scholarly journals 414 A randomized phase II study of systemic therapy plus WeiLeShu (WLS) versus systemic therapy alone in patients with metastatic colorectal cancer (mCRC)

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A445-A445
Author(s):  
Ruyi Zhang ◽  
Xiaoxuan Tu ◽  
Zhou Tong ◽  
Hangyu Zhang ◽  
Xudong Zhu ◽  
...  
Keyword(s):  
Random Forest ◽  
Systemic Therapy ◽  
Gut Microbiome ◽  
Clinical Benefit ◽  
Metabolic Pathways ◽  
Random Forest Model ◽  
Metagenomic Analysis ◽  
Dependent Manner ◽  
Inflammatory Microenvironment ◽  
Forest Model

BackgroundIn recent years, the role of inflammatory microenvironment induced by gut microbiome in the occurrence and development of CRC has received increased attention across a number of disciplines. WLS is a probiotics product consisted of with 6 billion live probiotics, mainly Lactobacillus helveticus and Bifidobacterium longum. To further explore the influence of gut microbiome in the anti-tumor efficacy of patients with mCRC, we conducted a randomized controlled trial (NCT04021589).MethodsPatients receiving corresponding systemic therapy were randomly included into the WLS-intervention and the control arms. Fecal samples were collected at baseline and about two months after treatment initiation. Gut microbiota composition was assessed using shotgun metagenomic sequencing. Best clinical response was dichotomized as partial remission (clinical benefit, CB) versus stable disease or disease progression (non-clinical benefit, NCB). Metagenomic analysis across patients with CB and NCB was conducted and random forest model training was employed to predict the efficacy of treatment.Abstract 414 Figure 1Metabolic pathways for differential enrichment. Metabolic pathways for differential enrichment of the gut microbiome genome in microbiota preparation group through KEGG analysisResultsA total of 40 patients with mCRC in two tertiary hospitals were enrolled. Dynamic metagenomic analysis indicated that during systemic treatment, the a diversity of the gut microbiome were all decreased in both arms. It has been reported that higher a diversity is associated with a better prognosis, while the degree of decline in WLS-intervention group was a relatively minor change. GO enrichment analysis of differential genes indicated a strong enrichment for genes related to lipid metabolism after WLS intervention (figure 1; p<0.01). Lipopolysaccharide (LPS) could regulate the accumulation of monocyte-like macrophages and promote the inflammatory microenvironment in a chemokine-dependent manner, while WLS intervention down-regulated genes related to its synthesis pathway, which may slow the development of CRC. Random forest model showed abundance of Desulfovibrio_vulgaris and Parvimonas_sp._oral_taxon_393 predominantly discriminated between CB and NCB. They were then used to construct a classifier, which achieved an AUC of 0.95 for efficacy prediction.ConclusionsThis prospective randomized pilot study provided insights for influence of the gut microbiome with probiotics in mCRC. WLS could maintain intestinal microecological balance of patients with mCRC by decreasing the degree of abundance of gut microbiome fall after chemotherapy and down-regulating lipopolysaccharide metabolism-related pathway. We established a novel classifier that accurately distinguished between patients with CB and NCB on systemic therapy.Trial RegistrationNCT04021589Ethics ApprovalThis study has been approved by Clinical Research Ethics Committee of the First Affiliated Hospital, College of Medicine, Zhejiang University. Acceptance number: IIT20200348A-R1

scholarly journals Disordered intestinal microbes are associated with the activity of Systemic Lupus Erythematosus

2019 ◽  
Vol 133 (7) ◽  
pp. 821-838 ◽  
Author(s):  
Yao Li ◽  
Hai-Fang Wang ◽  
Xin Li ◽  
Hai-Xia Li ◽  
Qiong Zhang ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Random Forest ◽  
Gut Microbiota ◽  
Disease Activity ◽  
Lupus Erythematosus ◽  
Gut Microbiome ◽  
Metabolic Pathways ◽  
Systemic Lupus ◽  
Forest Model ◽  
Active Patients

Abstract Intestinal dysbiosis is implicated in Systemic Lupus Erythematosus (SLE). However, the evidence of gut microbiome changes in SLE is limited, and the association of changed gut microbiome with the activity of SLE, as well as its functional relevance with SLE still remains unknown. Here, we sequenced 16S rRNA amplicon on fecal samples from 40 SLE patients (19 active patients, 21 remissive patients), 20 disease controls (Rheumatoid Arthritis (RA) patients), and 22 healthy controls (HCs), and investigated the association of functional categories with taxonomic composition by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt). We demonstrated SLE patients, particularly the active patients, had significant dysbiosis in gut microbiota with reduced bacterial diversity and biased community constitutions. Amongst the disordered microbiota, the genera Streptococcus, Campylobacter, Veillonella, the species anginosus and dispar, were positively correlated with lupus activity, while the genus Bifidobacterium was negatively associated with the disease activity. PICRUSt analysis showed metabolic pathways were different between SLE and HCs, and also between active and remissive SLE patients. Moreover, we revealed that a random forest model could distinguish SLE from RA and HCs (area under the curve (AUC) = 0.792), and another random forest model could well predict the activity of SLE patients (AUC = 0.811). In summary, SLE patients, especially the active patients, show an apparent dysbiosis in gut microbiota and its related metabolic pathways. Amongst the disordered microflora, four genera and two species are associated with lupus activity. Furthermore, the random forest models are able to diagnose SLE and predict disease activity.

scholarly journals Clinical trial registries as Scientometric data: A novel solution for linking and deduplicating clinical trials from multiple registries

2021 ◽  
Author(s):  
Christian Thiele ◽  
Gerrit Hirschfeld ◽  
Ruth von Brachel
Keyword(s):  
Clinical Trials ◽  
Random Forest ◽  
Random Forest Model ◽  
Scientometric Analysis ◽  
Data Set ◽  
The Public ◽  
Forest Model ◽  
Clinical Trial Registries ◽  
Multiple Primary ◽  
Clinical Trials Registry

AbstractRegistries of clinical trials are a potential source for scientometric analysis of medical research and serve important functions for the research community and the public at large. Clinical trials that recruit patients in Germany are usually registered in the German Clinical Trials Register (DRKS) or in international registries such as ClinicalTrials.gov. Furthermore, the International Clinical Trials Registry Platform (ICTRP) aggregates trials from multiple primary registries. We queried the DRKS, ClinicalTrials.gov, and the ICTRP for trials with a recruiting location in Germany. Trials that were registered in multiple registries were linked using the primary and secondary identifiers and a Random Forest model based on various similarity metrics. We identified 35,912 trials that were conducted in Germany. The majority of the trials was registered in multiple databases. 32,106 trials were linked using primary IDs, 26 were linked using a Random Forest model, and 10,537 internal duplicates on ICTRP were identified using the Random Forest model after finding pairs with matching primary or secondary IDs. In cross-validation, the Random Forest increased the F1-score from 96.4% to 97.1% compared to a linkage based solely on secondary IDs on a manually labelled data set. 28% of all trials were registered in the German DRKS. 54% of the trials on ClinicalTrials.gov, 43% of the trials on the DRKS and 56% of the trials on the ICTRP were pre-registered. The ratio of pre-registered studies and the ratio of studies that are registered in the DRKS increased over time.

scholarly journals Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model

2021 ◽  
Vol 10 (8) ◽  
pp. 503
Author(s):  
Hang Liu ◽  
Riken Homma ◽  
Qiang Liu ◽  
Congying Fang
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Cellular Automata ◽  
Land Use Change ◽  
Urban Land ◽  
Urban Land Use ◽  
Random Forest Model ◽  
Growth Trend ◽  
Related Factors ◽  
Forest Model

The simulation of future land use can provide decision support for urban planners and decision makers, which is important for sustainable urban development. Using a cellular automata-random forest model, we considered two scenarios to predict intra-land use changes in Kumamoto City from 2018 to 2030: an unconstrained development scenario, and a planning-constrained development scenario that considers disaster-related factors. The random forest was used to calculate the transition probabilities and the importance of driving factors, and cellular automata were used for future land use prediction. The results show that disaster-related factors greatly influence land vacancy, while urban planning factors are more important for medium high-rise residential, commercial, and public facilities. Under the unconstrained development scenario, urban land use tends towards spatially disordered growth in the total amount of steady growth, with the largest increase in low-rise residential areas. Under the planning-constrained development scenario that considers disaster-related factors, the urban land area will continue to grow, albeit slowly and with a compact growth trend. This study provides planners with information on the relevant trends in different scenarios of land use change in Kumamoto City. Furthermore, it provides a reference for Kumamoto City’s future post-disaster recovery and reconstruction planning.

scholarly journals Estimates of daily ground-level NO2 concentrations in China based on Random Forest model integrated K-means

2021 ◽  
pp. 100017
Author(s):  
Xinyu Dou ◽  
Cuijuan Liao ◽  
Hengqi Wang ◽  
Ying Huang ◽  
Ying Tu ◽  
...  
Keyword(s):  
Random Forest ◽  
Ground Level ◽  
Random Forest Model ◽  
Forest Model

scholarly journals Identification of candidate biomarkers of liver hydatid disease via microarray profiling, bioinformatics analysis, and machine learning

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199398
Author(s):  
Jinwu Peng ◽  
Zhili Duan ◽  
Yamin Guo ◽  
Xiaona Li ◽  
Xiaoqin Luo ◽  
...  
Keyword(s):  
Random Forest ◽  
Hydatid Disease ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Random Forest Model ◽  
Hepatic Hydatid Disease ◽  
Forest Model ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Microarray Profiling

Objectives Liver echinococcosis is a severe zoonotic disease caused by Echinococcus (tapeworm) infection, which is epidemic in the Qinghai region of China. Here, we aimed to explore biomarkers and establish a predictive model for the diagnosis of liver echinococcosis. Methods Microarray profiling followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed in liver tissue from patients with liver hydatid disease and from healthy controls from the Qinghai region of China. A protein–protein interaction (PPI) network and random forest model were established to identify potential biomarkers and predict the occurrence of liver echinococcosis, respectively. Results Microarray profiling identified 1152 differentially expressed genes (DEGs), including 936 upregulated genes and 216 downregulated genes. Several previously unreported biological processes and signaling pathways were identified. The FCGR2B and CTLA4 proteins were identified by the PPI networks and random forest model. The random forest model based on FCGR2B and CTLA4 reliably predicted the occurrence of liver hydatid disease, with an area under the receiver operator characteristic curve of 0.921. Conclusion Our findings give new insight into gene expression in patients with liver echinococcosis from the Qinghai region of China, improving our understanding of hepatic hydatid disease.

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