Publications by authors named "Aura A Rivera"

This work reports a sensitive SERS substrate based on graphene oxide (GO) and quantum-sized ZrO nanoparticles (GO/ZrO) for label-free determination of the organophosphate pesticide methyl parathion (MP). The enhanced light-matter interactions and the consequent SERS effect in these substrates resulted from the effective charge transfer (CT) mechanism attributed to synergistic contributions of three main factors: i) the strong molecular adherence of the MP molecules and the ZrO surface which allows the first layer-effect, ii) the relatively abundant surface defects in low dimensional ZrO semiconductor NPs, which act as intermediate electronic states that reduce the large bandgap barrier, and iii) the hindered charge recombination derived from the transference of the photoinduced holes to the GO layer. This mechanism allowed an enhancement factor of 8.

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Previous studies have suggested that PTEN loss is associated with p110β signaling dependency, leading to the clinical development of p110β-selective inhibitors. Here we use a panel pre-clinical models to reveal that PI3K isoform dependency is not governed by loss of PTEN and is impacted by feedback inhibition and concurrent PIK3CA/PIK3CB alterations. Furthermore, while pan-PI3K inhibition in PTEN-deficient tumors is efficacious, upregulation of Insulin Like Growth Factor 1 Receptor (IGF1R) promotes resistance.

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The increasing complexity of different cell types revealed by single-cell analysis of tissues presents challenges in efficiently elucidating their functions. Here we show, using prostate as a model tissue, that primary organoids and freshly isolated epithelial cells can be CRISPR edited ex vivo using Cas9-sgRNA (guide RNA) ribotnucleoprotein complex technology, then orthotopically transferred in vivo into immunocompetent or immunodeficient mice to generate cancer models with phenotypes resembling those seen in traditional genetically engineered mouse models. Large intrachromosomal (∼2 Mb) or multigenic deletions can be engineered efficiently without the need for selection, including in isolated subpopulations to address cell-of-origin questions.

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