Volume 01 · Issue 03 · 2021

To date, single-cell sequencing technology has penetrated all the major areas of biomedical research, including the cardiovascular field. When combined with other single-cell techniques, one can easily study various combinations of the genomes, epigenomes, transcriptomes, and proteomes of single cells, which is known as the "single-cell omics" approach. The surge in cardiovascular single-cell studies in recent years has yielded paradigm-shifting observations, ranging from unraveling developmental cell fates to identifying novel therapeutic targets.

Observational studies indicate that insomnia may increase the risk of developing and/or dying from cardiovascular diseases, especially coronary artery disease (CAD). Our purpose is to explore the underlying causal relationship between genetic variants susceptible to insomnia and the risk of CAD by Mendelian randomization analysis.

The study was conducted using publicly available statistical data on genetic variants identified from a genome-wide association meta-analysis of insomnia (n = 113,006 individuals) and a genome-wide association meta-analysis of CAD (n = 184,305 individuals), which consisted of both cases and non-cases. The genetic association between variants and CAD was assessed by the variants’ association with insomnia, and estimations were integrated by an inverse-variance weighted meta-analysis.

Among the Mendelian randomized analytical sample, 8 variants were associated with insomnia complaints and CAD. And there was no pleiotropic association with the latent confounders. In addition, in the inverse-variance weighted meta-analysis (the estimations combined from the 8 variants), the odds ratio was 1.15 (95% CI: 1.05-1.25; P= 0.002) for CAD, and in the weighted method analysis, the odds ratio was 1.14 (95% CI: 1.03-1.27; P= 0.015) for CAD.

All of the data indicated that some valuable variants might involve in the development of CAD by leading the insomnia. Therefore, insomnia might be a causal factor for CAD, and improving the quality of sleep might be a new way for populations with insomnia to prevent CAD.

Older patients with comorbidity, such as coronary heart disease (CHD) and malignant gastrointestinal tumors, are at a high risk of bleeding events. However, risk prediction models based on risk factor assessment remain unclear. This study aimed to establish an individualized bleeding risk assessment system based on the analysis of 10-year inpatient clinical big data.

Total clinical data of 56,819 patients with CHD and 25,988 patients with malignant digestive tract tumors (admitted from January 2008 to December 2017) were retrospectively collected at the First and Second Medical Centers of Chinese People’s Liberation Army General Hospital. Among them, 1307 patients with CHD and malignant digestive tract tumors were screened as the derivation cohort. The dependent variable was the occurrence of major clinical bleeding events. Baseline statistics and hypothesis tests of differences were performed for independent variables according to the occurrence of bleeding. Decision Tree, eXtreme Gradient Boosting (XGBoost), logistic regression, and random forest models were used for comparison. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) were applied as the criteria for evaluating and verifying model performance. To evaluate this developed model, another cohort comprising 454 patients (admitted from January 2018 to December 2019) was prospectively enrolled as the validation cohort based on the same inclusion and exclusion criteria.

Among the 64 variables with <50% missing values, the recursive feature elimination method with a random forest model was used to screen the selected variables. The highest accuracy was obtained following the selection of 10 scalars, and the final model was constructed accordingly. XGBoost demonstrated the best performance comprehensively. The AUC-ROC of this model was 0.981, with an accuracy, sensitivity, and specificity of 0.939, 0.950, and 0.927, respectively. In the validation cohort, the AUC-ROC, accuracy, sensitivity, and specificity of the XGBoost model were 0.702, 0.718, 0.636, and 0.725, respectively. The rate of major bleeding events has a positive correlation with the bleeding risk score quintiles. To allow for convenient clinical application, a smartphone application was developed for easy access and calculation (http://fir.master-wx.com/sghr).

We successfully established a risk model and score for predicting bleeding events in older patients with comorbidity, such as CHD and gastrointestinal cancer.

At some point after birth, myocyte hyperplasia ceases, and myocyte hypertrophy accounts for the remaining growth of myocytes composing the heart from that point forward. Heart weight increases in proportion to myocyte volume after this transition. In rat heart, this transition occurs abruptly at postnatal day 4.^[1]The transition point in humans is unknown and has proven very difficult to identify from a technical standpoint. For instance, even if autopsied hearts were optimally preserved, which is unlikely, it is difficult to definitively distinguish between mitosis in myocytes and non-myocytes in many cases. Additionally, karyokinesis (binucleation/multinucleation) continues as myocytes hypertrophy after cessation of cytokinesis. So, mitosis is not a definitive marker of myocyte proliferation. Identifying the transition point in humans is of scientific interest and potentially helpful in timing surgical procedures necessary to correct congenital heart defects. For instance, timing surgery before the transition may beneficially affect cardiac myocytes in adulthood. With conditions such as hypoplastic left heart syndrome, optimal surgical timing could make a difference in outcomes if surgery could be completed during the myocyte hyperplastic period before the heart has lost the ability to make new myocytes. Theoretically, these hearts may then have a normal complement of left ventricular myocytes upon reaching adulthood.