Machine learning and experimental validation identified autophagy signature in hepatic fibrosis

BackgroundThe molecular mechanisms of hepatic fibrosis (HF), closely related to autophagy, remain unclear. This study aimed to investigate autophagy characteristics in HF.MethodsGene expression profiles (GSE6764, GSE49541 and GSE84044) were downloaded, normalized, and merged. Autophagy-related differentially expressed genes (ARDEGs) were determined using the limma R package and the Wilcoxon rank sum test and then analyzed by GO, KEGG, GSEA and GSVA. The infiltration of immune cells, molecular subtypes and immune types of healthy control (HC) and HF were analyzed. Machine learning was carried out with two methods, by which, core genes were obtained. Models of liver fibrosis in vivo and in vitro were constructed to verify the expression of core genes and corresponding immune cells.ResultsA total of 69 ARDEGs were identified. Series functional cluster analysis showed that ARDEGs were significantly enriched in autophagy and immunity. Activated CD4 T cells, CD56bright natural killer cells, CD56dim natural killer cells, eosinophils, macrophages, mast cells, neutrophils, and type 17 T helper (Th17) cells showed significant differences in infiltration between HC and HF groups. Among ARDEGs, three core genes were identified, that were ATG5, RB1CC1, and PARK2. Considerable changes in the infiltration of immune cells were observed at different expression levels of the three core genes, among which the expression of RB1CC1 was significantly associated with the infiltration of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. In the mouse liver fibrosis experiment, ATG5, RB1CC1, and PARK2 were at higher levels in HF group than those in HC group. Compared with HC group, HF group showed low positive area in F4/80, IL-17 and CD56, indicating decreased expression of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. Meanwhile, knocking down RB1CC1 was found to inhibit the activation of hepatic stellate cells and alleviate liver fibrosis.ConclusionATG5, RB1CC1, and PARK2 are promising autophagy-related therapeutic biomarkers for HF. This is the first study to identify RB1CC1 in HF, which may promote the progression of liver fibrosis by regulating macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell

Cinnamaldehyde protects donor heart from cold ischemia–reperfusion injury via the PI3K/AKT/mTOR pathway

With the growing shortage of organs, improvements in donor organ protection are needed to meet the increasing demands for transplantation. Here, the aim was to investigate the protective effect of cinnamaldehyde against ischemia–reperfusion injury (IRI) in donor hearts exposed to prolonged cold ischemia. Donor hearts were harvested from rats pretreated with or without cinnamaldehyde, then subjected to 24 h of cold preservation and 1 h of ex vivo perfusion. Hemodynamic changes, myocardial inflammation, oxidative stress, and myocardial apoptosis were evaluated. The PI3K/AKT/mTOR pathway involved in the cardioprotective effects of cinnamaldehyde was explored through RNA sequencing and western blot analysis. Intriguingly, cinnamaldehyde pretreatment remarkably improved cardiac function through increasing coronary flow, left ventricular systolic pressure, +dp/dtmax, and −dp/dtmax, decreasing coronary vascular resistance and left ventricular end-diastolic pressure. Moreover, our findings indicated that cinnamaldehyde pretreatment protected the heart from IRI by alleviating myocardial inflammation, attenuating oxidative stress, and reducing myocardial apoptosis. Further studies showed that the PI3K/AKT/mTOR pathway was activated after cinnamaldehyde treatment during IRI. The protective effects of cinnamaldehyde were abolished by LY294002. In conclusion, cinnamaldehyde pretreatment alleviated IRI in donor hearts suffering from prolonged cold ischemia. Cinnamaldehyde exerted cardioprotective effects through the activation of the PI3K/AKT/mTOR pathway

Assessing mental stress on myocardial perfusion and myocardial blood flow in women without obstructive coronary disease: protocol for a mechanistic clinical trial

Introduction Two-thirds of women with symptoms of angina have ‘angina with no obstructive coronary artery disease’ (ANOCA). Growing evidence supports the use of coronary artery function testing for the diagnosis of ANOCA. Research into the prevalence of mental stress-induced myocardial ischaemia (MSIMI) among women with ANOCA is lacking. MSIMI is common in clinically stable patients with coronary artery disease. It is not associated coronary stenosis but is a prognostic risk factor. Here, we describe the rationale and protocol for a mechanistic clinical trial to test the following hypotheses: (1) that MSIMI is more common in women with ANOCA women than in age-matched and sex-matched controls, and (2) MSIMI is associated with mental stress-induced myocardial blood flow (MBF) change but not with adenosine vasodilator stress-induced MBF change.Methods and analysis This is a mechanistic clinical trial. 84 women with confirmed ANOCA and 42 aged-matched healthy women (neither angina symptoms nor coronary stenosis) are to be recruited for mental and adenosine vasodilator stress tests. Positron emission tomography CT with ammonia N-13 will be used to evaluate the myocardial perfusion and MBF changes between stress and rest. MSIMI is defined as a summed difference score (SDS) of ≥3 and adenosine stress-induced myocardial ischaemia is defined as an SDS of ≥4. Other assessments include Reactive Hyperemia Index for microvascular endothelial function, peripheral arterial tonometry or digital vasomotor response, and a series of blood and psychometric tests.Ethics and dissemination This mechanistic clinical trial was approved by the Ethics Committee of Guangdong Provincial People’s Hospital. Findings will be disseminated through peer-reviewed publications and conference presentations.Trial registration number NCT03982901; Pre-results

DataSheet_1_Machine learning and experimental validation identified autophagy signature in hepatic fibrosis.docx

BackgroundThe molecular mechanisms of hepatic fibrosis (HF), closely related to autophagy, remain unclear. This study aimed to investigate autophagy characteristics in HF.MethodsGene expression profiles (GSE6764, GSE49541 and GSE84044) were downloaded, normalized, and merged. Autophagy-related differentially expressed genes (ARDEGs) were determined using the limma R package and the Wilcoxon rank sum test and then analyzed by GO, KEGG, GSEA and GSVA. The infiltration of immune cells, molecular subtypes and immune types of healthy control (HC) and HF were analyzed. Machine learning was carried out with two methods, by which, core genes were obtained. Models of liver fibrosis in vivo and in vitro were constructed to verify the expression of core genes and corresponding immune cells.ResultsA total of 69 ARDEGs were identified. Series functional cluster analysis showed that ARDEGs were significantly enriched in autophagy and immunity. Activated CD4 T cells, CD56bright natural killer cells, CD56dim natural killer cells, eosinophils, macrophages, mast cells, neutrophils, and type 17 T helper (Th17) cells showed significant differences in infiltration between HC and HF groups. Among ARDEGs, three core genes were identified, that were ATG5, RB1CC1, and PARK2. Considerable changes in the infiltration of immune cells were observed at different expression levels of the three core genes, among which the expression of RB1CC1 was significantly associated with the infiltration of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. In the mouse liver fibrosis experiment, ATG5, RB1CC1, and PARK2 were at higher levels in HF group than those in HC group. Compared with HC group, HF group showed low positive area in F4/80, IL-17 and CD56, indicating decreased expression of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. Meanwhile, knocking down RB1CC1 was found to inhibit the activation of hepatic stellate cells and alleviate liver fibrosis.ConclusionATG5, RB1CC1, and PARK2 are promising autophagy-related therapeutic biomarkers for HF. This is the first study to identify RB1CC1 in HF, which may promote the progression of liver fibrosis by regulating macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell.</p

DataSheet_2_Machine learning and experimental validation identified autophagy signature in hepatic fibrosis.docx

BackgroundThe molecular mechanisms of hepatic fibrosis (HF), closely related to autophagy, remain unclear. This study aimed to investigate autophagy characteristics in HF.MethodsGene expression profiles (GSE6764, GSE49541 and GSE84044) were downloaded, normalized, and merged. Autophagy-related differentially expressed genes (ARDEGs) were determined using the limma R package and the Wilcoxon rank sum test and then analyzed by GO, KEGG, GSEA and GSVA. The infiltration of immune cells, molecular subtypes and immune types of healthy control (HC) and HF were analyzed. Machine learning was carried out with two methods, by which, core genes were obtained. Models of liver fibrosis in vivo and in vitro were constructed to verify the expression of core genes and corresponding immune cells.ResultsA total of 69 ARDEGs were identified. Series functional cluster analysis showed that ARDEGs were significantly enriched in autophagy and immunity. Activated CD4 T cells, CD56bright natural killer cells, CD56dim natural killer cells, eosinophils, macrophages, mast cells, neutrophils, and type 17 T helper (Th17) cells showed significant differences in infiltration between HC and HF groups. Among ARDEGs, three core genes were identified, that were ATG5, RB1CC1, and PARK2. Considerable changes in the infiltration of immune cells were observed at different expression levels of the three core genes, among which the expression of RB1CC1 was significantly associated with the infiltration of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. In the mouse liver fibrosis experiment, ATG5, RB1CC1, and PARK2 were at higher levels in HF group than those in HC group. Compared with HC group, HF group showed low positive area in F4/80, IL-17 and CD56, indicating decreased expression of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. Meanwhile, knocking down RB1CC1 was found to inhibit the activation of hepatic stellate cells and alleviate liver fibrosis.ConclusionATG5, RB1CC1, and PARK2 are promising autophagy-related therapeutic biomarkers for HF. This is the first study to identify RB1CC1 in HF, which may promote the progression of liver fibrosis by regulating macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell.</p

Automatic echocardiographic evaluation of the probability of pulmonary hypertension using machine learning

Abstract Echocardiography, a simple and noninvasive tool, is the first choice for screening pulmonary hypertension (PH). However, accurate assessment of PH, incorporating both the pulmonary artery pressures and additional signs for PH remained unsatisfied. Thus, this study aimed to develop a machine learning (ML) model that can automatically evaluate the probability of PH. This cohort included data from 346 (275 for training set and internal validation set and 71 for external validation set) patients with suspected PH patients and receiving right heart catheterization. Echocardiographic images on parasternal short axis‐papillary muscle level (PSAX‐PML) view from all patients were collected, labeled, and preprocessed. Local features from each image were extracted and subsequently integrated to build a ML model. By adjusting the parameters of the model, the model with the best prediction effect is finally constructed. We used receiver‐operating characteristic analysis to evaluate model performance and compared the ML model with the traditional methods. The accuracy of the ML model for diagnosis of PH was significantly higher than the traditional method (0.945 vs. 0.892, p = 0.027 [area under the curve [AUC]]). Similar findings were observed in subgroup analysis and validated in the external validation set (AUC = 0.950 [95% CI: 0.897−1.000]). In summary, ML methods could automatically extract features from traditional PSAX‐PML view and automatically assess the probability of PH, which were found to outperform traditional echocardiographic assessments