pH/glutathione-responsive theranostic nanoprobes for chemoimmunotherapy and magnetic resonance imaging of ovarian cancer cells.

The integration of immunotherapy and standard chemotherapy holds great promise for enhanced anticancer effects. In this study, we prepared a pH- and glutathione (GSH)-sensitive manganese-doped mesoporous silicon (MMSNs) based drug delivery system by integrating paclitaxel (PTX) and anti-programmed cell death-ligand 1 antibody (aPD-L1), and encapsulating with polydopamine (PDA) for chemoimmunosynergic treatment of ovarian cancer cells. The nanosystem was degraded in response to the tumor weakly acidic and reductive microenvironment. The Mn2+ produced by degradation can be used as a contrast agent for magnetic resonance (MR) imaging to provide visual exposure to tumor tissue. The released PTX can not only kill tumor cells directly, but also induce immunogenic death (ICD) of tumor cells, which can play a synergistic therapeutic effect with aPD-L1. Therefore, our study is expected to provide a promising strategy for improving the efficacy of cancer immunotherapy and the detection rate of cancer.

O2-Generating Fluorescent Carbon Dot-Decorated MnO2 Nanosheets for "Off/On" MR/Fluorescence Imaging and Enhanced Photodynamic Therapy.

Imaging-guided photodynamic therapy (PDT) has emerged as a promising protocol for cancer theragnostic. However, facile preparation of such a theranostic system for simultaneously achieving tumor location, real-time monitoring, and high-performance reactive oxygen species generation is highly desirable but remains challenging. Herein, we developed a reasonable tumor-targeting strategy based on carbon dots (CDs)-decorated MnO2 nanosheets (HA-MnO2-CDs) with an active magnetic resonance (MR)/fluorescence imaging and enhanced PDT effect. Under light irradiation, the addition of HA-MnO2-CDs increased the production of 1O2 by 2.5 times compared with CDs, providing favorable conditions for the PDT treatment effect on breast cancer. Moreover, HA-MnO2-CDs exhibited excellent performance in producing O2 in the presence of endogenous H2O2, which alleviated hypoxia in tumors and improved the therapeutic effect of PDT. In the presence of glutathione (GSH), the degraded MnO2 nanosheets released CDs and Mn2+ from HA-MnO2-CDs, restoring their fluorescence imaging function and increasing T1 relaxivity (r1) by 23 times. In vivo fluorescence and MR imaging suggested the excellent tumor-targeting property of HA-MnO2-CDs. By combining the complementary properties of nanoprobes and tumor microenvironments, the in vivo PDT therapeutic effect was significantly improved under the action of HA-MnO2-CDs. Overall, our reasonably designed HA-MnO2-CDs may inspire the future development of the next generation of high-performance tumor-responsive diagnostic and therapeutic agents to further enhance the targeted therapy effect of tumors.

Automated segmentation of normal and diseased coronary arteries - The ASOCA challenge.

Cardiovascular disease is a major cause of death worldwide. Computed Tomography Coronary Angiography (CTCA) is a non-invasive method used to evaluate coronary artery disease, as well as evaluating and reconstructing heart and coronary vessel structures. Reconstructed models have a wide array of for educational, training and research applications such as the study of diseased and non-diseased coronary anatomy, machine learning based disease risk prediction and in-silico and in-vitro testing of medical devices. However, coronary arteries are difficult to image due to their small size, location, and movement, causing poor resolution and artefacts. Segmentation of coronary arteries has traditionally focused on semi-automatic methods where a human expert guides the algorithm and corrects errors, which severely limits large-scale applications and integration within clinical systems. International challenges aiming to overcome this barrier have focussed on specific tasks such as centreline extraction, stenosis quantification, and segmentation of specific artery segments only. Here we present the results of the first challenge to develop fully automatic segmentation methods of full coronary artery trees and establish the first large standardized dataset of normal and diseased arteries. This forms a new automated segmentation benchmark allowing the automated processing of CTCAs directly relevant for large-scale and personalized clinical applications.