Weaverdejesus8530

Enhancement of protein synthesis from mRNA translation is one of the key steps supporting cardiomyocyte hypertrophy during cardiac remodeling. The methyltransferase-like5 (METTL5), which catalyzes m6A modification of 18S rRNA at position A1832, has been shown to regulate the efficiency of mRNA translation during the differentiation of ES cells and the growth of cancer cells. It remains unknown whether and how METTL5 regulates cardiac hypertrophy. In this study, we have generated a mouse model, METTL5-cKO, with cardiac-specific depletion of METTL5 in vivo. Loss function of METTL5 promotes pressure overload-induced cardiomyocyte hypertrophy and adverse remodeling. The regulatory function of METTL5 in hypertrophic growth of cardiomyocytes was further confirmed with both gain- and loss-of-function approaches in primary cardiomyocytes. Mechanically, METTL5 can modulate the mRNA translation of SUZ12, a core component of PRC2 complex, and further regulate the transcriptomic shift during cardiac hypertrophy. Altogether, our study may uncover an important translational regulator of cardiac hypertrophy through m6A modification.

Myocardial infarction with non-obstructive coronary arteries (MINOCA) is a heterogeneous entity with varying underlying etiologies and occurs in ~5-10% of patients with acute myocardial infarction. Sleep disorders and short sleep duration are common phenomena experienced by patients with coronary heart disease and are associated with poor clinical outcomes. However, the association between sleep quality, sleep duration, and the MINOCA prognosis is less clear.

We performed a prospective observational study of 607 patients with MINOCA between February 2016 and June 2018. The mean follow-up period was 3.9 years. Sleep quality and sleep duration were measured by the Chinese version of the Pittsburgh Sleep Quality Index. The primary endpoint was all-cause mortality, and the secondary endpoint was major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction, stroke and heart failure hospitalization.

During the follow-up period, all-cause death occ562;

< 0.001) and a 96.7% increased risk of MACE (HR = 1.967; 95% CI, 1.442-3.639;

< 0.001) than those with neither.

Sleep disorders were common among Chinese patients with MINOCA. Poor sleep quality and short sleep duration were independently associated with an increased risk of all-cause mortality and MACE in the MINOCA population. Meanwhile, a poor sleep profile has an additive effect with regard to cardiovascular risks; in these populations, efforts should be made to improve both sleep quality and sleep duration for secondary cardiovascular prevention.

http//www.chictr.org.cn, identifier ChiCTR2000040701.

http//www.chictr.org.cn, identifier ChiCTR2000040701.

Prior studies have found an unexplained inverse or U-shaped relationship between body mass index (BMI) and mortality in heart failure (HF) patients. selleck However, little is known about the independent effects of each body component, i.e., lean body mass (LBM) and fat mass (FM), on mortality.

We used data from the China Patient-centered Evaluative Assessment of Cardiac Events-Prospective Heart Failure Study. LBM and FM were calculated using equations developed from the National Health and Nutrition Examination Survey. LBM and FM index, calculated by dividing LBM or FM in kilograms by the square of height in meters, were used for analysis. We used restricted cubic spline and Cox model to examine the association of LBM and FM index with 1-year all-cause mortality.

Among 4,305 patients, median (interquartile range) age was 67 (57-76) years, 37.7% were women. During the 1-year follow-up, 691 (16.1%) patients died. After adjustments, LBM index was inversely associated with mortality in a linear way (

-overall association < 0.01;

-non-linearity = 0.52), but no association between FM index and mortality was observed (

-overall association = 0.19). Compared with patients in the 1st quartile of the LBM index, those in the 2nd, 3rd, and 4th quartiles had lower risk of death, with hazard ratio of 0.80 (95% CI 0.66-0.97), 0.65 (95% CI 0.52-0.83), and 0.61 (95% CI 0.45-0.82), respectively. In contrast, this association was not observed between FM index quartiles and mortality.

Higher LBM, not FM, was associated with lower 1-year mortality among HF patients.

Higher LBM, not FM, was associated with lower 1-year mortality among HF patients.

Atrial fibrillation (AF) and heart failure often co-exist. Early identification of AF patients at risk for AF-induced heart failure (AF-HF) is desirable to reduce both morbidity and mortality as well as health care costs. We aimed to leverage the characteristics of beat-to-beat-patterns in AF to prospectively discriminate AF patients with and without AF-HF.

A dataset of 10,234 5-min length RR-interval time series derived from 26 AF-HF patients and 26 control patients was extracted from single-lead Holter-ECGs. A total of 14 features were extracted, and the most informative features were selected. Then, a decision tree classifier with 5-fold cross-validation was trained, validated, and tested on the dataset randomly split. The derived algorithm was then tested on 2,261 5-min segments from six AF-HF and six control patients and validated for various time segments.

The algorithm based on the spectral entropy of the RR-intervals, the mean value of the relative RR-interval, and the root mean square of successive differences of the relative RR-interval yielded an accuracy of 73.5%, specificity of 91.4%, sensitivity of 64.7%, and PPV of 87.0% to correctly stratify segments to AF-HF. Considering the majority vote of the segments of each patient, 10/12 patients (83.33%) were correctly classified.

Beat-to-beat-analysis using a machine learning classifier identifies patients with AF-induced heart failure with clinically relevant diagnostic properties. Application of this algorithm in routine care may improve early identification of patients at risk for AF-induced cardiomyopathy and improve the yield of targeted clinical follow-up.

Beat-to-beat-analysis using a machine learning classifier identifies patients with AF-induced heart failure with clinically relevant diagnostic properties. Application of this algorithm in routine care may improve early identification of patients at risk for AF-induced cardiomyopathy and improve the yield of targeted clinical follow-up.

Rheumatic heart disease (RHD) accounts for a large proportion of Intensive Care Unit (ICU) deaths. Early prediction of RHD can help with timely and appropriate treatment to improve survival outcomes, and the XGBoost machine learning technology can be used to identify predictive factors; however, its use has been limited in the past. We compared the performance of logistic regression and XGBoost in predicting hospital mortality among patients with RHD from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.

The patients with RHD in the MIMIC-IV database were divided into two groups retrospectively according to the availability of data and its clinical significance based on whether they survived or died. Backward stepwise regression was used to analyze the independent factors influencing patients with RHD, and to compare the differences between the two groups. The XGBoost algorithm and logistic regression were used to establish two prediction models, and the areas under the receiver opert be clinically useful for patients with RHD and help clinicians to provide detailed treatments and precise management.

This study aimed to assess the circumferential and longitudinal strain of the fetal ascending aortic (AA) wall and establish a gestational age-associated longitudinal reference for aortic wall strain during the second half of pregnancy.

Singleton fetuses with gestational age (GA) at 20 + 0 to 24 + 6 weeks were prospectively collected from a low-risk population. Global circumferential strain (GCS) and mean longitudinal strain (MLS) of the ascending aorta were measured serially at 4-week intervals using the velocity vector imaging (VVI) technique. Fractional polynomials were conducted to obtain the best-fitting curves between GA and AA strains. GA-specific reference percentiles of GCS and MLS were established by multilevel modeling.

A total of 223 fetuses with a total of 1,127 serial observations were enrolled. GCS presented a second-degree fractional polynomial smoothing regression along GA (



= 0.635,

< 0.05). Fetal aortic GCS remained unchanged at ~27.29% (20.36-35.6%) before 31 weeks and increased significantly from 31.36% (26.38-37.12%) at 31 weeks to 43.29% (30.5-56.78%) at term. MLS presented a third-degree fractional polynomial smoothing regression along GA (



= 0.465,

< 0.05). MLS remained steady at ~10.03% (3.28-17.62%) between 20 and 31 weeks and then increased significantly from 12.68% (7.42-20.1%) at 32 weeks to 17.5% (9.67-25.34%) at term. The GCS was significantly higher than the MLS in the ascending aorta wall (

< 0.001).

The fetal ascending aorta wall demonstrates obviously greater circumferential strain than longitudinal strain. Both strains remained steady before the late trimester and then gradually increased until delivery, suggesting progressive maturation of aortic elasticity mechanics.

The fetal ascending aorta wall demonstrates obviously greater circumferential strain than longitudinal strain. Both strains remained steady before the late trimester and then gradually increased until delivery, suggesting progressive maturation of aortic elasticity mechanics.

Current guidewires for transradial coronary angiography had defects of passage difficulty or branch injury. This study sought to investigate the safety and efficiency of a novel method of active knuckle-angle 0.035-inch hydrophilic guidewire in transradial coronary angiography.

Patients undergoing a transradial coronary procedure in our team from August 2015 to June 2020 were retrospectively investigated. We compared the demographic and interventional characteristics of 1,457 patients receiving advancement of unmodified guidewires (Traditional group) and 1,322 patients receiving advancement of the knuckle guidewire (Knuckle group). Afterwards we included 239 patients and randomized them according to a random number table to either the unmodified or the knuckle guidewire to further confirm the efficiency and safety of knuckle guidewire advancement.

In the retrospective analysis, unwilling passage of guidewire into branches occurred more in the Traditional group than in the Knuckle group (9.5 vs. 0.08%,

< 0.001). Two patients in the Traditional group experienced guidewire-associated perforation. One patient was treated with covered stent for internal mammarian artery perforation, while the other was managed with compression for brachial branch perforation. In the randomized controlled study, unwilling passage of guidewire also occurred more in the Traditional group (10.8 vs. 1%,

< 0.001). Median duration of guidewire advancement from the sheath to aortic root significantly decreased from 33 seconds in the Traditional group to 21 seconds in the Knuckle group.

Active knuckle angle guidewire represented a novel method to prevent unwilling passage and associated perforation with efficiency improvement and a reduction in radiation exposure.

Active knuckle angle guidewire represented a novel method to prevent unwilling passage and associated perforation with efficiency improvement and a reduction in radiation exposure.