Stereoselective Ring Expansion Metathesis Polymerization with Cationic Molybdenum Alkylidyne N-Heterocyclic Carbene Complexes.

Molybdenum alkylidyne N-heterocyclic carbene (NHC) complexes of the type [Mo(C-p-C6H4Y)(OC(R)(CF3)2)2 (L)(NHC)][B(ArF)4] (Y = OMe, NO2; R = CH3, CF3; L = none, pivalonitrile, tetrahydrofuran; NHC = 1,3-dimesitylimidazol-2-ylidene (IMes), 1,3-dimesityl-3,4-dihydroimidazol-2-ylidene (IMesH2), 1,3-dimesityl-3,4-dichloroimidazol-2-ylidene (IMesCl2), 1,3-diisopropylimidazol-2-ylidene (IiPr); B(ArF)4- = tetrakis(3,5-bis(trifluoromethyl)phen-1-yl)borate) were used in the ring expansion metathesis polymerization (REMP) of cyclic olefins. With cis-cyclooctene (cCOE) cyclic, low molecular weight oligomers were obtained at low monomer concentrations and the cyclic nature of the polymer was confirmed by MALDI-TOF measurements. High-molecular weight cyclic poly(cCOE) became available at high monomer concentrations. Also, post-REMP allowed for converting low-molecular-weight cyclic poly(cCOE) into high-molecular-weight cyclic poly(cCOE). Tailored catalysts together with suitable additives offered access to the stereoselective REMP of functional norbornenes providing functional cis-isotactic (cis-it), cis-syndiotactic (cis-st) and trans-it poly(norbornene)s with up to 99% stereoselectivity. Mechanistic details supported by density functional theory (DFT) calculations are outlined.

Machine learning-driven investigation of the structure and dynamics of the BMIM-BF4 room temperature ionic liquid.

Room-temperature ionic liquids are an exciting group of materials with the potential to revolutionize energy storage. Due to their chemical structure and means of interaction, they are challenging to study computationally. Classical descriptions of their inter- and intra-molecular interactions require time intensive parametrization of force-fields which is prone to assumptions. While ab initio molecular dynamics approaches can capture all necessary interactions, they are too slow to achieve the time and length scales required. In this work, we take a step towards addressing these challenges by applying state-of-the-art machine-learned potentials to the simulation of 1-butyl-3-methylimidazolium tetrafluoroborate. We demonstrate a learning-on-the-fly procedure to train machine-learned potentials from single-point density functional theory calculations before performing production molecular dynamics simulations. Obtained structural and dynamical properties are in good agreement with computational and experimental references. Furthermore, our results show that hybrid machine-learned potentials can contribute to an improved prediction accuracy by mitigating the inherent shortsightedness of the models. Given that room-temperature ionic liquids necessitate long simulations to address their slow dynamics, achieving an optimal balance between accuracy and computational cost becomes imperative. To facilitate further investigation of these materials, we have made our IPSuite-based training and simulation workflow publicly accessible, enabling easy replication or adaptation to similar systems.

In-depth exploration of catalytic sites on amorphous solid water: I. The astrosynthesis of aminomethanol.

Chemical processes taking place on ice-grain mantles are pivotal to the complex chemistry of interstellar environments. In this study, we conducted a comprehensive analysis of the catalytic effects of an amorphous solid water (ASW) surface on the reaction between ammonia (NH3) and formaldehyde (H2CO) to form aminomethanol (NH2CH2OH) using density functional theory. We identified potential catalytic sites based on the binding energy distribution of NH3 and H2CO reactants, on a set-of-clusters surface model composed of 22 water molecules and found a total of 14 reaction paths. Our results indicate that the catalytic sites can be categorized into four groups, depending on the interactions of the carbonyl oxygen and the amino group with the ice surface in the reactant complex. A detailed analysis of the reaction mechanism using Intrinsic Reaction Coordinate and reaction force analysis, revealed three distinct chemical events for this reaction: formation of the C-N bond, breaking of the N-H bond, and formation of the O-H hydroxyl bond. Depending on the type of catalytic site, these events can occur within a single, concerted, albeit asynchronous, step, or can be isolated in a step-wise mechanism, with the lowest overall transition state energy observed at 1.3 kcal mol-1. A key requirement for the low-energy mechanism is the presence of a pair of dangling OH bonds on the surface, found at 5% of the potential catalytic sites on an ASW porous surface.

Health-related quality of life is an independent predictor of mortality and hospitalisations in transthyretin amyloid cardiomyopathy: a prospective cohort study.

PURPOSE: Transthyretin amyloid cardiomyopathy (ATTR-CM) is associated with severely impaired health-related quality of life (HRQL). HRQL is an independent predictor of outcome in heart failure (HF), but data on patients with ATTR-CM is scarce. This study therefore aims to evaluate the association of HRQL with outcome in ATTR-CM. METHODS: Patients from our prospective ATTR-CM registry were assessed using the Kansas City cardiomyopathy questionnaire (KCCQ), the Minnesota living with HF questionnaire (MLHFQ), and the EuroQol five dimensions questionnaire (EQ-5D). Cox regression analysis was utilised to assess the impact of HRQL on all-cause mortality. RESULTS: 167 patients [80 years; interquartile range (IQR): 76-84; 80.8% male] were followed for a median of 27.6 (IQR: 9.7-41.8) months. The primary endpoint of all-cause mortality was met by 43 (25.7%) patients after a median period of 16.2 (IQR: 9.1-28.1) months. In a univariate Cox regression for mortality, a 10-point change in the KCCQ implied a hazard ratio (HR) of 0.815 [95%-confidence interval (CI): 0.725-0.916; p = 0.001], in the EQ-5D VAS of 0.764 (95%-CI: 0.656-0.889; p < 0.001), and 1.163 (95%-CI: 1.114-1.433; p < 0.001) in the MLHFQ. After adjustment for established biomarkers of HF, all-cause mortality was predicted independently by the EQ-5D VAS (HR: 0.8; 95%-CI: 0.649-0.986; p = 0.037; per 10 points) and the MLHFQ (HR: 1.228; 95%-CI: 1.035-1.458; p = 0.019; per 10 points). CONCLUSION: HRQL is a predictor of outcome in ATTR-CM. The EQ-5D VAS and the MLHFQ predict survival independent of biomarkers of HF.

Patients with transthyretin amyloid cardiomyopathy, a condition causing heart failure and mostly seen in the elderly, suffer from shortness of breath and reduced maximum physical performance. Disease assessment is currently based on blood analysis for markers of heart failure. However, standardised patient questionnaires also allow to estimate disease severity. In this study, we analyse different standardised patient questionnaires for their ability to predict adverse events including death and heart failure-related hospitalisations. The analysis demonstrates that an increase of ten points in the Kansas City Cardiomyopathy questionnaire, a tool specifically designed for patients with heart failure, implies a reduction of mortality risk of close to 20%. Interestingly, even the very simple visual analogue scale, a quality-of-life measurement tool which asks the patient to rate their health on a scale from zero (worst) to one hundred (best) has demonstrated remarkable predictive utility. An increase of ten points on this scale resulted in a reduction of risk for death from any cause of almost a quarter. This analysis suggests that standardised patient questionnaires for the assessment of quality of life may play an important role in the evaluation of patients with transthyretin amyloid cardiomyopathy and estimation of prognosis.

Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly.

The field of machine learning potentials has experienced a rapid surge in progress, thanks to advances in machine learning theory, algorithms, and hardware capabilities. While the underlying methods are continuously evolving, the infrastructure for their deployment has lagged. The community, due to these rapid developments, frequently finds itself split into groups built around different implementations of machine-learned potentials. In this work, we introduce IPSuite, a Python-driven software package designed to connect different methods and algorithms from the comprehensive field of machine-learned potentials into a single platform while also providing a collaborative infrastructure, helping ensure reproducibility. Furthermore, the data management infrastructure of the IPSuite code enables simple model sharing and deployment in simulations. Currently, IPSuite supports six state-of-the-art machine learning approaches for the fitting of interatomic potentials as well as a variety of methods for the selection of training data, running of ab initio calculations, learning-on-the-fly strategies, model evaluation, and simulation deployment.

Probing Self-Diffusion of Guest Molecules in a Covalent Organic Framework: Simulation and Experiment.

Covalent organic frameworks (COFs) are a class of porous materials whose sorption properties have so far been studied primarily by physisorption. Quantifying the self-diffusion of guest molecules inside their nanometer-sized pores allows for a better understanding of confinement effects or transport limitations and is thus essential for various applications ranging from molecular separation to catalysis. Using a combination of pulsed field gradient nuclear magnetic resonance measurements and molecular dynamics simulations, we have studied the self-diffusion of acetonitrile and chloroform in the 1D pore channels of two imine-linked COFs (PI-3-COF) with different levels of crystallinity and porosity. The higher crystallinity and porosity sample exhibited anisotropic diffusion for MeCN parallel to the pore direction, with a diffusion coefficient of Dpar = 6.1(3) × 10-10 m2 s-1 at 300 K, indicating 1D transport and a 7.4-fold reduction in self-diffusion compared to the bulk liquid. This finding aligns with molecular dynamics simulations predicting 5.4-fold reduction, assuming an offset-stacked COF layer arrangement. In the low-porosity sample, more frequent diffusion barriers result in isotropic, yet significantly reduced diffusivities (DB = 1.4(1) × 10-11 m2 s-1). Diffusion coefficients for chloroform at 300 K in the pores of the high- (Dpar = 1.1(2) × 10-10 m2 s-1) and low-porosity (DB = 4.5(1) × 10-12 m2 s-1) samples reproduce these trends. Our multimodal study thus highlights the significant influence of real structure effects such as stacking faults and grain boundaries on the long-range diffusivity of molecular guest species while suggesting efficient intracrystalline transport at short diffusion times.

Amyloid Burden Correlates with Electrocardiographic Findings in Patients with Cardiac Amyloidosis-Insights from Histology and Cardiac Magnetic Resonance Imaging.

Cardiac amyloidosis (CA) is associated with several distinct electrocardiographic (ECG) changes. However, the impact of amyloid depositions on ECG parameters is not well investigated. We therefore aimed to assess the correlation of amyloid burden with ECG and test the prognostic power of ECG findings on outcomes in patients with CA. Consecutive CA patients underwent ECG assessment and cardiac magnetic resonance imaging (CMR), including the quantification of extracellular volume (ECV) with T1 mapping. Moreover, seven patients underwent additional amyloid quantification using immunohistochemistry staining of endomyocardial biopsies. A total of 105 CA patients (wild-type transthyretin: 74.3%, variant transthyretin: 8.6%, light chain: 17.1%) were analyzed for this study. We detected correlations of total QRS voltage with histologically quantified amyloid burden (r = -0.780, p = 0.039) and ECV (r = -0.266, p = 0.006). In patients above the ECV median (43.9%), PR intervals were significantly longer (p = 0.016) and left anterior fascicular blocks were more prevalent (p = 0.025). In our survival analysis, neither Kaplan-Meier curves (p = 0.996) nor Cox regression analysis detected associations of QRS voltage with adverse patient outcomes (hazard ratio: 0.995, p = 0.265). The present study demonstrated that an increased amyloid burden is associated with lower voltages in CA patients. However, baseline ECG findings, including QRS voltage, were not associated with adverse outcomes.

Cardiopulmonary exercise testing in transthyretin amyloid cardiomyopathy patients: a long-term follow-up study.

AIMS: Patients with transthyretin amyloid cardiomyopathy (ATTR-CM) experience reduced functional capacity. We evaluated changes in functional capacity over extensive follow-up using cardiopulmonary exercise testing (CPX). METHODS: ATTR-CM patients underwent CPX and blood testing at baseline, first [V1, 8 (6-10) months] and second follow-up (V2) at 35 (26-41) months after start of disease-specific therapy. RESULTS: We included 34 ATTR-CM patients, aged 77 (±6) years (88.2% men). CPX showed two patterns with functional capacity improvement at V1 and deterioration at V2. Peak work capacity ( P = 0.005) and peak oxygen consumption (VO 2 , P = 0.012) increased at V1 compared with baseline and decreased at V2. The ventilation to carbon dioxide relationship slope (VE/VCO 2 ) increased at V2 compared with baseline and V1 ( P = 0.044). A cut-off for peak VO 2 at 14 ml/kg·min showed more events (composite of death and heart failure hospitalization): less than 14 vs. greater than 14 ml/kg·min ( P  = 0.013). Cut-offs for VE/VCO 2 slope at 40 showed more events greater than 40 vs. less than 40 ( P  = 0.009). CONCLUSION: ATTR-CM patients showed an improvement and deterioration in the short-term and long-term follow-up, respectively, with a better prognosis for those with peak VO 2 above 14 ml/kg·min and for a VE/VCO 2 slope below 40.

Native skeletal muscle T1-time on cardiac magnetic resonance: A predictor of outcome in patients with heart failure with preserved ejection fraction.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is associated with heart failure (HF) hospitalizations and death. Previous studies have shown that altered muscle composition is associated with higher risk of adverse outcome in HFpEF patients. AIM: The purpose of our study was to investigate the association between skeletal muscle composition, as measured by skeletal muscle T1-times on cardiac magnetic resonance (CMR) imaging, and adverse outcome. METHODS: We measured skeletal muscle T1-times of the back muscles on standard CMR images in a prospective cohort of HFpEF patients. Cox regression models were used to test the association of skeletal muscle T1-times and adverse outcome defined as hospitalization for HF and/or cardiovascular death. RESULTS: We included 101 patients (mean age 72±7 years, 71 % female) in our study. The median skeletal muscle T1-times were 842 ms (IQR 806-881 ms). In univariate analysis high muscle T1-time was associated with adverse outcome (HR=1.96 [95 % CI, 1.31-2.94] per every 100 ms increase; p=.001). After adjustment for age, sex, body mass index, left- and right ventricular ejection fraction, N-terminal pro-brain natriuretic peptide and myocardial native T1-times, native skeletal muscle T1-time remained an independent predictor for adverse outcome (HR=1.94 [95 % CI, 1.24-3.03] per every 100 ms increase; p=.004). CONCLUSION: In patients with HFpEF, high skeletal muscle T1-times on standard CMR scans are associated with higher rates of HF hospitalizations and cardiovascular death. CONDENSED ABSTRACT: Skeletal muscle abnormalities are common in patients with heart failure with preserved ejection fraction (HFpEF). The present study evaluates skeletal muscle composition, as quantified by native skeletal muscle T1-times of the back muscles on standard cardiac magnetic resonance imaging, and assessed the association with adverse outcome, defined as hospitalization for heart failure and/or cardiovascular death. In a prospective cohort of 101 patients with HFpEF, we found that high native skeletal muscle T1-times are associated with an increased risk for adverse outcome. These findings suggest that native skeletal muscle T1-time may serve as marker for improved risk prediction.