Objective To evaluate the efficacy and safety of low-dose prourokinase (pro-UK) administration during primary percutaneous coronary intervention (PCI) for the treatment of acute ST-segment elevation myocardial infarction (STEMI) in patients with a high thrombus burden. Methods A prospective, randomized controlled trial was conducted at the Inner Mongolia People’s Hospital, China. Patients with STEMI and a high thrombus burden who underwent thrombus aspiration and primary PCI were randomly allocated to pro-UK administration or control groups. The primary endpoint was corrected thrombolysis in myocardial infarction (TIMI) frame count (CTFC). Results There were no significant differences in the baseline demographics or clinical characteristics of the two groups. The CTFC, tissue myocardial perfusion grade, ST-segment resolution, and myocardial blush grade of the pro-UK group were significantly better than those of the control group. In addition, after 30 days of follow-up, the pro-UK group had better cardiac function and perfusion than the control group. There were no differences in the clinical outcomes or incidence of hemorrhage. Conclusions Intracoronary low-dose pro-UK improves myocardial perfusion and cardiac function in patients with a high thrombus burden. Major hemorrhages still occur in patients administered pro-UK, but are no more frequent. Study registration: Chinese Clinical Trial Registry (ChiCTR1900022290).

No abstract

When searching code developers may express additional constraints ( e.g., functional constraints and nonfunctional constraints) on the implementations of desired functionalities in the queries. Existing code search tools treat the queries as a whole and ignore the different implications of different parts of the queries. Moreover, these tools usually return a ranked list of candidate code snippets without any explanations. Therefore, the developers often find it hard to choose the desired results and build confidence on them. In this paper, we conduct a developer survey to better understand and address these issues and induct some insights from the survey results. Based on the insights, we propose XCoS, an explainable code search approach based on query scoping and knowledge graph. XCoS extracts a background knowledge graph from general knowledge bases like Wikidata and Wikipedia. Given a code search query, XCoS identifies different parts ( i.e., functionalities, functional constraints, nonfunctional constraints) from it and use the expressions of functionalities and functional constraints to search the codebase. It then links both the query and the candidate code snippets to the concepts in the background knowledge graph and generates explanations based on the association paths between these two parts of concepts together with relevant descriptions. XCoS uses an interactive user interface that allows the user to better understand the associations between candidate code snippets and the query from different aspects and choose the desired results. Our evaluation shows that the quality of the extracted background knowledge and the concept linkings in codebase is generally high. Furthermore, the generated explanations are considered complete, concise, and readable and the approach can help developers find the desired code snippets more accurately and confidently.

No abstract