In vivo investigation of boron-rich nanodrugs for treating triple-negative breast cancers via boron neutron capture therapy.

Triple-negative breast cancer (TNBC) is characterized by highly proliferative cancer cells and is the only subtype of breast cancer that lacks a targeted therapy. Boron neutron capture therapy (BNCT) is an approach that combines chemotherapy with radiotherapy and can potentially offer beneficial targeted treatment for TNBC patients owing to its unique ability to eradicate cancer cells selectively while minimizing damage to the surrounding healthy cells. Since BNCT relies on specific delivery of a high loading of B10 to the tumor site, there is growing research interest to develop more potent boron-based drugs for BNCT that can overcome the limitations of small-molecule boron compounds. In this study, polyethylene-glycol-coated boron carbon oxynitride nanoparticles (PEG@BCNO) of size 134.2±23.6nm were prepared as a promising drug for BNCT owing to their high boron content and enhanced biocompatibility. The therapeutic efficiency of PEG@BCNO was compared with a state-of-the-art 10BPA boron drug in mice bearing MDA-MB-231 tumor. In the orthotopic mouse model, PEG@BCNO showed higher B10 accumulation in the tumor tissues (6 µg 10B/g tissue compared to 3 µg 10B/g tissue in mice administered B10-enriched 10BPA drug) despite using the naturally occurring 11B/10B boron precursor in the preparation of the BCNO nanoparticles. The in vivo biodistribution of PEG@BCNO in mice bearing MDA-MB-231 showed a tumor/blood ratio of ~3.5, which is comparable to that of the state-of-the-art 10BPA-fructose drug. We further demonstrated that upon neutron irradiation, the mice bearing MDA-MB-231 tumor cells treated with PEG@BCNO and 10BPA showed tumor growth delay times of 9 days and 1 day, respectively, compared to mice in the control group after BNCT. The doubling times (DTs) for mice treated with PEG@BCNO and 10BPA as well as mice in the control group were calculated to be 31.5, 19.8, and 17.7 days, respectively. Immunohistochemical staining for the p53 and caspase-3 antibodies revealed that mice treated with PEG@BCNO showed lower probability of cancer recurrence and greater level of cellular apoptosis than mice treated with 10BPA and mice in the control group. Our study thus demonstrates the potential of pegylated BCNO nanoparticles in effectively inhibiting the growth of TNBC tumors compared to the state-of-the-art boron drug 10BPA.

A Geo-AI-based ensemble mixed spatial prediction model with fine spatial-temporal resolution for estimating daytime/nighttime/daily average ozone concentrations variations in Taiwan.

High levels of ground level ozone (O3) are associated with detrimental health concerns. Most of the studies only focused on daily average and daytime trends due to the presence of sunlight that initiates its formation. However, atmospheric chemical reactions occur all day, thus, nighttime concentrations should be given equal importance. In this study, geospatial-artificial intelligence (Geo-AI) which combined kriging, land use regression (LUR), machine learning, an ensemble learning, was applied to develop ensemble mixed spatial models (EMSMs) for daily, daytime, and nighttime periods. These models were used to estimate the long-term O3 spatio-temporal variations using a two-decade worth of in-situ measurements, meteorological parameters, geospatial predictors, and social and season-dependent factors. From the traditional LUR approach, the performance of EMSMs improved by 60% (daytime), 49% (nighttime), and 57% (daily). The resulting daily, daytime, and nighttime EMSMs had a high explanatory power with and adjusted R2 of 0.91, 0.91, and 0.88, respectively. Estimation maps were produced to examine the changes before and during the implementation of nationwide COVID-19 restrictions. These results provide accurate estimates and its diurnal variation that will support pollution control measure and epidemiological studies.