The Systemic Immune Inflammatory Index Predicts No-Reflow Phenomenon after Primary Percutaneous Coronary Intervention in Older Patients with STEMI

Purpose:Coronary no-reflow phenomenon(NRP),a common adverse complication in patients with ST-segment eleva-tion myocardial infarction(STEMI)treated by percutaneous coronary intervention(PCI),is associated with poor patient prognosis.In this study,the correlation between the systemic immune-inflammation index(SII)and NRP in older patients with STEMI was studied,to provide a basis for early identification of high-risk patients and improve their prognosis.Materials and methods:Between January 2017 and June 2020,578 older patients with acute STEMI admitted to the Department of Cardiology of Hebei General Hospital for direct PCI treatment were selected for this retrospective study.Patients were divided into an NRP group and normal-flow group according to whether NRP occurred during the operation.Clinical data and the examination indexes of the two groups were collected.Logistic regression was used to analyze the independent predictors of NRP,and the receiver operating characteristic curve was used to further analyze the ability of SII to predict NRP in older patients with STEMI.Results:Multivariate logistic analysis indicated that hypertension(OR=2.048,95%CI:1.252–3.352,P=0.004),lymphocyte count(OR=0.571,95%CI:0.368–0.885,P=0.012),platelet count(OR=1.009,95%CI:1.005–1.013,P<0.001),hemoglobin(OR=1.015,95%CI:1.003–1.028,P=0.018),multivessel disease(OR=2.237,95%CI:1.407–3.558,P=0.001),and SII≥1814(OR=3.799,95%CI:2.190–6.593,P<0.001)were independent predictors of NRP after primary PCI in older patients with STEMI.Receiver operating characteristic curve analysis demonstrated that SII had a high predictive value for NRP(AUC=0.738;95%CI:0.686–0.790),with the best cut-off value of 1814,a sensitivity of 52.85%and a specificity of 85.71%.Conclusion:For older patients with STEMI undergoing primary PCI,SII is a valid predictor of NRP.

Oral glucose tolerance test in diabetes,the old method revisited

The oral glucose tolerance test(OGTT)has been widely used both in clinics and in basic research for a long time.It is applied to diagnose impaired glucose tolerance and/or type 2 diabetes mellitus in individuals.Additionally,it has been employed in research to investigate glucose utilization and insulin sensitivity in animals.The main aim of each was quite different,and the details are also somewhat varied.However,the time or duration of the OGTT was the same,using the 2-h post-glucose load glycemia in both,following the suggestions of the American Diabetes Association.Recently,the use of 30-min or 1-h post-glucose load glycemia in clinical practice has been recommended by several studies.In this review article,we describe this new view and suggest perspectives for the OGTT.Additionally,quantification of the glucose curve in basic research is also discussed.Unlike in clinical practice,the incremental area under the curve is not suitable for use in the studies involving animals receiving repeated treatments or chronic treatment.We discuss the potential mechanisms in detail.Moreover,variations between bench and bedside in the application of the OGTT are introduced.Finally,the newly identified method for the OGTT must achieve a recommendation from the American Diabetes Association or another official unit soon.In conclusion,we summarize the recent reports regarding the OGTT and add some of our own perspectives,including machine learning and others.

Target localization method of non-cooperative spacecraft on on-orbit service

With the explosion of the number of meteoroid/orbital debris in terrestrial space in recent years, the detection environment of spacecraft becomes more complex. This phenomenon causes most current detection methods based on machine learning intractable to break through the two difficulties of solving scale transformation problem of the targets in image and accelerating detection rate of high-resolution images. To overcome the two challenges, we propose a novel noncooperative target detection method using the framework of deep convolutional neural network.Firstly, a specific spacecraft simulation dataset using over one thousand images to train and test our detection model is built. The deep separable convolution structure is applied and combined with the residual network module to improve the network’s backbone. To count the different shapes of the spacecrafts in the dataset, a particular prior-box generation method based on K-means cluster algorithm is designed for each detection head with different scales. Finally, a comprehensive loss function is presented considering category confidence, box parameters, as well as box confidence. The experimental results verify that the proposed method has strong robustness against varying degrees of luminance change, and can suppress the interference caused by Gaussian noise and background complexity. The mean accuracy precision of our proposed method reaches 93.28%, and the global loss value is 13.252. The comparative experiment results show that under the same epoch and batchsize, the speed of our method is compressed by about 20% in comparison of YOLOv3, the detection accuracy is increased by about 12%, and the size of the model is reduced by nearly 50%.