RESUMEN

HCC is the most common fatal malignancy. Although surgical resection is the primary treatment strategy, most patients are not eligible for resection due to tumor heterogeneity, underlying liver disease, or comorbidities. Therefore, this study explores the possibility of multi-molecular targeted drug delivery in treating HCC. In this study, we constructed the recombinant adenovirus co-expressing apoptin and melittin (MEL) genes. The inhibitory effect of the recombinant adenovirus on hepatocellular carcinoma cells was detected through experiments on cell apoptosis, migration, invasion, and other factors. The tumor inhibitory effect in vivo was assessed using subcutaneous HCC mice. Results showed that recombinant adenovirus co-expressing anti-tumor genes TAT and apoptin, RGD and MEL can significantly inhibit the proliferation, migration, and invasion of HCC cells by inducing an increase in reactive oxygen species (ROS) levels, upregulation of apoptotic proteins such as Bax, cleaved caspase-3, and cleaved caspase-9, and downregulation of the anti-apoptotic protein Bcl-2. In subcutaneous HCC mice, recombinant adenovirus induced significant apoptosis in tumor, and inhibited tumor growth. In conclusion, recombinant adenovirus co-expressing apoptin and MEL can inhibit the growth and proliferation of tumor cells both in vivo and in vitro.

Asunto(s)

Adenoviridae , Apoptosis , Proteínas de la Cápside , Carcinoma Hepatocelular , Proliferación Celular , Neoplasias Hepáticas , Meliteno , Meliteno/farmacología , Animales , Carcinoma Hepatocelular/tratamiento farmacológico , Carcinoma Hepatocelular/terapia , Carcinoma Hepatocelular/patología , Carcinoma Hepatocelular/genética , Adenoviridae/genética , Neoplasias Hepáticas/tratamiento farmacológico , Neoplasias Hepáticas/patología , Neoplasias Hepáticas/terapia , Neoplasias Hepáticas/genética , Humanos , Proteínas de la Cápside/genética , Apoptosis/efectos de los fármacos , Proliferación Celular/efectos de los fármacos , Ratones , Movimiento Celular/efectos de los fármacos , Ratones Desnudos , Ratones Endogámicos BALB C , Línea Celular Tumoral , Especies Reactivas de Oxígeno/metabolismo , Ensayos Antitumor por Modelo de Xenoinjerto , Terapia Genética/métodos

RESUMEN

Clinical deployment of oligonucleotides requires delivery technologies that improve stability, target tissue accumulation and cellular internalization. Exosomes show potential as ideal delivery vehicles. However, an affordable generalizable system for efficient loading of oligonucleotides on exosomes remain lacking. Here, we identified an Exosomal Anchor DNA Aptamer (EAA) via SELEX against exosomes immobilized with our proprietary CP05 peptides. EAA shows high binding affinity to different exosomes and enables efficient loading of nucleic acid drugs on exosomes. Serum stability of thrombin inhibitor NU172 was prolonged by exosome-loading, resulting in increased blood flow after injury in vivo. Importantly, Duchenne Muscular Dystrophy PMO can be readily loaded on exosomes via EAA (EXOEAA-PMO). EXOEAA-PMO elicited significantly greater muscle cell uptake, tissue accumulation and dystrophin expression than PMO in vitro and in vivo. Systemic administration of EXOEAA-PMO elicited therapeutic levels of dystrophin restoration and functional improvements in mdx mice. Altogether, our study demonstrates that EAA enables efficient loading of different nucleic acid drugs on exosomes, thus providing an easy and generalizable strategy for loading nucleic acid therapeutics on exosomes.

Asunto(s)

Exosomas , Distrofia Muscular de Duchenne , Animales , Ratones , Distrofina/genética , Ratones Endogámicos mdx , Exosomas/metabolismo , Morfolinos/metabolismo , Morfolinos/farmacología , Morfolinos/uso terapéutico , Distrofia Muscular de Duchenne/tratamiento farmacológico , Distrofia Muscular de Duchenne/genética , Oligonucleótidos/metabolismo , Oligonucleótidos/uso terapéutico

RESUMEN

Machine learning models can automatically discover biomedical research trends and promote the dissemination of information and knowledge. Text feature representation is a critical and challenging task in natural language processing. Most methods of text feature representation are based on word representation. A good representation can capture semantic and structural information. In this paper, two fusion algorithms are proposed, namely, the Tr-W2v and Ti-W2v algorithms. They are based on the classical text feature representation model and consider the importance of words. The results show that the effectiveness of the two fusion text representation models is better than the classical text representation model, and the results based on the Tr-W2v algorithm are the best. Furthermore, based on the Tr-W2v algorithm, trend analyses of cancer research are conducted, including correlation analysis, keyword trend analysis, and improved keyword trend analysis. The discovery of the research trends and the evolution of hotspots for cancers can help doctors and biological researchers collect information and provide guidance for further research.