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• Component screening by intelligent MS data analysis and network pharmacology. • BE-DDA for non-targeted acquiring HRMS2 Data of TCM' s metabolites in vivo. • First report on comprehensive study on in vivo exposure of Xiaokewan in rats. Xiaokewan is a typical Traditional Chinese medicine (TCM) for diabetes and contains various natural chemicals, such as lignans, flavonoids, saponins, polysaccharides, and western medicine glibenclamide. In the current study, a highly efficient system for screening hypoglycemic efficacy constituents of Xiaokewan has been developed with the integration of intelligent data acquisition, data mining, network pharmacology, and computer assisted target fishing. With the combination of background exclusion data dependent acquisition (BE-DDA) and non-targeted precise-and-thorough background-subtraction (PATBS) techniques, a novel workflow has been established for the non-targeted recognition and identification of TCM constituents in vivo, and has been applied to the exposure study of Xiaokewan in rat. In this case, an interesting correlation among drug, target, and disease can be established, by combining the screening or characterization results with the strategy of network pharmacology and multiple computer assisted techniques. Consequently, five main constituents (puerarin, daidzein, formononetin, deoxyschizandrin and glibenclamide) exposed in vivo have been selected as effective hypoglycemic components. Meanwhile, the network pharmacology experimental results showed that these five constituents could act on various drug targets, such as PI3K, PTP1B, MAPK, AKT, TNF, and NF-κB. These five constituents might be involved in the regulation of β-cell function or exhibit inflammation inhibition ability to relieve the pathophysiological process of disease from multiple links. Furthermore, the pharmacological effects of these five constituents have been verified by diabetic zebrafish model. The zebrafish model results showed that the TCM monomer mixture without glibenclamide exhibited similar hypoglycemic activity with Xiaokewan. Although the monomer mixture with glibenclamide showed better activity than Xiaokewan only, the deoxyschizandrin (TCM constituent of Xiaokewan) exhibited best hypoglycemic performance. In summary, the above results indicated that the application of both intelligent recognition technology in mass spectrometry dataset and computerized network pharmacology might provide a pioneering approach for investigating the substance basis of TCM and searching lead compounds from natural sources. [ABSTRACT FROM AUTHOR]

The use of β-blockers for acute coronary syndrome (ACS) patients without heart failure (HF) is controversial, and lacks of evidence in the era of reperfusion and intensive secondary preventions. This study aimed to investigate the prognostic impacts of β-blockers on patients with ACS but no HF treated by percutaneous coronary intervention (PCI). A total of 2397 consecutive patients with ACS but no HF treated by PCI were retrospectively recruited from January 2010 to June 2017. Univariable Cox regression was used to assess the prognostic impacts of β-blockers, followed by adjusted analysis, one-to-one propensity score matching (PSM), and inverse probability treatment weighting (IPTW) analysis, in order to control for systemic between-group differences. The primary outcome was all-cause death. Among the included patients, 2060 (85.9%) were prescribed with β-blockers at discharge. The median follow-up time was 727 (433–2016) days, with 55 (2.3%) cases of all-cause death. Unadjusted analysis showed that the use of β-blockers was associated with lower risk of death (hazard ratio [HR]: 0.42, 95% confidence interval [CI]: 0.23–0.76, P = 0.004), which was sustained in adjusted analysis (HR: 0.53, 95% CI: 0.29–0.98, P = 0.044), PSM analysis (HR: 0.44, 95% CI: 0.20–0.96, P = 0.039) and IPTW analysis (HR: 0.49. 95% CI: 0.35–0.70, P < 0.001). Risk reduction was also seen in β-blocker users for cardiac death, but not for major adverse cardiovascular events. The use of β-blockers was associated with reduced long-term mortality for ACS-PCI patients without HF. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] High heterogeneity has been reported among epidemiological studies exploring the relationship between metformin and the risk of gastric cancer. Immortal time bias might be one of the vital factors causing heterogeneity because of its widespread existence in pharmacological observational studies and it could severely exaggerate the drug's effectiveness. Immortal time bias could occur in an observational study if exposure status is determined based on a measurement or event that occurs after baseline. In this study, we aimed to assess whether immortal time bias is responsible for the false assumption that metformin reduces the risk of gastric cancer. We searched PubMed, Embase, Web of Science and Cochrane Library databases for relevant studies from the inception to August 9, 2020. The strength of the relationship was assessed using pooled relative risks (RRs) with corresponding 95% confidence intervals (95% CIs). Statistical analyses were carried out using a random-effects model. Pooled RR from 6 cohort studies with immortal time bias found a clear 33% reduced risk associated with metformin use (RR = 0.67, 95% CI = 0.59, 0.77; P < 0.001; I2 = 48.5%). However, pooled RR from 8 cohort studies without immortal time bias indicated no association between the use of metformin and gastric cancer risk (RR = 0.95, 95% CI = 0.85, 1.05; P = 0.317; I2 = 64.5%). From a univariate meta-regression model, the presence of immortal time bias was associated with a significant reduction of 29% in the effect estimate of metformin on gastric cancer risk (ratio of RR = 0.71, 95% CI = 0.58, 0.86; P = 0.002). This meta-analysis indicates that metformin use has no protective effect on gastric cancer risk. The relationship between metformin use and gastric cancer risk has been exaggerated as a result of the presence of immortal time bias. Further studies are required to confirm the results by controlling for immortal time bias based on appropriate study designs and statistical methods. [ABSTRACT FROM AUTHOR]