Reversal of tamoxifen resistance by artemisinin in ER+breast cancer:bioinformatics analysis and experimental validation

Breast cancer is the leading cause of cancer-related deaths in women worldwide,with Hormone Receptor(HR)+being the predominant subtype.Tamoxifen(TAM)serves as the primary treatment for HR+breast cancer.However,drug resistance often leads to recurrence,underscoring the need to develop new therapies to enhance patient quality of life and reduce recurrence rates.Artemisinin(ART)has demonstrated efficacy in inhibiting the growth of drug-resistant cells,positioning art as a viable option for counteracting endocrine resistance.This study explored the interaction between artemisinin and tamoxifen through a combined approach of bioinformatics analysis and experimental validation.Five characterized genes(ar,cdkn1a,erbb2,esr1,hsp90aa1)and seven drug-disease crossover genes(cyp2e1,rorc,mapk10,glp1r,egfr,pgr,mgll)were identified using WGCNA crossover analysis.Subsequent functional enrichment analyses were conducted.Our findings confirm a significant correlation between key cluster gene expression and immune cell infiltration in tamoxifen-resistant and-sensitized patients.scRNA-seq analysis revealed high expression of key cluster genes in epithelial cells,suggesting artemisinin’s specific impact on tumor cells in estrogen receptor(ER)-positive BC tissues.Molecular target docking and in vitro experiments with artemisinin on LCC9 cells demonstrated a reversal effect in reducing migratory and drug resistance of drug-resistant cells by modulating relevant drug resistance genes.These results indicate that artemisinin could potentially reverse tamoxifen resistance in ER-positive breast cancer.

PowerDetector:Malicious PowerShell Script Family Classification Based on Multi-Modal Semantic Fusion and Deep Learning

Power Shell has been widely deployed in fileless malware and advanced persistent threat(APT)attacks due to its high stealthiness and live-off-theland technique.However,existing works mainly focus on deobfuscation and malicious detection,lacking the malicious Power Shell families classification and behavior analysis.Moreover,the state-of-the-art methods fail to capture fine-grained features and semantic relationships,resulting in low robustness and accuracy.To this end,we propose Power Detector,a novel malicious Power Shell script detector based on multimodal semantic fusion and deep learning.Specifically,we design four feature extraction methods to extract key features from character,token,abstract syntax tree(AST),and semantic knowledge graph.Then,we intelligently design four embeddings(i.e.,Char2Vec,Token2Vec,AST2Vec,and Rela2Vec) and construct a multi-modal fusion algorithm to concatenate feature vectors from different views.Finally,we propose a combined model based on transformer and CNN-Bi LSTM to implement Power Shell family detection.Our experiments with five types of Power Shell attacks show that PowerDetector can accurately detect various obfuscated and stealth PowerShell scripts,with a 0.9402 precision,a 0.9358 recall,and a 0.9374 F1-score.Furthermore,through singlemodal and multi-modal comparison experiments,we demonstrate that PowerDetector’s multi-modal embedding and deep learning model can achieve better accuracy and even identify more unknown attacks.