Mitochondrial Sequencing Identifies Long Non-Coding RNA Features that Promote Binding to PNPase.

Extranuclear localization of long non-coding RNAs (lncRNAs) is poorly understood. Based on machine learning evaluations, we propose a lncRNA-mitochondrial interaction pathway where Polynucleotide Phosphorylase (PNPase), through domains that provide specificity for primary sequence and secondary structure, binds nuclear-encoded lncRNAs to facilitate mitochondrial import. Using FVB/NJ mouse and human cardiac tissues, RNA from isolated subcellular compartments (cytoplasmic and mitochondrial) and crosslinked immunoprecipitate (CLIP) with PNPase within the mitochondrion were sequenced on the Illumina HiSeq and MiSeq, respectively. LncRNA sequence and structure were evaluated through supervised (Classification and Regression Trees (CART) and Support Vector Machines, (SVM)) machine learning algorithms. In HL-1 cells, qPCR of PNPase CLIP knockout mutants (KH and S1) were performed. In vitro fluorescence assays assessed PNPase RNA binding capacity and verified with PNPase CLIP. 112 (mouse) and 1,548 (human) lncRNAs were identified in the mitochondrion with Malat1 being the most highly expressed. Most non-coding RNAs binding PNPase were lncRNAs, including Malat1. LncRNA fragments bound to PNPase compared against randomly generated sequences of similar length showed stratification with SVM and CART algorithms. The lncRNAs bound to PNPase were used to create a criterion for binding, with experimental validation revealing increased binding affinity of RNA designed to bind PNPase compared to control RNA. Binding of lncRNAs to PNPase was decreased through knockout of RNA binding domains KH and S1. In conclusion, sequence and secondary structural features identified by machine learning enhance the likelihood of nuclear-encoded lncRNAs to bind to PNPase and undergo import into the mitochondrion.

A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data.

To provide accurate predictions, current machine learning-based solutions require large, manually labeled training datasets. We implement persistent homology (PH), a topological tool for studying the pattern of data, to analyze echocardiography-based strain data and differentiate between rare diseases like constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Patient population (retrospectively registered) included those presenting with heart failure due to CP (n = 51), RCM (n = 47), and patients without heart failure symptoms (n = 53). Longitudinal, radial, and circumferential strains/strain rates for left ventricular segments were processed into topological feature vectors using Machine learning PH workflow. In differentiating CP and RCM, the PH workflow model had a ROC AUC of 0.94 (Sensitivity = 92%, Specificity = 81%), compared with the GLS model AUC of 0.69 (Sensitivity = 65%, Specificity = 66%). In differentiating between all three conditions, the PH workflow model had an AUC of 0.83 (Sensitivity = 68%, Specificity = 84%), compared with the GLS model AUC of 0.68 (Sensitivity = 52% and Specificity = 76%). By employing persistent homology to differentiate the "pattern" of cardiac deformations, our machine-learning approach provides reasonable accuracy when evaluating small datasets and aids in understanding and visualizing patterns of cardiac imaging data in clinically challenging disease states.

Prediction of heart failure and all-cause mortality using cardiac ultrasomics in patients with breast cancer.

Breast cancer chemotherapy/immunotherapy can be associated with treatment-limiting cardiotoxicity. Radiomics techniques applied to ultrasound, known as ultrasomics, can be used in cardio-oncology to leverage echocardiography for added prognostic value. To utilize ultrasomics features collected prior to antineoplastic therapy to enhance prediction of mortality and heart failure (HF) in patients with breast cancer. Patients were retrospectively recruited in a study at the West Virginia University Cancer Institute. The final inclusion criteria were met by a total of 134 patients identified for the study. Patients were imaged using echocardiography in the parasternal long axis prior to receiving chemotherapy. All-cause mortality and HF, developed during treatment, were the primary outcomes. 269 features were assessed, grouped into four major classes: demographics (n = 21), heart function (n = 7), antineoplastic medication (n = 17), and ultrasomics (n = 224). Data was split into an internal training (60%, n = 81) and testing (40%, n = 53) set. Ultrasomics features augmented classification of mortality (area under the curve (AUC) 0.89 vs. 0.65, P = 0.003), when compared to demographic variables. When developing a risk prediction score for each feature category, ultrasomics features were significantly associated with both mortality (P = 0.031, log-rank test) and HF (P = 0.002, log-rank test). Further, only ultrasomics features provided significant improvement over demographic variables when predicting mortality (C-Index: 0.78 vs. 0.65, P = 0.044) and HF (C-Index: 0.77 vs. 0.60, P = 0.017), respectively. With further investigation, a clinical decision support tool could be developed utilizing routinely obtained patient data alongside ultrasomics variables to augment treatment regimens.

Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology.

Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative and, increasingly, quantitative data. These analytical approaches, however, may be significantly enhanced by applying additional methods arising from fields of mathematics such as differential geometry and algebraic topology that remain underexplored in this context. Geometry's strength lies in its ability to provide precise local measurements, such as curvature, that can be crucial for identifying abnormalities at multiple spatial levels. These measurements can augment the quantitative features extracted in conventional radiomics, leading to more nuanced diagnostics. By contrast, topology serves as a robust shape descriptor, capturing essential features such as connected components and holes. The field of topological data analysis was initially founded to explore the shape of data, with functional network connectivity in the brain being a prominent example. Increasingly, its tools are now being used to explore organizational patterns of physical structures in medical images and digitized pathology slides. By leveraging tools from both differential geometry and algebraic topology, researchers and clinicians may be able to obtain a more comprehensive, multi-layered understanding of medical images and contribute to precision medicine's armamentarium.

CT-derived pectoralis composition and incident pneumonia hospitalization using fully automated deep-learning algorithm: multi-ethnic study of atherosclerosis.

BACKGROUND: Pneumonia-related hospitalization may be associated with advanced skeletal muscle loss due to aging (i.e., sarcopenia) or chronic illnesses (i.e., cachexia). Early detection of muscle loss may now be feasible using deep-learning algorithms applied on conventional chest CT. OBJECTIVES: To implement a fully automated deep-learning algorithm for pectoralis muscle measures from conventional chest CT and investigate longitudinal associations between these measures and incident pneumonia hospitalization according to Chronic Obstructive Pulmonary Disease (COPD) status. MATERIALS AND METHODS: This analysis from the Multi-Ethnic Study of Atherosclerosis included participants with available chest CT examinations between 2010 and 2012. We implemented pectoralis muscle composition measures from a fully automated deep-learning algorithm (Mask R-CNN, built on the Faster Region Proposal Network (R-) Convolutional Neural Network (CNN) with an extension for mask identification) for two-dimensional segmentation. Associations between CT-derived measures and incident pneumonia hospitalizations were evaluated using Cox proportional hazards models adjusted for multiple confounders which include but are not limited to age, sex, race, smoking, BMI, physical activity, and forced-expiratory-volume-at-1 s-to-functional-vital-capacity ratio. Stratification analyses were conducted based on baseline COPD status. RESULTS: This study included 2595 participants (51% female; median age: 68 (IQR: 61, 76)) CT examinations for whom we implemented deep learning-derived measures for longitudinal analyses. Eighty-six incident pneumonia hospitalizations occurred during a median 6.67-year follow-up. Overall, pectoralis muscle composition measures did not predict incident pneumonia. However, in fully-adjusted models, only among participants with COPD (N = 507), CT measures like extramyocellular fat index (hazard ratio: 1.98, 95% CI: 1.22, 3.21, p value: 0.02), were independently associated with incident pneumonia. CONCLUSION: Reliable deep learning-derived pectoralis muscle measures could predict incident pneumonia hospitalization only among participants with known COPD. CLINICAL RELEVANCE STATEMENT: Pectoralis muscle measures obtainable at zero additional cost or radiation exposure from any chest CT may have independent predictive value for clinical outcomes in chronic obstructive pulmonary disease patients. KEY POINTS: •Identification of independent and modifiable risk factors of pneumonia can have important clinical impact on patients with chronic obstructive pulmonary disease. •Opportunistic CT measures of adipose tissue within pectoralis muscles using deep-learning algorithms can be quickly obtainable at zero additional cost or radiation exposure. •Deep learning-derived pectoralis muscle measurements of intermuscular fat and its subcomponents are independently associated with subsequent incident pneumonia hospitalization.

Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus.

Speckle tracking echocardiography (STE) has been utilized to evaluate independent spatial alterations in the diabetic heart, but the progressive manifestation of regional and segmental cardiac dysfunction in the type 2 diabetic (T2DM) heart remains understudied. Therefore, the objective of this study was to elucidate if machine learning could be utilized to reliably describe patterns of the progressive regional and segmental dysfunction that are associated with the development of cardiac contractile dysfunction in the T2DM heart. Non-invasive conventional echocardiography and STE datasets were utilized to segregate mice into two pre-determined groups, wild-type and Db/Db, at 5, 12, 20, and 25 weeks. A support vector machine model, which classifies data using a single line, or hyperplane, that best separates each class, and a ReliefF algorithm, which ranks features by how well each feature lends to the classification of data, were used to identify and rank cardiac regions, segments, and features by their ability to identify cardiac dysfunction. STE features more accurately segregated animals as diabetic or non-diabetic when compared with conventional echocardiography, and the ReliefF algorithm efficiently ranked STE features by their ability to identify cardiac dysfunction. The Septal region, and the AntSeptum segment, best identified cardiac dysfunction at 5, 20, and 25 weeks, with the AntSeptum also containing the greatest number of features which differed between diabetic and non-diabetic mice. Cardiac dysfunction manifests in a spatial and temporal fashion, and is defined by patterns of regional and segmental dysfunction in the T2DM heart which are identifiable using machine learning methodologies. Further, machine learning identified the Septal region and AntSeptum segment as locales of interest for therapeutic interventions aimed at ameliorating cardiac dysfunction in T2DM, suggesting that machine learning may provide a more thorough approach to managing contractile data with the intention of identifying experimental and therapeutic targets.

Topological data analysis in medical imaging: current state of the art.

Machine learning, and especially deep learning, is rapidly gaining acceptance and clinical usage in a wide range of image analysis applications and is regarded as providing high performance in detecting anatomical structures and identification and classification of patterns of disease in medical images. However, there are many roadblocks to the widespread implementation of machine learning in clinical image analysis, including differences in data capture leading to different measurements, high dimensionality of imaging and other medical data, and the black-box nature of machine learning, with a lack of insight into relevant features. Techniques such as radiomics have been used in traditional machine learning approaches to model the mathematical relationships between adjacent pixels in an image and provide an explainable framework for clinicians and researchers. Newer paradigms, such as topological data analysis (TDA), have recently been adopted to design and develop innovative image analysis schemes that go beyond the abilities of pixel-to-pixel comparisons. TDA can automatically construct filtrations of topological shapes of image texture through a technique known as persistent homology (PH); these features can then be fed into machine learning models that provide explainable outputs and can distinguish different image classes in a computationally more efficient way, when compared to other currently used methods. The aim of this review is to introduce PH and its variants and to review TDA's recent successes in medical imaging studies.

Lung-gut axis of microbiome alterations following co-exposure to ultrafine carbon black and ozone.

BACKGROUND: Microbial dysbiosis is a potential mediator of air pollution-induced adverse outcomes. However, a systemic comparison of the lung and gut microbiome alterations and lung-gut axis following air pollution exposure is scant. In this study, we exposed male C57BL/6J mice to inhaled air, CB (10 mg/m3), O3 (2 ppm) or CB + O3 mixture for 3 h/day for either one day or four consecutive days and were euthanized 24 h post last exposure. The lung and gut microbiome were quantified by 16 s sequencing. RESULTS: Multiple CB + O3 exposures induced an increase in the lung inflammatory cells (neutrophils, eosinophils and B lymphocytes), reduced absolute bacterial load in the lungs and increased load in the gut. CB + O3 exposure was more potent as it decreased lung microbiome alpha diversity just after a single exposure. CB + O3 co-exposure uniquely increased Clostridiaceae and Prevotellaceae in the lungs. Serum short chain fatty acids (SCFA) (acetate and propionate) were increased significantly only after CB + O3 co-exposure. A significant increase in SCFA producing bacterial families (Ruminococcaceae, Lachnospiraceae, and Eubacterium) were also observed in the gut after multiple exposures. Co-exposure induced significant alterations in the gut derived metabolite receptors/mediator (Gcg, Glp-1r, Cck) mRNA expression. Oxidative stress related mRNA expression in lungs, and oxidant levels in the BALF, serum and gut significantly increased after CB + O3 exposures. CONCLUSION: Our study confirms distinct gut and lung microbiome alterations after CB + O3 inhalation co-exposure and indicate a potential homeostatic shift in the gut microbiome to counter deleterious impacts of environmental exposures on metabolic system.

Distal cholangiocarcinoma: case report and brief review of the literature.

Adenocarcinomas of the distal bile duct are traditionally classified as either pancreatobiliary or intestinal type, with pancreatic adenocarcinoma and cholangiocarcinoma included within the former classification. Cholangiocarcinoma is a rare and deadly malignancy that occurs within three clinically defined regions: intrahepatic, perihilar, and in the distal bile duct. We present a 68-year-old male with a past medical history of human immunodeficiency virus, hepatitis B, hypertension, and hyperlipidemia who presented to the emergency department with a 3-week history of diarrhea, diffuse abdominal pain, malaise, and nausea. Contrast enhanced CT of the abdomen and pelvis revealed a periampullary mass. Endoscopic ultrasound biopsy was performed, with histopathology suggestive of distal cholangiocarcinoma. Endoscopic retrograde cholangiopancreatography was utilized for palliative stent placement until patient received pancreaticoduodenectomy (ie, Whipple procedure). In this case, we highlight the imaging presentation and histopathology of a distal cholangiocarcinoma.

NUDT7 regulates total hepatic CoA levels and the composition of the intestinal bile acid pool in male mice fed a Western diet.

Nudix hydrolase 7 (NUDT7) is an enzyme that hydrolyzes CoA species, is highly expressed in the liver, and resides in the peroxisomes. Peroxisomes are organelles where the preferential oxidation of dicarboxylic fatty acids occurs and where the hepatic synthesis of the primary bile acids cholic acid and chenodeoxycholic acid is completed. We previously showed that liver-specific overexpression of NUDT7 affects peroxisomal lipid metabolism but does not prevent the increase in total liver CoA levels that occurs during fasting. We generated Nudt7-/- mice to further characterize the role that peroxisomal (acyl-)CoA degradation plays in the modulation of the size and composition of the acyl-CoA pool and in the regulation of hepatic lipid metabolism. Here, we show that deletion of Nudt7 alters the composition of the hepatic acyl-CoA pool in mice fed a low-fat diet, but only in males fed a Western diet does the lack of NUDT7 activity increase total liver CoA levels. This effect is driven by the male-specific accumulation of medium-chain dicarboxylic acyl-CoAs, which are produced from the ß-oxidation of dicarboxylic fatty acids. We also show that, under conditions of elevated synthesis of chenodeoxycholic acid derivatives, Nudt7 deletion promotes the production of tauromuricholic acid, decreasing the hydrophobicity index of the intestinal bile acid pool and increasing fecal cholesterol excretion in male mice. These findings reveal that NUDT7-mediated hydrolysis of acyl-CoA pathway intermediates in liver peroxisomes contributes to the regulation of dicarboxylic fatty acid metabolism and the composition of the bile acid pool.

Eosinophilic myocarditis: Case report and brief review of the literature.

Eosinophilic myocarditis (EM) is a cardiac manifestation of hypereosinophilic syndrome with a high mortality rate. EM shares imaging features similar to other restrictive cardiopathies, and include patchy intramural late gadolinium enhancement on cardiac magnetic resonance with or without presence of biventricular thrombus. Diagnosis is confirmed on histopathology, and is the current gold standard. Here we report clinical presentation and imaging findings of EM in a 70-year-old woman who presented with fever and chills.

N6-methyladenosine (M6A) in fetal offspring modifies mitochondrial gene expression following gestational nano-TiO2 inhalation exposure.

N6-methyladenosine (m6A) is the most prominent epitranscriptomic modification to RNA in eukaryotes, but it's role in adaptive changes within the gestational environment are poorly understood. We propose that gestational exposure to nano titanium dioxide (TiO2) contributes to cardiac m6A methylation in fetal offspring and influences mitochondrial gene expression. 10-week-old pregnant female FVB/NJ wild-type mice underwent 6 nonconsecutive days of whole-body inhalation exposure beginning on gestational day (GD) 5. Mice were exposed to filtered room air or nano-TiO2 with a target aerosol mass concentration of 12 mg/m3. At GD 15 mice were humanely killed and cardiac RNA and mitochondrial proteins extracted. Immunoprecipitation with m6A antibodies was performed followed by sequencing of immunoprecipitant (m6A) and input (mRNA) on the Illumina NextSeq 2000. Protein extraction, preparation, and LC-MS/MS were used for mitochondrial protein quantification. There were no differences in maternal or fetal pup weights, number of pups, or pup heart weights between exposure and control groups. Transcriptomic sequencing revealed 3648 differentially expressed mRNA in nano-TiO2 exposed mice (Padj ≤ 0.05). Transcripts involved in mitochondrial bioenergetics were significantly downregulated (83 of 85 genes). 921 transcripts revealed significant m6A methylation sites (Padj ≤ 0.10). 311 of the 921 mRNA were identified to have both 1) significantly altered expression and 2) differentially methylated sites. Mitochondrial proteomics revealed decreased expression of ATP Synthase subunits in the exposed group (P ≤ 0.05). The lack of m6A modifications to mitochondrial transcripts suggests a mechanism for decreased transcript stability and reduced protein expression due to gestational nano-TiO2 inhalation exposure.

Ultrasonic Texture Features for Assessing Cardiac Remodeling and Dysfunction.

BACKGROUND: Changes in cardiac size, myocardial mass, cardiomyocyte appearance, and, ultimately, the function of the entire organ are interrelated features of cardiac remodeling that profoundly affect patient outcomes. OBJECTIVES: This study proposes that the application of radiomics for extracting cardiac ultrasonic textural features (ultrasomics) can aid rapid, automated assessment of left ventricular (LV) structure and function without requiring manual measurements. METHODS: This study developed machine-learning models using cardiac ultrasound images from 1,915 subjects in 3 clinical cohorts: 1) an expert-annotated cardiac point-of-care-ultrasound (POCUS) registry (n = 943, 80% training/testing and 20% internal validation); 2) a prospective POCUS cohort for external validation (n = 275); and 3) a prospective external validation on high-end ultrasound systems (n = 484). In a type 2 diabetes murine model, echocardiography of wild-type (n = 10) and Leptr-/- (n = 8) mice were assessed longitudinally at 3 and 25 weeks, and ultrasomics features were correlated with histopathological features of hypertrophy. RESULTS: The ultrasomics model predicted LV remodeling in the POCUS and high-end ultrasound external validation studies (area under the curve: 0.78 [95% CI: 0.68-0.88] and 0.79 [95% CI: 0.73-0.86], respectively). Similarly, the ultrasomics model predicted LV remodeling was significantly associated with major adverse cardiovascular events in both cohorts (P < 0.0001 and P = 0.0008, respectively). Moreover, on multivariate analysis, the ultrasomics probability score was an independent echocardiographic predictor of major adverse cardiovascular events in the high-end ultrasound cohort (HR: 8.53; 95% CI: 4.75-32.1; P = 0.0003). In the murine model, cardiomyocyte hypertrophy positively correlated with 2 ultrasomics biomarkers (R2 = 0.57 and 0.52, Q < 0.05). CONCLUSIONS: Cardiac ultrasomics-based biomarkers may aid development of machine-learning models that provide an expert-level assessment of LV structure and function.

Aortic root abscess from Enterococcus faecalis infective endocarditis: Case report and brief review of the literature.

Infective endocarditis is a life-threatening disease that is associated with a significant risk of morbidity and mortality. One of the most serious complications of infective endocarditis is perivalvular and aortic root abscess formation. Due to the high propensity for rupture and continued spread within the aorta and surrounding organs, surgical management is recommended and can improve long-term survival. Imaging plays a critical role in diagnosis of infective endocarditis and its sequalae. Initial workup includes transthoracic and/or transesophageal echocardiography, as part of the modified Duke criteria for diagnosing infective endocarditis. If paravalvular abscesses are suspected, CTA chest can characterize invasion and spread of the abscess. Here, we present a 55-year-old male with recurrent infective endocarditis with an aortic root abscess. The abscess was first identified through transesophageal echocardiography and subsequently confirmed using CTA chest. Surgically, the patient required pulmonic and aortic valve replacement along with aortic root reconstruction.

Traumatic right ventricular rupture: Case report and brief review of the literature.

Rupture of the right ventricular (RV) myocardium is associated with serious morbidity and mortality. Under very rare conditions, a tear in the ventricular wall can lead to the formation of a pseudoaneurysm: an external outpouching of the ventricle that is stabilized by the pericardium, thrombus formation, and/or adhesions. Here, we present a 75-year-old man with RV free wall rupture with pseudoaneurysm following a motor vehicle collision. With concerns for blunt cardiac trauma, initial CTA chest revealed focal outpouching and extension of contrast outside of the confines of the RV chamber, compatible with pseudoaneurysm formation. In this case, conservative management of the pseudoaneurysm was preferred over surgical management, due to the thin RV free wall and present comorbid conditions. We highlight how CTA chest offers a reliable tool for tracking the stability of pseudoaneurysms in the RV and can guide clinical management through directing treatment strategies and appropriate follow-up intervals.

Left Ventricular Segmental Strain Identifies Unique Myocardial Deformation Patterns After Intrinsic and Extrinsic Stressors in Mice.

We used segmental strain analysis to evaluate whether intrinsic (diet-induced obesity [DIO]) and extrinsic (unpredictable chronic mild stress [UCMS]) stressors can alter deformational patterns of the left ventricle. Six-week-old male C57BL/6J mice were randomized into the lean or obese group (n = 24/group). Mice underwent 12 wk of DIO with a high-fat diet (HFD). At 18 wk, lean and obese mice were further randomized into UCMS and non-UCMS groups (UCMS, 7 h/d, 5 d/wk, for 8 wk). Echocardiography was performed at baseline (6 wk), post-HFD (18 wk) and post-UCMS (26 wk). Machine learning was applied to the DIO and UCMS groups. There was robust predictive accuracy (area under the receiver operating characteristic curve [AUC] = 0.921) when comparing obese with lean mice, with radial strain changes in the lateral (-64%, p ≤ 0.001) and anterior free (-53%, p < 0.001) walls being most informative. The ability to predict mice that underwent UCMS, irrespective of diet, was assessed (AUC = 0.886), revealing longitudinal strain rate of the anterior midwall and radial strain of the posterior septal wall as the top features. The wall segments indicate a predilection for changes in deformation patterns to the free wall (DIO) and septal wall (UCMS), indicating disease-specific alterations to the myocardium.