RESUMEN
Manganese oxide (MnO) nanoparticles (NPs) can serve as robust pH-sensitive contrast agents for magnetic resonance imaging (MRI) due to Mn2+ release at low pH, which generates a ~30 fold change in T1 relaxivity. Strategies to control NP size, composition, and Mn2+ dissolution rates are essential to improve diagnostic performance of pH-responsive MnO NPs. We are the first to demonstrate that MnO NP size and composition can be tuned by the temperature ramping rate and aging time used during thermal decomposition of manganese(II) acetylacetonate. Two different temperature ramping rates (10°C/min and 20°C/min) were applied to reach 300°C and NPs were aged at that temperature for 5, 15, or 30 min. A faster ramping rate and shorter aging time produced the smallest NPs of ~23 nm. Shorter aging times created a mixture of MnO and Mn3O4 NPs, whereas longer aging times formed MnO. Our results indicate that a 20°C/min ramp rate with an aging time of 30 min was the ideal temperature condition to form the smallest pure MnO NPs of ~32 nm. However, Mn2+ dissolution rates at low pH were unaffected by synthesis conditions. Although Mn2+ production was high at pH 5 mimicking endosomes inside cells, minimal Mn2+ was released at pH 6.5 and 7.4, which mimic the tumor extracellular space and blood, respectively. To further elucidate the effects of NP composition and size on Mn2+ release and MRI contrast, the ideal MnO NP formulation (~32 nm) was compared with smaller MnO and Mn3O4 NPs. Small MnO NPs produced the highest amount of Mn2+ at acidic pH with maximum T1 MRI signal; Mn3O4 NPs generated the lowest MRI signal. MnO NPs encapsulated within poly(lactide-co-glycolide) (PLGA) retained significantly higher Mn2+ release and MRI signal compared to PLGA Mn3O4 NPs. Therefore, MnO instead of Mn3O4 should be targeted intracellularly to maximize MRI contrast.
Asunto(s)
Compuestos de Manganeso/química , Nanopartículas del Metal/química , Óxidos/química , Medios de Contraste/química , Imagen por Resonancia Magnética/métodos , Nanopartículas/química , Temperatura , Factores de TiempoRESUMEN
BACKGROUND: Diabetes mellitus is a chronic disease that impacts an increasing percentage of people each year. Among its comorbidities, diabetics are two to four times more likely to develop cardiovascular diseases. While HbA1c remains the primary diagnostic for diabetics, its ability to predict long-term, health outcomes across diverse demographics, ethnic groups, and at a personalized level are limited. The purpose of this study was to provide a model for precision medicine through the implementation of machine-learning algorithms using multiple cardiac biomarkers as a means for predicting diabetes mellitus development. METHODS: Right atrial appendages from 50 patients, 30 non-diabetic and 20 type 2 diabetic, were procured from the WVU Ruby Memorial Hospital. Machine-learning was applied to physiological, biochemical, and sequencing data for each patient. Supervised learning implementing SHapley Additive exPlanations (SHAP) allowed binary (no diabetes or type 2 diabetes) and multiple classification (no diabetes, prediabetes, and type 2 diabetes) of the patient cohort with and without the inclusion of HbA1c levels. Findings were validated through Logistic Regression (LR), Linear Discriminant Analysis (LDA), Gaussian Naïve Bayes (NB), Support Vector Machine (SVM), and Classification and Regression Tree (CART) models with tenfold cross validation. RESULTS: Total nuclear methylation and hydroxymethylation were highly correlated to diabetic status, with nuclear methylation and mitochondrial electron transport chain (ETC) activities achieving superior testing accuracies in the predictive model (~ 84% testing, binary). Mitochondrial DNA SNPs found in the D-Loop region (SNP-73G, -16126C, and -16362C) were highly associated with diabetes mellitus. The CpG island of transcription factor A, mitochondrial (TFAM) revealed CpG24 (chr10:58385262, P = 0.003) and CpG29 (chr10:58385324, P = 0.001) as markers correlating with diabetic progression. When combining the most predictive factors from each set, total nuclear methylation and CpG24 methylation were the best diagnostic measures in both binary and multiple classification sets. CONCLUSIONS: Using machine-learning, we were able to identify novel as well as the most relevant biomarkers associated with type 2 diabetes mellitus by integrating physiological, biochemical, and sequencing datasets. Ultimately, this approach may be used as a guideline for future investigations into disease pathogenesis and novel biomarker discovery.