Transcatheter Edge-to-Edge Repair in Valvular Heart Disease: A Comprehensive Exploration of Equipment, Efficacy, Gender, Racial, and Socioeconomic Disparities, and Future Prospects.

The 2 primary components of valvular heart disease are mitral regurgitation (MR) and tricuspid regurgitation (TR). Transcatheter edge-to-edge repair (TEER) is an advanced, minimally invasive procedure that has recently displayed encouraging outcomes in the treatment of these pathologies. TEER offers a nonsurgical alternative for individuals diagnosed with conditions deemed to be high-risk surgical candidates. Currently, the TEER procedure employs devices such as MitraCLIP and TriCLIP, as well as innovative PASCAL (transcatheter valve repair system used for mitral and tricuspid valve repair) and FORMA (repair system used for tricuspid valve repair) repair systems. In the COAPT (Cardiovascular Outcomes Assessment of the MitraClip Percutaneous Therapy for Heart Failure Patients with Functional Mitral Regurgitation) trial enrolling 614 patients to test the efficacy of TEER in MR, a significant reduction in hospitalization due to heart failure was observed at 24 months in the MitraClip + guideline-directed medical therapy (GDMT) group (35.8%) than in the GDMT-alone group (67.9%), HR, 0.53; P < 0.001, lower rate of all-cause mortality at 29.1% compared with 46.1% (P < 0.001), lower risk of cerebrovascular events (P = 0.001), and lower mortality due to cardiovascular events (P < 0.001). In another trial, patients with moderate TR or greater than New York Heart Association Class II or higher underwent TEER using the TriClip for the management of TR. The outcomes were encouraging, with 86% of patients showing a reduction in TR severity of at least one grade. As the technology and research surrounding TEER continue to progress, a more extensive range of patients are expected to qualify for TEER procedures. Our comprehensive review sought to extensively explore the background, equipment used, effectiveness of MR and TR, potential side effects, future prospects, and ongoing trials associated with TEER. We further discuss the existing gender, racial, and socioeconomic disparities in the realm of TEER.

Direct Oral Anticoagulants Exhibit Lower Risks of Mortality and Bleeding Compared to Vitamin K Antagonists in Atrial Fibrillation Patients on Chronic Hemodialysis: A Systematic Review and Meta-Analysis.

This meta-analysis aimed to assess the outcomes of patients with atrial fibrillation undergoing chronic hemodialysis, comparing the effectiveness of direct oral anticoagulants (DOACs) and vitamin K antagonists. A systematic search was conducted across various databases including PubMed, Embase, and Google Scholar. Efficacy outcomes focused on the risk of stroke and mortality, whereas safety outcomes assessed the risk of bleeding. Review Manager generated forest plots for data synthesis. Statistical significance was set at P < 0.05, and random-effects models were used. Subgroup analysis identified the sources of heterogeneity. Nine studies met the inclusion criteria for the final analysis. The risk of all-cause stroke [risk ratio (RR): 0.64; 95% confidence interval (CI): 0.51-0.81; P = 0.0001; I2 = 0%], ischemic stroke (RR: 0.53; 95% CI: 0.29-0.96; P = 0.04; I2 = 0%), all-cause mortality (RR: 0.73; 95% CI: 0.60-0.88; P = 0.001; I2 = 71%), major bleeding (RR: 0.63; 95% CI: 0.52-0.76; P < 0.00001; I2 = 44%), gastrointestinal bleeding (RR: 0.67; 95% CI: 0.53-0.85; P = 0.0009; I2 = 36%), intracranial hemorrhage (RR: 0.57; 95% CI: 0.38-0.84; P = 0.004; I2 = 0%) were lower in the DOAC group compared with the vitamin K antagonist group. The risk of cardiovascular-related death (RR: 1.34; 95% CI: 0.69-2.60; P = 0.39; I2 = 0%), clinically relevant nonmajor bleeding (RR: 0.90; 95% CI: 0.75-1.08; P = 0.26; I2 = 28%), and hemorrhagic stroke (RR: 0.36; 95% CI: 0.06-2.24; P = 0.28; I2 = 10%) showed no significant differences. In conclusion, the risks of all-cause stroke, ischemic stroke, all-cause mortality, major bleeding, gastrointestinal bleeding, and intracranial hemorrhage in patients with atrial fibrillation undergoing chronic hemodialysis were lower in the DOAC group.

The Presence of Chronic Total Occlusion in Noninfarct-Related Arteries Is Associated With Higher Mortality and Worse Patient Outcomes Following Percutaneous Coronary Intervention for STEMI: A Systematic Review, Meta-Analysis and Meta-Regression.

Reperfusion therapy with percutaneous coronary intervention improves outcomes in patients with ST-elevation myocardial infarction. We conducted a meta-analysis to assess the impact of chronic total occlusion (CTO) in noninfarct-related artery on the outcomes of these patients. Comprehensive searches were performed using PubMed, Google Scholar, and EMBASE. The primary endpoint was the 30-day mortality rate, with secondary endpoints including all-cause mortality, repeat myocardial infarction, and stroke. Forest plots were created for the pooled analysis of the results, with statistical significance set at P < 0.05. A total of 19 studies were included in this meta-analysis, with 23,989 patients (3589 in CTO group and 20,400 in no-CTO group). The presence of CTO was associated with significantly higher odds of 30-day mortality [18.38% vs 5.74%; relative risk (RR), 3.69; 95% confidence intervals (CI), 2.68-5.07; P < 0.00001], all-cause mortality (31.00% vs 13.40%; RR, 2.79; 95% CI, 2.31-3.37; P < 0.00001), cardiovascular-related deaths (12.61% vs 4.1%; RR, 2.61; 95% CI, 1.99-3.44; P < 0.00001), and major adverse cardiovascular events (13.64% vs 9.88%; RR, 2.08; 95% CI, 1.52-2.86; P < 0.00001) than the non-CTO group. No significant differences in repeated myocardial infarction or stroke were observed between the CTO and non-CTO groups. Our findings underscore the need for further research on the benefits and risks of performing staged or simultaneous percutaneous coronary intervention for CTO in the noninfarct-related artery in patients with ST-elevation myocardial infarction.

Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks.

BACKGROUND: Deciphering complete networks of interactions between proteins is the key to comprehend cellular regulatory mechanisms. A significant effort has been devoted to expanding the coverage of the proteome-wide interaction space at molecular level. Although a growing body of research shows that protein docking can, in principle, be used to predict biologically relevant interactions, the accuracy of the across-proteome identification of interacting partners and the selection of near-native complex structures still need to be improved. RESULTS: In this study, we developed a new method to discover and model protein interactions employing an exhaustive all-to-all docking strategy. This approach integrates molecular modeling, structural bioinformatics, machine learning, and functional annotation filters in order to provide interaction data for the bottom-up assembly of protein interaction networks. Encouragingly, the success rates for dimer modeling is 57.5 and 48.7% when experimental and computer-generated monomer structures are employed, respectively. Further, our protocol correctly identifies 81% of protein-protein interactions at the expense of only 19% false positive rate. As a proof of concept, 61,913 protein-protein interactions were confidently predicted and modeled for the proteome of E. coli. Finally, we validated our method against the human immune disease pathway. CONCLUSIONS: Protein docking supported by evolutionary restraints and machine learning can be used to reliably identify and model biologically relevant protein assemblies at the proteome scale. Moreover, the accuracy of the identification of protein-protein interactions is improved by considering only those protein pairs co-localized in the same cellular compartment and involved in the same biological process. The modeling protocol described in this communication can be applied to detect protein-protein interactions in other organisms and pathways as well as to construct dimer structures and estimate the confidence of protein interactions experimentally identified with high-throughput techniques.

Template-based identification of protein-protein interfaces using eFindSitePPI.

Protein-protein interactions orchestrate virtually all cellular processes, therefore, their exhaustive exploration is essential for the comprehensive understanding of cellular networks. A reliable identification of interfacial residues is vital not only to infer the function of individual proteins and their assembly into biological complexes, but also to elucidate the molecular and physicochemical basis of interactions between proteins. With the exponential growth of protein sequence data, computational approaches for detecting protein interface sites have drawn an increased interest. In this communication, we discuss the major features of eFindSite(PPI), a recently developed template-based method for interface residue prediction available at http://brylinski.cct.lsu.edu/efindsiteppi. We describe the requirements and installation procedures for the stand-alone version, and explain the content and format of output data. Furthermore, the functionality of the eFindSite(PPI) web application that is designed to provide a simple and convenient access for the scientific community is presented with illustrative examples. Finally, we discuss common problems encountered in predicting protein interfaces and set forth directions for the future development of eFindSite(PPI).

Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures.

BACKGROUND: Protein-protein interactions (PPIs) mediate the vast majority of biological processes, therefore, significant efforts have been directed to investigate PPIs to fully comprehend cellular functions. Predicting complex structures is critical to reveal molecular mechanisms by which proteins operate. Despite recent advances in the development of new methods to model macromolecular assemblies, most current methodologies are designed to work with experimentally determined protein structures. However, because only computer-generated models are available for a large number of proteins in a given genome, computational tools should tolerate structural inaccuracies in order to perform the genome-wide modeling of PPIs. RESULTS: To address this problem, we developed eRank(PPI), an algorithm for the identification of near-native conformations generated by protein docking using experimental structures as well as protein models. The scoring function implemented in eRank(PPI) employs multiple features including interface probability estimates calculated by eFindSite(PPI) and a novel contact-based symmetry score. In comparative benchmarks using representative datasets of homo- and hetero-complexes, we show that eRank(PPI) consistently outperforms state-of-the-art algorithms improving the success rate by ~10 %. CONCLUSIONS: eRank(PPI) was designed to bridge the gap between the volume of sequence data, the evidence of binary interactions, and the atomic details of pharmacologically relevant protein complexes. Tolerating structure imperfections in computer-generated models opens up a possibility to conduct the exhaustive structure-based reconstruction of PPI networks across proteomes. The methods and datasets used in this study are available at www.brylinski.org/erankppi.

Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning.

The identification of protein-protein interactions is vital for understanding protein function, elucidating interaction mechanisms, and for practical applications in drug discovery. With the exponentially growing protein sequence data, fully automated computational methods that predict interactions between proteins are becoming essential components of system-level function inference. A thorough analysis of protein complex structures demonstrated that binding site locations as well as the interfacial geometry are highly conserved across evolutionarily related proteins. Because the conformational space of protein-protein interactions is highly covered by experimental structures, sensitive protein threading techniques can be used to identify suitable templates for the accurate prediction of interfacial residues. Toward this goal, we developed eFindSite(PPI) , an algorithm that uses the three-dimensional structure of a target protein, evolutionarily remotely related templates and machine learning techniques to predict binding residues. Using crystal structures, the average sensitivity (specificity) of eFindSite(PPI) in interfacial residue prediction is 0.46 (0.92). For weakly homologous protein models, these values only slightly decrease to 0.40-0.43 (0.91-0.92) demonstrating that eFindSite(PPI) performs well not only using experimental data but also tolerates structural imperfections in computer-generated structures. In addition, eFindSite(PPI) detects specific molecular interactions at the interface; for instance, it correctly predicts approximately one half of hydrogen bonds and aromatic interactions, as well as one third of salt bridges and hydrophobic contacts. Comparative benchmarks against several dimer datasets show that eFindSite(PPI) outperforms other methods for protein-binding residue prediction. It also features a carefully tuned confidence estimation system, which is particularly useful in large-scale applications using raw genomic data. eFindSite(PPI) is freely available to the academic community at http://www.brylinski.org/efindsiteppi.

Predicting protein interface residues using easily accessible on-line resources.

It has been more than a decade since the completion of the Human Genome Project that provided us with a complete list of human proteins. The next obvious task is to figure out how various parts interact with each other. On that account, we review 10 methods for protein interface prediction, which are freely available as web servers. In addition, we comparatively evaluate their performance on a common data set comprising different quality target structures. We find that using experimental structures and high-quality homology models, structure-based methods outperform those using only protein sequences, with global template-based approaches providing the best performance. For moderate-quality models, sequence-based methods often perform better than those structure-based techniques that rely on fine atomic details. We note that post-processing protocols implemented in several methods quantitatively improve the results only for experimental structures, suggesting that these procedures should be tuned up for computer-generated models. Finally, we anticipate that advanced meta-prediction protocols are likely to enhance interface residue prediction. Notwithstanding further improvements, easily accessible web servers already provide the scientific community with convenient resources for the identification of protein-protein interaction sites.