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Background: The prevalence of hypertension (HTN) has significantly increased among younger adults (15-45 yrs) in the U.S. Despite this, there is limited data on trends of HTN-related mortality within this population., Methods: Data from the CDC WONDER multiple-cause of death database was analyzed from 1999 to 2021, focusing on HTN-related mortality in young adults aged 15 to 45 years. Age-adjusted mortality rates (AAMRs) per 100,000 persons and annual percent changes (APCs) were calculated and stratified by year, sex, race/ethnicity, urbanization status, and census region., Results: Between 1999 and 2021, there were 201,860 HTN-related deaths among young adults in the U.S. The AAMR increased from 2.8 in 1999 to 5.0 in 2001 (APC 35.3; 95 % CI 20.6 to 44.5) and then to 9.4 in 2019 (APC 3.1; 95 % CI 2.7 to 3.5) before sharply rising to 13.9 in 2021 (APC 22.3; 95 % CI 15.1 to 26.4). Men consistently exhibited higher AAMRs than women from 1999 (AAMR men: 3.6 vs women: 1.9) to 2021 (AAMR men: 18.9 vs women: 8.8). In 2020, the highest AAMR was observed among non-Hispanic (NH) Black or African American young adults (30.2), followed by NH American Indian/Alaska Natives (29.6), NH White (9.9), Hispanics or Latino (9.3) and NH Asian or Pacific Islander (5.0). The Southern region had the highest AAMR (9.3), followed by the Midwest (6.4), West (5.8), and Northeast (5.4). Nonmetropolitan areas consistently had higher AAMR (8.5) than metropolitan areas (7.0). States in the top 90 th percentile for AAMRs included Mississippi, the District of Columbia, Oklahoma, West Virginia, and Arkansas, with these states exhibiting approximately five times the AAMRs of those in the lower 10th percentile., Conclusion: HTN-related mortality among young adults in the U.S. increased steadily until 2019, followed by a sharp rise in 2020 and 2021. The highest AAMRs were observed among men, NH Black young adults, and individuals residing in the Southern and non-metropolitan areas of the U.S. These findings underscore the need for targeted interventions to reduce the burden and address disparities in HTN-related mortality among young adults in the U.S., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The widespread use of Machine Learning (ML) techniques in chemical applications has come with the pressing need to analyze extremely large molecular libraries. In particular, clustering remains one of the most common tools to dissect the chemical space. Unfortunately, most current approaches present unfavorable time and memory scaling, which makes them unsuitable to handle million- and billion-sized sets. Here, we propose to bypass these problems with a time- and memory-efficient clustering algorithm, BitBIRCH. This method uses a tree structure similar to the one found in the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm to ensure O N time scaling. BitBIRCH leverages the instant similarity (iSIM) formalism to process binary fingerprints, allowing the use of Tanimoto similarity, and reducing memory requirements. Our tests show that BitBIRCH is already > 1,000 times faster than standard implementations of the Taylor-Butina clustering for libraries with 1,500,000 molecules. BitBIRCH increases efficiency without compromising the quality of the resulting clusters. We explore strategies to handle large sets, which we applied in the clustering of one billion molecules under 5 hours using a parallel/iterative BitBIRCH approximation.

Recent studies have demonstrated that coronary plaque burden carries greater prognostic value in predicting adverse atherosclerotic cardiovascular disease outcomes than myocardial ischemia, thereby challenging the existing paradigm. Advances in plaque quantification through both noncontrast and contrast-enhanced computed tomography (CT) methods have led to earlier and more cost-effective detection of coronary disease compared with traditional stress testing. The 2 principal techniques of noninvasive coronary plaque quantification assessment are coronary artery calcium scoring by noncontrast CT and coronary CT angiography, both of which correlate with disease burden on invasive angiography. Plaque quantification from these imaging modalities has shown utility in risk stratification and prognostication of adverse cardiovascular events, leading to increased incorporation into clinical practice guidelines and preventive care pathways. Furthermore, due to their expanding clinical value, emerging technologies such as artificial intelligence are being integrated into plaque quantification platforms, placing more advanced measures of plaque burden at the forefront of coronary plaque evaluation. In this review, we summarize recent clinical data on coronary artery calcium scoring and coronary CT angiography plaque quantification in the evaluation of adverse atherosclerotic cardiovascular disease in patients with and without chest pain, highlight how these methods compare to invasive quantification approaches, and directly compare the performance characteristics of coronary artery calcium scoring and coronary CT angiography., Competing Interests: None.

Molecular dynamics (MD) simulations are ideally suited to describe conformational ensembles of biomolecules such as proteins and nucleic acids. Microsecond-long simulations are now routine, facilitated by the emergence of graphical processing units. Clustering, which groups objects based on structural similarity, is typically used to process ensembles, leading to different states, their populations, and the identification of representative structures. A popular pipeline combines hierarchical clustering for clustering and selecting the cluster centroid as representative of the cluster. Here, we propose to improve on this approach, by developing a module-Protein Retrieval via Integrative Molecular Ensembles (PRIME), that consists of tools to improve the prediction of the representative in the most populated cluster using extended continuous similarity. PRIME is integrated with our Molecular Dynamics Analysis with N -ary Clustering Ensembles (MDANCE) package and can be used as a postprocessing tool for arbitrary clustering algorithms, compatible with several MD suites. PRIME predictions produced structures that when aligned to the experimental structure were better superposed (lower RMSD). A further benefit of PRIME is its linear scaling─rather than the traditional O( N 2 ) traditionally associated with comparisons of elements in a set.

One of the key challenges of k -means clustering is the seed selection or the initial centroid estimation since the clustering result depends heavily on this choice. Alternatives such as k -means++ have mitigated this limitation by estimating the centroids using an empirical probability distribution. However, with high-dimensional and complex data sets such as those obtained from molecular simulation, k -means++ fails to partition the data in an optimal manner. Furthermore, stochastic elements in all flavors of k -means++ will lead to a lack of reproducibility. K -means N -Ary Natural Initiation (NANI) is presented as an alternative to tackle this challenge by using efficient n -ary comparisons to both identify high-density regions in the data and select a diverse set of initial conformations. Centroids generated from NANI are not only representative of the data and different from one another, helping k -means to partition the data accurately, but also deterministic, providing consistent cluster populations across replicates. From peptide and protein folding molecular simulations, NANI was able to create compact and well-separated clusters as well as accurately find the metastable states that agree with the literature. NANI can cluster diverse data sets and be used as a standalone tool or as part of our MDANCE clustering package.

Purpose: Coronary artery plaque burden, low attenuation non-calcified plaque (LAP), and pericoronary adipose tissue (PCAT) on coronary CT angiography (CCTA), have been linked to future cardiac events. The purpose of this study was to evaluate intra- and inter reader reproducibility in the quantification of coronary plaque burden and its characteristics using an artificial intelligence-enhanced semi-automated software., Materials and Methods: A total of 10 women and 6 men, aged 52 (IQR 49-58) underwent CCTA using a Siemens Somatom Force, Somatom Definition AS and Somatom Definition Flash scanners. Two expert readers utilized dedicated semi-automatic software (vascuCAP, Elucid Bioimaging, Wenham, MA) to assess calcified plaque, low attenuation plaque and PCAT. Readers were blinded to all clinical information and repeated their analysis at 6 weeks in random order to minimize recall bias. Data analysis was performed on the right and left coronary arteries. Intra- and inter-reader reproducibility was compared using Pearson correlation coefficient, while absolute values between analyses and readers were compared with paired non-parametric tests. This is a sub-study of the Specialized Center of Research Excellence (SCORE) clinical trial (5U54AG062334)., Results: A total of 64 vessels from 16 patients were analyzed. Intra-reader Pearson correlation coefficients for calcified plaque volume, LAP volume and PCAT volumes were 0.96, 0.99 and 0.92 for reader 1 and 0.94, 0.94 and 0.95 for reader 2, respectively, (all p < 0.0001). Inter-reader Pearson correlation coefficients for calcified plaque volume, LAP and PCAT volumes were 0.92, 0.96 and 0.78, and 0.99, 0.99 and 0.93 on the second analyses, all had a p value <0.0001. There was no significant bias on the corresponding Bland-Altman analyses., Conclusion: Volume measurement of coronary plaque burden and PCAT volume can be performed with high intra- and inter-reader agreement., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Purpose of Review: To provide a comprehensive review of hypertension among patients with cancer. Several cancer therapies cause hypertension which has resulted in a growing and vulnerable population of patients with difficult to control hypertension which has significant downstream effects., Recent Findings: Hypertension affects up to 50% of cancer patients and higher comorbidity when compared to the general population. Many anticancer therapies can cause hypertension through their treatment effect. Antihypertensive treatment is crucial given cardiovascular mortality is a leading cause of death among cancer patients. It is already known that hypertension is poorly controlled in the general population, and there are additional challenges in management among patients with cancer. Patients with cancer suffer from multimorbidity, are on multiple medications creating concern for drug interactions, and often have blood pressure lability, which can worsen clinical inertia among patients and their providers. It is crucial to effectively treat hypertension in cancer patients to mitigate downstream adverse cardiovascular events., Summary: In recent years, there have been significant changes in management guidelines of hypertension and simultaneously as influx of new cancer therapeutics. We provide an update on hypertension treatment among patients with cancer on different chemotherapeutic agents., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

The quantification of molecular similarity has been present since the beginning of cheminformatics. Although several similarity indices and molecular representations have been reported, all of them ultimately reduce to the calculation of molecular similarities of only two objects at a time. Hence, to obtain the average similarity of a set of molecules, all the pairwise comparisons need to be computed, which demands a quadratic scaling in the number of computational resources. Here we propose an exact alternative to this problem: iSIM (instant similarity). iSIM performs comparisons of multiple molecules at the same time and yields the same value as the average pairwise comparisons of molecules represented by binary fingerprints and real-value descriptors. In this work, we introduce the mathematical framework and several applications of iSIM in chemical sampling, visualization, diversity selection, and clustering., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

We propose a new perturbation theory framework that can be used to help with the projective solution of the Schrödinger equation for arbitrary wave functions. This Flexible Ansatz for N -body Perturbation Theory (FANPT) is based on our previously proposed Flexible Ansatz for the N -body Configuration Interaction (FANCI). We derive recursive FANPT expressions, including arbitrary orders in the perturbation hierarchy. We show that the FANPT equations are well-behaved across a wide range of conditions, including static correlation-dominated configurations and highly nonlinear wave functions.

Electron pairs have an illustrious history in chemistry, from powerful concepts to understanding structural stability and reactive changes to the promise of serving as building blocks of quantitative descriptions of the electronic structure of complex molecules and materials. However, traditionally, two-electron wavefunctions (geminals) have not enjoyed the popularity and widespread use of the more standard single-particle methods. This has changed recently, with a renewed interest in the development of geminal wavefunctions as an alternative to describing strongly correlated phenomena. Hence, there is a need to find geminal methods that are accurate, computationally tractable, and do not demand significant input from the user (particularly via cumbersome and often ill-behaved orbital optimization steps). Here, we propose new families of geminal wavefunctions inspired by the pair coupled cluster doubles ansatz. We present a new hierarchy of two-electron wavefunctions that extends the one-reference orbital idea to other geminals. Moreover, we show how to incorporate single-like excitations in this framework without leaving the quasiparticle picture. We explore the role of imposing seniority restrictions on these wavefunctions and benchmark these new methods on model strongly correlated systems., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at each sampled x , y position, resulting in thousands of individual mass spectra, each comprising a pixel in the resulting ion images. However, efficient analysis of imaging mass spectrometry datasets can be challenging due to the hyperspectral characteristics of the data. Each spectrum contains several thousand unique compounds at discrete m / z values that result in unique ion images, which demands robust and efficient algorithms for searching, statistical analysis, and visualization. Some traditional post-processing techniques are fundamentally ill-equipped to dissect these types of data. For example, while principal component analysis (PCA) has long served as a useful tool for mining imaging mass spectrometry datasets to identify correlated analytes and biological regions of interest, the interpretation of the PCA scores and loadings can be non-trivial. The loadings often contain negative peaks in the PCA-derived pseudo-spectra, which are difficult to ascribe to underlying tissue biology. Herein, we have utilized extended similarity indices to streamline the interpretation of imaging mass spectrometry data. This novel workflow uses PCA as a pixel-selection method to parse out the most and least correlated pixels, which are then compared using the extended similarity indices. The extended similarity indices complement PCA by removing all non-physical artifacts and streamlining the interpretation of large volumes of imaging mass spectrometry spectra simultaneously. The linear complexity, O( N ), of these indices suggests that large imaging mass spectrometry datasets can be analyzed in a 1 : 1 scale of time and space with respect to the size of the input data. The extended similarity indices algorithmic workflow is exemplified here by identifying discrete biological regions of mouse brain tissue., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

We introduce certain concepts and expressions from conceptual density functional theory (DFT) to study the properties of the Hildebrand solubility parameter. The original form of the Hildebrand solubility parameter is used to qualitatively estimate solubilities for various apolar and aprotic substances and solvents and is based on the square root of the cohesive energy density. Our results show that a revised expression allows the replacement of cohesive energy densities by electrophilicity densities, which are numerically accessible by simple DFT calculations. As an extension, the reformulated expression provides a deeper interpretation of the main contributions and, in particular, emphasizes the importance of charge transfer mechanisms. All calculated values of the Hildebrand parameters for a large number of common solvents are compared with experimental values and show good agreement for non- or moderately polar aprotic solvents in agreement with the original formulation of the Hildebrand solubility parameters. The observed deviations for more polar and protic solvents define robust limits from the original formulation which remain valid. Likewise, we show that the use of machine learning methods leads to only slightly better predictability., (© 2023 The Authors. ChemPhysChem published by Wiley-VCH GmbH.)

Purpose of Review: To describe the relationship between three pandemics: hypertension, obesity, and heart failure. From pathophysiology to treatment, understanding how these disease entities are linked can lead to breakthroughs in their prevention and treatment. The relevance of this review lies in its discussion of novel pharmacological and surgical treatment strategies for obesity and hypertension, and their role in the prevention and treatment of heart failure., Recent Findings: Novel medications such as GLP-1 agonists have demonstrated sustained weight loss in patients with obesity, and concurrent improvements in their cardiometabolic profile, and possibly also reductions in hypertension-related comorbidities including heart failure. Surgical therapies including laparoscopic bariatric surgery represent an important treatment strategy in obese patients, and recent studies describe their use even in patients with advanced heart failure, including those with ventricular assist devices., Summary: These developments have deep implications on our efforts to understand, mitigate, and ultimately prevent the three pandemics, and offer promising improvements to quality of life, survival, and the cost burden of these diseases., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Visualization of the chemical space is useful in many aspects of chemistry, including compound library design, diversity analysis, and exploring structure-property relationships, to name a few. Examples of notable research areas where the visualization of chemical space has strong applications are drug discovery and natural product research. However, the sheer volume of even comparatively small sub-sections of chemical space implies that we need to use approximations at the time of navigating through chemical space. ChemMaps is a visualization methodology that approximates the distribution of compounds in large datasets based on the selection of satellite compounds that yield a similar mapping of the whole dataset when principal component analysis on a similarity matrix is performed. Here, we show how the recently proposed extended similarity indices can help find regions that are relevant to sample satellites and reduce the amount of high-dimensional data needed to describe a library's chemical space.

Understanding structure-activity landscapes is essential in drug discovery. Similarly, it has been shown that the presence of activity cliffs in compound data sets can have a substantial impact not only on the design progress but also can influence the predictive ability of machine learning models. With the continued expansion of the chemical space and the currently available large and ultra-large libraries, it is imperative to implement efficient tools to analyze the activity landscape of compound data sets rapidly. The goal of this study is to show the applicability of the n-ary indices to quantify the structure-activity landscapes of large compound data sets using different types of structural representation rapidly and efficiently. We also discuss how a recently introduced medoid algorithm provides the foundation to finding optimum correlations between similarity measures and structure-activity rankings. The applicability of the n-ary indices and the medoid algorithm is shown by analyzing the activity landscape of 10 compound data sets with pharmaceutical relevance using three fingerprints of different designs, 16 extended similarity indices, and 11 coincidence thresholds., (© 2023 The Authors. Molecular Informatics published by Wiley-VCH GmbH.)

Purpose of the Review: Hypertension accounts for the largest proportion of cardiovascular (CV) mortality worldwide and its prevalence continues to rise. While prominent CV societies have offered strong recommendations on the management of hypertension in adults, the role of noninvasive CV imaging in the evaluation of hypertensive patients remains incompletely defined., Recent Findings: Noninvasive imaging is a rapidly expanding field with a growing number of sophisticated and readily applicable modalities to assess how cardiac structure and function changes after periods of sustained, elevated blood pressure. Echocardiography remains the initial modality to screen these patients while developments in nuclear, computed tomography and cardiac magnetic resonance complement and expand investigations for alternative diagnoses that may complement or conflict with the diagnosis of left ventricular hypertrophy., Summary: In this review article, we summarize the application of echocardiography, nuclear imaging, cardiac computed tomography, and cardiac magnetic resonance imaging in the evaluation and management of hypertensive heart disease., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

The hard/soft acid/base (HSAB) principle is a cornerstone in our understanding of chemical reactivity preferences. Motivated by the success of the original ("global") version of this rule, a "local" counterpart was readily proposed to account for regioselectivity preferences, in particular, in ambident reactions. However, ample experimental evidence indicates that the local HSAB principle often fails to provide meaningful predictions. Here we examine the assumptions behind the standard proof of the local HSAB rule, showing that it is based on a flawed premise. By solving this issue, we show that it is critical to consider not only the charge transferred between the different reacting centers but also the charge reorganization within the non-reacting parts of the molecule. We propose different reorganization models and derive the corresponding regioselectivity rules for each.

Cardiac amyloidosis (CA) is a complex disease considered to be the most common underdiagnosed form of restrictive cardiomyopathy. Accumulation of misfolded proteins called amyloid fibrils in the extracellular space results in clinical deterioration and late diagnosis is associated with morbidity and mortality. Both types of this disease, light chain CA and transthyretin-related CA share many cardiac and extracardiac features that compromise multiple organs such as kidneys, musculoskeletal system, autonomic nervous system, and gastrointestinal tract. Early diagnosis and detection of CA are imperative. Clinicians should maintain a high degree of suspicion among patients with unexplained diastolic heart failure to implement different disease-altering therapies at the early stages of the disease. In this article, we provided a comprehensive review of multiple invasive and non-invasive cardiac imaging modalities with their respective degrees of sensitivities and specificity., (Copyright © 2022 Elsevier Inc. All rights reserved.)

We present a first-principles approach for the calculation of solvation energies and enthalpies with respect to different ion pair combinations in various solvents. The method relies on the conceptual density functional theory (DFT) of solvation, from which detailed expressions for the solvation energies can be derived. In addition to fast and straightforward gas phase calculations, we also study the influence of modified chemical reactivity descriptors in terms of electronic perturbations. The corresponding phenomenological changes in molecular energy levels can be interpreted as the influence of continuum solvents. Our approach shows that the introduction of these modified expressions is essential for a quantitative agreement between the calculated and the experimental results.

Fanpy is a free and open-source Python library for developing and testing multideterminant wavefunctions and related ab initio methods in electronic structure theory. The main use of Fanpy is to quickly prototype new methods by making it easier to convert the mathematical formulation of a new wavefunction ansätze to a working implementation. Fanpy is designed based on our recently introduced Flexible Ansatz for N-electron Configuration Interaction (FANCI) framework, where multideterminant wavefunctions are represented by their overlaps with Slater determinants of orthonormal spin-orbitals. In the simplest case, a new wavefunction ansatz can be implemented by simply writing a function for evaluating its overlap with an arbitrary Slater determinant. Fanpy is modular in both implementation and theory: the wavefunction model, the system's Hamiltonian, and the choice of objective function are all independent modules. This modular structure makes it easy for users to mix and match different methods and for developers to quickly explore new ideas. Fanpy is written purely in Python with standard dependencies, making it accessible for various operating systems. In addition, it adheres to principles of modern software development, including comprehensive documentation, extensive testing, quality assurance, and continuous integration and delivery protocols. This article is considered to be the official release notes for the Fanpy library., (© 2022 Wiley Periodicals LLC.)

We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15-17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at https://www.difacquim.com/english/events/2022-colloquium/ ., (© 2022. The Author(s).)

We present explainable machine learning approaches for the accurate prediction and understanding of solvation free energies, enthalpies, and entropies for different salts in various protic and aprotic solvents. As key input features, we use fundamental contributions from the conceptual density functional theory (DFT) of solutions. The most accurate models with the highest prediction accuracy for the experimental validation data set are decision tree-based approaches such as extreme gradient boosting and extra trees, which highlight the non-linear influence of feature values on target predictions. The detailed assessment of the importance of features in terms of Gini importance criteria as well as Shapley Additive Explanations (SHAP) and permutation and reduction approaches underlines the prominent role of anion and cation solvation effects in combination with fundamental electronic properties of the solvents. These results are reasonably consistent with previous assumptions and provide a solid rationale for more recent theoretical approaches.

We demonstrate the utility of basic chemical principles like the "|Δμ| big is good" (DMB) rule for the study of solvation interactions between distinct solutes such as ions and solvents. The corresponding approach allows us to define relevant criteria for maximum solvation energies of ion pairs in different solvents in terms of electronegativities and chemical hardnesses. Our findings reveal that the DMB principle culminates into the strong and weak acids and bases concept as recently derived for specific ion effects in various solvents. The further application of the DMB approach highlights a similar condition for the chemical hardnesses with a reminiscence to the hard/soft acids and bases principle. Comparable conclusions can also be drawn with regard to the change of the solvent. We show that favorable solvent interactions are mainly driven by low chemical hardnesses as well as high electronegativity differences between the ions and the solvent. Our findings highlight that solvation interactions are governed by basic chemical principles, which demonstrates the close similarity between solvation mechanisms and chemical reactions.

Multi-modality imaging plays critical roles during and after procedures associated with atrial fibrillation. Transesophageal echocardiography is an invaluable tool for left atrial appendage occlusion during the procedure and at follow-up. Both cardiac computed tomography and cardiac magnetic resonance contribute to postprocedural evaluation of pulmonary vein isolation ablation. The present review is the second of a 2-part series where we discuss the roles of cardiac imaging in the evaluation and management of patients with atrial fibrillation, focusing on intraprocedural and postprocedural assessment, including the clinical evidence and outcomes data supporting this future applications.

We show that the "|Δμ| big is good" principle holds at temperatures above absolute zero (the so-called "finite-T regime"). We also provide the first conditions hinting at the validity of this reactivity rule in cases where the chemical reactions involved have different signs in their chemical potential variations.

Atrial fibrillation (AF) is the most common arrhythmia worldwide and is associated with increased risk of heart failure, stroke, and death. In current medical practice, multimodality imaging is routinely used in the management of AF. Twenty-one years ago, the ACUTE trial (Assessment of Cardioversion Using Transesophageal Echocardiography) results were published, and the management of AF changed forever by incorporating transesophageal echocardiography guided cardioversion of patients in AF for the first time. Current applications of multimodality imaging in AF in 2022 include the use of transesophageal echocardiography and computed tomography before cardioversion to exclude left atrial thrombus and in left atrial appendage occlusion device implantation. Transesophageal echocardiography, cardiac computed tomography, and cardiac magnetic resonance are clinically used for AF ablation planning. The decision to use a particular imaging modality in AF is based on patient's characteristics, guideline recommendation, institutional preferences, expertise, and cost. In this first of 2-part review series, we discuss the preprocedural role of echocardiography, computed tomography, and cardiac magnetic resonance in the AF, with regard to their clinical applications, relevant outcomes data and unmet needs, and highlights future directions in this rapidly evolving field.

We present a new classification scheme for amino acids and nucleobases based on the electronic properties of the individual molecules. Using chemical reactivity indices such as electronegativity, electrophilicity, and chemical hardness, we can identify similarities and differences between each class of amino acids and nucleobases. Notable differences emerge in particular with regard to high, neutral or low electronegativity as well as different combinations of chemical hardness. Our approach allows us to relate these insights to the properties of the side groups in terms of a unique reference scheme. We further show that hydrophobic differences between amino acids are rather negligible in the context of electronic properties. Our classification scheme also rationalizes the occurrence of distinct stable nucleobase pairs and clearly emphasizes certain differences between individual molecules. The stability and abundant occurrence of Watson-Crick nucleobase pairs is further discussed in the context of the minimum electrophilicity principle.

Molecular dynamics (MD) is a core methodology of molecular modeling and computational design for the study of the dynamics and temporal evolution of molecular systems. MD simulations have particularly benefited from the rapid increase of computational power that has characterized the past decades of computational chemical research, being the first method to be successfully migrated to the GPU infrastructure. While new-generation MD software is capable of delivering simulations on an ever-increasing scale, relatively less effort is invested in developing postprocessing methods that can keep up with the quickly expanding volumes of data that are being generated. Here, we introduce a new idea for sampling frames from large MD trajectories, based on the recently introduced framework of extended similarity indices. Our approach presents a new, linearly scaling alternative to the traditional approach of applying a clustering algorithm that usually scales as a quadratic function of the number of frames. When showcasing its usage on case studies with different system sizes and simulation lengths, we have registered speedups of up to 2 orders of magnitude, as compared to traditional clustering algorithms. The conformational diversity of the selected frames is also noticeably higher, which is a further advantage for certain applications, such as the selection of structural ensembles for ligand docking. The method is available open-source at https://github.com/ramirandaq/MultipleComparisons.

In the first paper of this series, the authors derived an expression for the interaction energy between two reagents in terms of the chemical reactivity indicators that can be derived from density functional perturbation theory. While negative interaction energies can explain reactivity, reactivity is often more simply explained using the "|dμ| big is good" rule or the maximum hardness principle. Expressions for the change in chemical potential ( μ ) and hardness when two reagents interact are derived. A partial justification for the maximum hardness principle is that the terms that appear in the interaction energy expression often reappear in the expression for the interaction hardness, but with opposite sign., Competing Interests: The reviewer (WT) declared a past co-authorship with the author (CC) to the handling editor., (Copyright © 2022 Miranda-Quintana, Heidar-Zadeh, Fias, Chapman, Liu, Morell, Gómez, Cárdenas and Ayers.)

Reactivity descriptors indicate where a reagent is most reactive and how it is most likely to react. However, a reaction will only occur when the reagent encounters a suitable reaction partner. Determining whether a pair of reagents is well-matched requires developing reactivity rules that depend on both reagents. This can be achieved using the expression for the minimum-interaction-energy obtained from the density functional reactivity theory. Different terms in this expression will be dominant in different circumstances; depending on which terms control the reactivity, different reactivity indicators will be preferred., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Miranda-Quintana, Heidar-Zadeh, Fias, Chapman, Liu, Morell, Gómez, Cárdenas and Ayers.)

The quantification of chemical diversity has many applications in drug discovery, organic chemistry, food, and natural product chemistry, to name a few. As the size of the chemical space is expanding rapidly, it is imperative to develop efficient methods to quantify the diversity of large and ultralarge chemical libraries and visualize their mutual relationships in chemical space. Herein, we show an application of our recently introduced extended similarity indices to measure the fingerprint-based diversity of 19 chemical libraries typically used in drug discovery and natural products research with over 18 million compounds. Based on this concept, we introduce the Chemical Library Networks (CLNs) as a general and efficient framework to represent visually the chemical space of large chemical libraries providing a global perspective of the relation between the libraries. For the 19 compound libraries explored in this work, it was found that the (extended) Tanimoto index offers the best description of extended similarity in combination with RDKit fingerprints. CLNs are general and can be explored with any structure representation and similarity coefficient for large chemical libraries.

Extended (or n-ary) similarity indices have been recently proposed to extend the comparative analysis of binary strings. Going beyond the traditional notion of pairwise comparisons, these novel indices allow comparing any number of objects at the same time. This results in a remarkable efficiency gain with respect to other approaches, since now we can compare N molecules in O(N) instead of the common quadratic O(N 2 ) timescale. This favorable scaling has motivated the application of these indices to diversity selection, clustering, phylogenetic analysis, chemical space visualization, and post-processing of molecular dynamics simulations. However, the current formulation of the n-ary indices is limited to vectors with binary or categorical inputs. Here, we present the further generalization of this formalism so it can be applied to numerical data, i.e. to vectors with continuous components. We discuss several ways to achieve this extension and present their analytical properties. As a practical example, we apply this formalism to the problem of feature selection in QSAR and prove that the extended continuous similarity indices provide a convenient way to discern between several sets of descriptors., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

We discuss the current state of research as well as the future challenges for a deeper understanding of specific ion effects in protic and aprotic solvents as well as various additional media. Despite recent interest in solute or interfacial effects, we focus exclusively on the specific properties of ions in bulk electrolyte solutions. Corresponding results show that many mechanisms remain unknown for these simple media, although theoretical, computational, and experimental studies have provided some insights into explaining individual observations. In particular, the importance of local interactions and electronic properties is emphasized, which enabled a more consistent interpretation of specific ion effects over the past years. Despite current insufficient knowledge, we also discuss future challenges in relation to dynamic properties as well as the influence of different concentrations, different solvents, and solute contributions to gain a deeper understanding of specific ion effects for technological applications.

We present new algorithms to classify structural ensembles of macromolecules based on the recently proposed extended similarity measures. Molecular dynamics provides a wealth of structural information on systems of biological interest. As computer power increases, we capture larger ensembles and larger conformational transitions between states. Typically, structural clustering provides the statistical mechanics treatment of the system to identify relevant biological states. The key advantage of our approach is that the newly introduced extended similarity indices reduce the computational complexity of assessing the similarity of a set of structures from O( N 2 ) to O( N ). Here we take advantage of this favorable cost to develop several highly efficient techniques, including a linear-scaling algorithm to determine the medoid of a set (which we effectively use to select the most representative structure of a cluster). Moreover, we use our extended similarity indices as a linkage criterion in a novel hierarchical agglomerative clustering algorithm. We apply these new metrics to analyze the ensembles of several systems of biological interest such as folding and binding of macromolecules (peptide, protein, DNA-protein). In particular, we design a new workflow that is capable of identifying the most important conformations contributing to the protein folding process. We show excellent performance in the resulting clusters (surpassing traditional linkage criteria), along with faster performance and an efficient cost-function to identify when to merge clusters.

We study the electronic properties of low-weight organic co-solutes by means of conceptual density functional theory calculations. Our results highlight the important role of certain chemical reactivity descriptors such as chemical hardness, electronegativity, nucleofugality, and the electrofugality as important criteria to classify protein stabilizers and destabilizers. Our results imply Lewis basic properties with lower chemical hardness for stabilizers, while destabilizers show higher Lewis acidity with higher chemical hardness. Further consideration of analytical calculations in terms of transfer energies reveals the crucial role of co-solute-protein interactions which significantly change the interaction pattern of the stabilizing or destabilizing species. The corresponding outcomes connect statistical thermodynamics with the electronic properties of co-solutes and also allow us to define general principles for strong stabilizers and destabilizers.

In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study focuses only on classification models with large datasets (i.e. more than a thousand compounds). A comprehensive literature search and meta-analysis was carried out for nine different targets: hERG-mediated cardiotoxicity, blood-brain barrier penetration, permeability glycoprotein (P-gp) substrate/inhibitor, cytochrome P450 enzyme family, acute oral toxicity, mutagenicity, carcinogenicity, respiratory toxicity and irritation/corrosion. The comparison of the best classification models was targeted to reveal the differences between machine learning algorithms and modeling types, endpoint-specific performances, dataset sizes and the different validation protocols. Based on the evaluation of the data, we can say that tree-based algorithms are (still) dominating the field, with consensus modeling being an increasing trend in drug safety predictions. Although one can already find classification models with great performances to hERG-mediated cardiotoxicity and the isoenzymes of the cytochrome P450 enzyme family, these targets are still central to ADMET-related research efforts., (© 2021. The Author(s).)

Similarity measures are widely used in various areas from taxonomy to cheminformatics. To this end, a large number of similarity and distance measures (or, collectively, comparative measures) have been introduced, with only a few studies directed to revealing their inner relationships. We present a thorough analytical study of the conditions leading to two comparative measures providing equivalent results over a given set of molecules. A key part of this work is the introduction of a novel way to study the consistency between comparative measures: the differential consistency analysis (DCA). This tool reveals how the consistency can be established in an analytical way with minimal (or no) assumptions. We found that the consensus between Tanimoto and the Cosine coefficients improved by choosing a reference whose similarity to the rest of the molecules varies less, or by representing the molecules in a way that does not depend strongly on their size (i. e. bit frequency in the chosen fingerprint representation). The presented derivations are just some generic examples; DCA can be applied widely and for all binary similarity coefficients introduced so far, independently from the molecular representations., (© 2021 Wiley-VCH GmbH.)

Quantification of similarities between protein sequences or DNA/RNA strands is a (sub-)task that is ubiquitously present in bioinformatics workflows, and is usually accomplished by pairwise comparisons of sequences, utilizing simple ( e.g. percent identity) or more intricate concepts ( e.g. substitution scoring matrices). Complex tasks (such as clustering) rely on a large number of pairwise comparisons under the hood, instead of a direct quantification of set similarities. Based on our recently introduced framework that enables multiple comparisons of binary molecular fingerprints ( i.e. , direct calculation of the similarity of fingerprint sets), here we introduce novel symmetric similarity indices for analogous calculations on sets of character sequences with more than two ( t ) possible items ( e.g. DNA/RNA sequences with t  = 4, or protein sequences with t  = 20). The features of these new indices are studied in detail with analysis of variance (ANOVA), and demonstrated with three case studies of protein/DNA sequences with varying degrees of similarity (or evolutionary proximity). The Python code for the extended many-item similarity indices is publicly available at: https://github.com/ramirandaq/tn&#95;Comparisons., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

Despite being a central concept in cheminformatics, molecular similarity has so far been limited to the simultaneous comparison of only two molecules at a time and using one index, generally the Tanimoto coefficent. In a recent contribution we have not only introduced a complete mathematical framework for extended similarity calculations, (i.e. comparisons of more than two molecules at a time) but defined a series of novel idices. Part 1 is a detailed analysis of the effects of various parameters on the similarity values calculated by the extended formulas. Their features were revealed by sum of ranking differences and ANOVA. Here, in addition to characterizing several important aspects of the newly introduced similarity metrics, we will highlight their applicability and utility in real-life scenarios using datasets with popular molecular fingerprints. Remarkably, for large datasets, the use of extended similarity measures provides an unprecedented speed-up over "traditional" pairwise similarity matrix calculations. We also provide illustrative examples of a more direct algorithm based on the extended Tanimoto similarity to select diverse compound sets, resulting in much higher levels of diversity than traditional approaches. We discuss the inner and outer consistency of our indices, which are key in practical applications, showing whether the n-ary and binary indices rank the data in the same way. We demonstrate the use of the new n-ary similarity metrics on t-distributed stochastic neighbor embedding (t-SNE) plots of datasets of varying diversity, or corresponding to ligands of different pharmaceutical targets, which show that our indices provide a better measure of set compactness than standard binary measures. We also present a conceptual example of the applicability of our indices in agglomerative hierarchical algorithms. The Python code for calculating the extended similarity metrics is freely available at: https://github.com/ramirandaq/MultipleComparisons.

Quantification of the similarity of objects is a key concept in many areas of computational science. This includes cheminformatics, where molecular similarity is usually quantified based on binary fingerprints. While there is a wide selection of available molecular representations and similarity metrics, there were no previous efforts to extend the computational framework of similarity calculations to the simultaneous comparison of more than two objects (molecules) at the same time. The present study bridges this gap, by introducing a straightforward computational framework for comparing multiple objects at the same time and providing extended formulas for as many similarity metrics as possible. In the binary case (i.e. when comparing two molecules pairwise) these are naturally reduced to their well-known formulas. We provide a detailed analysis on the effects of various parameters on the similarity values calculated by the extended formulas. The extended similarity indices are entirely general and do not depend on the fingerprints used. Two types of variance analysis (ANOVA) help to understand the main features of the indices: (i) ANOVA of mean similarity indices; (ii) ANOVA of sum of ranking differences (SRD). Practical aspects and applications of the extended similarity indices are detailed in the accompanying paper: Miranda-Quintana et al. J Cheminform. 2021. https://doi.org/10.1186/s13321-021-00504-4 . Python code for calculating the extended similarity metrics is freely available at: https://github.com/ramirandaq/MultipleComparisons .

We present a detailed study of specific ion effects, volcano plots and the law of matching solvent affinities by means of a conceptual density functional theory (DFT) approach. Our results highlight that specific ion effects and the corresponding implications on the solvation energy are mainly due to differences in the electric chemical potentials and chemical hardnesses of the ions and the solvent. Our approach can be further used to identify reliable criteria for the validity of the law of matching solvent affinities. Basic expressions are derived, which allow us to study the limiting conditions for this empirical observation with regard to matching chemical reactivity indices. Moreover, we show that chaotropic and kosmotropic concepts and their implications for the stability of ion pairs are directly related to a generalized strong and weak acids and bases (SWAB) principle for ions in solution, which is also applicable to rationalize the shape of volcano plots for different solvents. In contrast to previous assumptions, all empirical findings can be explained by the properties of local solvent-ion complexes which dominate the specific global behavior of ion pairs in solution., (© 2020 The Authors. ChemPhysChem published by Wiley-VCH GmbH.)

The knowledge of thermodynamic properties for novel electrolyte formulations is of fundamental interest for industrial applications as well as academic research. Herewith, we present an artificial neural networks (ANN) approach for the prediction of solvation energies and entropies for distinct ion pairs in various protic and aprotic solvents. The considered feed-forward ANN is trained either by experimental data or computational results from conceptual density functional theory calculations. The proposed concept of mapping computed values to experimental data lowers the amount of time-consuming and costly experiments and helps to overcome certain limitations. Our findings reveal high correlation coefficients between predicted and experimental values which demonstrate the validity of our approach.

Background: It is currently unknown if outcomes after transcatheter aortic valve replacement (TAVR) differ according to the prosthetic valve deployed in patients with bicuspid aortic valves (BAV)., Objectives: This study evaluated valve-specific outcomes post-TAVR in patients with BAV., Methods: Literature search was performed using the Cochrane databases, PubMed, ClinicalTrials, SCOPUS and EMBASE databases from inception until July 2018. We computed risk ratios and their 95% confidence intervals for all outcomes of interest. For each outcome, the data were pooled using a multivariate random-effects meta-analysis including multiple treatment as well as direct and indirect comparisons., Results: Ten studies enrolling a total of 1547 BAV patients undergoing TAVR using 6 different prosthetic valve types were analyzed. There were no significant differences in 30-day all-cause mortality, life-threatening bleeding and device success among the diverse prosthetic valve types implanted. However, 2nd generation balloon-expandable valves had consistently lower risk of moderate-to-severe prosthetic valve regurgitation., Conclusion: In patients with BAV, there were no significant differences in 30-day all-cause mortality after TAVR among the various prosthetic valve types., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Speckle-tracking echocardiography has enabled clinicians to detect changes in myocardial function with more sensitivity than that afforded by traditional diastolic and systolic functional measurements, including left ventricular ejection fraction. Speckle-tracking echocardiography enables evaluation of myocardial strain in terms of strain (percent change in length of a myocardial segment relative to its length at baseline) and strain rate (strain per unit of time). Both measurements have potential for use in diagnosing and monitoring the cardiovascular side effects of cancer therapy. Regional and global strain measurements can independently predict outcomes not only in patients who experience cardiovascular complications of cancer and cancer therapy, but also in patients with a variety of other clinical conditions. This review and case series examine the clinical applications and overall usefulness of speckle-tracking echocardiography in cardio-oncology and, more broadly, in clinical cardiology., (© 2020 by the Texas Heart® Institute, Houston.)

Background: Randomized clinical trials demonstrated the benefits of percutaneous coronary interventions (PCI) in diverse clinical settings. Patients with cancer were not routinely included in these studies., Methods/results: Literature search of PubMed, Cochrane, Medline, SCOPUS, EMBASE, and ClinicalTrials was conducted to identify studies that assessed one-year all-cause, cardiovascular and non-cardiovascular mortality in patients with historical or active cancer. Using the random effects model, we computed risk ratios (RRs) and standardized mean differences and their 95% confidence intervals for the dichotomous and continuous measures and outcomes, respectively. Of 171 articles evaluated in total, 5 eligible studies were included in this meta-analysis. In total, 33,175 patients receiving PCI were analyzed, of whom 3323 patients had cancer and 29,852 no cancer history. Patients in the cancer group had greater all-cause mortality [RR 2.22 (1.51-3.26; p<0.001)], including cardiovascular mortality [RR 1.34 (1.1-1.65; p=0.005)] and non-cardiovascular mortality [RR 3.42 (1.74-6.74; p≤0.001], at one-year compared to non-cancer patients. Patients in the cancer group had greater one-month all-cause mortality [RR 2.01 (1.24-3.27; p=0.005)] and greater non-cardiovascular mortality [RR 6.87 (3.10-15.21; p≤0.001)], but no difference in one-month cardiovascular mortality compared to non-cancer patients. Meta-regression analyses showed that the difference in one-year all-cause and cardiovascular mortality between both groups was not attributable to differences in baseline characteristics, index PCI characteristics, or medications prescribed at discharge., Conclusions: Patients with cancer undergoing PCI have worse mid-term outcomes compared to non-cancer patients. Cancer patients should be managed by a multi-specialist team, in an effort to close the mortality gap., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Rationale: The mitochondrial Poldip2 (protein polymerase interacting protein 2) is required for the activity of the tricarboxylic acid cycle. As a consequence, Poldip2 deficiency induces metabolic reprograming with repressed mitochondrial respiration and increased glycolytic activity. Though homozygous deletion of Poldip2 is lethal, heterozygous mice are viable and show protection against aneurysm and injury-induced neointimal hyperplasia, diseases linked to loss of vascular smooth muscle differentiation. Thus, we hypothesize that the metabolic reprograming induced by Poldip2 deficiency controls VSMC differentiation., Objective: To determine the role of Poldip2-mediated metabolic reprograming in phenotypic modulation of VSMC., Methods and Results: We show that Poldip2 deficiency in vascular smooth muscle in vitro and in vivo induces the expression of the SRF (serum response factor), myocardin, and MRTFA (myocardin-related transcription factor A) and dramatically represses KLF4 (Krüppel-like factor 4). Consequently, Poldip2-deficient VSMC and mouse aorta express high levels of contractile proteins and, more significantly, these cells do not dedifferentiate nor acquire macrophage-like characteristics when exposed to cholesterol or PDGF (platelet-derived growth factor). Regarding the mechanism, we found that Poldip2 deficiency upregulates the hexosamine biosynthetic pathway and OGT (O-linked N-acetylglucosamine transferase)-mediated protein O-GlcNAcylation. Increased protein glycosylation causes the inhibition of a nuclear ubiquitin proteasome system responsible for SRF stabilization and KLF4 repression and is required for the establishment of the differentiated phenotype in Poldip2-deficient cells., Conclusions: Our data show that Poldip2 deficiency induces a highly differentiated phenotype in VSMCs through a mechanism that involves regulation of metabolism and proteostasis. Additionally, our study positions mitochondria-initiated signaling as key element of the VSMC differentiation programs that can be targeted to modulate VSMC phenotype during vascular diseases.

The present work aimed to study the guest-host complexes of β-hexachlorocyclohexane (β-HCH), a pesticide with high environmental stability that can cause severe health problems, with the most common cyclodextrins (α-, β-, and γ-CDs). The formation reactions of these molecular inclusion complexes were addressed in this research. The multiple minima hypersurface methodology, quantum calculations based on density functional theory and a topological exploration of the electron density based on the quantum theory of atoms in molecules approach were used to characterize the interaction spaces of the pollutant with the three CDs. Additionally, charge distribution, charge transfer and dual descriptor analyses were employed to elucidate the driving forces involved in the formation of these molecular inclusion complexes. Three types of fundamental interactions were observed: total occlusion, partial occlusion and external interaction (non-occlusion). Finally, experiments were performed to confirm the formation of the studied complexes. The most stable complexes were obtained when γ-CD was the host molecule. The interactions between the pesticide and CDs have fundamentally dispersive natures, as was confirmed experimentally by spectroscopic results. All the obtained results suggest the possibility of using CDs for the purification and treatment of water polluted with β-HCH., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)