Value of Bax and Bcl2 expression in peripheral blood mononuclear cells for clinical prognosis of patients with chronic heart failure.

To investigate the expression of Bax and Bcl2 protein in peripheral blood mononuclear cells (PBMC) of patients with chronic heart failure (CHF), and to analyze their value for predicting major adverse cardiovascular event (MACE) in CHF patients. A total of 154 fasting venous blood samples from CHF patients were collected in our hospital from January 2017 to June 2019, and they were divided into 2 group according to whether MACE occurred during 3 years follow-up, MACE group and No-MACE group. Levels of Bax and Bcl2 protein expression in PBMC of CHF patients using enzyme-linked immunosorbent assay (ELISA), and then evaluated the predictive power of Bax and Bcl2 expression for MACE using logistic regression analysis and ROC curve. 62 (40.26%) of 154 CHF patients occurred MACE during follow-up, and there were significant differences in age, diabetes, LVEF, LDL-C and NYHA grade between MACE group and No-MACE group. Levels of Bax protein expression in PBMC of CHF patients in MACE group were significantly higher than those in No-MACE group, while levels of Bcl2 protein expression were significantly lower than those in No-MACE group, and Bax and Bcl2 protein levels increased and decreased with NYHA grades in MACE group and No-MACE group, respectively. Results of univariate and multivariate logistic regression analysis showed that Bax (OR, 1.026; 95% CI, 1.003-1.049; P = .027) and Bcl2 levels (OR, 0.952; 95% CI, 0.908-0.998; P = .041) were independent predictive factors for MACE in CHF patients. In addition, Bax and Bcl2 levels could be used to differentiate CHF patients at risk for MACE with an AUC of 0.744 (95% CI: 0.660-0.827) and an AUC of 0.743 (95% CI: 0.667-0.819), respectively. Levels of Bax and Bcl2 protein in PBMC could be used as independent predictive factors for MACE in CHF patients.

Urban commuting dynamics in response to public transit upgrades: A big data approach.

Public transit, especially urban rail systems, plays a vital role in shaping commuting patterns. Compared with census data and survey data, large-scale and real-time big data can track the impacts of urban policy implementations at finer spatial and temporal scales. Therefore, this study proposed a multi-level analytical framework using transit smartcard data to examine urban commuting dynamics in response to rail transit upgrades. The study area was Shenzhen, one of the most highly urbanized and densely populated cities in China, which provides the opportunity to examine the effects of rail transit upgrades on commuting patterns in a rapidly developing urban context. Changes in commuting patterns were examined at three levels: city, region, and individual. At the city level, we considered the average commuting time, commuting speed, and commuting distance across the whole city. At the region level, we analyzed changes in the job accessibility of residential zones. Finally, this study evaluated the potential effects of rail transit upgrades on the jobs-housing relationship at the individual level. Difference-in-difference models were used for causal inference between rail transit upgrades and commuting patterns. In the very short term, the opening of new rail transit lines resulted in no significant changes in overall commuting patterns across the whole city; however, two effects of rail transit upgrades on commuting patterns were identified. First, rail transit upgrades enhanced regional connectivity between residential zones and employment centers, thus improving job accessibility. Second, rail transit improvement increased the commuting distances of individuals and contributed to the separation of workplaces and residences. This study provides meaningful insights into the effects of rail transit upgrades on commuting patterns.