Nobel Prizes for nuclear magnetic resonance: 2003 and historical perspectives

Phenotyping heart failure by cardiac magnetic resonance imaging of cardiac macro- and microscopic structure: state of the art review

Heart failure demographics have evolved in past decades with the development of improved diagnostics, therapies, and prevention. Cardiac magnetic resonance (CMR) has developed in a similar timeframe to become the gold-standard non-invasive imaging modality for characterizing diseases causing heart failure. CMR techniques to assess cardiac morphology and function have progressed since their first use in the 1980s. Increasingly efficient acquisition protocols generate high spatial and temporal resolution images in less time. This has enabled new methods of characterizing cardiac systolic and diastolic function such as strain analysis, exercise real-time cine imaging and four-dimensional flow. A key strength of CMR is its ability to non-invasively interrogate the myocardial tissue composition. Gadolinium contrast agents revolutionized non-invasive cardiac imaging with the late gadolinium enhancement technique. Further advances enabled quantitative parametric mapping to increase sensitivity at detecting diffuse pathology. Novel methods such as diffusion tensor imaging and artificial intelligence-enhanced image generation are on the horizon. Magnetic resonance spectroscopy (MRS) provides a window into the molecular environment of the myocardium. Phosphorus (31P) spectroscopy can inform the status of cardiac energetics in health and disease. Proton (1H) spectroscopy complements this by measuring creatine and intramyocardial lipids. Hyperpolarized carbon (13C) spectroscopy is a novel method that could further our understanding of dynamic cardiac metabolism. CMR of other organs such as the lungs may add further depth into phenotypes of heart failure. The vast capabilities of CMR should be deployed and interpreted in context of current heart failure challenges.

X-ray crystallography and sickle cell disease drug discovery-a tribute to Donald Abraham

X-ray crystallography and structure-based drug discovery have played a major role in the discovery of antisickling agents that target hemoglobin (Hb) for the treatment of sickle cell disease (SCD). Sickle cell disease, the most common inherited hematologic disorder, occurs as a result of a single point mutation of βGlu6 in normal human adult hemoglobin (HbA) to βVal6 in sickle hemoglobin (HbS). The disease is characterized by polymerization of HbS and sickling of red blood cells (RBCs), leading to several secondary pathophysiologies, including but not limited to vaso-occlusion, hemolytic anemia, oxidative stress, inflammation, stroke, pain crisis, and organ damage. Despite the fact that SCD was the first disease to have its molecular basis established, the development of therapies was for a very long time a challenge and took several decades to find therapeutic agents. The determination of the crystal structure of Hb by Max Perutz in the early 60s, and the pioneering X-ray crystallography research by Donald J. Abraham in the early 80s, which resulted in the first structures of Hb in complex with small molecule allosteric effectors of Hb, gave much hope that structure-based drug discovery (SBDD) could be used to accelerate development of antisickling drugs that target the primary pathophysiology of hypoxia-induced HbS polymerization to treat SCD. This article, which is dedicated to Donald J. Abraham, briefly reviews structural biology, X-ray crystallography and structure-based drug discovery from the perspective of Hb. The review also presents the impact of X-ray crystallography in SCD drug development using Hb as a target, emphasizing the major and important contributions by Don Abraham in this field.

Quantum computation of molecular structure using data from challenging-to-classically-simulate nuclear magnetic resonance experiments

We propose a quantum algorithm for inferring the molecular nuclear spin Hamiltonian from time-resolved measurements of spin-spin correlators, which can be obtained via nuclear magnetic resonance (NMR). We focus on learning the anisotropic dipolar term of the Hamiltonian, which generates dynamics that are challenging to classically simulate in some contexts. We demonstrate the ability to directly estimate the Jacobian and Hessian of the corresponding learning problem on a quantum computer, allowing us to learn the Hamiltonian parameters. We develop algorithms for performing this computation on both noisy near-term and future fault-tolerant quantum computers. We argue that the former is promising as an early beyond-classical quantum application since it only requires evolution of a local spin Hamiltonian. We investigate the example of a protein (ubiquitin) confined on a membrane as a benchmark of our method. We isolate small spin clusters, demonstrate the convergence of our learning algorithm on one such example, and then investigate the learnability of these clusters as we cross the ergodic to non-ergodic phase transition by suppressing the dipolar interaction. We see a clear correspondence between a drop in the multifractal dimension measured across many-body eigenstates of these clusters, and a transition in the structure of the Hessian of the learning cost function (from degenerate to learnable). Our hope is that such quantum computations might enable the interpretation and development of new NMR techniques for analyzing molecular structure.

Re-examining physician-scientist training through the prism of the discovery-invention cycle

The training of physician-scientists lies at the heart of future medical research. In this commentary, we apply Narayanamurti and Odumosu’s framework of the “discovery-invention cycle” to analyze the structure and outcomes of the integrated MD/PhD program. We argue that the linear model of “bench-to-bedside” research, which is also reflected in the present training of MD/PhDs, merits continual re-evaluation to capitalize on the richness of opportunities arising in clinical medicine. In addition to measuring objective career outcomes, as existing research has done, we suggest that detailed characterization of researchers’ efforts using both qualitative and quantitative techniques is necessary to understand if dual-degree training is being utilized. As an example, we propose that the application of machine learning and data science to corpora of biomedical literature and anonymized clinical data might allow us to see if there are objective “signatures” of research uniquely enabled by MD/PhD training. We close by proposing several hypotheses for shaping physician-scientist training, the relative merits of which could be assessed using the techniques proposed above. Our overarching message is the importance of deeply understanding individual career trajectories as well as characterizing organizational details and cultural nuances to drive new policy which shapes the future of the physician-scientist workforce.