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Metabolic reprogramming is pivotal to sustain cancer growth and progression. As such dietary restriction therapy represents a promising approach to starve and treat cancers. Nonetheless, tumors are dynamic and heterogeneous populations of cells with metabolic activities modulated by spatial and temporal contexts. Autophagy is a major pathway controlling cell metabolism. It can downregulate cell metabolism, leading to cancer cell quiescence, survival, and chemoresistance. To understand treatment dynamics and provide rationales for better future therapeutic strategies, we investigated whether and how autophagy is involved in the chemo-cytotoxicity and -resistance using two commonly used human glioblastoma (GBM) cell lines U87 and U251 together with primary cancer cells from the GBM patients. Our results suggest that autophagy mediates chemoresistance through reprogramming cancer cell metabolism and promoting quiescence and survival. Further unbiased transcriptome profiling identified a number of clinically relevant pathways and genes, strongly correlated with TCGA data. Our analyses have not only reported many well-known tumor players, but also uncovered a number of genes that were not previously implicated in cancers and/or GBM. The known functions of these genes are highly suggestive. It would be of high interest to investigate their potential involvement in GBM tumorigenesis, progression, and/or drug resistance. Taken together, our results suggest that autophagy inhibition could be a viable approach to aid GBM chemotherapy and combat drug resistance.

Lycium barbarum, extensively utilized as a medicinal plant in China for years, exhibits antitumor, immunoregulative, hepatoprotective, and neuroprotective properties. The present study aims to investigate the hyperglycemic and antidiabetic nephritic effects of polysaccharide which is separated from Lycium barbarum (LBPS) in high-fat diet-streptozotocin- (STZ-) induced rat models. The reduced bodyweight and enhanced blood glucose concentration in serum were observed in diabetic rats, and they were significantly normalized to the healthy level by 100 mg/kg of metformin (Met) and LBPS at doses of 100, 250, and 500 mg/kg. LBPS inhibited albuminuria and blood urea nitrogen concentration and serum levels of inflammatory factors including IL-2, IL-6, TNF-α, IFN-α, MCP-1, and ICAM-1 compared with diabetic rats, and it indicates the protection on renal damage. Furthermore, the activities of SOD and GSH-Px in serum were enhanced strikingly by LBPS which suggests its antioxidation effects. LBPS, compared with nontreated diabetic rats, inhibited the expression of phosphor-nuclear factors kappa B (NF-κB) and inhibitor kappa B alpha in kidney tissues. Collectively, LBPS possesses antidiabetic and antinephritic effects related to NF-κB-mediated antioxidant and antiinflammatory activities.

Path planning is important to the efficiency and navigation safety of USV autonomous operation offshore. To improve path planning, this study proposes the improved ant colony optimization-artificial potential field (ACO-APF) algorithm, which is based on a grid map for both local and global path planning of USVs in dynamic environments. The improved ant colony optimization (ACO) mechanism is utilized to search for a globally optimal path from the starting point to the endpoint for a USV in a grid environment, and the improved artificial potential field (APF) algorithm is subsequently employed to avoid unknown obstacles during USV navigation. The primary contributions of this paper are as follows: (1) this paper proposes a new heuristic function, pheromone update rule, and dynamic pheromone volatilization factor to improve convergence and mitigate finding local optima with the traditional ant colony algorithm; (2) we propose an equipotential line outer tangent circle and redefine potential functions to eliminate goals unreachable by nearby obstacles (GNRONs) and local minimum problems, respectively; (3) to adapt the USV to a complex environment, this paper proposes a dynamic early-warning step-size adjustment strategy in which the moving distance and safe obstacle avoidance range in each step are adjusted based on the complexity of the surrounding environment; (4) the improved ant colony optimization algorithm and artificial potential field algorithm are effectively combined to form the algorithm proposed in this paper, which is verified as an effective solution for USV local and global path planning using a series of simulations. Finally, in contrast to most papers, we successfully perform field experiments to verify the feasibility and effectiveness of the proposed algorithm. INDEX TERMS-Unmanned surface vehicles (USVs); Path planning; Improved ant colony optimization-artificial potential filed (ACO-APF) algorithm; Unknown obstacle avoidance; Field experiment