Lack of Laminar Shear Stress Facilitates the Endothelial Uptake of Very Small Superparamagnetic Iron Oxide Nanoparticles by Modulating the Endothelial Surface Layer.

Purpose: To study whether the absence of laminar shear stress (LSS) enables the uptake of very small superparamagnetic iron oxide nanoparticles (VSOP) in endothelial cells by altering the composition, size, and barrier function of the endothelial surface layer (ESL). Methods and Results: A quantitative particle exclusion assay with living human umbilical endothelial cells using spinning disc confocal microscopy revealed that the dimension of the ESL was reduced in cells cultivated in the absence of LSS. By combining gene expression analysis, flow cytometry, high pressure freezing/freeze substitution immuno-transmission electron microscopy, and confocal laser scanning microscopy, we investigated changes in ESL composition. We found that increased expression of the hyaluronan receptor CD44 by absence of shear stress did not affect the uptake rate of VSOPs. We identified collagen as a previously neglected component of ESL that contributes to its barrier function. Experiments with inhibitor halofuginone and small interfering RNA (siRNA) demonstrated that suppression of collagen expression facilitates VSOP uptake in endothelial cells grown under LSS. Conclusion: The absence of laminar shear stress disturbs the barrier function of the ESL, facilitating membrane accessibility and endocytic uptake of VSOP. Collagen, a previously neglected component of ESL, contributes to its barrier function.

KRAS, NRAS, and BRAF mutation prevalence, clinicopathological association, and their application in a predictive model in Mexican patients with metastatic colorectal cancer: A retrospective cohort study.

Mutations in KRAS, NRAS, and BRAF (RAS/BRAF) genes are the main predictive biomarkers for the response to anti-EGFR monoclonal antibodies (MAbs) targeted therapy in metastatic colorectal cancer (mCRC). This retrospective study aimed to report the mutational status prevalence of these genes, explore their possible associations with clinicopathological features, and build and validate a predictive model. To achieve these objectives, 500 mCRC Mexican patients were screened for clinically relevant mutations in RAS/BRAF genes. Fifty-two percent of these specimens harbored clinically relevant mutations in at least one screened gene. Among these, 86% had a mutation in KRAS, 7% in NRAS, 6% in BRAF, and 2% in both NRAS and BRAF. Only tumor location in the proximal colon exhibited a significant correlation with KRAS and BRAF mutational status (p-value = 0.0414 and 0.0065, respectively). Further t-SNE analyses were made to 191 specimens to reveal patterns among patients with clinical parameters and KRAS mutational status. Then, directed by the results from classical statistical tests and t-SNE analysis, neural network models utilized entity embeddings to learn patterns and build predictive models using a minimal number of trainable parameters. This study could be the first step in the prediction for RAS/BRAF mutational status from tumoral features and could lead the way to a more detailed and more diverse dataset that could benefit from machine learning methods.