Manganese oxide (MnO) nanoparticles (NPs) can serve as robust pH-sensitive contrast agents for magnetic resonance imaging (MRI) due to Mn 2+ release at low pH, which generates a~30 fold change in T 1 relaxivity. Strategies to control NP size, composition, and Mn 2+ 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 Mn 3 O 4 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, Mn 2+ dissolution rates at low pH were unaffected by synthesis conditions. Although Mn 2+ production was high at pH 5 mimicking endosomes inside cells, minimal Mn 2+ 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 Mn 2+ release and MRI contrast, the ideal MnO NP formulation (~32 nm) was compared with smaller MnO and Mn 3 O 4 NPs. Small MnO NPs produced the highest amount of Mn 2+ at acidic pH with maximum T 1 MRI signal; Mn 3 O 4 NPs generated the lowest MRI signal. MnO NPs encapsulated within poly (lactide-co-glycolide) (PLGA) retained significantly higher Mn 2+ release and MRI signal compared to PLGA Mn 3 O 4 NPs. Therefore, MnO instead of Mn 3 O 4 should be targeted intracellularly to maximize MRI contrast.

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. Electronic supplementary material The online version of this article (10.1186/s12933-019-0879-0) contains supplementary material, which is available to authorized users.

BackgroundDiabetes mellitus is a chronic, debilitating disease that continues to affect a greater percentage of people each year. Among its systemic comorbidities, diabetics are two to four times more likely to develop cardiovascular diseases. While glycosylated hemoglobin (HbA1c) remains the primary diagnostic for diabetes mellitus onset, predicting health outcomes through a single measure is difficult, with disparities existing between ethnic and demographic groups. The purpose of this study was to use machine learning algorithms to develop personalized prognostics in predicting the development and progression of diabetes mellitus in the heart.MethodsRight atrial appendages from 48 patients, 29 non‐diabetic and 19 type 2 diabetic, were procured from the WVU Ruby Memorial Hospital. Machine learning was applied to physiological (sex, BMI, HbA1c, comorbidities), biochemical (total methylation and mitochondrial bioenergetics functional parameters), and sequencing data (mitochondrial genomic DNA single nucleotide polymorphisms (SNPs) and nuclear genomic DNA CpG island methylation) for each patient. Supervised (including HbA1c) and unsupervised (without HbA1c) learning evaluated the efficiency of testing and training sets through classification and regression trees, logistic regression, support vector machines, and k nearest neighbors.ResultsNuclear 5‐methylcytosine content and s‐adenosyl methionine activity matched diagnostic accuracy of HbA1c (~91% testing) for all patients. Mitochondrial DNA SNPs found in the D‐Loop region (SNP‐152C, ‐16126C, and ‐16362C) were significantly associated with diabetes incidence. The CpG island for the mitochondrial transcription factor A (TFAM) revealed specific CpG sites (chr10:58384915 and 58385262) that showed high accuracy (~89% testing) in predicting diabetes incidence. Unsupervised learning, both binary (diabetic or non‐diabetic) and multi‐classification (diabetic, pre‐diabetic, or non‐diabetic), confirmed these results.ConclusionsUsing machine learning, we were able to predict health outcomes through the combination of multiple physiological, biochemical, and sequencing approaches, assessing the development and progression of diabetes. Linking mitochondrial and genomic/epigenomic data, we detail how learning algorithms can be applied to forwarding personalized medicine and developing novel, more advanced prognostics in the heart.Support or Funding InformationR01 HL‐128485 (JMH), AHA‐17PRE33660333 (QAH), WV‐INBRE support by NIH Grant P20GM103434, WVU Flow Cytometry & Single Cell Core supported by MBRCC CoBRE Grant GM103488 and Fortessa S10 Grant OD016165, and the Community Foundation for the Ohio Valley Whipkey Trust. We would like to acknowledge the WVU Genomics Core Facility, Morgantown WV for support provided to help make this work possible.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.