97 research outputs found

    Model Checking Race-freedom When "Sequential Consistency for Data-race-free Programs" is Guaranteed

    Full text link
    Many parallel programming models guarantee that if all sequentially consistent (SC) executions of a program are free of data races, then all executions of the program will appear to be sequentially consistent. This greatly simplifies reasoning about the program, but leaves open the question of how to verify that all SC executions are race-free. In this paper, we show that with a few simple modifications, model checking can be an effective tool for verifying race-freedom. We explore this technique on a suite of C programs parallelized with OpenMP

    Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study

    Get PDF
    IntroductionPostoperative systemic inflammatory response syndrome (SIRS) is common in surgical patients especially in older patients, and the geriatric population with SIRS is more susceptible to sepsis, MODS, and even death. We aimed to develop and validate a model for predicting postoperative SIRS in older patients.MethodsPatients aged ≥65 years who underwent general anesthesia in two centers of Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2020 were included. The cohort was divided into training and validation cohorts. A simple nomogram was developed to predict the postoperative SIRS in the training cohort using two logistic regression models and the brute force algorithm. The discriminative performance of this model was determined by area under the receiver operating characteristics curve (AUC). The external validity of the nomogram was assessed in the validation cohort.ResultsA total of 5,904 patients spanning from January 2015 to December 2019 were enrolled in the training cohort and 1,105 patients from January 2020 to September 2020 comprised the temporal validation cohort, in which incidence rates of postoperative SIRS were 24.6 and 20.2%, respectively. Six feature variables were identified as valuable predictors to construct the nomogram, with high AUCs (0.800 [0.787, 0.813] and 0.822 [0.790, 0.854]) and relatively balanced sensitivity (0.718 and 0.739) as well as specificity (0.718 and 0.729) in both training and validation cohorts. An online risk calculator was established for clinical application.ConclusionWe developed a patient-specific model that may assist in predicting postoperative SIRS among the aged patients

    Upregulation of TLR2/4 Expression in Mononuclear Cells in Postoperative Systemic Inflammatory Response Syndrome after Liver Transplantation

    Get PDF
    Background. To explore the relationship between Toll-like rpheral blood mononuclear cells (PBMC) and systemic inflammatory response syndrome (SIRS) in postoperative patients of liver transplantation (LT). Methods. Blood samples of 27 patients receiving LT were collected at T1 (after induction of anaesthesia), T2 (25 minutes after the beginning of anhepatic phase), T3 (3 hours after graft reperfusion), and T4 (24 hours after graft reperfusion). The expression of TLR2/4 on PBMC and serum concentration of tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-8 were measured. The patients were divided into SIRS group (n=12) and non-SIRS group (n=15) for analysis. Results. Blood loss and transfusion were higher in the SIRS group than in the non-SIRS group. Both the preanhepatic and anhepatic phase were significantly longer in the SIRS group. The TLR2/4 expression on PBMC as well as serum TNF-α, IL-1β, and IL-8 were significantly higher at T3 and T4 than that at T1 and T2 in the SIRS patients. The expression of TLR4 on PBMC is positively correlated to serum TNF-α, IL-8. Expression of TLR2/4 on PBMC and serum concentrations of TNF-α, IL-1β, did not differ among the 4-time points in non-SIRS patients. Conclusions. Upregulation of TLR2/4 expression on PBMC may contribute to the development of postoperative SIRS during perioperative period of LT
    corecore