Optimized therapeutic potential of Yinchenhao decoction for cholestatic hepatitis by combined network meta-analysis and network pharmacology.

BACKGROUND: Cholestatic hepatitis is recognized as a significant contributor to the development of liver fibrosis and cirrhosis. As a well-known classic formula for the treatment of cholestatic hepatitis, Yinchenhao decoction (YCHD) is widely used in countries in Asia, including China, Japan, and Korea. However, in recent years, a risk of liver injury has been reported from Rheum palmatum L. and Gardenia jasmonoides J.Ellis which are the main ingredients of YCHD. Therefore, the question arises whether YCHD is still safe enough for the treatment of cholestatic hepatitis or whether an optimized ratio of ingredients should be applied. These is inevitable questions for the clinical application of YCHD. PURPOSE: To provide a scientific basis for the clinical application of YCHD through a combination of meta-analysis and network pharmacology and to find the best ratio of components to ensure optimal therapeutic efficacy and safety. At the same time, a deeper understanding of the mechanisms of YCHD was explored. METHODS: We retrieved relevant trials from various databases including PubMed, Web of Science, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI), VIP and Wanfang databases up to August 2023. After screening for inclusion and exclusion criteria, we assessed efficiency, ALT, AST, and TBIL as outcome parameters. The relevant data underwent a network meta-analysis using STATA 16.0 software. Based on network pharmacology, we screened the disease targets, active ingredients, and targets related to YCHD. The targets were visualized using Cytoscape 3.9.1. Then, potential mechanisms were explored based on bioinformatic techniques. RESULTS: Twenty eligible studies were finally screened and a total of 1,591 patients who fulfilled the inclusion criteria were enrolled in the study. The meta-analysis results indicated that TG-c (treatment group c) [(Artemisia capillaris Thunb. : Gardenia jasminoides J.Ellis : Rheum palmatum L. = 10:5:2-10:5:3) + CT] was the most promising therapeutic approach, demonstrating superior efficacy and notable improvements in both AST and TBIL levels. For ALT, TG-d [(Artemisia capillaris : Gardenia jasminoides : Rheum palmatum = 5:1:1-5:2:1) + CT] exhibited the greatest potential as optimal therapy option. Based on the surface under the cumulative ranking curve (SUCRA) values, TG-c was the best therapy in terms of efficiency and improvement in TBIL levels, while TG-d was the most effective in reducing ALT levels. For AST levels, TG-e [(Artemisia capillaris : Gardenia jasminoides : Rheum palmatum = 5:2:2-5:3:3) + CT] was the most effective therapy. The comprehensive analysis revealed that TG-c exhibited the most pronounced efficacy. Combined network pharmacology, GO enrichment analysis and KEGG pathway enrichment analysis displayed that the key target genes of Artemisia capillaris, Rheum palmatum, and Gardenia jasminoides were closely involved in inflammation response, bile transport, apoptosis, oxidative stress, and regulation of leukocyte migration. Notably, bile secretion dominated the common pathway of the three herbs. On the other hand, Artemisia capillaris exhibited a unique mode of action by regulating the IL-17 signaling pathway, which may play a crucial role in its effectiveness. CONCLUSION: Based on our findings, the optimal TG-C demonstrated the most favorable overall therapeutic efficacy by increasing the dosage of Artemisia capillaris while reducing the dosage of Gardenia jasminoides and Rheum palmatum. This is attributed to the potent ability of Artemisia capillaris. to effectively modulate the IL-17 signaling pathway, thereby exerting a beneficial therapeutic effect. Conversely, Gardenia jasminoides and Rheum palmatum may potentially enhance the activation of the NF-кB signaling pathway, thereby elevating the risk of hepatotoxicity.

Molecular insights into experimental models and therapeutics for cholestasis.

Cholestatic liver disease (CLD) is a range of conditions caused by the accumulation of bile acids (BAs) or disruptions in bile flow, which can harm the liver and bile ducts. To investigate its pathogenesis and treatment, it is essential to establish and assess experimental models of cholestasis, which have significant clinical value. However, owing to the complex pathogenesis of cholestasis, a single modelling method can merely reflect one or a few pathological mechanisms, and each method has its adaptability and limitations. We summarize the existing experimental models of cholestasis, including animal models, gene-knockout models, cell models, and organoid models. We also describe the main types of cholestatic disease simulated clinically. This review provides an overview of targeted therapy used for treating cholestasis based on the current research status of cholestasis models. In addition, we discuss the respective advantages and disadvantages of different models of cholestasis to help establish experimental models that resemble clinical disease conditions. In sum, this review not only outlines the current research with cholestasis models but also projects prospects for clinical treatment, thereby bridging basic research and practical therapeutic applications.

A systematic review of astragaloside IV effects on animal models of diabetes mellitus and its complications.

Context: Diabetes mellitus (DM) is one of the fastest-growing diseases worldwide; however, its pathogenesis remains unclear. Complications seriously affect the quality of life of patients in the later stages of diabetes, ultimately leading to suffering. Natural small molecules are an important source of antidiabetic agents. Objective: Astragaloside IV (AS-IV) is an active ingredient of Astragalus mongholicus (Fisch.) Bunge. We reviewed the efficacy and mechanism of action of AS-IV in animal and cellular models of diabetes and the mechanism of action of AS-IV on diabetic complications in animal and cellular models. We also summarized the safety of AS-IV and provided ideas and rationales for its future clinical application. Methods: Articles on the intervention in DM and its complications using AS-IV, such as those published in SCIENCE, PubMed, Springer, ACS, SCOPUS, and CNKI from the establishment of the database to February 2022, were reviewed. The following points were systematically summarized: dose/concentration, route of administration, potential mechanisms, and efficacy of AS-IV in animal models of DM and its complications. Results: AS-IV has shown therapeutic effects in animal models of DM, such as alleviating gestational diabetes, delaying diabetic nephropathy, preventing myocardial cell apoptosis, and inhibiting vascular endothelial dysfunction; however, the potential effects of AS-IV on DM should be investigated. Conclusion: AS-IV is a potential drug for the treatment of diabetes and its complications, including diabetic vascular disease, cardiomyopathy, retinopathy, peripheral neuropathy, and nephropathy. In addition, preclinical toxicity studies indicate that it appears to be safe, but the safe human dose limit is yet to be determined, and formal assessments of adverse drug reactions among humans need to be further investigated. However, additional formulations or structural modifications are required to improve the pharmacokinetic parameters and facilitate the clinical use of AS-IV.

Natural products targeting macroautophagy signaling in hepatocellular carcinoma therapy: Recent evidence and perspectives.

Hepatocellular carcinoma (HCC), presently the second leading cause of global cancer-related mortality, continues to pose significant challenges in the realm of medical oncology, impacting both clinical drug selection and mechanistic research. Recent investigations have unveiled autophagy-related signaling as a promising avenue for HCC treatment. A growing body of research has highlighted the pivotal role of autophagy-modulating natural products in inhibiting HCC progression. In this context, we provide a concise overview of the fundamental autophagy mechanism and delineate the involvement of autophagic signaling pathways in HCC development. Additionally, we review pertinent studies demonstrating how natural products regulate autophagy to mitigate HCC. Our findings indicate that natural products exhibit cytotoxic effects through the induction of excessive autophagy, simultaneously impeding HCC cell proliferation by autophagy inhibition, thereby depriving HCC cells of essential energy. These effects have been associated with various signaling pathways, including PI3K/AKT, MAPK, AMPK, Wnt/ß-catenin, Beclin-1, and ferroautophagy. These results underscore the considerable therapeutic potential of natural products in HCC treatment. However, it is important to note that the present study did not establish definitive thresholds for autophagy induction or inhibition by natural products. Further research in this domain is imperative to gain comprehensive insights into the dual role of autophagy, equipping us with a better understanding of this double-edged sword in HCC management.

Protective role of curcumin in disease progression from non-alcoholic fatty liver disease to hepatocellular carcinoma: a meta-analysis.

Background: Pathological progression from non-alcoholic fatty liver disease (NAFLD) to liver fibrosis (LF) to hepatocellular carcinoma (HCC) is a common dynamic state in many patients. Curcumin, a dietary supplement derived from the turmeric family, is expected to specifically inhibit the development of this progression. However, there is a lack of convincing evidence. Methods: The studies published until June 2023 were searched in PubMed, Web of Science, Embase, and the Cochrane Library databases. The SYstematic Review Center for Laboratory animal Experimentation (SYRCLE) approach was used to evaluate the certainty of evidence. StataSE (version 15.1) and Origin 2021 software programs were used to analyze the critical indicators. Results: Fifty-two studies involving 792 animals were included, and three disease models were reported. Curcumin demonstrates a significant improvement in key indicators across the stages of NAFLD, liver fibrosis, and HCC. We conducted a detailed analysis of common inflammatory markers IL-1ß, IL-6, and TNF-α, which traverse the entire disease process. The research results reveal that curcumin effectively hinders disease progression at each stage by suppressing inflammation. Curcumin exerted hepatoprotective effects in the dose range from 100 to 400 mg/kg and treatment duration from 4 to 10 weeks. The mechanistic analysis reveals that curcumin primarily exerts its hepatoprotective effects by modulating multiple signaling pathways, including TLR4/NF-κB, Keap1/Nrf2, Bax/Bcl-2/Caspase 3, and TGF-ß/Smad3. Conclusion: In summary, curcumin has shown promising therapeutic effects during the overall progression of NAFLD-LF-HCC. It inhibited the pathological progression by synergistic mechanisms related to multiple pathways, including anti-inflammatory, antioxidant, and apoptosis regulation.

Transformer-Based Network with Optimization for Cross-Subject Motor Imagery Identification.

Exploring the effective signal features of electroencephalogram (EEG) signals is an important issue in the research of brain-computer interface (BCI), and the results can reveal the motor intentions that trigger electrical changes in the brain, which has broad research prospects for feature extraction from EEG data. In contrast to previous EEG decoding methods that are based solely on a convolutional neural network, the traditional convolutional classification algorithm is optimized by combining a transformer mechanism with a constructed end-to-end EEG signal decoding algorithm based on swarm intelligence theory and virtual adversarial training. The use of a self-attention mechanism is studied to expand the receptive field of EEG signals to global dependence and train the neural network by optimizing the global parameters in the model. The proposed model is evaluated on a real-world public dataset and achieves the highest average accuracy of 63.56% in cross-subject experiments, which is significantly higher than that found for recently published algorithms. Additionally, good performance is achieved in decoding motor intentions. The experimental results show that the proposed classification framework promotes the global connection and optimization of EEG signals, which can be further applied to other BCI tasks.

Filter bank sinc-convolutional network with channel self-attention for high performance motor imagery decoding.

Objective.Motor Imagery Brain-Computer Interface (MI-BCI) is an active Brain-Computer Interface (BCI) paradigm focusing on the identification of motor intention, which is one of the most important non-invasive BCI paradigms. In MI-BCI studies, deep learning-based methods (especially lightweight networks) have attracted more attention in recent years, but the decoding performance still needs further improving.Approach.To solve this problem, we designed a filter bank structure with sinc-convolutional layers for spatio-temporal feature extraction of MI-electroencephalography in four motor rhythms. The Channel Self-Attention method was introduced for feature selection based on both global and local information, so as to build a model called Filter Bank Sinc-convolutional Network with Channel Self-Attention for high performance MI-decoding. Also, we proposed a data augmentation method based on multivariate empirical mode decomposition to improve the generalization capability of the model.Main results.We performed an intra-subject evaluation experiment on unseen data of three open MI datasets. The proposed method achieved mean accuracy of 78.20% (4-class scenario) on BCI Competition IV IIa, 87.34% (2-class scenario) on BCI Competition IV IIb, and 72.03% (2-class scenario) on Open Brain Machine Interface (OpenBMI) dataset, which are significantly higher than those of compared deep learning-based methods by at least 3.05% (p= 0.0469), 3.18% (p= 0.0371), and 2.27% (p= 0.0024) respectively.Significance.This work provides a new option for deep learning-based MI decoding, which can be employed for building BCI systems for motor rehabilitation.

Clinical efficacy of acupuncture in patients with adhesive intestinal obstruction: A meta-analysis.

BACKGROUND: Adhesive intestinal obstruction (AIO) is a common surgical emergency. Surgical exploration has a considerable risk of intestinal injury, and surgical treatment may greatly reduce the quality of life after surgery and cause AIO after re-operation. The nonsurgical treatment is effective for approximately 70% to 90% of patients with adhesive small bowel obstruction (ASBO). However, the high recurrence (30%) and mortality (2%) rates of ASBO are concerning. Moreover, the ideal management method of ASBO remains debatable. Studies have shown that acupuncture can also promote postoperative gastrointestinal function recovery and prevent postoperative complications such as nausea, vomiting, and visceral pain. AIM: We aimed to evaluate the effectiveness of acupuncture in the treatment of AIO. METHODS: Randomized controlled trials investigating the effectiveness of acupuncture for adhesive bowel obstruction published until November 2021 were identified by searching 8 comprehensive databases. Data analysis was performed using RevMan v. 5.4 and Stata software v. 16.0. The random-effects model and the fixed-effects model were used to perform the meta-analysis on the experimental group and control group. RESULTS: Twelve studies with a total of 892 participants were included. The results showed that the experimental group had a significantly higher effective rate (relative risk: 1.20; 95% confidence interval (CI): 1.11-1.28; P < .00001) and a markedly shorter time of the first defecation (mean difference: -11.49, 95% CI: -19.31 to -3.66; P = .004) than the control group. The experimental group also showed a reduction in the duration of abdominal pain, and the reduced length of hospital stay. However, no statistical differences were observed between the 2 groups in terms of the surgery conversion rate. CONCLUSION: Acupuncture is effective in the treatment of AIO. It can remarkably alleviate some clinical symptoms in patients with AIO.

A Systematic Review and Meta-Analysis of Randomized Controlled Trials of Fecal Microbiota Transplantation for the Treatment of Inflammatory Bowel Disease.

Objectives: Inflammatory bowel disease (IBD) is a chronic recurrent inflammatory disease of the gastrointestinal tract, and its prevalence is increasing worldwide. Fecal microbiota transplantation (FMT) is an emerging therapy that modifies the patient's gut microbiota by transplanting feces from a healthy donor to achieve disease remission. However, its efficacy and safety need to be further investigated. Methods: PubMed, the Cochrane Library, Web of Science, Embase, and Google Scholar databases (up to 8th November 2021) were searched and literature was screened by title and abstract as well as full text. The primary outcome was clinical remission, with the clinical response as a secondary outcome. Risk ratios (RR) with 95% confidence intervals (CI) were reported. Results: A total of 14 trials were included in this study. In terms of clinical remission, FMT had a significant effect compared to placebo (RR = 1.44, 95 CI%: 1.03 to 2.02, I 2 = 38%, P=0.03), with no significant risk of study heterogeneity. Moreover, FMT led to significant results in clinical response compared to placebo with moderate between-study heterogeneity (RR = 1.34, 95 CI%: 0.92 to 1.94, I 2 = 51%, P=0.12). Subgroup analysis showed a higher clinical remission for fresh fecal FMT (40.9%) than that for frozen fecal FMT (32.2%); the efficacy of gastrointestinal (GI) pretreatment, the severity of disease, route of administration, and the donor selection remain unclear and require more extensive study. Safety analysis concluded that most adverse events were mild and self-resolving. The microbiological analysis found that the patient's gut microbiota varied in favor of the donor, with increased flora diversity and species richness. Conclusion: FMT is a safe, effective, and well-tolerated therapy. Studies have found that fresh fecal microbiota transplant can increase clinical remission rates. However, more randomized controlled trials and long-term follow-ups are needed to assess its long-term effectiveness and safety.

A Fishery Water Quality Monitoring and Prediction Evaluation System for Floating UAV Based on Time Series.

In recent years, fishery has developed rapidly. For the vital interests of the majority of fishermen, this paper makes full use of Internet of Things and air-water amphibious UAV technology to provide an integrated system that can meet the requirements of fishery water quality monitoring and prediction evaluation. To monitor target water quality in real time, the water quality monitoring of the system is mainly completed by a six-rotor floating UAV that carries water quality sensors. The GPRS module is then used to realize remote data transmission. The prediction of water quality transmission data is mainly realized by the algorithm of time series comprehensive analysis. The evaluation rules are determined according to the water quality evaluation standards to evaluate the predicted water quality data. Finally, the feasibility of the system is proved through experiments. The results show that the system can effectively evaluate fishery water quality under different weather conditions. The prediction accuracy of the pH, dissolved oxygen content, and ammonia nitrogen content of fishery water quality can reach 99%, 98%, and 99% on sunny days, and reach 92%, 98%, and 91% on rainy days.

Efficient Mining Template of Predictive Temporal Clinical Event Patterns From Patient Electronic Medical Records.

Exploring the temporal relationship among events in patient electronic medical records (EMR) is an important problem in biomedical informatics and the results can reveal patients' impending disease conditions. In this paper, we investigate the problem of mining patterns from a sequence of point events, i.e., we only have the information on when the event happens but no duration or numerical value available. We propose a whole pipeline, including event preprocessing, pattern mining, and outcome analysis to mine the patterns and evaluate their effectiveness and discriminative power. Finally, we treat those mined patterns as additional features and evaluate them in a predictive modeling task for the early detection of congestive heart failure. On a real-world EMR data warehouse, we found that by adding those sequential pattern features, the prediction performance could be significantly improved approximately 0.1.