • Targetable genetic alterations of TCF4 (E2-2) drive immunoglobulin expression in diffuse large B cell lymphoma
  • 2019
  • Ingår i: Science Translational Medicine. - : American Association for the Advancement of Science (AAAS). - 1946-6234 .- 1946-6242. ; 11:497
  • Tidskriftsartikel (refereegranskat)abstract
    • The activated B cell (ABC-like) subtype of diffuse large B cell lymphoma (DLBCL) is characterized by chronic activation of signaling initiated by immunoglobulin m (IgM). By analyzing the DNA copy number profiles of 1000 DLBCL tumors, we identified gains of 18q21.2 as the most frequent genetic alteration in ABC-like DLBCL. Using integrative analysis of matched gene expression profiling data, we found that the TCF4 (E2-2) transcription factor gene was the target of these alterations. Overexpression of TCF4 in ABC-like DLBCL cell lines led to its occupancy on immunoglobulin (IGHM) and MYC gene enhancers and increased expression of these genes at the transcript and protein levels. Inhibition of TCF4 activity with dominant-negative constructs was synthetically lethal to ABC-like DLBCL cell lines harboring TCF4 DNA copy gains, highlighting these gains as an attractive potential therapeutic target. Furthermore, the TCF4 gene was one of the top BRD4-regulated genes in DLBCL cell lines. BET proteolysistargeting chimera (PROTAC) ARV771 extinguished TCF4, MYC, and IgM expression and killed ABC-like DLBCL cells in vitro. In DLBCL xenograft models, ARV771 treatment reduced tumor growth and prolonged survival. This work highlights a genetic mechanism for promoting immunoglobulin signaling in ABC-like DLBCL and provides a functional rationale for the use of BET inhibitors in this disease.
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3.
  • Javeed, A, et al. (författare)
  • A Clinical Decision Support System (CDSS) for Unbiased Prediction of Caesarean Section Based on Features Extraction and Optimized Classification
  • 2022
  • Ingår i: Computational intelligence and neuroscience. - : Hindawi Limited. - 1687-5273 .- 1687-5265. ; 2022, s. 1901735-
  • Tidskriftsartikel (refereegranskat)abstract
    • Nowadays, caesarean section (CS) is given preference over vaginal birth and this trend is rapidly rising around the globe, although CS has serious complications such as pregnancy scar, scar dehiscence, and morbidly adherent placenta. Thus, CS should only be performed when it is absolutely necessary for mother and fetus. To avoid unnecessary CS, researchers have developed different machine-learning- (ML-) based clinical decision support systems (CDSS) for CS prediction using electronic health record of the pregnant women. However, previously proposed methods suffer from the problems of poor accuracy and biasedness in ML. To overcome these problems, we have designed a novel CDSS where random oversampling example (ROSE) technique has been used to eliminate the problem of minority classes in the dataset. Furthermore, principal component analysis has been employed for feature extraction from the dataset while, for classification purpose, random forest (RF) model is deployed. We have fine-tuned the hyperparameter of RF using a grid search algorithm for optimal classification performance. Thus, the newly proposed system is named ROSE-PCA-RF and it is trained and tested using an online CS dataset available on the UCI repository. In the first experiment, conventional RF model is trained and tested on the dataset while in the second experiment, the proposed model is tested. The proposed ROSE-PCA-RF model improved the performance of traditional RF by 4.5% with reduced time complexity, while only using two extracted features through the PCA. Moreover, the proposed model has obtained 96.29% accuracy on training data while improving the accuracy of 97.12% on testing data.
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