Effects of remote ischaemic preconditioning on myocardial injury after major abdominal surgery in patients at high risk for cardiovascular adverse events in China (RIPC-MAS): protocol for a randomised, sham-controlled, observer-blinded trial.

INTRODUCTION: Myocardial injury after non-cardiac surgery (MINS) caused by an ischaemic mechanism is common and is associated with adverse short-term and long-term prognoses. However, MINS is a recent concept, and few studies have prospectively used it as a primary outcome. Remote ischaemic preconditioning (RIPC) is a non-invasive procedure that induces innate cardioprotection and may reduce MINS. METHODS AND ANALYSIS: This is a multicentre, randomised, sham-controlled, observer-blinded trial. Patients with a high clinical risk of cardiovascular events who are scheduled to undergo major abdominal surgery will be enrolled. A total of 766 participants will be randomised (1:1 ratio) to receive RIPC or control treatment before anaesthesia. RIPC will comprise four cycles of cuff inflation for 5 min to 200 mm Hg and deflation for 5 min. In the controls, an identical-looking cuff will be placed around the arm but will not be actually inflated. The primary outcome will be MINS, defined as at least one postoperative cardiac troponin (cTn) concentration above the 99th percentile upper reference limit of the cTn assay as a result of a presumed ischaemic mechanism. This trial will test the concentration of high-sensitivity cardiac troponin T (hs-cTnT). The secondary outcomes will be hs-cTnT levels reaching/above the prognostically important thresholds, peak hs-cTnT and total hs-cTnT release during the initial 3 days after surgery, length of hospital stay after surgery, length of stay in the intensive care unit, myocardial infarction, major adverse cardiovascular events, cardiac-related death, all-cause death within 30 days, 6 months, 1 year and 2 years after surgery, and postoperative complications and adverse events within 30 days after surgery. ETHICS AND DISSEMINATION: This study protocol (version 5.0 on 7 April 2023) was approved by the Ethics Committee of Sixth Affiliated Hospital of Sun Yat-sen University. The findings will be published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT05733208.

Non-Graph Data Clustering via O(n) Bipartite Graph Convolution.

Since the representative capacity of graph-based clustering methods is usually limited by the graph constructed on the original features, it is attractive to find whether graph neural networks (GNNs), a strong extension of neural networks to graphs, can be applied to augment the capacity of graph-based clustering methods. The core problems mainly come from two aspects. On the one hand, the graph is unavailable in the most general clustering scenes so that how to construct graph on the non-graph data and the quality of graph is usually the most important part. On the other hand, given n samples, the graph-based clustering methods usually consume at least O(n2) time to build graphs and the graph convolution requires nearly O(n2) for a dense graph and O(|E|) for a sparse one with |E| edges. Accordingly, both graph-based clustering and GNNs suffer from the severe inefficiency problem. To tackle these problems, we propose a novel clustering method, AnchorGAE, with the self-supervised estimation of graph and efficient graph convolution. We first show how to convert a non-graph dataset into a graph dataset, by introducing the generative graph model and anchors. A bipartite graph is built via generating anchors and estimating the connectivity distributions of original points and anchors. We then show that the constructed bipartite graph can reduce the computational complexity of graph convolution from O(n2) and O(|E|) to O(n). The succeeding steps for clustering can be easily designed as O(n) operations. Interestingly, the anchors naturally lead to siamese architecture with the help of the Markov process. Furthermore, the estimated bipartite graph is updated dynamically according to the features extracted by GNN modules, to promote the quality of the graph by exploiting the high-level information by GNNs. However, we theoretically prove that the self-supervised paradigm frequently results in a collapse that often occurs after 2-3 update iterations in experiments, especially when the model is well-trained. A specific strategy is accordingly designed to prevent the collapse. The experiments support the theoretical analysis and show the superiority of AnchorGAE.

Clinical effect of antibiotic combined with fiber bronchoscope perfusion in treatment of pneumonia after severe cerebral hemorrhage.

To explore the curative effect of antibiotic combined with mucosolvan perfusion under fiber bronchoscope in treatment of pneumonia after severe cerebral hemorrhage. The clinical data of 120 patients with pneumonia after severe cerebral hemorrhage admitted to our hospital from January 2017 to December 2019 were collected. All patients were divided into the lavage group and perfusion group by random number method, with 60 patients in each group. Patients in the lavage group received antibiotics combined with mucosolvan lavage therapy under fiber bronchoscope, while patients in the perfusion group received antibiotics combined with mucosolvan perfusion therapy under fiber bronchoscope. Clinical pulmonary infection score (CPIS), arterial blood gas index, clinical symptom improvement, and hospitalization costs were compared between the two groups before and after treatment. CPIS scores were improved after treatment in both groups (P < .05), and CPIS scores of patients in the perfusion group were lower than those in the lavage group at 3, 5, and 7 days after treatment (P < .05). The blood oxygen saturation and partial arterial oxygen pressure of the perfusion group were all higher than those of the lavage group (P < .05), while the partial carbon dioxide pressure was lower than that of the lavage group (P < .05). In the perfusion group, the duration of cough, adequacy of fever, disappearance of rhonchus in the lungs, and the length of hospital stay were less than those in the lavage group (P < .05). The hospitalization costs of perfusion group were lower than that of lavage group (P < .05). The combination of antibiotics and mucosolvan infusion therapy under fiber bronchoscope can effectively improve the clinical efficacy, reduce the hospitalization cost, and improve the prognosis of patients with pneumonia after severe cerebral hemorrhage.