Conventional cancer therapies, such as radiotherapy and chemotherapy, often damage normal cells and may induce new tumors. Oncolytic viruses (OVs) selectively target tumor cells while sparing normal cells. Most OVs used in clinical trials have been genetically engineered to enhance their ability to target tumor cells and activate immune responses. To develop a specific OV-based approach for treating cervical cancer, this study constructed an oncolytic adenovirus that delivered a base editor targeting oncogenes to achieve efficient killing of tumor cells through inhibiting tumor growth and directly lysing tumor cells. We utilized the human telomerase reverse transcriptase (TERT) promoter to drive the expression of adenovirus early region 1A (E1A) and successfully constructed the P-hTERT-E1A-GFP vector, which was validated for its activity in cervical cancer cells. Given the critical role of the MYC oncogene in the research of oncology, identifying efficient editing sites for the MYC oncogene is a key step in this study.Three MYC-targeting gRNAs were engineered and co-delivered with ABE8e base editor plasmids into HEK293T cells. Following puromycin selection, Sanger sequencing demonstrated differential editing efficiencies: MYC-1 (43%), MYC-2 (25%), and MYC-3 (35%), identifying MYC-1 as the most efficient editing locus. By constructing the P-ABEs-hTERT-E1A-GFP and P-MYC gRNA-hTERT-E1A-GFP vectors, we successfully packaged the virus and confirmed its specificity and efficacy. The experimental results demonstrate that this novel oncolytic adenovirus effectively inhibits the growth of HeLa cells in vitro, providing new experimental evidence and potential strategies for treating cervical cancer based on the HeLa cell model.
Humans ; Uterine Cervical Neoplasms/pathology* ; Oncolytic Viruses/genetics* ; Female ; HEK293 Cells ; Oncolytic Virotherapy/methods* ; Adenoviridae/genetics* ; Gene Editing/methods* ; Telomerase/genetics* ; Adenovirus E1A Proteins/genetics* ; Genetic Vectors/genetics* ; HeLa Cells

Objective To evaluate the effect of the 3D-printed heart model on congenital heart disease (CHD) education through systematic review and meta-analysis. Methods The literature about the application of the 3D-printed heart model in CHD education was systematically searched by computer from PubMed, Web of Science, and EMbase from inception to November 10, 2022. The two researchers independently screened the literature, extracted data and evaluated the quality of the literature. Cochrane literature evaluation standard was used to evaluate the quality of randomized controlled trials, and JBI evaluation scale was used for cross-sectional and cohort studies. Results After screening, 23 literatures were included, including 7 randomized controlled trials, 15 cross-sectional studies and 1 cohort study. Randomized controlled trials were all at low-risk, cross-sectional studies and and the cohort study had potential bias. There were 4 literatures comparing 3D printing heart model with 2D image teaching and the meta-analysis result showed that the effect of 3D printing heart model on theoretical achievement was more significant compared with 2D image teaching (SMD=0.31, 95%CI –0.28 to 0.91, P=0.05). Conclusion The application of the 3D-printed heart model in CHD education can be beneficial. But more randomized controlled trials are still needed to verify this result.

Objective: To investigate trends of mean age of diagnosis for liver cancer during 2000 to 2014, which may provide basic information for making feasible cancer prevention strategies. Methods: Based on the continuous cancer incidence data from 22 cancer registries of China between 1 January 2000 and 31 December 2014, the incidence by birth-cohort (year of birth between 1925 and 1994) and age specific incidence rates were calculated. The incidence of different age groups were also calculated. World Segi's population was used for age standardization. The liner regression model was applied to analyze the changing trend of mean age of diagnosis. Results: In 2014, the incidence rate for population with 80 years older and above was 108.21 per 100 000, whereas the rate for population at 30-39 years old was 5.09 per 100 000. But the mean age of diagnosis for liver cancer showed an increasing trend from 2000 to 2014. For male, it had increased from 58.80 to 62.35 (t=18.70, P<0.001) . For female, it had increased from 64.02 to 68.99 (t=20.50, P<0.001) . After age standardization, the mean age of diagnosis still showed increasing trend. Meanwhile, the proportion of liver cancer in people above 70 years old was 25.05% in 2014, which was higher than that in 2000 (22.49%). Conclusion The mean age of liver cancer incidence was increasing during 2000-2014.