Search

Aims Quantitative assessment of tricuspid regurgitation (TR) is challenging, and the prognostic implications of cardiac magnetic resonance (CMR)-quantified measures of TR remain unclear in patients with heart failure with reduced ejection fraction (HFrEF). This study investigated the prognostic value of functional TR quantified by CMR in patients with HFrEF. Methods and results A total of 262 patients with HFrEF who underwent CMR were analysed. Patients who had primary TR, who had acute HF, or for whom cardiac surgery was planned were excluded. TR volume and fraction were indirectly calculated via subtracting methods. The primary outcome was defined as a composite of all-cause death and hospitalization for HF. Renal outcome was defined as a composite of a decrease in estimated glomerular filtration rate ≥50% or progression to end-stage renal disease. During the follow-up period (median 921 days), 62 primary outcomes and 48 renal outcomes occurred. When divided into two or three groups based on TR fraction in Kaplan–Meier analysis, patients with higher TR fractions showed worse primary outcomes and renal outcomes than those with lower TR fractions. In Cox regression analysis, a 10% increase in TR fraction was significantly associated with primary outcome [hazard ratio (HR) 1.49, 95% confidence interval (CI) 1.29–1.73, P < 0.001] and renal outcome (HR 1.31, 95% CI 1.12–1.55, P = 0.001). TR fraction exhibited a strong positive linear relationship with primary outcomes and renal outcomes in restricted cubic spline curves. Conclusion CMR-quantified measures of TR were independently associated with adverse clinical outcomes in patients with HFrEF. [ABSTRACT FROM AUTHOR]

Aims: Increased red cell distribution width (RDW) is a poor prognostic factor in patients with heart failure (HF). However, only a few large‐scale studies have identified the clinical utility of RDW after adjusting for covariates affecting RDW. Methods and results: From January 2010 to April 2021, we retrospectively enrolled patients diagnosed with HF from three referral hospitals with available RDW data (taken within 3 months of HF diagnosis) using an integrated clinical data system. Patients with an ejection fraction (EF) < 50% or HFA‐PEFF (Heart Failure Association Pre‐test assessment, Echocardiography and natriuretic peptide, Functional testing, Final aetiology) score ≥ 2 without severe valvular heart disease or coronary revascularization were enrolled. The primary endpoint was all‐cause mortality, and cardiovascular mortality was also collected. Multivariable Cox regression analysis and stabilized inverse probability of treatment weighting (IPTW) were used to identify any association between RDW and all‐cause death by balancing covariates or compounding factors. The global χ2 score was calculated and discrimination analysis was performed to evaluate the incremental value of RDW in predicting prognosis. Among the 6599 participants enrolled in this study, 1256 (19.0%) cases of all‐cause death occurred, and the median duration of follow‐up was 887 (interquartile range 351–1589) days. Elevated RDW at the initial diagnosis was associated with poor prognosis [cumulative incidence: 819 (30.2%) vs. 437 (11.2%), relative risk 1.58, 95% confidence interval (CI) 1.51–1.67, log‐rank P < 0.001]. Multivariable Cox analysis showed that elevated RDW was a poor prognostic factor for the primary endpoint [hazard ratio (HR) 1.11, 95% CI 1.06–1.16, P < 0.001], independent of clinical risk factors, N‐terminal pro‐brain natriuretic peptide (NT‐proBNP), and EF, which was concordant with the stabilized IPTW (HR 1.29, 95% CI 1.10–1.49, P < 0.001). Adding RDW to model composed of traditional risk factors, NT‐proBNP, and echocardiographic parameters showed incremental prognostic value for predicting poor prognosis (area under the receiver operating characteristic curve, 0.799–0.826; P < 0.001). Conclusions: Increased RDW at the time of diagnosis is associated with poor prognosis in patients with HF, independent of clinical risk factors, such as NT‐proBNP, and echocardiographic parameters. Therefore, RDW may aid in the management of these patients beyond traditional risk factors. [ABSTRACT FROM AUTHOR]

Hygrothermal simulation programs are widely used nowadays, but reliable results can only be achieved with the knowledge of input parameter values. This study shows how the difficulties, obtaining accurate material properties and specifying boundary conditions, affect the hygrothermal performance of the construction. A wood-based construction is investigated under climate conditions of South Korea. Sensitive material parameters and boundary conditions, which significantly influence the relative humidity in the construction, are identified by means of the method of Morris. The most influential material parameters are varied for specific boundary conditions using Latin Hypercube sampling, resulting in a wide range of relative humidity values (~51 % until ~99 %) on OSB surface. Low relative humidity values on OSB are obtained with a Pareto-optimal material parameter set.

Aims We aimed to evaluate the efficacy of the measured effective orifice area (EOA)/reference EOA ratio in discriminating mechanical prosthetic aortic valve (PAV) obstruction. Methods and results This is a retrospective study of 193 mechanical PAV patients with an elevated mean transprosthetic pressure gradient (PG) over 20 mmHg or peak velocity over 3 m/s. Of those, 143 patients were objectively proven PAV obstruction with cardiac computed tomography or surgical inspection. The EOA was measured using the continuity equation, and the reference EOA values were obtained from previous guidelines. The measured/reference EOA ratio was significantly lower in the obstruction group (0.63 ± 0.18 vs. 0.86 ± 0.17; P < 0.001). The EOA ratio added incremental value for discriminating obstruction from the conventional parameters recommended in the guidelines. Receiver operating characteristic curve analysis revealed that the measured/reference EOA ratio discriminated PAV obstruction from those without obstruction [area under the curve (AUC), 0.840; 95% confidence interval, 0.783–0.898; P < 0.001]. A cutoff of 0.71 had 73.4% sensitivity and 82.0% specificity. The novel diagnostic algorithm adding the EOA ratio had similar accuracy to previous guideline algorithms, including reference EOA, and conventional Doppler parameters (AUC, 0.763 vs. 0.731; P = 0.309). In patients with a large PAV (≥23 mm), the novel algorithm had higher accuracy than the previous algorithm (AUC, 0.788 vs. 0.642; P = 0.019). Conclusion The ratio of measured/reference EOA adds incremental value over conventional Doppler parameters and might be helpful for distinguishing PAV obstruction. [ABSTRACT FROM AUTHOR]

Natural metalloenzymes stabilize metal centers by utilizing multiple imidazole moieties. Inspired by nature's design principles, the introduction of multiple azoles into ligands has been an effective method for constructing transition metal complexes. Herein, we describe a post‐synthetic modification of peptoids to incorporate multiple azoles on side chains. A simple substitution reaction between an azole (imidazole, pyrazole, 1,2,3‐triazole, and tetrazole) and a chloroalkyl‐containing peptoid provided access to a variety of azole‐containing peptoids. Ten azole‐containing peptoids were synthesized from a single chloroalkyl‐containing peptoid, and the efficiency of each azole for the substitution reaction was evaluated. We have identified that several of the azole‐containing peptoids are capable of binding with Cu(II) and Fe(III). Our synthetic approach can contribute to the expansion of peptoids' chemical diversity and the development of novel peptoids for metal recognition and catalysis. [ABSTRACT FROM AUTHOR]

The accurate prediction of cancer drug sensitivity according to the multiomics profiles of individual patients is crucial for precision cancer medicine. However, the development of prediction models has been challenged by the complex crosstalk of input features and the resistance-dominant drug response information contained in public databases. In this study, we propose a novel multidrug response prediction framework, response-aware multitask prediction (RAMP), via a Bayesian neural network and restrict it by soft-supervised contrastive regularization. To utilize network embedding vectors as representation learning features for heterogeneous networks, we harness response-aware negative sampling, which applies cell line–drug response information to the training of network embeddings. RAMP overcomes the prediction accuracy limitation induced by the imbalance of trained response data based on the comprehensive selection and utilization of drug response features. When trained on the Genomics of Drug Sensitivity in Cancer dataset, RAMP achieved an area under the receiver operating characteristic curve > 89%, an area under the precision-recall curve > 59% and an |$\textrm{F}_1$| score > 52% and outperformed previously developed methods on both balanced and imbalanced datasets. Furthermore, RAMP predicted many missing drug responses that were not included in the public databases. Our results showed that RAMP will be suitable for the high-throughput prediction of cancer drug sensitivity and will be useful for guiding cancer drug selection processes. The Python implementation for RAMP is available at https://github.com/hvcl/RAMP. [ABSTRACT FROM AUTHOR]