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Abstract Two common features of dietary polyphenols have hampered our mechanistic understanding of their beneficial effects for decades: targeting multiple organs and extremely low bioavailability. We show here that resveratrol intervention (REV-I) in high-fat diet (HFD)-challenged male mice inhibits chylomicron secretion, associated with reduced expression of jejunal but not hepatic scavenger receptor class B type 1 (SR-B1). Intestinal mucosa-specific SR-B1-/- mice on HFD-challenge exhibit improved lipid homeostasis but show virtually no further response to REV-I. SR-B1 expression in Caco-2 cells cannot be repressed by pure resveratrol compound while fecal-microbiota transplantation from mice on REV-I suppresses jejunal SR-B1 in recipient mice. REV-I reduces fecal levels of bile acids and activity of fecal bile-salt hydrolase. In Caco-2 cells, chenodeoxycholic acid treatment stimulates both FXR and SR-B1. We conclude that gut microbiome is the primary target of REV-I, and REV-I improves lipid homeostasis at least partially via attenuating FXR-stimulated gut SR-B1 elevation.

Soil microbial characteristics are considered to be an index for soil quality evaluation. It is generally believed that organic amendments replacing chemical fertilizers have positive effects on changing microbial activity and community structure. However, their effects on different agro-ecosystems on a global scale and their differences in different environmental conditions and experimental durations are unclear. This study performed a meta-analysis based on 94 studies with 204 observations to evaluate the overall effects and their differences in different experimental conditions and duration. The results indicated that compared to chemical fertilizer, organic amendments significantly increased total microbial biomass, bacterial biomass, fungal biomass, Gram-positive bacterial biomass and Gram-negative bacterial biomass, and had no effect on the ratio of fungi to bacteria and ratio of Gram-positive bacteria to Gram-negative bacteria. Meanwhile, land use type, mean annual precipitation and soil initial pH are essential factors affecting microbial activity response. Organic-amendment-induced shifts in microbial biomass can be predominantly explained by soil C and nutrient availability changes. Additionally, we observed positive relationships between microbial functionality and microbial biomass, suggesting that organic-amendment-induced changes in microbial activities improved soil microbial functionality.

Application of organic fertilizers or their combination with chemical fertilizers is a feasible practice for improving soil fertility and reducing soil degradation in agroecosystems, and these regulations are mainly mediated though soil microbial communities. Despite bacteria ranking among the most abundant and diverse groups of soil microorganisms, the effects of long-term organic fertilization (OF) and chemical–organic fertilization (COF) on soil bacterial diversity and community composition remain unclear. In this study, we conducted a meta-analysis and demonstrated that OF had no significant effect on bacterial alpha diversity. Application of chemical fertilizer and crop residue significantly decreased bacterial Richness index. Both OF and COF significantly altered bacterial community structure, with these changes being predominately attributed to shifts in soil pH. For bacterial phyla, both OF and COF significantly increased the relative abundance of Proteobacteria and Bacteroidetes, suggesting that OF and COF may cause the enrichment of copiotrophic taxa. In addition, COF significantly increased the relative abundance of Gammaproteobacteria but decreased the relative abundance of Acidobacteria. Overall, our results suggest that organic and chemical–organic fertilization can effectively maintain bacterial diversity and enhance soil fertility in agroecosystems, and the alteration of soil bacterial community structure is closely intertwined with soil pH.

Cardiovascular and cerebrovascular diseases are leading causes of death worldwide, accounting for more than 40% of all deaths in China. Acute myocardial infarction (AMI) is a common cardiovascular disease and traditionally divided into ST-segment (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI), which are known with different prognoses and treatment strategies. However, key regulatory genes and pathways involved in AMI that may be used as potential biomarker for prognosis are unknown. In this study, we employed both bulk and single-cell RNA-seq to construct gene regulatory networks and cell-cell communication networks. We first constructed weighted gene co-expression networks for differential expressed genes between STEMI and NSTEMI patients based on whole-blood RNA-seq transcriptomics. Network topological attributes (e.g., node degree, betweenness) were analyzed to identify key genes involved in different functional network modules. Furthermore, we used single-cell RNA-seq data to construct multilayer signaling network to infer regulatory mechanisms of the above key genes. PLAUR (receptor for urokinase plasminogen activator) was found to play a vital role in transducing inter-cellular signals from endothelial cells and fibroblast cells to intra-cellular pathways of myocardial cells, leading to gene expression involved in cellular response to hypoxia. Our study sheds lights on identifying molecular biomarkers for diagnosis and prognosis of AMI, and provides candidate key regulatory genes for further experimental validation.

BackgroundCurrent electrocardiogram (ECG) criteria of left ventricular hypertrophy (LVH) have low sensitivity. Deep learning (DL) techniques have been widely used to detect cardiac diseases due to its ability of automatic feature extraction of ECG. However, DL was rarely applied in LVH diagnosis. Our study aimed to construct a DL model for rapid and effective detection of LVH using 12-lead ECG.MethodsWe built a DL model based on convolutional neural network-long short-term memory (CNN-LSTM) to detect LVH using 12-lead ECG. The echocardiogram and ECG of 1,863 patients obtained within 1 week after hospital admission were analyzed. Patients were evenly allocated into 3 sets at 3:1:1 ratio: the training set (n = 1,120), the validation set (n = 371) and the test set 1 (n = 372). In addition, we recruited 453 hospitalized patients into the internal test set 2. Different DL model of each subgroup was developed according to gender and relative wall thickness (RWT).ResultsThe LVH was predicted by the CNN-LSTM model with an area under the curve (AUC) of 0.62 (sensitivity 68%, specificity 57%) in the test set 1, which outperformed Cornell voltage criteria (AUC: 0.57, sensitivity 48%, specificity 72%) and Sokolow-Lyon voltage (AUC: 0.51, sensitivity 14%, specificity 96%). In the internal test set 2, the CNN-LSTM model had a stable performance in predicting LVH with an AUC of 0.59 (sensitivity 65%, specificity 57%). In the subgroup analysis, the CNN-LSTM model predicted LVH by 12-lead ECG with an AUC of 0.66 (sensitivity 72%, specificity 60%) for male patients, which performed better than that for female patients (AUC: 0.59, sensitivity 50%, specificity 71%).ConclusionOur study established a CNN-LSTM model to diagnose LVH by 12-lead ECG with higher sensitivity than current ECG diagnostic criteria. This CNN-LSTM model may be a simple and effective screening tool of LVH.

Early diagnosis of acute ST-segment elevation myocardial infarction (STEMI) and early determination of the culprit vessel are associated with a better clinical outcome. We developed three deep learning (DL) models for detecting STEMIs and culprit vessels based on 12-lead electrocardiography (ECG) and compared them with conclusions of experienced doctors, including cardiologists, emergency physicians, and internists. After screening the coronary angiography (CAG) results, 883 cases (506 control and 377 STEMI) from internal and external datasets were enrolled for testing DL models. Convolutional neural network-long short-term memory (CNN-LSTM) (AUC: 0.99) performed better than CNN, LSTM, and doctors in detecting STEMI. Deep learning models (AUC: 0.96) performed similarly to experienced cardiologists and emergency physicians in discriminating the left anterior descending (LAD) artery. Regarding distinguishing RCA from LCX, DL models were comparable to doctors (AUC: 0.81). In summary, we developed ECG-based DL diagnosis systems to detect STEMI and predict culprit vessel occlusion, thus enhancing the accuracy and effectiveness of STEMI diagnosis.

Electrocardiogram (ECG) is an important tool for the detection of acute ST-segment elevation myocardial infarction (STEMI). However, machine learning (ML) for the diagnosis of STEMI complicated with arrhythmia and infarct-related arteries is still underdeveloped based on real-world data. Therefore, we aimed to develop an ML model using the Least Absolute Shrinkage and Selection Operator (LASSO) to automatically diagnose acute STEMI based on ECG features. A total of 318 patients with STEMI and 502 control subjects were enrolled from Jan 2017 to Jun 2019. Coronary angiography was performed. A total of 180 automatic ECG features of 12-lead ECG were input into the model. The LASSO regression model was trained and validated by the internal training dataset and tested by the internal and external testing datasets. A comparative test was performed between the LASSO regression model and different levels of doctors. To identify the STEMI and non-STEMI, the LASSO model retained 14 variables with AUCs of 0.94 and 0.93 in the internal and external testing datasets, respectively. The performance of LASSO regression was similar to that of experienced cardiologists (AUC: 0.92) but superior (p < 0.05) to internal medicine residents, medical interns, and emergency physicians. Furthermore, in terms of identifying left anterior descending (LAD) or non-LAD, LASSO regression achieved AUCs of 0.92 and 0.98 in the internal and external testing datasets, respectively. This LASSO regression model can achieve high accuracy in diagnosing STEMI and LAD vessel disease, thus providing an assisting diagnostic tool based on ECG, which may improve the early diagnosis of STEMI.