Larson, Nicholas B.

Angiogenesis has an essential role in the de novo evolution of choroidal melanoma as well as choroidal nevus transformation into melanoma. Differentiating early-stage melanoma from nevus is of high clinical importance; thus, imaging techniques that provide objective information regarding tumor microvasculature structures could aid accurate early detection. Herein, we investigated the feasibility of quantitative high-definition microvessel imaging (qHDMI) for differentiation of choroidal tumors in humans. This new ultrasound-based technique encompasses a series of morphological filtering and vessel enhancement techniques, enabling the visualization of tumor microvessels as small as 150 microns and extracting vessel morphological features as new tumor biomarkers. Distributional differences between the malignant melanomas and benign nevi were tested on 37 patients with choroidal tumors using a non-parametric Wilcoxon rank-sum test, and statistical significance was declared for biomarkers with p-values < 0.05. The ocular oncology diagnosis was choroidal melanoma (malignant) in 21 and choroidal nevus (benign) in 15 patients. The mean thickness of benign and malignant masses was 1.70 ± 0.40 mm and 3.81 ± 2.63 mm, respectively. Six HDMI biomarkers, including number of vessel segments (p = 0.003), number of branch points (p = 0.003), vessel density (p = 0.03), maximum tortuosity (p = 0.001), microvessel fractal dimension (p = 0.002), and maximum diameter (p = 0.003) exhibited significant distributional differences between the two groups. Contrast-free HDMI provided noninvasive imaging and quantification of microvessels of choroidal tumors. The results of this pilot study indicate the potential use of qHDMI as a complementary tool for characterization of small ocular tumors and early detection of choroidal melanoma.

Objective CYP2C9 and CYP2C19 are highly polymorphic pharmacogenes that encodes enzymes that metabolize a large number of clinically relevant drugs. While the majority of variability in drug metabolism can be explained by a few common alleles, identification of gene sequence variation that alters enzyme activity and incorporation of that information into pharmacogenomic rules for clinical decision support might lead to improved therapy. Using sequence data derived from the Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment (RIGHT Protocol), we sought to identify variants of unknown significance (VUSs) for CYP2C9 and CYP2C19, and to characterize the function of the encoded enzymes. Methods Targeted next‐generation sequencing for 84 pharmacogenes using the PGRN‐Seq v1.0 capture reagent was performed with DNA samples from 1013 RIGHT Protocol participants. Plasmids containing VUSs were created using site directed mutagenesis and were transiently expressed in COS‐1 cells. Initial determination of enzyme activity was performed using fluorogenic substrates. Enzyme kinetic parameters (Km and Vmax) for each of the variant allozymes were determined using liquid chromatography mass spectrometry with tolbutamide (CYP2C9) and mephenytoin (CYP2C19) as prototypic substrates. We also compared our results with in silico functional predictions by SIFT and Polyphen. Results Seven VUSs were identified for each gene, none of which had been functionally characterized. There were significant positive correlations between protein content and fluorometric enzyme activity for all variants for both CYP2C9 (P<0.05) and CYP2C19 (P<0.005). Notably, CYP2C9 V237L and CYP2C19 A173V activity was much lower than predicted based on protein content. The substrate intrinsic clearance of CYP2C9 variants P73L , S115R , V237L [6.23E‐05±1.08E‐05; 2.55E‐05±4.1E‐06; 7.28E‐05±2.4E‐05 (uL/mg protein/min)], and CYP2C19 variants V113I , A173V , R186C and R186H [2.49E‐04±2.78E‐05; 1.80E‐05±2.19E‐06; 2.33E‐05±9.2E‐06;1.45E‐04±1.2E‐05 (uL/mg protein/min)] were less than 25% of their respective WT enzyme . Conclusion Sequencing of CYP2C9 and CYP2C19 from 1013 subjects identified low frequency variants that had not been functionally characterized. In silico predictions which showed variable results, can be helpful but are not always accurate, hence functional validation is necessary for VUSs requiring protein expression and determination of kinetic parameters. Efforts to identify variation in diverse populations is resulting in a very large number of VUSs, so there is a need for the application of high throughput mutagenesis approaches and functional characterization methods to help pharmacogenomics contribute to precision medicine. Support or Funding Information NIH RO1 GM28157 and the Mayo Center for Individualized Medicine Pharmacogenomics Program.

Mendelian randomization has become a popular tool to assess causal relationships using existing observational data. While randomized controlled trials are considered the gold standard for establishing causality between exposures and outcomes, it is not always feasible to conduct a trial. Mendelian randomization is a causal inference method that uses observational data to infer causal relationships by using genetic variation as a surrogate for the exposure of interest. Publications using the approach have increased dramatically in recent years, including in the field of hepatology. In this concise review, we describe the concepts, assumptions, and interpretation of Mendelian randomization as related to studies in hepatology. We focus on the strengths and weaknesses of the approach for a non-statistical audience, using an illustrative example to assess the causal relationship between body mass index and NAFLD.

Background: Antidepressants are among the most prescribed medications in the United States. The aim of this study was to explore the prevalence of antidepressant prescriptions and investigate sex differences and age-sex interactions in adults enrolled in the Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment (RIGHT) study. Materials and Methods: We conducted a retrospective analysis of the RIGHT study. Using electronic prescriptions, we assessed 12-month prevalence of antidepressant treatment. Sex differences and age-sex interactions were evaluated using multivariable logistic regression and flexible recursive smoothing splines. Results: The sample consisted of 11,087 participants (60% women). Antidepressant prescription prevalence was 22.24% (27.96% women, 13.58% men). After adjusting for age and enrollment year, women had significantly greater odds of antidepressant prescription (odds ratio = 2.29; 95% confidence interval = 2.07, 2.54). Furthermore, selective serotonin reuptake inhibitors (SSRIs) had a significant age-sex interaction. While SSRI prescriptions in men showed a sustained decrease with age, there was no such decline for women until after reaching ∼50 years of age. There are important limitations to consider in this study. Electronic prescription data were cross-sectional; information on treatment duration or adherence was not collected; this cohort is not nationally representative; and enrollment occurred over a broad period, introducing confounding by changes in temporal prescribing practices. Conclusions: Underscored by the significant interaction between age and sex on odds of SSRI prescription, our results warrant age to be incorporated as a mediator when investigating sex differences in mental illness, especially mood disorders and their treatment.

Background The relationship between mitochondrial DNA copy number (mtDNA CN) and cardiovascular disease remains elusive. Methods and Results We performed cross‐sectional and prospective association analyses of blood‐derived mtDNA CN and cardiovascular disease outcomes in 27 316 participants in 8 cohorts of multiple racial and ethnic groups with whole‐genome sequencing. We also performed Mendelian randomization to explore causal relationships of mtDNA CN with coronary heart disease (CHD) and cardiometabolic risk factors (obesity, diabetes, hypertension, and hyperlipidemia). P <0.01 was used for significance. We validated most of the previously reported associations between mtDNA CN and cardiovascular disease outcomes. For example, 1‐SD unit lower level of mtDNA CN was associated with 1.08 (95% CI, 1.04–1.12; P <0.001) times the hazard for developing incident CHD, adjusting for covariates. Mendelian randomization analyses showed no causal effect from a lower level of mtDNA CN to a higher CHD risk (β=0.091; P =0.11) or in the reverse direction (β=−0.012; P =0.076). Additional bidirectional Mendelian randomization analyses revealed that low‐density lipoprotein cholesterol had a causal effect on mtDNA CN (β=−0.084; P <0.001), but the reverse direction was not significant ( P =0.059). No causal associations were observed between mtDNA CN and obesity, diabetes, and hypertension, in either direction. Multivariable Mendelian randomization analyses showed no causal effect of CHD on mtDNA CN, controlling for low‐density lipoprotein cholesterol level ( P =0.52), whereas there was a strong direct causal effect of higher low‐density lipoprotein cholesterol on lower mtDNA CN, adjusting for CHD status (β=−0.092; P <0.001). Conclusions Our findings indicate that high low‐density lipoprotein cholesterol may underlie the complex relationships between mtDNA CN and vascular atherosclerosis.

Breast cancer is the second-leading cause of mortality among women around the world. Ultrasound (US) is one of the noninvasive imaging modalities used to diagnose breast lesions and monitor the prognosis of cancer patients. It has the highest sensitivity for diagnosing breast masses, but it shows increased false negativity due to its high operator dependency. Underserved areas do not have sufficient US expertise to diagnose breast lesions, resulting in delayed management of breast lesions. Deep learning neural networks may have the potential to facilitate early decision-making by physicians by rapidly yet accurately diagnosing and monitoring their prognosis. This article reviews the recent research trends on neural networks for breast mass ultrasound, including and beyond diagnosis. We discussed original research recently conducted to analyze which modes of ultrasound and which models have been used for which purposes, and where they show the best performance. Our analysis reveals that lesion classification showed the highest performance compared to those used for other purposes. We also found that fewer studies were performed for prognosis than diagnosis. We also discussed the limitations and future directions of ongoing research on neural networks for breast ultrasound.