Biology subjects

Yousefi, S.

Publications and source records attributed to Yousefi, S..

3 recordsLinked to original sources

bioRxiv2018

Gold Nanoprisms as Optical Coherence Tomography Contrast Agents in the Second Near Infrared Window for Enhanced Angiography in Live Animals

Optical coherence tomography angiography (OCTA) is an important tool for investigating vascular networks and microcirculation in living tissue. Traditional OCTA detects blood vessels via intravascular dynamic scattering signals derived from the movements of red blood cells (RBCs). However, the low hematocrit and long latency between RBCs in capillaries makes these OCTA signals discontinuous, leading to incomplete mapping of the vascular networks. OCTA imaging of microvascular circulation is particularly challenging in tumors due to the abnormally slow blood flow in angiogenic tumor vessels and strong attenuation of light by tumor tissue. Here we demonstrate in vivo that gold nanoprisms (GNPRs) can be used as OCT contrast agents working in the second near infrared window, significantly enhancing the dynamic scattering signals in microvessels and improving the sensitivity of OCTA in skin tissue and melanoma tumors in live mice. This is the first demonstration that nanoparticle-based OCT contrast agent work in vivo in the second near infrared window, which allows deeper imaging depth by OCT. With GNPRs as contrast agents, the post-injection OCT angiograms showed 41% and 59% more microvasculature than pre-injection angiograms in healthy mouse skin and melanoma tumors, respectively. By enabling better characterization of microvascular circulation in vivo, GNPR-enhanced OCTA could lead to better understanding of vascular functions during pathological conditions, more accurate measurements of therapeutic response, and improved patient prognoses.

bioengineering↗

bioRxiv2017

Predicting cancer outcomes from histology and genomics using convolutional networks

Cancer histology reflects underlying molecular processes and disease progression, and contains rich phenotypic information that is predictive of patient outcomes. In this study, we demonstrate a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how this approach can integrate information from both histology images and genomic biomarkers to predict time-to-event patient outcomes, and demonstrate performance surpassing the current clinical paradigm for predicting the survival of patients diagnosed with glioma. We also provide techniques to visualize the tissue patterns learned by these deep learning survival models, and establish a framework for addressing intratumoral heterogeneity and training data deficits.

bioinformatics↗

bioRxiv2017

Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models

Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated by these platforms, and rely on experts to hand-select a small number of features for training prediction models. In this paper, we demonstrate how deep learning and Bayesian optimization methods that have been remarkably successful in general high-dimensional prediction tasks can be adapted to the problem of predicting cancer outcomes. We perform an extensive comparison of Bayesian optimized deep survival models and other state of the art machine learning methods for survival analysis, and describe a framework for interpreting deep survival models using a risk backpropagation technique. Finally, we illustrate that deep survival models can successfully transfer information across diseases to improve prognostic accuracy. We provide an open-source software implementation of this framework called SurvivalNet that enables automatic training, evaluation and interpretation of deep survival models.

bioinformatics↗