Accuracy of triggering receptor expressed on myeloid cells 1 in diagnosis and prognosis of acute myocardial infarction: a prospective cohort study

Background Acute myocardial infarction (AMI) is one of the fatal cardiac emergencies. The detection of triggering receptor expressed on myeloid cells 1 (TREM1), a cell surface immunoglobulin that amplifies pro-inflammatory responses, screened by bioinformatics was shown to be significant in diagnosing and predicting the prognosis of AMI. Methods GSE66360, GSE61144 and GSE60993 were downloaded from the Gene Expression Omnibus (GEO) database to explore the differentially expressed genes (DEGs) between AMI and control groups using R software. A total of 147 patients in total were prospectively enrolled from October 2018 to June 2019 and divided into two groups, the normal group (n = 35) and the AMI group (n = 112). Plasma was collected from each patient at admission and all patients received 6-month follow-up care. Results According to bioinformatic analysis, TREM1 was an important DEG in patients with AMI. Compared with the normal group, TREM1 expression was markedly increased in the AMI group (p < 0.001). TREM1 expression was positively correlated with fasting plasma glucose (FPG), glycosylated hemoglobin (HbAC), and the number of lesion vessels, although it had no correlation with Gensini score. TREM1 expression in the triple-vessels group was significantly higher than that of the single-vessel group (p < 0.05). Multiple linear regression showed that UA and HbAC were two factors influencing TREM1 expression. The ROC curve showed that TREM1 had a diagnostic significance in AMI (p < 0.001), especially in AMI patients without diabetes. Cox regression showed increased TREM1 expression was closely associated with 6-month major adverse cardiac events (MACEs) (p < 0.001). Conclusions TREM1 is a potentially significant biomarker for the diagnosis of AMI and may be closely associated with the severity of coronary lesions and diabetes. TREM1 may also be helpful in predicting the 6-month MACEs after AMI

High-density lipoprotein cholesterol to apolipoprotein A-1 ratio is an important indicator predicting in-hospital death in patients with acute coronary syndrome

Background: Dyslipidemia plays a pivotal role in the pathogenesis of acute coronary syndrome (ACS). This study aims to investigate the value of two indices associated with lipid metabolism, low-density lipoprotein cholesterol to apolipoprotein B ratio (LBR) and high-density lipoprotein cholesterol to apolipoprotein A-1 ratio (HAR), to predict in-hospital death in patients with ACS. Methods: This single-center, retrospective, observational study included 3,366 consecutive ACS patients in Zhongda Hospital, Southeast University from July 2013 to January 2018. The clinical and laboratory data were extracted, and the in-hospital death and hospitalization days were also recorded. Results: All patients were equally divided into four groups according to quartiles of HAR: Q1 (HAR &lt; 1.0283), Q2 (1.0283 ≤ HAR &lt; 1.0860), Q3 (1.0860 ≤ HAR &lt; 1.1798), and Q4 (HAR ≥ 1.1798). Overall, HAR was positively associated with the counts of neutrophils and monocytes, whereas negatively correlated to lymphocyte counts. HAR was negatively correlated to left ventricular ejection fraction (LVEF). Compared to other three groups, in-hospital mortality (vs. Q1, Q2, and Q3, p &lt; 0.001) and hospitalization length (vs. Q1, Q2, and Q3, p &lt; 0.001) were significantly higher in the Q4 group. When grouped by LBR, however, there was no significant difference in LVEF, in-hospital mortality, and hospitalization length among groups. After adjusting potential impact from age, systolic blood pressure, creatine, lactate dehydrogenase, albumin, glucose, and uric acid, multivariate analysis indicated that HAR was an independent factor predicting in-hospital death among ACS patients. Conclusions: HAR had good predictive value for patients’ in-hospital death after the occurrence of acute coronary events, but LBR was not related to in-hospital adverse events

Semi-Supervised Gastrointestinal Stromal Tumor Detection via Self-Training

The clinical diagnosis of gastrointestinal stromal tumors (GISTs) requires time-consuming tumor localization by physicians, while automated detection of GIST can help physicians develop timely treatment plans. Existing GIST detection methods based on fully supervised deep learning require a large amount of labeled data for the model training, but the acquisition of labeled data is often time-consuming and labor-intensive, hindering the optimization of the model. However, the semi-supervised learning method can perform better than the fully supervised learning method with only a small amount of labeled data because of the full use of unlabeled data, which effectively compensates for the lack of labeled data. Therefore, we propose a semi-supervised gastrointestinal stromal tumor (GIST) detection method based on self-training using the new selection criterion to guarantee the quality of pseudo-labels and adding the pseudo-labeled data to the training set together with the labeled data after linear mixing. In addition, we introduce the improved Faster RCNN with the multiscale module and the feature enhancement module (FEM) for semi-supervised GIST detection. The multiscale module and the FEM can better fit the characteristics of GIST and obtain better detection results. The experiment results showed that our approach achieved the best performance on our GIST image dataset with the joint optimization of the self-training framework, the multiscale module, and the FEM