Doppler strain imaging closely reflects myocardial energetic status in acute progressive ischemia and indicates energetic recovery after reperfusion.

BACKGROUND: Capitalizing on mechanoenergetic coupling, we investigated whether strain echocardiography can noninvasively estimate the ratio of adenosine triphosphate (ATP) to adenosine diphosphate (ADP), a marker of energetic status during acute myocardial ischemia and reperfusion. METHODS: Twenty-eight pigs were divided into 7 groups (1 baseline, 4 ischemic, and 2 reperfusion). Ischemia was induced by left anterior descending coronary artery occlusion. Longitudinal systolic lengthening (SL) and postsystolic shortening (PSS) strain were measured by echocardiography. The ATP/ADP ratio was obtained from myocardial biopsies in the ischemic and control regions. RESULTS: SL and PSS strain and the ATP/ADP ratio progressively decreased (P < .05) with increased duration (12, 40, 120, and 200 minutes) of ischemia. A mathematical formula (ATP/ADP = -0.97 + 0.25 x PSS strain + 0.20 x SL strain) estimated best the ATP/ADP ratio (r = 0.94, P < .05). Reperfusion after 12 but not after 120 minutes of ischemia significantly improved the ATP/ADP ratio and decreased SL and PSS strain. CONCLUSIONS: Strain echocardiography closely reflected changes and enabled the noninvasive estimation of the ATP/ADP ratio. A higher ATP/ADP ratio is associated with functional improvement after reperfusion.

Does implantation of sonomicrometry crystals alter regional cardiac muscle function?

BACKGROUND: Sonomicrometry is a gold standard in experimental studies on myocardial motion. However, limited information exists regarding mechanical and biochemical changes produced by sonomicrometry crystal (SC) insertion into the myocardial wall. METHODS: In 10 open-chest pigs, we implanted SCs into the inner half of apical anterior and midposterior regions. Longitudinal strains (systolic lengthening, end-systolic, peak shortening, and postsystolic shortening strains) and strain rate (SR) measurements (peak systolic ejection and early and late diastolic SRs) were obtained by Doppler SR echocardiography along with troponin I levels measured from peripheral blood before and after SC insertion. RESULTS: SR and strain parameters did not change significantly after SC implantation. Troponin I levels increased significantly from less than 0.010 to 0.129 +/- 0.138 microg/L (P < .005) after SC implantation. CONCLUSIONS: Our study demonstrates that despite biochemical evidence of myocardial injury, carefully implanted SCs do not alter systolic or diastolic regional myocardial function assessed by Doppler echocardiography.

Inferential backbone assignment for sparse data.

This paper develops an approach to protein backbone NMR assignment that effectively assigns large proteins while using limited sets of triple-resonance experiments. Our approach handles proteins with large fractions of missing data and many ambiguous pairs of pseudoresidues, and provides a statistical assessment of confidence in global and position-specific assignments. The approach is tested on an extensive set of experimental and synthetic data of up to 723 residues, with match tolerances of up to 0.5 ppm for Calpha and Cbeta resonance types. The tests show that the approach is particularly helpful when data contain experimental noise and require large match tolerances. The keys to the approach are an empirical Bayesian probability model that rigorously accounts for uncertainty in the data at all stages in the analysis, and a hybrid stochastic tree-based search algorithm that effectively explores the large space of possible assignments.

Reconsidering complete search algorithms for protein backbone NMR assignment.

MOTIVATION: Nuclear magnetic resonance (NMR) spectroscopy is widely used to determine and analyze protein structures. An essential step in NMR studies is determining the backbone resonance assignment, which maps individual atoms to experimentally measured resonance frequencies. Performing assignment is challenging owing to the noise and ambiguity in NMR spectra. Although automated procedures have been investigated, by-and-large they are still struggling to gain acceptance because of inherent limits in scalability and/or unacceptable levels of assignment error. To have confidence in the results, an algorithm should be complete, i.e. able to identify all solutions consistent with the data, including all arbitrary configurations of extra and missing peaks. The ensuing combinatorial explosion in the space of possible assignments has led to the perception that complete search is hopelessly inefficient and cannot scale to realistic datasets. RESULTS: This paper presents a complete branch-contract-and-bound search algorithm for backbone resonance assignment. The algorithm controls the search space by hierarchically agglomerating partial assignments and employing statistically sound pruning criteria. It considers all solutions consistent with the data, and uniformly treats all combinations of extra and missing data. We demonstrate our approach on experimental data from five proteins ranging in size from 70 to 154 residues. The algorithm assigns >95% of the positions with >98% accuracy. We also present results on simulated data from 259 proteins from the RefDB database, ranging in size from 25 to 257 residues. The median computation time for these cases is 1 min, and the assignment accuracy is >99%. These results demonstrate that complete search not only has the advantage of guaranteeing fair treatment of all feasible solutions, but is efficient enough to be employed effectively inpractice. AVAILABILITY: The MBA(2) software package is made available under an open-source software license. The datasets featured in the Results section can also be obtained from the contact author.

Model-based assignment and inference of protein backbone Nuclear Magnetic Resonances.

Nuclear Magnetic Resonance (NMR) spectroscopy is a key experimental technique used to study protein structure, dynamics, and interactions. NMR methods face the bottleneck of spectral analysis, in particular determining the resonance assignments, which help define the mapping between atoms in the protein and peaks in the spectra. A substantial amount of noise in spectral data, along with ambiguities in interpretation, make this analysis a daunting task, and there exists no generally accepted measure of uncertainty associated with the resulting solutions. This paper develops a model-based inference approach that addresses the problem of characterizing uncertainty in backbone resonance assignment. We argue that NMR spectra are subject to random variation, and ignoring this stochasticity can lead to false optimism and erroneous conclusions. We propose a Bayesian statistical model that accounts for various sources of uncertainty and provides an automatable framework for inference. While assignment has previously been viewed as a deterministic optimization problem, we demonstrate the importance of considering all solutions consistent with the data, and develop an algorithm to search this space within our statistical framework. Our approach is able to characterize the uncertainty associated with backbone resonance assignment in several ways: 1) it quantifies of uncertainty in the individually assigned resonances in terms of their posterior standard deviations; 2) it assesses the information content in the data with a posterior distribution of plausible assignments; and 3) it provides a measure of the overall plausibility of assignments. We demonstrate the value of our approach in a study of experimental data from two proteins, Human Ubiquitin and Cold-shock protein A from E. coli. In addition, we provide simulations showing the impact of experimental conditions on uncertainty in the assignments.