Background: Invasive detection methods such as liver biopsy are currently the gold standard for diagnosing liver cirrhosis and can be used to determine the degree of liver fibrosis and cirrhosis. In contrast, non-invasive diagnostic methods, such as ultrasonography, elastography, and clinical prediction scores, can prevent patients from invasiveness-related discomfort and risks and are often chosen as alternative or supplementary diagnostic methods for liver fibrosis or cirrhosis. However, these non-invasive methods cannot specify the pathological grading and early diagnosis of the lesions. Recent studies have revealed that gut microbiome-based machine learning can be utilized as a non-invasive diagnostic technique for liver cirrhosis or fibrosis, but there is no evidence-based support. Therefore, this study conducted a systematic review and meta-analysis for the first time to investigate the accuracy of machine learning based on the gut microbiota in the prediction of liver fibrosis and cirrhosis. Methods: A comprehensive and systematic search of publications published before April 2th, 2023 in PubMed, Cochrane Library, Embase, and Web of Science was conducted for relevant studies on the application of gut microbiome-based metagenomic sequencing modeling technology to the diagnostic prediction of liver cirrhosis or fibrosis. A bivariate mixed-effects model and Stata software 15.0 were adopted for the meta-analysis. Results: Ten studies were included in the present study, involving 11 prediction trials and 838 participants, 403 of whom were fibrotic and cirrhotic patients. Meta-analysis showed the pooled sensitivity (SEN) = 0.81 [0.75, 0.85], specificity (SEP) = 0.85 [0.77, 0.91], positive likelihood ratio (PLR) = 5.5 [3.6, 8.7], negative likelihood ratio (NLR) = 0.23 [0.18, 0.29], diagnostic odds ratio (DOR) = 24 [14, 41], and area under curve (AUC) = 0.86 [0.83–0.89]. The results demonstrated that machine learning methods had excellent potential to analyze gut microbiome data and could effectively predict liver cirrhosis or fibrosis. Machine learning provides a powerful tool for non-invasive prediction and diagnosis of liver cirrhosis or liver fibrosis, with broad clinical application prospects. However, these results need to be interpreted with caution due to limited clinical data. Conclusion: Gut microbiome-based machine learning can be utilized as a practical, non-invasive technique for the diagnostic prediction of liver cirrhosis or fibrosis. However, most of the included studies applied the random forest algorithm in modeling, so a diversified prediction system based on microorganisms is needed to improve the non-invasive detection of liver cirrhosis or fibrosis.

Virtual Screening and Molecular Docking to Study the Mechanism of Chinese Medicines in the Treatment of Coronavirus Infection - Article abstract #934102

Background: Stroke is ranked second among diseases that cause mortality worldwide. Owing to its complicated pathogenesis, no satisfactory treatment strategies for stroke are available. Dachengqi decoction (DCQD), a traditional Chinese herbal medicine, has been widely used in China for a long time, as it has a good effect on stroke. However, the molecular mechanism underlying this effect of DCQD is unclear. Objective: In the present study, we aimed to reveal and explore the multi-pathway and multi-gene regulatory molecular mechanism of Dachengqi decoction in the treatment of stroke. Methods: In this study, a network pharmacology method, in combination with oral bioavailability prediction and drug-likeness evaluation, was employed to predict the active ingredients of DCQD. The target genes of the active components and the traced pathways related to these target genes were predicted. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using clusterProfiler software package on the R platform and ClueGo+CluePedia plug-ins. Finally, the key DCQD targets were verified using the Gene Expression Omnibus (GEO) dataset. Results and Discussion: According to the ADME model, 52 active components were screened from 296 active components of DCQD. After prediction and screening, 215 stroke-related targets were obtained and analyzed via GO and KEGG analyses. GO analysis showed that DCQD targets were mainly involved in the regulation of oxidative stress, lipid metabolism, inflammation, and other biological processes. KEGG pathway analysis further revealed pathways involved in stroke, such as arachidonic acid metabolic, HIF-1 signaling pathway, estrogen signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, platelet activation pathway, VEGF signaling pathway, and cAMP signaling pathway. Network analysis revealed that DCQD might be involved in the regulation of lipid metabolism, blood pressure, inflammation, angiogenesis, neuroprotection, platelet aggregation, apoptosis, and oxidation in stroke treatment. GEO dataset analysis showed that DCQD’s therapeutic effects might be exerted via the bidirectional regulation principle. Conclusion: Based on the methods of network pharmacology and GEO analysis, it was found that, during stroke treatment, DCQD regulates and controls multiple genes and multiple pathways in a synergistic manner, providing a new strategy for stroke treatment.

Aim and Objective: Myocardial infarction, cerebral infarction and other diseases caused by vascular obstruction have always jeopardized human life and health. More and more reports indicate that Rhei Radix et Rhizoma has a good clinical effect in the prevention and treatment of cardiovascular disease. Owing to the complexity of herbal medicine, the pharmacodynamic mechanism of Rhei Radix et Rhizoma is still not clear. The objective of this study was to explore the two-way adjustment mechanism of Rhei Radix et Rhizoma and provided a new solution for the prevention and treatment of cardiovascular disease. Materials and Methods: Based on the systems pharmacology method and big data analysis technology, this study used data mining, reverse pharmacophore matching, network construction, GO and KEGG Analysis, and molecular docking to investigate the two-way adjustment mechanism of Rhei Radix et Rhizoma. Results: The results suggest that Rhei Radix et Rhizoma plays a two-way adjustment of activating blood circulation as well as blood coagulation in the prevention and treatment of cardiovascular diseases. The components involved in activating blood circulation were mainly Anthraquinones components. The corresponding targets were NOS2, NOS3, CALM1, and the corresponding pathways were Calcium signaling pathway, VEGF signaling pathway, Platelet activation, PI3K-Akt signaling pathway. For blood coagulation, the components were mainly Tannins components, the corresponding targets were F2, F10, ELANE, and the corresponding pathways were Neuroactive ligand-receptor interaction, Complement and coagulation cascades. Conclusion: Through this study, it was indicated that Rhei Radix et Rhizoma exerts the two-way adjustment of activating blood circulation and blood coagulation in the prevention and treatment of cardiovascular diseases. And it can make up for the existing blood circulation drugs for cardiovascular disease, only blood circulation is used, and the side effects of uncontrollable large-area bleeding because of the long-term use of the drugs. This study provides a material basis for the development of new blood-activating drugs based on natural medicines.

Yougui pills have long been used to treat hypothyroidism, usually in combination with levothyroxine sodium in clinical treatment, while their clinical efficacy and safety are still controversial when compared to levothyroxine treatment alone. To explore the clinical efficacy and safety of Yougui pills combined with levothyroxine sodium in the treatment of hypothyroidism. This meta-analysis was performed in accordance with the PRISMA guidelines. Randomized controlled trials on Yougui pills in the treatment of hypothyroidism published from 2008 to May 2021 were searched in a total of 8 databases (4 databases in Chinese and 4 databases in English). The quality of the included studies was evaluated according to the Cochrane risk assessment tool. Weighted mean difference (WMD) was used for continuous variables, and relative risk (RR) was used for binary variables. Data were extracted, and the meta-analysis was conducted with the statistical software of Stata15.0 and RevMan5.0. A total of 140 articles were retrieved, and 9 of them were finally included, with a total sample size of 936 cases. The main meta-analysis results are as follows: (1) The group of Yougui pills combined with levothyroxine sodium had a significantly higher overall response rate than the group of levothyroxine sodium (RR = 1.20, 95%CI 1.12, 1.28, P < 0.00001); (2) Yougui pills combined with levothyroxine sodium achieved significantly better efficacy than levothyroxine sodium alone in alleviating adverse symptoms [standard mean difference (SMD) = -1.10, 95%CI: -1.37, -0.84, P < 0.00001]; (3) The level of thyrotropin stimulating hormone in the group of Yougui pills combined with levothyroxine sodium was significantly lower than in the control group of levothyroxine sodium (WMD = -1.38, 95%CI: -2.10, -0.67, P = 0.00001); (4) The level of free triiodothyronine in the group of Yougui pills combined with levothyroxine sodium was higher than that in the control group of levothyroxine sodium (WMD = 0.41, 95%CI: 0.03, 0.79, P = 0.03); (5) The level of free thyroxine in the group of Yougui pills combined with levothyroxine sodium was significantly higher than that in the control group of levothyroxine sodium (SMD = 0.83, 95%CI: 0.44, 1.22, P ≤ 0.0001); and (6) The adverse reactions in the group of Yougui pills combined with levothyroxine sodium were significantly less than those in the control group of levothyroxine sodium (RR = 0.33, 95%CI: 0.20, -0.53, P < 0.00001). In the treatment of hypothyroidism, the combination of Yougui pills with levothyroxine sodium may be better than levothyroxine sodium treatment alone.

Colon cancer (COAD) poses great challenges to clinical treatment due to its heterogeneity and complex immune microenvironment. M2-like macrophages significantly influence COAD’s onset, progression, and treatment. Yet, existing M2-like macrophage markers are limited in prognostic efficacy, prompting the exploration of new M2 signatures. Extensive data analysis aimed to unveil prognosis-associated M2-derived signatures. Bulk transcriptome, single-cell RNA sequencing, and clinical data from The Cancer Genome Atlas and Gene Expression Omnibus databases for patients with COAD were amassed. Cell-Type Identification by Estimating Relative Subsets of RNA Transcripts identified immune cell infiltration, and the Kaplan-Meier test identified crucial immune populations associated with prognosis. Genetic signatures linked to M2 tumor–associated macrophage were crafted utilizing weighted gene coexpression network analysis, least absolute shrinkage and selection operator, and Cox regression. The M2 tumor–associated macrophage gene signature was validated in GSE17536. The expression profile of the M2 gene signature was investigated in single-cell RNA sequencing dataset GSE166555. Systemic immune profile identified that M2-like macrophage has the most significant prognostic significance in The Cancer Genome Atlas-COAD. The core genes related to M2 macrophage infiltration were extracted by weighted gene coexpression network analysis. Least absolute shrinkage and selection operator-stepwise COX regression–derived M2-derived signatures (snail family zinc finger 1, gastrin-releasing peptide, gamma-aminobutyric acid type A receptor delta subunit, cluster of differentiation 1B, poly(A)-binding protein cytoplasmic 2, manic fringe, and death-associated protein kinase 1) as a risk model, which was confirmed as independent prognosis factors, validated by external dataset. This M2-based prognostic model reflected M2 macrophage infiltration. Mendelian randomization established cytotoxic T lymphocyte associate protein-4 and cluster of differentiation 274 immune checkpoints’ causality with COAD. In conclusion, our study developed novel markers for discriminating M2-like macrophages and predicting the prognosis of patients with COAD, offering fresh perspectives for clinical interventions.