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Abstract Background: Salvianolate is a compound mainly composed of salvia magnesium acetate, which is extracted from the Chinese herb Salvia miltiorrhiza. In recent years, salvianolate injection has been widely used in the treatment of cardiovascular diseases, but the mechanism of how it can alleviate cardiotoxicity remains unclear. Methods: The cardiac injury model was constructed by treatment with doxorubicin (Dox) or azithromycin (Azi) in zebrafish larvae. Heart phenotype, heart rate, and cardiomyocyte apoptosis were observed in the study. RNA-sequencing (RNA-seq) analysis was used to explore the underlying mechanism of salvianolate treatment. Moreover, cardiomyocyte autophagy was assessed by in situ imaging. In addition, the miR-30a/becn1 axis regulation by salvianolate was further investigated. Results: Salvianolate treatment reduced the proportion of pericardial edema, recovered heart rate, and inhibited cardiomyocyte apoptosis in Dox/Azi-administered zebrafish larvae. Mechanistically, salvianolate regulated the lysosomal pathway and promoted autophagic flux in zebrafish cardiomyocytes. The expression level of becn1 was increased in Dox-induced myocardial tissue injury after salvianolate administration; overexpression of becn1 in cardiomyocytes alleviated the Dox/Azi-induced cardiac injury and promoted autophagic flux in cardiomyocytes, while becn1 knockdown blocked the effects of salvianolate. In addition, miR-30a, negatively regulated by salvianolate, partially inhibited the cardiac amelioration of salvianolate by targeting becn1 directly. Conclusion: This study has proved that salvianolate reduces cardiomyopathy by regulating autophagic flux through the miR-30a/becn1 axis in zebrafish and is a potential drug for adjunctive Dox/Azi therapy.

Background Early diagnosis and treatment effectiveness of early-onset coronary artery disease (EOCAD) are crucial, and non-invasive predictive biomarkers are needed for young adults. We aimed to evaluate the usefulness of the triglyceride-glucose (TyG) index, a novel marker of insulin resistance, in identifying young CAD patients and predicting their risk of developing target lesion failure (TLF). Methods We recruited EOCAD patients (luminal narrowing ≥ 70%) and controls free from CAD (luminal narrowing < 30%), both aged 45 years or younger, from 38 hospitals in China between 2017 and 2020. EOCAD patients who underwent successful percutaneous coronary intervention were followed for incident TLF. TyG index was defined as Ln [fasting triglyceride (mg/dL) × fasting blood glucose (mg/dL)/2]. We used logistic regression and Cox proportional hazards modeling to evaluate the association of TyG index with prevalent EOCAD and incident TLF, respectively. The discriminatory ability of TyG index was assessed by the area under the receiver-operating characteristic curve (AUC). Results Among the included 1513 EOCAD patients (39.6 ± 4.4 years, 95.4% male) and 1513 age-matched controls (39.0 ± 4.4 years, 46.4% male), TyG index was positively associated with the prevalence of EOCAD (adjusted odds ratio: 1.40, 95% confidence interval [CI] 1.23–1.60, per standard deviation [SD] increase in TyG index). The addition of TyG index to an empirical risk model provided an improvement in diagnostic ability for EOCAD, with a net reclassification improvement of 0.10 (95% CI 0.03–0.17, p  = 0.005). During a medium of 33 month (IQR: 31–34 months) follow-up, 43 (3.3%) patients experienced TLF. Multivariate Cox regression model revealed that TyG index was an independent risk factor for TLF (adjusted hazard ratio [HR]: 2.410, 95% CI 1.07–5.42 comparing the top to bottom TyG index tertile groups; HR: 1.30, 95% CI 1.01–1.73, per SD increase in TyG index). Compared with a model of conventional risk factors alone, the addition of the TyG index modestly improved the AUC (0.722–0.734, p  = 0.04) to predict TLF. Conclusions TyG index is positively associated with prevalent EOCAD and incident TLF. TyG index appeared to be a valuable component of future efforts to improve CAD risk stratification and TLF outcome prediction among young adults.

The water–energy–food (W-E-F) nexus approach has become the basis for a host of many methods addressing the security of global resources, whose methods are often nonparametric, due to the complex and indefinable relationship among the three. In this work, the nonparametric evaluation method data envelopment analysis (DEA) is further extended to a three-stage dual-boundary network model (TD-NDEA) for dealing with the “black box” problem in W-E-F Nexus. In the empirical study, the TD-NDEA method is applied to assess the efficiencies of W-E-F nexus in 13 selected cities of Jiangsu Province in China, where W-E-F nexus is innovatively decomposed into three stages, “W-E,” “WE-F,” and “WEF.” External factors such as labor force, urbanization, and economy are included in the assessment. On this basis, environmental governance and policy interventions are utilized as indicators of eco-sustainability to reshape the third stage as “WEF-Eco.” It is perceived from the numeric analysis that (i) regional disparities in the efficiencies of W-E-F nexus expand significantly; (ii) compared with the labor force, urbanization, and economy are more remarkable toward influencing the efficiencies; and (iii) ecological sustainable planning plays an effective role in reducing regional heterogeneity and speeding up the process of regional coordination. Based upon the findings, relevant policy recommendations are carefully designed.

Many epidemiological reports have indicated an increase in the incidence of breast cancer among psychotic patients, suggesting that the targets of antipsychotics, neurotransmitter receptors, may have a role in tumorigenesis. However, the functions of neurotransmitter receptors in cancer are barely known. Here, we analyzed 44 neurotransmitter receptors in breast cancer and revealed that the expression of 34 receptors was positively correlated with relapse-free survival rates (RFS) of patients using the public database (n = 3951). Among all these receptors, we revealed decreased expression of HTR6 in human advanced breast cancer versus tumors in situ using our original data (n = 44). After a pan-cancer analysis including 22 cancers (n = 11262), we disclosed that HTR6 was expressed in 12 tumors and uncovered its influence on survival in seven tumors. Using multi-omics datasets from Linkedomics, we revealed a potential regulatory role of HTR6 in MAPK, JUN, and leukocyte-differentiation pathways through enriching 294 co-expressed phosphorylated proteins of HTR6. Furthermore, we proclaimed a close association of HTR6 expression with the immune microenvironment. Finally, we uncovered two possible reasons for HTR6 down-regulation in breast cancer, including deep deletion in the genome and the up-regulation of FOXA1 in breast cancer, which was a potential negatively regulatory transcription factor of HTR6. Taken together, we revealed a new function of neurotransmitter receptors in breast cancer and identified HTR6 as a survival-related gene potentially regulating the immune microenvironment. The findings in our study would improve our understanding of the pathogenesis of breast cancer and provided a theoretical basis for personalized medication in psychotic patients.

Improving the survival rate of cardiomyocytes is the key point to treat most of the heart diseases, and targeting autophagy is a potential advanced therapeutic approach. Monitoring autophagic activity in cardiomyocytes in situ will be useful for studying autophagy-related heart disease and screening autophagy-modulating drugs. Zebrafish, Danio rerio , has been proven as an animal model for studying heart diseases in situ . Taken the advantage of zebrafish, especially the imaging of intact animals, here we generated two stable transgenic zebrafish lines that specifically expressed EGFP-map1lc3b or mRFP-EGFP-map1lc3b in cardiomyocytes under the promoter of myosin light chain 7. We first used a few known autophagy-modulating drugs to confirm their usefulness. By quantifying the density of autophagosomes and autolysosomes, autophagy inducers and inhibitors showed their regulatory functions, which were consistent with previous studies. With the two lines, we then found a significant increase in the density of autophagosomes but not autolysosomes in zebrafish cardiomyocytes at the early developmental stages, indicating the involvement of autophagy in early heart development. To prove their applicability, we also tested five clinical statins by the two lines. And we found that statins did not change the density of autophagosomes but reduced the density of autolysosomes in cardiomyocytes, implying their regulation in autophagic flux. Our study provides novel animal models for monitoring autophagic activity in cardiomyocytes in situ , which could be used to study autophagy-related cardiomyopathy and drug screening.

Based on data from 30 provinces in China from 2013 to 2022, this paper employs the Spatial Durbin Model to analyze the effect of integration development of digital intelligence on the quantity and quality of green technology innovation and its regional heterogeneity. The moderating effects of degree of nationalization and green purchasing are further explored. The results show the following: (1) The integration development of digital intelligence can not only increase the quantity of green technology innovation, but also significantly improve the quality of green technology innovation. Meanwhile, the integration development of digital intelligence has a negative spatial spillover effect on the quantity and quality of green technology innovation in neighboring regions. (2) There is significant regional heterogeneity in the improvement effect of digital intelligence integration development on the quantity and quality of green technology innovation and its spatial spillover effect. Moreover, the integration development of digital intelligence realizes the “quantity increase and quality improvement” of green technology innovation mainly by generating a resource allocation effect, scale economy effect and technology promotion effect. (3) Degree of nationalization negatively moderates the impact of integration development of digital intelligence on the quantity and quality of green technology innovation, while green purchasing positively moderates the impact of integration development of digital intelligence on the quantity and quality of green technology innovation, both of which have significant spatial spillover effects.

In the Martian induced magnetosphere, the motion of planetary ions is significantly controlled by the ambient electric fields, which can be decomposed into three components: the motional, Hall, and ambipolar electric fields. Each of them is dominant in different regions and provides the ion acceleration with a particular effectiveness. Therefore, it is necessary to characterize the global distribution of these electric field components. In this study, a global multifluid Hall-MHD model is applied, which considers the motional, Hall, and ambipolar electric fields in ion transport and magnetic induction equations to self-consistently investigate the morphology of the electric fields in the Martian space environment. Numerical results suggest that the motional electric field is dominant in the upstream of the bow shock and in the magnetosheath along the Z MSE direction, leading to the formation of the ion plume escape channel. At the bow shock, the ambipolar electric field points outward, to decelerate and deflect the solar wind plasma flow. In the magnetosheath region, the ambipolar and motional electric fields with inward direction tend to reaccelerate the solar wind ions. However, along the magnetic pileup boundary, the Hall electric field pointing outward prevents the solar wind ions from penetrating the Martian induced magnetosphere, which also prevails in the Martian magnetotail region, to accelerate the ions’ tailward escape. This is the first systematic investigation of the global distribution of electric fields, which is helpful to understand the processes of ion acceleration/deceleration and escape within the Mars–solar wind interaction.

Xanthine oxidase (XO) is a crucial enzyme in the development of hyperuricemia and gout. This study focuses on LWM and ALPM, two food-derived inhibitors of XO. We used molecular docking to obtain three systems and then conducted 200 ns molecular dynamics simulations for the Apo, LWM, and ALPM systems. The results reveal a stronger binding affinity of the LWM peptide to XO, potentially due to increased hydrogen bond formation. Notable changes were observed in the XO tunnel upon inhibitor binding, particularly with LWM, which showed a thinner, longer, and more twisted configuration compared to ALPM. The study highlights the importance of residue F914 in the allosteric pathway. Methodologically, we utilized the perturbed response scan (PRS) based on Python, enhancing tools for MD analysis. These findings deepen our understanding of food-derived anti-XO inhibitors and could inform the development of food-based therapeutics for reducing uric acid levels with minimal side effects.

As the stock market is an important part of the national economy, more and more investors have begun to pay attention to the methods to improve the return on investment and effectively avoid certain risks. Many factors affect the trend of the stock market, and the relevant information has the nature of time series. This paper proposes a composite model CNN-BiSLSTM to predict the closing price of the stock. Bidirectional special long short-term memory (BiSLSTM) improved on bidirectional long short-term memory (BiLSTM) adds 1 − tanh(x) function in the output gate which makes the model better predict the stock price. The model extracts advanced features that influence stock price through convolutional neural network (CNN), and predicts the stock closing price through BiSLSTM after the data processed by CNN. To verify the effectiveness of the model, the historical data of the Shenzhen Component Index from July 1, 1991, to October 30, 2020, are used to train and test the CNN-BiSLSTM. CNN-BiSLSTM is compared with multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), BiLSTM, CNN-LSTM, and CNN-BiLSTM. The experimental results show that the mean absolute error (MAE), root-mean-squared error (RMSE), and R-square (R2) evaluation indicators of the CNN-BiSLSTM are all optimal. Therefore, CNN-BiSLSTM can accurately predict the closing price of the Shenzhen Component Index of the next trading day, which can be used as a reference for the majority of investors to effectively avoid certain risks.

The interplanetary magnetic field (IMF) is one of the primary factors influencing the Martian plasma environment. In this study, a multifluid magnetohydrodynamic model is adopted to investigate how variations in IMF affect planetary ion escape, particularly the tailward escape flux. Our results reveal that for nominal IMF direction ( 56° Parker spiral), as IMF intensity increases, the ion escape rate decreases considerably. This reduction is primarily due to the decrease in planetary ion density in the plume and the magnetotail, which is caused by the lower ion production rate through the charge exchange process under high IMF conditions. With high IMF conditions, the dynamo at the bow shock is significantly enhanced, leading to a more severe deceleration of solar wind protons and fewer protons entering the magnetosheath. Consequently, intensified electromagnetic fields create a stronger induced magnetosphere, which shields the Martian ionosphere and atmosphere. Although the enhanced loading process for planetary ions results in higher ion escape velocities, the overall ion escape fluxes decrease due to the significant reduction in planetary ion density.