ATP-binding cassette (ABC) transporters, acting as cellular "pumps," facilitate solutes translocation through membranes via ATP hydrolysis. Their overexpression is closely tied to multidrug resistance (MDR), a major obstacle in chemotherapy and neurological disorder treatment, hampering drug accumulation and delivery. Extensive research has delved into the intricate interplay between ABC transporter structure, function, and potential inhibition for MDR reversal. Cryo-electron microscopy has been instrumental in unveiling structural details of various MDR-causing ABC transporters, encompassing ABCB1, ABCC1, ABCG2, as well as recently revealed ABCC3 and ABCC4 structures. The newly obtained structural insight has deepened our understanding of substrate and drug binding, translocation mechanisms, and inhibitor interactions. Given the growing body of structural information available for human MDR transporters and their associated mechanisms, we believe it is timely to compile a comprehensive review of these transporters and compare their functional mechanisms in the context of multidrug resistance. Therefore, this review primarily focuses on the structural aspects of clinically significant human ABC transporters linked to MDR, with the aim of providing valuable insights to enhance the effectiveness of MDR reversal strategies in clinical therapies.

Semantic image segmentation has recently witnessed considerable progress by training deep convolutional neural networks (CNNs). The core issue of this technique is the limited capacity of CNNs to depict visual objects. Existing approaches tend to utilize approximate inference in a discrete domain or additional aides and do not have a global optimum guarantee. We propose the use of the multi-label manifold ranking (MR) method in solving the linear objective energy function in a continuous domain to delineate visual objects and solve these problems. We present a novel embedded single stream optimization method based on the manifold ranking (MR) model to avoid approximations without sacrificing expressive power. In addition, we propose a novel network, which we refer to as dual multi-scale manifold ranking (\textbf{DMSMR}) network, that combines the dilated, multi-scale strategies with the single stream MR optimization method in the deep learning architecture to further improve the performance. Experiments on high resolution images, including close-range and remote sensing datasets, demonstrate that the proposed approach can achieve competitive accuracy without additional aides in an end-to-end manner.

This study investigated the neuroprotective effects of methyl 3,4-dihydroxybenzoate (MDHB) against t-butylhydroperoxide(TBHP) induced oxidative damage in SH-SY5Y (human neuroblastoma cells) and the underlying mechanisms. SH-SY5Y were cultured in DMEM+10% FBS for 24 hours and pretreated with different concentrations of MDHB or N-acetyl-L-cysteine (NAC) for 4 hours prior to the addition of 40 μM TBHP for 24 hours. Cell viability was analyzed using the methyl thiazolyl tetrazolium (MTT) and lactate dehydrogenase (LDH) assays. An annexin V-FITC assay was used to detect cell apoptosis rate. The 2',7'-dichlorofluorescin diacetate (DCFH-DA) assay was used to determine intracellular ROS levels. The activities of antioxidative enzymes (GSH-Px and SOD) were measured using commercially available kits. The oxidative DNA damage marker 8-OHdG was detected using ELISA. Western blotting was used to determine the expression of Bcl-2, Bax, caspase 3, p-Akt and Akt proteins in treated SH-SY5Y cells. Our results showed that MDHB is an effective neuroprotective compound that can mitigate oxidative stress and inhibit apoptosis in SH-SY5Y cells