Search

Abstract Oncolytic virotherapy is emerging as a promising therapeutic avenue for cancer treatment, harnessing both innate and tumor-specific immune responses for targeted tumor elimination. In this study, we present a novel oncolytic virus (oHSV1-IL15B) derived from herpes simplex virus-1 (HSV-1), armed with IL-15/IL-15Rα complex, with a focus on treating colon cancer combined with oncolytic HSV-1 expressing anti-PD-1 antibody (oHSV1-aPD1). Results from our study reveal that recombinant oHSV-1 virus equipped with IL-15/IL-15Rα complex exhibited significant anti-tumor effects in a murine CT26 colon adenocarcinoma model. Notably, oHSV1-IL15B combined with oHSV-1-aPD1 demonstrates superior tumor inhibition and prolonged overall survival compared to oHSV1-mock and monotherapy groups. Further exploration highlights the impact of oHSV1-IL15B, oHSV-1-aPD1 and combined group on antitumor capacity, revealing a substantial increase in CD8+ T and CD4+ T cell proportions of CT26-bearing BALB/c mice and promoting apoptosis in tumor tissue. The study emphasizes the pivotal role of cytotoxic CD8+T cells in oncolytic virotherapy, demonstrating that recombinant oHSV1-IL15B combined with oncolytic HSV-1-aPD1 induces a robust tumor-specific T cell response. RNA sequence analysis highlighted oHSV1-IL15B combined with oHSV1-aPD1 improved tumors immune microenvironment on immune response, antiviral response-related genes and apoptosis-related genes, which contributed to anti-tumor immunotherapy. The findings underscore the promising antitumor activity achieved through the combination of IL-15/IL-15Rα complex and anti-PD-1 antibody with oHSV-1. This research opens avenues for diverse therapeutic strategies, suggesting the potential of synergistically utilizing cytokines and anti-PD-1 antibody with oncolytic viruses to enhance immunotherapy for cancer management.

Patients with hepatic fibrosis (HF) have a high risk of developing liver cirrhosis and hepatocellular carcinoma, and there is an urgent need for preventive strategies to block this process. Previous studies have found that disordered inflammation and oxidative damage play important roles in HF progression, suggesting two attractive therapeutic targets. Herein, a new kind of bioinspired microcapsules with a core-shell structure is generated using microfluidics. Polydopamine nanoparticles (PDANPs), a synthetic analogue of natural melanin, are embedded in the polymer shell to provide antioxidative properties for these microcapsules. The aqueous core is used to encapsulate ketone body β-hydroxybutyrate (BHB), an energy metabolite recently known to have regulating effects of cellular signals involved in chronic inflammation. In a HF mouse model, the BHB-encapsulated PDANPs-embedded microcapsules (BHB-PDA-MCs) can not only decrease the severity of inflammatory response, but also the level of oxidative stress. As a result, this combinational strategy is demonstrated to prevent the activation of hepatic stellate cells, the accumulation of extracellular matrix, and the damage of hepatic lobules. These findings indicate that BHB-PDA-MCs can be a promising drug delivery system and have a synergistic effect on HF management.

The most common method of word embedding is to learn word vector representations from context information of large-scale text. However, Chinese words usually consist of characters, subcharacters, and strokes, and each part contains rich semantic information. The quality of Chinese word vectors is related to the accuracy of prediction. Therefore, to obtain high-quality Chinese character embedding, we propose a continuously enhanced word embedding model. The model starts with fine-grained strokes and adjacent stroke information and enhances subcharacter embedding by combining the relationship vector representation between strokes. Similarly, we combine the subcharacter relationship vector and the character relationship vector to learn Chinese word embedding based on the enhanced subcharacter embedding. We construct the underlying stroke n-grams and adjacent stroke n-grams and extract the relationship vector used to enhance the relationship between the components, which can be used to learn Chinese word embedding and improve the accuracy. Finally, we evaluate our model on the word similarity calculations and word reasoning tasks.