Noninvasive detection of graft injury after heart transplant using donor-derived cell-free DNA: A prospective multicenter study

Standardized donor-derived cell-free DNA (dd-cfDNA) testing has been introduced into clinical use to monitor kidney transplant recipients for rejection. This report describes the performance of this dd-cfDNA assay to detect allograft rejection in samples from heart transplant (HT) recipients undergoing surveillance monitoring across the United States. Venous blood was longitudinally sampled from 740 HT recipients from 26 centers and in a single-center cohort of 33 patients at high risk for antibody-mediated rejection (AMR). Plasma dd-cfDNA was quantified by using targeted amplification and sequencing of a single nucleotide polymorphism panel. The dd-cfDNA levels were correlated to paired events of biopsy-based diagnosis of rejection. The median dd-cfDNA was 0.07% in reference HT recipients (2164 samples) and 0.17% in samples classified as acute rejection (35 samples; P = .005). At a 0.2% threshold, dd-cfDNA had a 44% sensitivity to detect rejection and a 97% negative predictive value. In the cohort at risk for AMR (11 samples), dd-cfDNA levels were elevated 3-fold in AMR compared with patients without AMR (99 samples, P = .004). The standardized dd-cfDNA test identified acute rejection in samples from a broad population of HT recipients. The reported test performance characteristics will guide the next stage of clinical utility studies of the dd-cfDNA assay

High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning

Left ventricular hypertrophy (LVH) results from chronic remodeling caused by a broad range of systemic and cardiovascular disease including hypertension, aortic stenosis, hypertrophic cardiomyopathy, and cardiac amyloidosis. Early detection and characterization of LVH can significantly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating etiologies of LVH. To overcome this challenge, we present EchoNet-LVH - a deep learning workflow that automatically quantifies ventricular hypertrophy with precision equal to human experts and predicts etiology of LVH. Trained on 28,201 echocardiogram videos, our model accurately measures intraventricular wall thickness (mean absolute error [MAE] 1.4mm, 95% CI 1.2-1.5mm), left ventricular diameter (MAE 2.4mm, 95% CI 2.2-2.6mm), and posterior wall thickness (MAE 1.2mm, 95% CI 1.1-1.3mm) and classifies cardiac amyloidosis (area under the curve of 0.83) and hypertrophic cardiomyopathy (AUC 0.98) from other etiologies of LVH. In external datasets from independent domestic and international healthcare systems, EchoNet-LVH accurately quantified ventricular parameters (R2 of 0.96 and 0.90 respectively) and detected cardiac amyloidosis (AUC 0.79) and hypertrophic cardiomyopathy (AUC 0.89) on the domestic external validation site. Leveraging measurements across multiple heart beats, our model can more accurately identify subtle changes in LV geometry and its causal etiologies. Compared to human experts, EchoNet-LVH is fully automated, allowing for reproducible, precise measurements, and lays the foundation for precision diagnosis of cardiac hypertrophy. As a resource to promote further innovation, we also make publicly available a large dataset of 23,212 annotated echocardiogram videos

A framework for protein structure classification and identification of novel protein structures

BACKGROUND: Protein structure classification plays a central role in understanding the function of a protein molecule with respect to all known proteins in a structure database. With the rapid increase in the number of new protein structures, the need for automated and accurate methods for protein classification is increasingly important. RESULTS: In this paper we present a unified framework for protein structure classification and identification of novel protein structures. The framework consists of a set of components for comparing, classifying, and clustering protein structures. These components allow us to accurately classify proteins into known folds, to detect new protein folds, and to provide a way of clustering the new folds. In our evaluation with SCOP 1.69, our method correctly classifies 86.0%, 87.7%, and 90.5% of new domains at family, superfamily, and fold levels. Furthermore, for protein domains that belong to new domain families, our method is able to produce clusters that closely correspond to the new families in SCOP 1.69. As a result, our method can also be used to suggest new classification groups that contain novel folds. CONCLUSION: We have developed a method called proCC for automatically classifying and clustering domains. The method is effective in classifying new domains and suggesting new domain families, and it is also very efficient. A web site offering access to proCC is freely available a

Report from the American Society of Transplantation on frailty in solid organ transplantation

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148387/1/ajt15198_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148387/2/ajt15198.pd

Mitochondria-Associated MicroRNAs in Rat Hippocampus Following Traumatic Brain Injury

Traumatic brain injury (TBI) is a major cause of death and disability. However, the molecular events contributing to the pathogenesis are not well understood. Mitochondria serve as the powerhouse of cells, respond to cellular demands and stressors, and play an essential role in cell signaling, differentiation, and survival. There is clear evidence of compromised mitochondrial function following TBI; however, the underlying mechanisms and consequences are not clear. MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression post-transcriptionally, and function as important mediators of neuronal development, synaptic plasticity, and neurodegeneration. Several miRNAs show altered expression following TBI; however, the relevance of mitochondria in these pathways is unknown. Here, we present evidence supporting the association of miRNA with hippocampal mitochondria, as well as changes in mitochondria-associated miRNA expression following a controlled cortical impact (CCI) injury in rats. Specifically, we found that the miRNA processing proteins Argonaute (AGO) and Dicer are present in mitochondria fractions from uninjured rat hippocampus, and immunoprecipitation of AGO associated miRNA from mitochondria suggests the presence of functional RNA-induced silencing complexes. Interestingly, RT-qPCR miRNA array studies revealed that a subset of miRNA is enriched in mitochondria relative to cytoplasm. At 12h following CCI, several miRNAs are significantly altered in hippocampal mitochondria and cytoplasm. In addition, levels of miR-155 and miR-223, both of which play a role in inflammatory processes, are significantly elevated in both cytoplasm and mitochondria. We propose that mitochondria-associated miRNAs may play an important role in regulating the response to TBI