Volume 01 · Issue 03 · 2021

迄今为止，单细胞测序技术已经渗透到生物医学研究的所有主要领域，包括心血管领域。当与其他单细胞技术结合时，人们可以容易地研究单细胞的基因组、表观基因组、转录组和蛋白质组的各种组合，这被称为“单细胞组学”方法。近年来心血管单细胞研究的激增产生了范式转变的观察结果，从揭示发育细胞的命运到确定新的治疗靶点。

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.

在出生后的某个时间点，肌细胞增生停止，并且肌细胞肥大解释了从该时间点开始构成心脏的肌细胞的剩余生长。在这种转变之后，心脏重量与肌细胞体积成比例地增加。在大鼠心脏中，这种转变在出生后第4天突然发生。^[1]人类的转变点是未知的，并且从技术角度来看很难识别。例如，即使解剖的心脏被最佳地保存，这是不太可能的，在许多情况下也很难明确地区分肌细胞和非肌细胞的有丝分裂。此外，核分裂（双核/多核）在胞质分裂停止后继续作为肌细胞肥大。因此，有丝分裂不是肌细胞增殖的明确标志。确定人类的过渡点具有科学意义，并可能有助于确定纠正先天性心脏缺陷所需的外科手术的时机。例如，在转变之前的定时手术可以有益地影响成年期的心肌细胞。对于左心发育不全综合征等疾病，如果手术可以在心脏失去制造新肌细胞的能力之前在肌细胞增生期完成，则最佳手术时机可能会对结果产生影响。理论上，这些心脏在成年后可能具有正常的左心室肌细胞补体。