Ak Dutta Anatomy

[Jules Altier]

Whatdo proteins, DNA, and RNA look like? Where do these molecules fit in your body and how do they work? This seminar will introduce you to the basics of structural biology using human anatomy, physiology, and disease as themes.

The focus of this course will be to understand the structures and functions of proteins that play key roles in Cancer Biology. Student learning and discussions will focus on molecular mechanisms of causes and treatments of human cancers. During the first half of the semester, students will learn the fundamentals of structural biology -how proteins, DNA, and RNA are shaped and how their three-dimensional structures are experimentally determined. They will be introduced to specific enzymes, receptors, and signaling molecules (such as Kinases and G-Protein Coupled Receptors or GPCRs) that perform important functions and are impacted in cancers. Students will learn to appreciate how knowledge about the structures of relevant molecules can play an important role in understanding their biological functions and in turn enable validation of drug targets. Through the second half of the seminar, students will conduct supervised research on contemporary ideas concerning the precise molecular mechanism of action of small-molecule drugs currently used to treat human cancers.

Shuchismita Dutta, Ph.D. is a structural biologist with training in X-ray crystallography and bioinformatics. She is an Associate Research Professor in the Institute for Quantitative Biomedicine at Rutgers, the Scientific Educational Development Lead at the RCSB Protein Data Bank, and a Member of the Cancer Institute of NJ. She has been teaching a wide range of audiences about visualization of structural data for over 15 years. Dutta has extensive experience with curating and using structural data and has been involved in research and management of several data remediation projects organized by the worldwide PDB. About 14 years ago she initiated the honors seminar titled Molecular View of Human Anatomy. She has also collaborated with educators, scientists and clinicians to develop curricular modules for learning about biological molecules in general, and also molecules related to specific global health topics (such as HIV/AIDS, Diabetes, and Antimicrobial resistance, COVID-19). Dutta has authored many scholarly and scientific articles and continues to train a wide range of audiences in promoting molecular structural view of biology and medicine.

Stephen K. Burley, M.D., D.Phil.is an expert in structural biology, proteomics, bioinformatics, structure/fragment-based drug discovery, and clinical medicine/oncology. He is the Founding Director of the Institute for Quantitative Biomedicine at Rutgers, The State University of New Jersey. He currently serves as University Professor and Henry Rutgers Chair; Director, RCSB Protein Data Bank; and is a Member of Rutgers Cancer Institute of New Jersey. From 2008 to 2012, Burley was a Distinguished Lilly Research Scholar in Lilly Research Laboratories. Prior to joining Lilly, Burley served as the Chief Scientific Officer and Senior Vice President of SGX Pharmaceuticals, Inc., a publicly traded biotechnology company that was acquired by Lilly in 2008. Until 2002, Burley was the Richard M. and Isabel P. Furlaud Professor at The Rockefeller University, and an Investigator in the Howard Hughes Medical Institute. Burley has authored/coauthored more than 250 scholarly scientific articles. He is a Fellow of the Royal Society of Canada and of the New York Academy of Sciences.

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A key element of contact lens practice involves clinical evaluation of anterior eye health, including the cornea and limbus, conjunctiva and sclera, eyelids and eyelashes, lacrimal system and tear film. This report reviews the fundamental anatomy and physiology of these structures, including the vascular supply, venous drainage, lymphatic drainage, sensory innervation, physiology and function. This is the foundation for considering the potential interactions with, and effects of, contact lens wear on the anterior eye. This information is not consistently published as academic research and this report provides a synthesis from all available sources. With respect to terminology, the report aims to promote the consistent use of nomenclature in the field, and generally adopts anatomical terms recommended by the Federative Committee for Anatomical Terminology. Techniques for the examination of the ocular surface are also discussed.

Background: The application of deep learning on medical imaging is growing in prevalence in the recent literature. One of the most studied areas is coronary artery disease (CAD). Imaging of coronary artery anatomy is fundamental, which has led to a high number of publications describing a variety of techniques. The aim of this systematic review is to review the evidence behind the accuracy of deep learning applications in coronary anatomy imaging.

Methods: The search for the relevant studies, which applied deep learning on coronary anatomy imaging, was performed in a systematic approach on MEDLINE and EMBASE databases, followed by reviewing of abstracts and full texts. The data from the final studies was retrieved using data extraction forms. A meta-analysis was performed on a subgroup of studies, which looked at fractional flow reserve (FFR) prediction. Heterogeneity was tested using tau2, I2 and Q tests. Finally, a risk of bias was performed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) approach.

Conclusions: Deep learning has been used in many applications on coronary anatomy imaging, most of which are yet to be externally validated and prepared for clinical use. The performance of deep learning, especially CNN models, proved to be powerful and some applications have already translated into medical practice, such as computed tomography (CT)-FFR. These applications have the potential to translate technology into better care of CAD patients.

Coronary artery disease (CAD) is considered a leading cause of death and hospitalisation in high-income countries, and worldwide (1). The progressive nature of coronary atherosclerosis is the main underlying pathological process. Therefore, it is essential to have timely diagnosis of CAD to aid the management of patients and reduce both morbidity and mortality.

The last two decades have witnessed significant advancements in CAD imaging, from functional assessment of coronary artery stenoses and how they impact on the myocardium at stress and rest, using cardiac magnetic resonance (CMR), myocardial perfusion scintigraphy (MPS), and echocardiography, to anatomical assessment by means of coronary computed tomography angiography (CCTA) and invasive X-rays coronary angiography.

Computer vision technology on the other hand is going through an exciting era following the revolution of deep learning and artificial intelligence (AI) algorithms. CAD imaging is one of the key applications which has been targeted by many computer vision experts and deep learning practitioners.

There has been an explosion in the number of deep learning publications in CAD over the recent years with a focus on atherosclerosis and coronary anatomy imaging. The wide range of methodology presented in the recent literature opened the door for applications in various coronary artery imaging modalities.

This review aims to unravel this challenge by summarising the new information we gained so far in this field, evaluating the performance of the presented deep learning algorithms, and drawing some conclusions on potential meaningful applications. We present the following article in accordance with the PRISMA reporting checklist (available at -22-36/rc).

This review follows the Cochrane Review structure of diagnostic test accuracy (DTA) (2). The umbrella protocol for this systematic review is registered in the International Prospective Register of Systematic Reviews (PROSPERO, CRD42020204164), and reported according to PRISMA guidelines. All searching activities were performed by two independent reviewers (EA and UD), with divergences solved after consensus.

Without restrictions on minimal sample sizes or recruitment process, both prospective and retrospective studies were included. The included studies had participants with known or suspected CAD who had atherosclerosis imaging (invasive and non-invasive) with the application of deep learning technology, and compared with the gold standard (reference) test used in clinical practice.

Competitions presented in conferences on deep learning techniques, such as at the Medical Image Computing and Computer Assisted Intervention (MICCAI) conference, animal studies, and simulation studies were not included due to ambiguity in their direct relation to patient care. Studies which used atherosclerosis data as a target for outcome prediction were excluded, as were studies, which focused on clinical data and imaging reports rather than imaging data for prediction. Studies, which used deep learning software with no details on the deep learning architecture were also excluded. Fusion imaging studies were not part of this review, and studies of automated coronary anatomy and atherosclerosis quantification, which relied mainly on hand crafted or non-learning algorithms were not included.

For fractional flow reserve (FFR) derived from CCTA using deep learning, only the original publications were included in this review, all subsequent publications, which used the same algorithms for different clinical applications were considered external validation papers and were not included in this review.

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