A flexible approach for variable selection in large-scale healthcare database studies with missing covariate and outcome data.

BACKGROUND: Prior work has shown that combining bootstrap imputation with tree-based machine learning variable selection methods can provide good performances achievable on fully observed data when covariate and outcome data are missing at random (MAR). This approach however is computationally expensive, especially on large-scale datasets. METHODS: We propose an inference-based method, called RR-BART, which leverages the likelihood-based Bayesian machine learning technique, Bayesian additive regression trees, and uses Rubin's rule to combine the estimates and variances of the variable importance measures on multiply imputed datasets for variable selection in the presence of MAR data. We conduct a representative simulation study to investigate the practical operating characteristics of RR-BART, and compare it with the bootstrap imputation based methods. We further demonstrate the methods via a case study of risk factors for 3-year incidence of metabolic syndrome among middle-aged women using data from the Study of Women's Health Across the Nation (SWAN). RESULTS: The simulation study suggests that even in complex conditions of nonlinearity and nonadditivity with a large percentage of missingness, RR-BART can reasonably recover both prediction and variable selection performances, achievable on the fully observed data. RR-BART provides the best performance that the bootstrap imputation based methods can achieve with the optimal selection threshold value. In addition, RR-BART demonstrates a substantially stronger ability of detecting discrete predictors. Furthermore, RR-BART offers substantial computational savings. When implemented on the SWAN data, RR-BART adds to the literature by selecting a set of predictors that had been less commonly identified as risk factors but had substantial biological justifications. CONCLUSION: The proposed variable selection method for MAR data, RR-BART, offers both computational efficiency and good operating characteristics and is utilitarian in large-scale healthcare database studies.

Effect and mechanism of Banxia Xiexin decoction in colorectal cancer: A network pharmacology approach.

BACKGROUND: Banxia Xiexin decoction (BXD) is a traditional Chinese medicine with anti-colorectal cancer (CRC) activity. However, its bioactive constituents and its mechanism of action remain unclear. Herein, we explored the mechanism of action of BXD against CRC using a network pharmacology approach. METHODS: First, the targets of the main chemical components of BXD were predicted and collected through a database, and the intersection of compound targets and disease targets was obtained. Subsequently, protein-protein interaction network analysis, Gene Ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed to explore the potential mechanisms underlying the effects of BXD on CRC. Finally, a CRC cell model and a CRC xenograft model in nude mice were utilized to further determine the mechanism of action. RESULTS: A compound-therapeutic target network of BXD was constructed, revealing 146 cellular targets of BXD. The phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling axis was identified as the main target of BXD. Using in vitro and in vivo models, the activity of BXD against CRC was found to be mediated through ferritinophagy by targeting the PI3K/AKT/mTOR axis, leading to intracellular iron accumulation, reactive oxygen species activation, and finally ferroptosis. CONCLUSIONS: Through the application of network pharmacology and in vitro/in vivo validation experiments, we discovered that BXD exerts anti-CRC effects via the ferritinophagy pathway. Furthermore, we elucidated the potential mechanism underlying its induction of ferritinophagy. These findings demonstrate the significant potential of traditional drugs in managing CRC and support their wider clinical application in combination chemotherapy, targeted therapy, and immunotherapy.

Research progress and application of liver organoids for disease modeling and regenerative therapy.

The liver is a major metabolic organ of the human body and has a high incidence of diseases. In recent years, the annual incidence of liver disease has increased, seriously endangering human life and health. The study of the occurrence and development mechanism of liver diseases, discovery of new therapeutic targets, and establishment of new methods of medical treatment are major issues related to the national economy and people's livelihood. The development of stable and effective research models is expected to provide new insights into the pathogenesis of liver diseases and the search for more effective treatment options. Organoid technology is a new in vitro culture system, and organoids constructed by human cells can simulate the morphological structure, gene expression, and glucose and lipid metabolism of organs in vivo, providing a new model for related research on liver diseases. This paper reviews the latest research progress on liver organoids from the establishment of cell sources and application of liver organoids and discusses their application potential in the field of liver disease research.

Liquiritin inhibits MRGPRX2-mediated pseudo-allergy through the PI3K/AKT and PLCγ signaling pathways.

Liquiritin is a natural flavone with a variety of pharmacological effects derived from the medicinal food homology plant Glycyrrhiza uralensis Fisch. As a kind of lethal allergic reactions, pseudo-allergic reactions (PARs) arise from the Mas-related G protein coupled receptor X2 (MRGPRX2)-triggered fast degranulation of mast cells (MCs). In the current work, the anti-pseudo-allergy action and potential mechanisms of liquiritin were explored in vivo and in vitro. Liquiritin suppressed the calcium influx and degranulation elicited by Compound 48/80 (C48/80) in mouse peritoneal mast cells (MPMCs). In mice, liquiritin also inhibited the C48/80-elicited hind paw extravasation, as well as the elevations in TNF-α and histamine levels. Molecular docking combined with detection of HEK293T cells expressing human MRGPRX2 showed that liquiritin was a potential MRGPRX2 antagonist and inhibited PARs through the PI3K/AKT and PLCγ signaling pathways downstream of MRGPRX2. The present work opens a new avenue for the PARs management.