Gene therapy for heart failure: A novel treatment for the age old disease.

Across the globe, cardiovascular disease (CVD) is the leading cause of mortality. According to reports, around 6.2 million people in the United states have heart failure. Current standards of care for heart failure can delay but not prevent progression of disease. Gene therapy is one of the novel treatment modalities that promises to fill this limitation in the current standard of care for Heart Failure. In this paper we performed an extensive search of the literature on various advances made in gene therapy for heart failure till date. We review the delivery methods, targets, current applications, trials, limitations and feasibility of gene therapy for heart failure. Various methods have been employed till date for administering gene therapies including but not limited to arterial and venous infusion, direct myocardial injection and pericardial injection. Various strategies such as AC6 expression, S100A1 protein upregulation, VEGF-B and SDF-1 gene therapy have shown promise in recent preclinical trials. Furthermore, few studies even show that stimulation of cardiomyocyte proliferation such as through cyclin A2 overexpression is a realistic avenue. However, a considerable number of obstacles need to be overcome for gene therapy to be part of standard treatment of care such as definitive choice of gene, gene delivery systems and a suitable method for preclinical trials and clinical trials on patients. Considering the challenges and taking into account the recent advances in gene therapy research, there are encouraging signs to indicate gene therapy for heart failure to be a promising treatment modality for the future. However, the time and feasibility of this option remains in a situation of balance.

Machine Learning Techniques Differentiate Alcohol-Associated Hepatitis From Acute Cholangitis in Patients With Systemic Inflammation and Elevated Liver Enzymes.

OBJECTIVE: To develop machine learning algorithms (MLAs) that can differentiate patients with acute cholangitis (AC) and alcohol-associated hepatitis (AH) using simple laboratory variables. METHODS: A study was conducted of 459 adult patients admitted to Mayo Clinic, Rochester, with AH (n=265) or AC (n=194) from January 1, 2010, to December 31, 2019. Ten laboratory variables (white blood cell count, hemoglobin, mean corpuscular volume, platelet count, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, total bilirubin, direct bilirubin, albumin) were collected as input variables. Eight supervised MLAs (decision tree, naive Bayes, logistic regression, k-nearest neighbor, support vector machine, artificial neural networks, random forest, gradient boosting) were trained and tested for classification of AC vs AH. External validation was performed with patients with AC (n=213) and AH (n=92) from the MIMIC-III database. A feature selection strategy was used to choose the best 5-variable combination. There were 143 physicians who took an online quiz to distinguish AC from AH using the same 10 laboratory variables alone. RESULTS: The MLAs demonstrated excellent performances with accuracies up to 0.932 and area under the curve (AUC) up to 0.986. In external validation, the MLAs showed comparable accuracy up to 0.909 and AUC up to 0.970. Feature selection in terms of information-theoretic measures was effective, and the choice of the best 5-variable subset produced high performance with an AUC up to 0.994. Physicians did worse, with mean accuracy of 0.790. CONCLUSION: Using a few routine laboratory variables, MLAs can differentiate patients with AC and AH and may serve valuable adjunctive roles in cases of diagnostic uncertainty.

Explanatory Analysis of a Machine Learning Model to Identify Hypertrophic Cardiomyopathy Patients from EHR Using Diagnostic Codes.

Hypertrophic cardiomyopathy (HCM) is a genetic heart disease that is the leading cause of sudden cardiac death (SCD) in young adults. Despite the well-known risk factors and existing clinical practice guidelines, HCM patients are underdiagnosed and sub-optimally managed. Developing machine learning models on electronic health record (EHR) data can help in better diagnosis of HCM and thus improve hundreds of patient lives. Automated phenotyping using HCM billing codes has received limited attention in the literature with a small number of prior publications. In this paper, we propose a novel predictive model that helps physicians in making diagnostic decisions, by means of information learned from historical data of similar patients. We assembled a cohort of 11,562 patients with known or suspected HCM who have visited Mayo Clinic between the years 1995 to 2019. All existing billing codes of these patients were extracted from the EHR data warehouse. Target ground truth labeling for training the machine learning model was provided by confirmed HCM diagnosis using the gold standard imaging tests for HCM diagnosis echocardiography (echo), or cardiac magnetic resonance (CMR) imaging. As the result, patients were labeled into three categories of "yes definite HCM", "no HCM phenotype", and "possible HCM" after a manual review of medical records and imaging tests. In this study, a random forest was adopted to investigate the predictive performance of billing codes for the identification of HCM patients due to its practical application and expected accuracy in a wide range of use cases. Our model performed well in finding patients with "yes definite", "possible" and "no" HCM with an accuracy of 71%, weighted recall of 70%, the precision of 75%, and weighted F1 score of 72%. Furthermore, we provided visualizations based on multidimensional scaling and the principal component analysis to provide insights for clinicians' interpretation. This model can be used for the identification of HCM patients using their EHR data, and help clinicians in their diagnosis decision making.

Novel Quantitative Analytical Approaches for Rotor Identification and Associated Implications for Mapping.

GOAL: Clinical studies identifying rotors and confirming these sites for ablation in treating cardiac arrhythmias have had inconsistent results with the currently available analysis techniques. The aim of this study is to evaluate four new signal analysis approaches-multiscale frequency (MSF), Shannon entropy (SE), Kurtosis (Kt), and multiscale entropy (MSE)-in their ability to identify the pivot point of rotors. METHODS: Optical mapping movies of ventricular tachycardia were used to evaluate the performance and robustness of SE, Kt, MSF, and MSE techniques with respect to several clinical limitations: decreased time duration, reduced spatial resolution, and the presence of meandering rotors. To quantitatively assess the robustness of the four techniques, results were compared to the "true" rotor(s) identified using optical mapping-based phase maps. RESULTS: The results demonstrate that MSF, Kt, and MSE accurately identified both stationary and meandering rotors. In addition, these techniques remained accurate under simulated clinical limitations: shortened electrogram duration and decreased spatial resolution. Artifacts mildly affected the performance of MSF, Kt, and MSE, but strongly impacted the performance of SE. CONCLUSION: These results motivate further validation using intracardiac electrograms to see if these approaches can map rotors in a clinical setting and whether they apply to more complex arrhythmias including atrial or ventricular fibrillation. SIGNIFICANCE: New techniques providing more accurate rotor localization could improve characterization of arrhythmias and, in turn, offer a means to accurately evaluate whether rotor ablation is a viable and effective treatment for chaotic cardiac arrhythmias.

Should there be sex-specific criteria for the diagnosis and treatment of heart failure?

All-cause mortality from cardiovascular disease is declining in the USA. However, there remains a significant difference in risk factors for disease and in mortality between men and women. For example, prevalence and outcomes for heart failure with preserved ejection fraction differ between men and women. The reasons for these differences are multifactorial, but reflect, in part, an incomplete understanding of sex differences in the etiology of cardiovascular diseases and a failure to account for sex differences in pre-clinical studies including those designed to develop new diagnostic and treatment modalities. This review focuses on the underlying physiology of these sex differences and provides evidence that inclusion of female animals in pre-clinical studies of heart failure and in development of imaging modalities to assess cardiac function might provide new information from which one could develop sex-specific diagnostic criteria and approaches to treatment.

Ectopic expression of miR160 results in auxin hypersensitivity, cytokinin hyposensitivity, and inhibition of symbiotic nodule development in soybean.

Symbiotic root nodules in leguminous plants result from interaction between the plant and nitrogen-fixing rhizobia bacteria. There are two major types of legume nodules, determinate and indeterminate. Determinate nodules do not have a persistent meristem, while indeterminate nodules have a persistent meristem. Auxin is thought to play a role in the development of both these types of nodules. However, inhibition of rootward auxin transport at the site of nodule initiation is crucial for the development of indeterminate nodules but not determinate nodules. Using the synthetic auxin-responsive DR5 promoter in soybean (Glycine max), we show that there is relatively low auxin activity during determinate nodule initiation and that it is restricted to the nodule periphery subsequently during development. To examine if and what role auxin plays in determinate nodule development, we generated soybean composite plants with altered sensitivity to auxin. We overexpressed microRNA393 to silence the auxin receptor gene family, and these roots were hyposensitive to auxin. These roots nodulated normally, suggesting that only minimal/reduced auxin signaling is required for determinate nodule development. We overexpressed microRNA160 to silence a set of repressor auxin response factor transcription factors, and these roots were hypersensitive to auxin. These roots were not impaired in epidermal responses to rhizobia but had significantly reduced nodule primordium formation, suggesting that auxin hypersensitivity inhibits nodule development. These roots were also hyposensitive to cytokinin and had attenuated expression of key nodulation-associated transcription factors known to be regulated by cytokinin. We propose a regulatory feedback loop involving auxin and cytokinin during nodulation.

Calibration and Evaluation of a Magnetically Tracked ICE Probe for Guidance of Left Atrial Ablation Therapy.

The novel prototype system for advanced visualization for image-guided left atrial ablation therapy developed in our laboratory permits ready integration of multiple imaging modalities, surgical instrument tracking, interventional devices and electro-physiologic data. This technology allows subject-specific procedure planning and guidance using 3D dynamic, patient-specific models of the patient's heart, augmented with real-time intracardiac echocardiography (ICE). In order for the 2D ICE images to provide intuitive visualization for accurate catheter to surgical target navigation, the transducer must be tracked, so that the acquired images can be appropriately presented with respect to the patient-specific anatomy. Here we present the implementation of a previously developed ultrasound calibration technique for a magnetically tracked ICE transducer, along with a series of evaluation methods to ensure accurate imaging and faithful representation of the imaged structures. Using an engineering-designed phantom, target localization accuracy is assessed by comparing known target locations with their transformed locations inferred from the tracked US images. In addition, the 3D volume reconstruction accuracy is also estimated by comparing a truth volume to that reconstructed from sequential 2D US images. Clinically emulating validation studies are conducted using a patient-specific left atrial phantom. Target localization error of clinically-relevant surgical targets represented by nylon fiducials implanted within the endocardial wall of the phantom was assessed. Our studies have demonstrated 2.4 ± 0.8 mm target localization error in the engineering-designed evaluation phantoms, 94.8 ± 4.6 % volume reconstruction accuracy, and 3.1 ± 1.2 mm target localization error in the left atrial-mimicking phantom. These results are consistent with those disseminated in the literature and also with the accuracy constraints imposed by the employed technology and the clinical application.