Combination of novel oncolytic herpesvirus with paclitaxel as an efficient strategy for breast cancer therapy.

BACKGROUND: New strategies are needed to improve the treatment of patients with breast cancer (BC). Oncolytic virotherapy is a promising new tool for cancer treatment but still has a limited overall durable antitumor response. A novel replicable recombinant oncolytic herpes simplex virus type 1 called VG161 has been developed and has demonstrated antitumor effects in several cancers. Here, we explored the efficacy and the antitumor immune response of VG161 cotreatment with paclitaxel (PTX) which as a novel oncolytic viral immunotherapy for BC. METHODS: The antitumor effect of VG161 and PTX was confirmed in a BC xenograft mouse model. The immunostimulatory pathways were tested by RNA-seq and the remodeling of tumor microenvironment was detected by Flow cytometry analysis or Immunohistochemistry. Pulmonary lesions were analyzed by the EMT6-Luc BC model. RESULTS: In this report, we demonstrate that VG161 can significantly represses BC growth and elicit a robust antitumor immune response in a mouse model. The effect is amplified when combined with PTX treatment. The antitumor effect is associated with the infiltration of lymphoid cells, including CD4+ T cells, CD8+ T cells, and NK cells (expressing TNF and IFN-γ), and myeloid cells, including macrophages, myeloid-derived suppressor cells, and dendritic cell cells. Additionally, VG161 cotreatment with PTX showed a significant reduction in BC lung metastasis, which may result from the enhanced CD4+ and CD8+ T cell-mediated responses. CONCLUSIONS: The combination of PTX and VG161 is effective for repressing BC growth by inducing proinflammatory changes in the tumor microenvironment and reducing BC pulmonary metastasis. These data will provide a new strategy and valuable insight for oncolytic virus therapy applications in primary solid or metastatic BC tumors.

Global Burden of Respiratory Diseases Attributable to Ambient Particulate Matter Pollution: Findings From the Global Burden of Disease Study 2019.

Background: Exposure to ambient particulate matter pollution (APMP) is a global health issue that directly affects the human respiratory system. Thus, we estimated the spatiotemporal trends in the burden of APMP-related respiratory diseases from 1990 to 2019. Methods: Based on the Global Burden of Disease Study 2019, data on the burden of APMP-related respiratory diseases were analyzed by age, sex, cause, and location. Joinpoint regression analysis was used to analyze the temporal trends in the burden of different respiratory diseases over the 30 years. Results: Globally, in 2019, APMP contributed the most to chronic obstructive pulmonary disease (COPD), with 695.1 thousand deaths and 15.4 million disability-adjusted life years (DALYs); however, the corresponding age-standardized death and DALY rates declined from 1990 to 2019. Similarly, although age-standardized death and DALY rates since 1990 decreased by 24% and 40%, respectively, lower respiratory infections (LRIs) still had the second highest number of deaths and DALYs attributable to APMP. This was followed by tracheal, bronchus, and lung (TBL) cancer, which showed increased age-standardized death and DALY rates during the past 30 years and reached 3.78 deaths per 100,000 persons and 84.22 DALYs per 100,000 persons in 2019. Among children aged < 5 years, LRIs had a huge burden attributable to APMP, whereas for older people, COPD was the leading cause of death and DALYs attributable to APMP. The APMP-related burdens of LRIs and COPD were relatively higher among countries with low and low-middle socio-demographic index (SDI), while countries with high-middle SDI showed the highest burden of TBL cancer attributable to APMP. Conclusions: APMP contributed substantially to the global burden of respiratory diseases, posing a significant threat to human health. Effective actions aimed at air pollution can potentially avoid an increase in the PM2.5-associated disease burden, especially in highly polluted areas.

Modelling the effects of Wuhan's lockdown during COVID-19, China.

OBJECTIVE: To design a simple model to assess the effectiveness of measures to prevent the spread of coronavirus disease 2019 (COVID-19) to different regions of mainland China. METHODS: We extracted data on population movements from an internet company data set and the numbers of confirmed cases of COVID-19 from government sources. On 23 January 2020 all travel in and out of the city of Wuhan was prohibited to control the spread of the disease. We modelled two key factors affecting the cumulative number of COVID-19 cases in regions outside Wuhan by 1 March 2020: (i) the total the number of people leaving Wuhan during 20-26 January 2020; and (ii) the number of seed cases from Wuhan before 19 January 2020, represented by the cumulative number of confirmed cases on 29 January 2020. We constructed a regression model to predict the cumulative number of cases in non-Wuhan regions in three assumed epidemic control scenarios. FINDINGS: Delaying the start date of control measures by only 3 days would have increased the estimated 30 699 confirmed cases of COVID-19 by 1 March 2020 in regions outside Wuhan by 34.6% (to 41 330 people). Advancing controls by 3 days would reduce infections by 30.8% (to 21 235 people) with basic control measures or 48.6% (to 15 796 people) with strict control measures. Based on standard residual values from the model, we were able to rank regions which were most effective in controlling the epidemic. CONCLUSION: The control measures in Wuhan combined with nationwide traffic restrictions and self-isolation reduced the ongoing spread of COVID-19 across China.