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Background: Little is known regarding the characteristics, treatment patterns, and outcomes in patients with adult congenital heart disease (ACHD) admitted to cardiac intensive care units (CICUs). Objectives: The authors sought to better define the contemporary epidemiology, treatment patterns, and outcomes of ACHD admissions in the CICU. Methods: The Critical Care Cardiology Trials Network is a multicenter network of CICUs in North America. Participating centers contributed prospective data from consecutive admissions during 2-month annual snapshots from 2017 to 2022. We analyzed characteristics and outcomes of admissions with ACHD compared with those without ACHD. Multivariable logistic regression was used to assess mortality in ACHD vs non-ACHD admissions. Results: Of 23,299 CICU admissions across 42 sites, there were 441 (1.9%) ACHD admissions. Shunt lesions were most common (46.1%), followed by right-sided lesions (29.5%) and complex lesions (28.7%). ACHD admissions were younger (median age 46 vs 67 years) than non-ACHD admissions. ACHD admissions were more commonly for heart failure (21.3% vs 15.7%, P

Abstract Background Drug resistance is a critical problem limiting effective antiviral therapy for HIV/AIDS. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques can also be used to identify protease mutants for experimental studies of resistance and thereby assist in the development of next-generation therapies. Few studies, however, have assessed the evolution of resistance from genotype–phenotype data. Results The machine learning produced highly accurate and robust classification of resistance to HIV protease inhibitors. Genotype data were mapped to the enzyme structure and encoded using Delaunay triangulation. Estimates of evolutionary relationships, based on this encoding, and using Minimum Spanning Trees, showed clusters of mutations that closely resemble the wild type. These clusters appear to evolve uniquely to more resistant phenotypes. Conclusions Using the triangulation metric and spanning trees results in paths that are consistent with evolutionary theory. The majority of the paths show bifurcation, namely they switch once from non-resistant to resistant or from resistant to non-resistant. Paths that lose resistance almost uniformly have far lower levels of resistance than those which either gain resistance or are stable. This strongly suggests that selection for stability in the face of a rapid rate of mutation is as important as selection for resistance in retroviral systems.

A 69-year-old man with a history of coronary artery ectasia, potentially resulting from an underlying heritable connective tissue disorder, presented with ventricular fibrillation. Despite medical management of ischemia, he developed recurrent ventricular tachycardia with poor neurological recovery. We highlight challenges in the management of coronary artery ectasia. (Level of Difficulty: Beginner.)

Abstract Background Drug resistance in HIV is the major problem limiting effective antiviral therapy. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques can also be used to select protease mutants for experimental studies of resistance and thereby assist in the development of next-generation therapies. Results The machine learning produced highly accurate and robust classification of HIV protease resistance. Genotype data were mapped to the enzyme structure and encoded using Delaunay triangulation. Generative machine learning models trained on one inhibitor could classify resistance from other inhibitors with varying levels of accuracy. Generally, the accuracy was best when the inhibitors were chemically similar. Conclusions Restricted Boltzmann Machines are an effective machine learning tool for classification of genomic and structural data. They can also be used to compare resistance profiles of different protease inhibitors.

The retroviral protease of human immunodeficiency virus (HIV) is an excellent target for antiviral inhibitors for treating HIV/AIDS. Despite the efficacy of therapy, current efforts to control the disease are undermined by the growing threat posed by drug resistance. This review covers the historical background of studies on the structure and function of HIV protease, the subsequent development of antiviral inhibitors, and recent studies on drug-resistant protease variants. We highlight the important contributions of Dr. Stephen Oroszlan to fundamental knowledge about the function of the HIV protease and other retroviral proteases. These studies, along with those of his colleagues, laid the foundations for the design of clinical inhibitors of HIV protease. The drug-resistant protease variants also provide an excellent model for investigating the molecular mechanisms and evolution of resistance.

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Materials exposed to plasmas in magnetic confinement nuclear reactors will accumulate radiation-induced defects and energetically implanted gas atoms (from the plasma and transmutations), of which insoluble helium (He) is likely to be the most problematic. The large surface-area-to-volume ratio exhibited by nanoporous materials provides an unsaturable sink with the potential to continuously remove both point defects and He. This property enhances the possibilities for these materials to be tailored for high radiation-damage resistance. In order to explore the potential effect of this on the individual ligaments of nanoporous materials, we present results on the response of tungsten (W) nanoparticles (NPs) to 15 keV He ion irradiation. Tungsten foils and various sizes of NPs were ion irradiated concurrently and imaged in-situ via transmission electron microscopy at 750 °C. Helium bubbles were not observed in NPs with diameters less than 20 nm but did form in larger NPs and the foils. No dislocation loops or black spot damage were observed in any NPs up to 100 nm in diameter but were found to accumulate in the W foils. These results indicate that a nanoporous material, particularly one made up of ligaments with characteristic dimensions of 30 nm or less, is likely to exhibit significant resistance to He accumulation and structural damage and, therefore, be highly tolerant to radiation.

How a unique three-dimensional structure is rapidly formed from the linear sequence of a polypeptide is one of the important questions in contemporary science. Apart from biological context of in vivo protein folding (which has been studied only for a few proteins), the roles of the fundamental physical forces in the in vitro folding remain largely unstudied. Despite a degree of success in using descriptions based on statistical and/or thermodynamic approaches, few of the current models explicitly include more basic physical forces (such as electrostatics and Van Der Waals forces). Moreover, the present-day models rarely take into account that the protein folding is, essentially, a rapid process that produces a highly specific architecture. This review considers several physical models that may provide more direct links between sequence and tertiary structure in terms of the physical forces. In particular, elaboration of such simple models is likely to produce extremely effective computational techniques with value for modern genomics.

Members of the TCL1 family of oncogenes are abnormally expressed in mature T-cell leukemias and B-cell lymphomas. The proteins are involved in the coactivation of protein kinase B (Akt/PKB), a key intracellular kinase. The sequences and crystal structures of three Tcl1 proteins were analyzed in order to understand their interactions with Akt/PKB and the implications for lymphocyte malignancies. Tcl1 proteins are ~15 kD and share 25—80% amino acid sequence identity. The tertiary structures of mouse Tcl1, human Tcl1, and Mtcp1 are very similar. Analysis of the structures revealed conserved semi-planar surfaces that have characteristics of surfaces involved in protein-protein interactions. The Tcl1 proteins show differences in surface charge distribution and oligomeric state suggesting that they do not interact in the same way with Akt/PKB and other cellular protein(s).

A new sliding window scheme is introduced with multiple windows to form the protein data for SVM. Two new tertiary classifiers are introduced; one of them makes use of support vector machines as neurons in neural network architecture and the other tertiary classifier is a granular decision tree based on granular computing, decision tree and SVM. Binary classifier using multiple windows is compared with single window scheme. The accuracy levels of the new classifiers are better than most available techniques.