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Tam, K.

Publications and source records attributed to Tam, K..

2 recordsLinked to original sources

Two-hit mouse model of heart failure with preserved ejection fraction combining diet-induced obesity and renin-mediated hypertension

Heart failure with preserved ejection fraction (HFpEF) is increasingly common but its pathogenesis is poorly understood. The ability to assess genetic and pharmacologic interventions is hampered by the lack of robust preclinical mouse models of HFpEF. We have developed a novel "2-hit" model, which combines obesity and insulin resistance with chronic pressure overload to recapitulate clinical features of HFpEF. C57BL6/NJ mice fed a high fat diet for >10 weeks were administered an AAV8-driven vector resulting in constitutive overexpression of mouse Renin1d. Control mice, HFD only, Renin only and HFD-Renin (aka "HFpEF") littermates underwent a battery of cardiac and extracardiac phenotyping. HFD-Renin mice demonstrated obesity and insulin resistance, a 2-3-fold increase in circulating renin levels that resulted in 30-40% increase in left ventricular hypertrophy, preserved systolic function, and diastolic dysfunction indicated by altered E/e, IVRT, and strain measurements; increased left atrial mass; elevated natriuretic peptides; and exercise intolerance. Transcriptomic and metabolomic profiling of HFD-Renin myocardium demonstrated upregulation of pro-fibrotic pathways and downregulation of metabolic pathways, in particular branched chain amino acid catabolism, similar to findings in human HFpEF. Treatment of these mice with the sodium-glucose cotransporter 2 inhibitor empagliflozin, an effective but incompletely understood HFpEF therapy, improved exercise tolerance, left heart enlargement, and insulin homeostasis. The HFD-Renin mouse model recapitulates key features of human HFpEF and will enable studies dissecting the contribution of individual pathogenic drivers to this complex syndrome. Addition of HFD-Renin mice to the preclinical HFpEF model platform allows for orthogonal studies to increase validity in assessment of interventions. NEW & NOTEWORTHYHeart failure with preserved ejection fraction (HFpEF) is a complex disease to study due to limited preclinical models. We rigorously characterize a new two-hit HFpEF mouse model, which allows for dissecting individual contributions and synergy of major pathogenic drivers, hypertension and diet-induced obesity. The results are consistent and reproducible in two independent laboratories. This high-fidelity pre-clinical model increases the available, orthogonal models needed to improve our understanding of the causes and assessment treatments for HFpEF.

physiology↗

AI-Enabled Classification of Head and Neck Tumors using Convolutional Neural Networks with Dynamic Optical Contrast Imaging

BackgroundRecent advances in artificial intelligence (AI) in the field of imaging have resulted in new opportunities for automated tumor detection and margin assessment. In particular, AI deep learning techniques such as the Convolutional Neural Network (CNN) have greatly advanced the field of computer vision. Here we introduce the application of a CNN model for use with Dynamic Optical Contrast Imaging (DOCI), an imaging technique developed by our group that creates a unique molecular signature on tissue targets by obtaining the autofluorescence decay of several spectral bands in the UV-Vis range. Methods21 patients undergoing surgical resection for tonsillar squamous cell carcinoma (SCC) were identified on a prospective basis. DOCI images were analyzed and compared to the pathology results as ground truth. A CNN model was used to segment sections of DOCI images and provide a percentage chance of tumor presence, allowing for automated tumor margin delineation without a-priori knowledge of the tumor tissue composition. ResultsCNN outputs yielded a 99.98% confidence in classifying non-tumor tissue and 76.02% confidence in classifying tumor tissue. ConclusionsOur results indicate that a CNN-based classification model for DOCI allows for real-time analysis of tissue, providing improved sensitivity and accuracy of determining true margins and thus enabling the head and neck cancer surgeon to save healthy tissue and improve patient outcomes.

bioinformatics↗