C-Phycocyanin Ameliorates the Senescence of Mesenchymal Stem Cells through ZDHHC5-Mediated Autophagy via PI3K/AKT/mTOR Pathway.

The senescence of mesenchymal stem cells (MSCs) impairs their regenerative capacity to maintain tissue homeostasis. Numerous studies are focusing on the interventions and mechanisms to attenuate the senescence of MSCs. C-phycocyanin (C-PC) is reported to have multiple functions such as antitumor, antioxidation, anti-inflammation and anti-aging roles, but there is little research about the effects of C-PC on the senescence of MSCs. Here we investigated the roles and mechanism of C-PC on MSCs senescence. In vitro results showed that C-PC could reduce senescence, enhance proliferation, promote the adipogenic and osteogenic differentiation in senescent MSCs induced by oxidative stress. In vivo D-Galactose (D-Gal) induced rats aging models showed C-PC also increased the viability and differentiation of intrinsic senescent bone marrow derived MSCs (BMSCs). Furthermore, C-PC also decreased the levels of oxidative stress markers ROS or MDA, elevated the SOD activity, and increased the anti-inflammatory factors. Proteomic chip analysis showed that C-PC interacted with ZDHHC5, and their interaction was verified by pull down assay. Overexpression of ZDHHC5 aggravated the senescence of MSCs and greatly lessened the beneficial effects of C-PC on senescence. In addition, we found ZDHHC5 regulated autophagy by altering LC3, Beclin1 and PI3K/AKT/mTOR pathway. In summary, our data indicated that C-PC ameliorates the senescence of MSCs through zinc finger Asp-His-His-Cys (DHHC) domain-containing protein 5 (ZDHHC5) mediated autophagy via PI3K/AKT/mTOR pathway. The present study uncovered the key role of autophagy in MSCs senescence and PI3K/AKT/mTOR pathway may be a potential target for anti-senescence studies of MSCs.

A Review on Drug Delivery System for Tumor Therapy.

In recent years, with the development of nanomaterials, the research of drug delivery systems has become a new field of cancer therapy. Compared with conventional antitumor drugs, drug delivery systems such as drug nanoparticles (NPs) are expected to have more advantages in antineoplastic effects, including easy preparation, high efficiency, low toxicity, especially active tumor-targeting ability. Drug delivery systems are usually composed of delivery carriers, antitumor drugs, and even target molecules. At present, there are few comprehensive reports on a summary of drug delivery systems applied for tumor therapy. This review introduces the preparation, characteristics, and applications of several common delivery carriers and expounds the antitumor mechanism of different antitumor drugs in delivery carriers in detail which provides a more theoretical basis for clinical application of personalized cancer nanomedicine in the future.

Introspective False Negative Prediction for Black-Box Object Detectors in Autonomous Driving.

Object detection plays a critical role in autonomous driving, but current state-of-the-art object detectors will inevitably fail in many driving scenes, which is unacceptable for safety-critical automated vehicles. Given the complexity of the real traffic scenarios, it is impractical to guarantee zero detection failure; thus, online failure prediction is of crucial importance to mitigate the risk of traffic accidents. Of all the failure cases, False Negative (FN) objects are most likely to cause catastrophic consequences, but little attention has been paid to the online FN prediction. In this paper, we propose a general introspection framework that can make online prediction of FN objects for black-box object detectors. In contrast to existing methods which rely on empirical assumptions or handcrafted features, we facilitate the FN feature extraction by an introspective FN predictor we designed in this framework. For this purpose, we extend the original concept of introspection to object-wise FN predictions, and propose a multi-branch cooperation mechanism to address the distinct foreground-background imbalance problem of FN objects. The effectiveness of the proposed framework is verified through extensive experiments and analysis, and the results show that our method successfully predicts the FN objects with 81.95% precision for 88.10% recall on the challenging KITTI Benchmark, and effectively improves object detection performance by taking FN predictions into consideration.