Frameshift variants in C10orf71 cause dilated cardiomyopathy in human, mouse, and organoid models.

Research advances over the past 30 years have confirmed a critical role for genetics in the etiology of dilated cardiomyopathies (DCMs). However, full knowledge of the genetic architecture of DCM remains incomplete. We identified candidate DCM causal gene, C10orf71, in a large family with 8 patients with DCM by whole-exome sequencing. Four loss-of-function variants of C10orf71 were subsequently identified in an additional group of492 patients with sporadic DCM from 2 independent cohorts. C10orf71 was found to be an intrinsically disordered protein specifically expressed in cardiomyocytes. C10orf71-KO mice had abnormal heart morphogenesis during embryonic development and cardiac dysfunction as adults with altered expression and splicing of contractile cardiac genes. C10orf71-null cardiomyocytes exhibited impaired contractile function with unaffected sarcomere structure. Cardiomyocytes and heart organoids derived from human induced pluripotent stem cells with C10orf71 frameshift variants also had contractile defects with normal electrophysiological activity. A rescue study using a cardiac myosin activator, omecamtiv mecarbil, restored contractile function in C10orf71-KO mice. These data support C10orf71 as a causal gene for DCM by contributing to the contractile function of cardiomyocytes. Mutation-specific pathophysiology may suggest therapeutic targets and more individualized therapy.

Machine learning-based coronary artery calcium score predicted from clinical variables as a prognostic indicator in patients referred for invasive coronary angiography.

OBJECTIVES: Utilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary calcification, and further assessed its prognostic significance. METHODS: This retrospective study enrolled patients who underwent coronary CT angiography and subsequent invasive coronary angiography. Multiple ML algorithms were used to train the models for predicting severe coronary calcification (cardiac CT-measured coronary artery calcium [CT-CAC] score ≥ 400). The ML-based CAC (ML-CAC) score derived from the ML predictive probability was stratified into quartiles for prognostic analysis. The primary endpoint was a composite of all-cause death, nonfatal myocardial infarction, or nonfatal stroke. RESULTS: Overall, 5785 patients were divided into training (80%) and test sets (20%). For clinical practicability, we selected the nine-feature support vector machine model with good and satisfactory performance regarding both discrimination and calibration based on five repetitions of the 10-fold cross-validation in the training set (mean AUC = 0.715, Brier score = 0.202), and based on the test in the test set (AUC = 0.753, Brier score = 0.191). In the test set cohort (n = 1137), the primary endpoint was observed in 50 (4.4%) patients during a median 2.8 years' follow-up. The ML-CAC system was significantly associated with an increased risk of the primary endpoint (adjusted hazard ratio for trend 2.26, 95% CI 1.35-3.79, p = 0.002). There was no significant difference in the prognostic value between the ML-CAC and CT-CAC systems (C-index, 0.67 vs. 0.69; p = 0.618). CONCLUSION: ML-CAC score predicted from clinical variables can serve as a novel prognostic indicator in patients referred for invasive coronary angiography. CLINICAL RELEVANCE STATEMENT: In patients referred for invasive coronary angiography who have not undergone preoperative CT-measured coronary artery calcium scoring, machine learning-based coronary artery calcium score assessment can serve as an alternative for predicting the prognosis. KEY POINTS: • The coronary artery calcium (CAC) score, a solid prognostic indicator, can be predicted using non-CT methods. • We developed a machine learning (ML)-CAC model utilising nine clinical variables to predict severe coronary calcification. • The ML-CAC system offers significant prognostic value in patients referred for invasive coronary angiography.

Kinetic compensation effect in logistic distributed activation energy model for lignocellulosic biomass pyrolysis.

The kinetic compensation effect in the logistic distributed activation energy model (DAEM) for lignocellulosic biomass pyrolysis was investigated. The sum of square error (SSE) surface tool was used to analyze two theoretically simulated logistic DAEM processes for cellulose and xylan pyrolysis. The logistic DAEM coupled with the pattern search method for parameter estimation was used to analyze the experimental data of cellulose pyrolysis. The results showed that many parameter sets of the logistic DAEM could fit the data at different heating rates very well for both simulated and experimental processes, and a perfect linear relationship between the logarithm of the frequency factor and the mean value of the activation energy distribution was found. The parameters of the logistic DAEM can be estimated by coupling the optimization method and isoconversional kinetic methods. The results would be helpful for chemical kinetic analysis using DAEM.