Reduction-sensitive polymeric carrier for the targeted delivery of a quinazoline derivative for enhanced generation of reactive oxygen species against cancer.

Hepatocellular carcinoma (HCC) is one of the deadliest malignant tumors and the development of effective therapeutics against HCC is urgently needed. A novel quinazoline derivative 04NB-03 (Qd04) has been proved to be highly effective against HCC without obvious toxic side-effects. However, the poor water solubility and low bioavailability in vivo severely limit its clinical application. In addition, Qd04 kills tumor cells by inducing an accumulation of endogenous reactive oxygen species (ROS), which is highly impeded by the overexpression of glutathione (GSH) in tumor cells. Herein, we designed a disulfide cross-linked polyamino acid micelle to deliver Qd04 for HCC therapy. The disulfide linkage not only endowed a tumor-targeted delivery of Qd04 by responding to tumor cell GSH but also depleted GSH to achieve increased levels of ROS generation, which improved the therapeutic efficiency of Qd04. Both in vitro and in vivo results demonstrated that the synthesized nanodrug exerted good anti-hepatoma effects, which provided a potential application for HCC therapy in clinics.

Generalized Focal Loss: Towards Efficient Representation Learning for Dense Object Detection.

Object detection is a fundamental computer vision task that simultaneously predicts the category and localization of the targets of interest. Recently one-stage (also termed "dense") detectors have gained much attention over two-stage ones due to their simple pipeline and friendly application to end devices. Dense object detectors basically formulate object detection as dense classification and localization (i.e., bounding box regression). The classification is usually optimized by Focal Loss and the box location is commonly learned under Dirac delta distribution. A recent trend for dense detectors is to introduce an individual prediction branch to estimate the quality of localization, which facilitates the classification to improve detection performance. This paper delves into the representations of the above three fundamental elements: quality estimation, classification and localization. Three problems are discovered in existing practices, including (1) the inconsistent usage of the quality estimation and classification between training and inference, (2) the inflexible Dirac delta distribution for localization, and (3) the deficient and implicit guidance for accurate quality estimation. To address these problems, we design new representations for these elements. Specifically, we merge the quality estimation into the class prediction vector to form a joint representation, use a vector to represent arbitrary distribution of box locations, and extract discriminant feature descriptors from the distribution vector for more reliable quality estimation. The improved representations eliminate the inconsistency risk and accurately depict the flexible distribution in real data, but contain continuous labels, which is beyond the scope of Focal Loss. We then propose Generalized Focal Loss (GFocal) that generalizes Focal Loss from its discrete form to the continuous version for successful optimization. Extensive experiments demonstrate the effectiveness of our method, without sacrificing the efficiency both in training and inference. Based on GFocal, we construct a considerably fast and lightweight detector termed NanoDet under mobile settings, which is 1.8 AP higher, 2x faster and 6x smaller than scaled YoloV4-Tiny.

[Preliminary study of akermanite's effect on stem cells from goat deciduous teeth in proliferation and osteogenic differentiation].

PURPOSE: To explore the characters of attachment, proliferation and osteogenic differentiation of stem cells from goat deciduous teeth (SGDs) cultured on akermanite ceramic. METHODS: SGDs were collected with enzyme digestion, and cultured. Under culture condition in growth medium, actin filament labeling and CCK-8 analysis were used to reveal attachment and proliferation. Expression level of alkaline phosphatase (ALP) and osteogenic differentiation genes (ALP, COL I, OPN and OCN) were examined by pNPP method and real-time PCR. The data was analyzed using SPSS 13.0 software package. RESULTS: Compared with ß-TCP, SGDs seeded on akermanite ceramic not only spread out better, but also grew faster since day 4. In osteogenic medium, cells of akermanite group had stronger ALP activity at day 4 and 7. The gene expression of akermanite was more than the ß-TCP group. The gene OCN expression level increased significantly at day 7 (P<0.01). CONCLUSIONS: SGDs can attach and grow well on akermanite ceramic. Compared with ß-TCP, akermanite ceramic shows an advantage in osteogenic differentiation of SGDs.