Background: Acute myocardial infarction (AMI) has two clinical characteristics: high missed diagnosis and dysfunction of leukocytes. Transcriptional RNA on leukocytes is closely related to the course evolution of AMI patients. We hypothesized that transcriptional RNA in leukocytes might provide potential diagnostic value for AMI. Integration machine learning (IML) was first used to explore AMI discrimination genes. The following clinical study was performed to validate the results. Methods: A total of four AMI microarrays (derived from the Gene Expression Omnibus) were included in bioanalysis (220 sample size). Then, the clinical validation was finished with 20 AMI and 20 stable coronary artery disease patients (SCAD). At a ratio of 5:2, GSE59867 was included in the training set, while GSE60993, GSE62646, and GSE48060 were included in the testing set. IML was explicitly proposed in this research, which is composed of six machine learning algorithms, including support vector machine (SVM), neural network (NN), random forest (RF), gradient boosting machine (GBM), decision trees (DT), and least absolute shrinkage and selection operator (LASSO). IML had two functions in this research: filtered optimized variables and predicted the categorized value. Finally, The RNA of the recruited patients was analyzed to verify the results of IML. Results: Thirty-nine differentially expressed genes (DEGs) were identified between controls and AMI individuals from the training sets. Among the thirty-nine DEGs, IML was used to process the predicted classification model and identify potential candidate genes with overall normalized weights > 1. Finally, two genes (AQP9 and SOCS3) show their diagnosis value with the area under the curve (AUC) > 0.9 in both the training and testing sets. The clinical study verified the significance of AQP9 and SOCS3. Notably, more stenotic coronary arteries or severe Killip classification indicated higher levels of these two genes, especially SOCS3. These two genes correlated with two immune cell types, monocytes and neutrophils. Conclusion: AQP9 and SOCS3 in leukocytes may be conducive to identifying AMI patients with SCAD patients. AQP9 and SOCS3 are closely associated with monocytes and neutrophils, which might contribute to advancing AMI diagnosis and shed light on novel genetic markers. Multiple clinical characteristics, multicenter, and large-sample relevant trials are still needed to confirm its clinical value.

Drug-induced liver injury (DILI) is the most common adverse effect of numerous drugs and a leading cause of drug withdrawal from the market. In recent years, the incidence of DILI has increased. However, diagnosing DILI remains challenging because of the lack of specific biomarkers. Hence, we used machine learning (ML) to mine multiple microarrays and identify useful genes that could contribute to diagnosing DILI. In this prospective study, we screened six eligible microarrays from the Gene Expression Omnibus (GEO) database. First, 21 differentially expressed genes (DEGs) were identified in the training set. Subsequently, a functional enrichment analysis of the DEGs was performed. We then used six ML algorithms to identify potentially useful genes. Based on receiver operating characteristic (ROC), four genes, DDIT3, GADD45A, SLC3A2, and RBM24, were identified. The average values of the area under the curve (AUC) for these four genes were higher than 0.8 in both the training and testing sets. In addition, the results of immune cell correlation analysis showed that these four genes were highly significantly correlated with multiple immune cells. Our study revealed that DDIT3, GADD45A, SLC3A2, and RBM24 could be biomarkers contributing to the identification of patients with DILI.

To assess the reporting quality of randomized controlled trials (RCTs) with multicenter design, particularly whether necessary information related to multicenter characteristics was adequately reported. Through a search of 4 international electronic databases, we identified multicenter RCTs published in English from 1975 to 2019. Reporting quality was assessed by the CONSORT (Consolidated Standards of Reporting Trials) checklist (37 items) and by a self-designed multicenter-specific checklist (27 items covering multicenter design, implement and analysis). The scores of trials published in three time periods (1975-1995; 1996-2009; and 2010-2019) were also compared. A total of 2,844 multicenter RCTs were included. For the CONSORT checklist, the mean (standard deviation) reporting score was 24.1 (5.5), 12 items were assessed as excellent (>90%), 12 items as good (50%-90%), and 13 items as poor (<50%). For the multicenter checklist, the reporting score was 3.9 (2.2), only 3 items were excellent or good, and the remaining 24 items were poor. Time period comparison showed that reporting quality improved over time, especially after the CONSORT 2010 issued. Although CONSORT appears to have enhanced the reporting quality of multicenter RCTs, further improvement is needed. A "CONSORT extension for multicenter trials" should be developed.

Plasma volume fluctuations limit the utility of circulating lipidomics in chronic heart failure (CHF). In contrast, red blood cell (RBC) membrane lipidomics may serve as stable biomarkers that are unaffected by plasma volume changes. Two cohorts are included to investigate the association between RBC indicators and CHF. RBC membrane lipidomics is first used to characterize CHF and its impact on plasma volume overload (PVO) and major adverse cardiovascular events (MACE). The first cohort (n = 507,638) shows that the erythrocyte stress index (ESI), better than traditional RBC indicators, is associated with the prevalence of CHF with odds ratio (OR) and confidence interval (CI) of 1.57 (95% CI,1.55‐1.59). ESI is also linked with in‐hospital death in CHF. Another cohort (n = 1,550) indicated RBC membrane lipidomics ceramide subtype Cer 18:0;O2/16:0 and lysophosphatidylethanolamine subtype LPE 18:0 has an AUC of PVO with 0.75 and 0.61. The above two lipids are risk factors of PVO with OR of 1.62 (95% CI, 1.47‐1.80) and 1.11 (95% CI, 1.06‐1.16). They also are risk factors for MACE, with hazard ratios (HR) of 1.04 (95% CI, 1.01‐1.07) and 1.06 (95% CI, 1.01‐1.10). This research emphasizes the potential value of RBC membrane lipidomics for CHF development, prognosis, and treatment.

Aim: To assess whether the erythrocyte stress index (ESI), a novel indicator to characterize erythrocytes, promotes the type 2 diabetes (T2D) patients developed into heart failure. Methods: Two cohorts were contained, the Medical Information Mart for Intensive Care (MIMIC) database, and the Tianjin HF with Integrated Treatment (TJHFIT). Among the 15,813 T2D patients with follow-up time (mean of 1,532 days and 959 days in the two cohorts), 3,711 finally developed HF. ESI was compared with two indicators associated with HF onset: red blood cell distribution width coefficient of variation (RDWCV) and estimated plasma volume status (ePVS). ESI was stratification with quartiles (ESIQ). The Cox analysis, restricted cubic spline (RCS), and Kaplan–Meier (KM) curves were applied. Results: The incidence of HF in T2D patients across ESI quartiles was 17%, 22%, 27%, and 29%, respectively. In MIMIC, the adjusted hazard ratios (aHR) with 95% confidence interval (CI) of ESI, RDWCV, and ePVS were 1.27 (1.20, 1.35), 1.07 (1.05, 1.10), and 1.05 (1.02, 1.07), the ESI had the highest aHR. Restricted cubic splines demonstrated that ESI, RDWCV, and ePVS exhibited an S-shaped non-linear relationship, with cut-off values of 4.19 mL/g for ESI. Kaplan-Meier curves indicated that T2D patients with ESI >4.19 mL/g had a higher probability of HF than those with ESI ≤ 4.19mL/g. Notably, ESI remained a significant predictor of HF even in T2D patients with RDWCV, and ePVS lost their predictive value. Conclusion : The ESI served as an independent prognostic marker for the development of HF in patients with T2D. Even though both RDWCV and ePVS lost their predictive value, ESI can still play its role.

Objective: Diarrhea-predominant irritable bowel syndrome (IBS-D) is a subtype of IBS. Many patients are dissatisfied with the efficacy of drug treatment and seek complementary and alternative medical therapies, especially acupuncture treatment. In this study, we evaluated the effect of manual acupuncture (MA) on IBS-D using a rat model and investigated the underlying mechanism of action by analyzing the changes in untargeted metabolomics of plasma/liver/colon and targeted plasma lipidomics. Methods: Thirty male Sprague–Dawley rats were divided into Control, IBS-D and MA groups (n = 10 each). IBS-D/MA groups received acetic acid enemas combined with restraint stress. The Control group received saline enemas. The MA group received daily MA at ST36 for 2 weeks after modeling. Pain and stool quality were measured by the abdominal withdrawal reflex (AWR) and Bristol stool form scale (BSFS). Plasma/liver/colon samples were collected for ultra-high performance liquid chromatography (UHPLC)/Q-Orbitrap mass spectromy (MS) untargeted metabolomics. Plasma was also subjected to UHPLC-MS/MS targeted lipidomics. Results: MA treatment improved BSFS and AWR scores in IBS-D model rats, suggesting reduced intestinal hypersensitivity. Metabolomic results showed that MA impacted 18 metabolites in the plasma, liver and colon, which were mainly involved in arachidonic acid metabolism, biosynthesis of unsaturated fatty acids, and amino sugar and nucleotide sugar metabolism. Targeted lipidomic results showed MA restored the low levels of four medium-chain and one long-chain fatty acids to normal levels in IBS-D, suggesting that acupuncture treatment of IBS-D involves fatty acid metabolic pathways. Conclusion: MA reduced markers of intestinal hypersensitivity and impacted a number of metabolic pathways in a rat model of IBS-D.