Publications by authors named "M Notaro"

During limb bud formation, axis polarities are established as evidenced by the spatially restricted expression of key regulator genes. In particular, the mutually antagonistic interaction between the GLI3 repressor and HAND2 results in distinct and non-overlapping anterior-distal Gli3 and posterior Hand2 expression domains. This is a hallmark of the establishment of antero-posterior limb axis polarity, together with spatially restricted expression of homeodomain and other transcriptional regulators.

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  • * FLT3-ITD promotes the expression of PDP1, leading to increased activity of the pyruvate dehydrogenase complex, which drives metabolic shifts essential for cell proliferation and survival under both normoxic and hypoxic conditions.
  • * Targeting PDP1 may present a new strategy for overcoming resistance to FLT3 inhibitors, as its regulation affects glucose metabolism and drug response in AML cells.
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Liver metastases are associated with poor response to current pharmacological treatments, including immunotherapy. We describe a lentiviral vector (LV) platform to selectively engineer liver macrophages, including Kupffer cells and tumor-associated macrophages (TAMs), to deliver type I interferon (IFNα) to liver metastases. Gene-based IFNα delivery delays the growth of colorectal and pancreatic ductal adenocarcinoma liver metastases in mice.

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  • Healthcare datasets from Electronic Health Records (EHRs) are valuable for studying patient outcomes, but often have missing data that can lead to bias if not handled properly.* -
  • Multiple imputation algorithms aim to fill in missing values, but there's no clear consensus on the best one, and selecting parameters for these algorithms can be challenging.* -
  • This paper presents a new framework for evaluating methods to handle missing data, demonstrating its effectiveness using a large dataset of type-2 diabetes patients and providing insights into how various imputation techniques perform.*
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Patient similarity networks (PSNs), where patients are represented as nodes and their similarities as weighted edges, are being increasingly used in clinical research. These networks provide an insightful summary of the relationships among patients and can be exploited by inductive or transductive learning algorithms for the prediction of patient outcome, phenotype and disease risk. PSNs can also be easily visualized, thus offering a natural way to inspect complex heterogeneous patient data and providing some level of explainability of the predictions obtained by machine learning algorithms.

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