Background: The variability in patients' risk of oral mucositis (OM) has been, in part, attributed to differences in host genomics. The aim better define the role of genomics as an OM risk by investigating the association between genetic variants and the presence and severity of OM in pediatric patients with osteosarcoma (OS) undergoing chemotherapy (CT).
Methods: A longitudinal observational retrospective study was conducted.
The aim of this study was to develop a virtual learning object (VLO) to teach undergraduate dental students about the diagnostic and therapeutic approaches to oral ulcerative lesions. The VLO was developed with information on the diagnostic process, lesion classification, and clinical-surgical management of oral ulcerative lesions. The VLO content was initially validated by a group of specialists.
View Article and Find Full Text PDFThe situation of limited data concerning the response to COVID-19 mRNA vaccinations in immunocom-promised children hinders evidence-based recommendations. This prospective observational study investigated humoral and T cell responses after primary BNT162b2 vaccination in secondary immunocompromised and healthy children aged 5-11 years. Participants were categorized as: children after kidney transplantation (KTx, = 9), proteinuric glomerulonephritis (GN, = 4) and healthy children (controls, = 8).
View Article and Find Full Text PDFNanoparticle-mediated cancer immunotherapy holds great promise, but more efforts are needed to obtain nanoformulations that result in a full scale activation of innate and adaptive immune components that specifically target the tumors. We generated a series of copper-doped TiO nanoparticles in order to tune the kinetics and full extent of Cu ion release from the remnant TiO nanocrystals. Fine-tuning nanoparticle properties resulted in a formulation of 33% Cu-doped TiO which enabled short-lived hyperactivation of dendritic cells and hereby promoted immunotherapy.
View Article and Find Full Text PDFMotivation: Metabolic networks are complex systems of chemical reactions proceeding via physical interactions between metabolites and proteins. We aimed to predict previously unknown compound-protein interactions (CPI) in metabolic networks by applying biclique extension, a network-structure-based prediction method.
Results: We developed a workflow, named BiPredict, to predict CPIs based on biclique extension and applied it to and human using their respective known CPI networks as input.