In situ injectable hydrogel encapsulating Mn/NO-based immune nano-activator for prevention of postoperative tumor recurrence

Postoperative tumor recurrence remains a predominant cause of treatment failure. In this study, we developed an in situ injectable hydrogel, termed MPB-NO@DOX + ATRA gel, which was locally formed within the tumor resection cavity. The MPB-NO@DOX + ATRA gel was fabricated by mixing a thrombin solution, a fibrinogen solution containing all-trans retinoic acid (ATRA), and a Mn/NO-based immune nano-activator termed MPB-NO@DOX. ATRA promoted the differentiation of cancer stem cells, inhibited cancer cell migration, and affected the polarization of tumor-associated macrophages. The outer MnO2 shell disintegrated due to its reaction with glutathione and hydrogen peroxide in the cytoplasm to release Mn2+ and produce O2, resulting in the release of doxorubicin (DOX). The released DOX entered the nucleus and destroyed DNA, and the fragmented DNA cooperated with Mn2+ to activate the cGAS-STING pathway and stimulate an anti-tumor immune response. In addition, when MPB-NO@DOX was exposed to 808 nm laser irradiation, the Fe-NO bond was broken to release NO, which downregulated the expression of PD-L1 on the surface of tumor cells and reversed the immunosuppressive tumor microenvironment. In conclusion, the MPB-NO@DOX + ATRA gel exhibited excellent anti-tumor efficacy. The results of this study demonstrated the great potential of in situ injectable hydrogels in preventing postoperative tumor recurrence.

Efficient Ship:A Hybrid Deep Learning Framework for Ship Detection in the River

Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters.Current deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of detection.To solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this paper.The core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship objects.We also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model overfitting.We compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called Seaships.Experiments show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.