Tumor metastasis is a major concern in cancer therapy. In this context, focal adhesion kinase (FAK) gene overexpression, which mediates cancer cell migration and invasion, has been reported in several human tumors and is considered a potential therapeutic target. However, gene-based treatment has certain limitations, including a lack of stability and low transfection ability. In this study, a biocompatible lipopolyplex was synthesized to overcome the aforementioned limitations. First, polyplexes were prepared using poly(2-Hydroxypropyl methacrylamide-co-methylacrylate-hydrazone-pyridoxal) (P(HPMA-co-MA-hyd-VB6)) copolymers, which bore positive charges at low pH value owing to protonation of pyridoxal groups and facilitated electrostatic interactions with negatively charged FAK siRNA. These polyplexes were then encapsulated into methoxy polyethylene glycol (mPEG)-modified liposomes to form lipopolyplexes. Doxorubicin (DOX) was also loaded into lipopolyplexes for combination therapy with siRNA. Experimental results revealed that lipopolyplexes successfully released DOX at low pH to kill cancer cells and induced siRNA out of endosomes to inhibit the translation of FAK proteins. Furthermore, the efficient accumulation of lipopolyplexes in the tumors led to excellent cancer therapeutic efficacy. Overall, the synthesized lipopolyplex is a suitable nanocarrier for the co-delivery of chemotherapeutic agents and genes to treat cancers.
We propose a new embedded processor power analysis approach that maps instruction executions to microarchitecture components for highly efficient and accurate power evaluations, which are crucial for embedded system designs. We observe that in practice, the execution of each high-level instruction in a processor always triggers the same microcomponent activity sequence while the difference of power consumption values of different instructions is mainly due to timing variations caused by hazards and cache misses. Hence, by incorporating accurately pre-characterized microcomponent power consumption values into an efficient instruction-microcomponent processor timing simulation tool, we construct a highly accurate embedded processor power analysis tool. Additionally, based on the proposed approach, we accurately and effortlessly capture the power waveform at any time point for power profiling, peak power and dynamic thermal distribution analysis. The experimental results show that the proposed approach is nearly as accurate as gate-level simulators, with an error rate of less than 1.2% while achieving simulation speeds of up to 20 MIPS, five orders faster than a commercial gate-level simulator.
In recent years, the demand for memory performance has grown rapidly due to the increasing number of cores on a single CPU, along with the integration of graphics processing units and other accelerators. Caching has been a very effective way to relieve bandwidth demand and to reduce average memory latency. As shown by the cache feature table in Fig. 23.9.1, there is a big latency gap between SRAM caches in the CPU and the external DRAM main memory. As a key element for future computing systems, the last level cache (LLC) should have a high random access bandwidth, a low random access latency, a density of 1 to 8Gb, and all signal pads located on one side of the chip [1]. A logic-process-based solution was proposed [2], but it is not scalable, and has a high standby current due to its need for frequent refresh. HBM2 was also proposed [3], but its row latency is not better than conventional DRAM, and its random-access bandwidth is still limited by t FAW , as shown in Fig. 23.9.1. This paper describes the high-bandwidth low-latency (HBLL) RAM design: how it overcomes these challenges and meets requirements in a cost-effective way.
An effective full-system virtual prototype is critical for early-stage systems design exploration. Generally, however, traditional acceleration approaches of virtual prototypes cannot accurately analyze system performance and model non-deterministic inter-component interactions due to the unpredictability of simulation progress. In this paper, we propose an effective virtualization-assisted approach for modeling and performance analysis. First, we develop a deterministic synchronization process that manages the interactions affecting the data dependency in chronological order to model inter-component interactions consistently. Second, we create accurate timing and bus contention models based on runtime operation statistics for analyzing system performance. We implement the proposed virtualization-assisted approach on an off-the-shelf System-on-Chip (SoC) board to demonstrate the effectiveness of our idea. The experimental results show that the proposed approach runs 12~77 times faster than a commercial virtual prototyping tool and performance estimation is only 3~6% apart from real systems.
Abstract Activation of the receptor tyrosine kinase Axl by its ligand gas6 is implicated in several diseases included inflammation and cancer. Our previous report demonstrated that Axl signal promotes oral squamous cell carcinoma (OSCC) carcinogenesis and progression. Recent studies also suggest that tumor-associated macrophage (TAM) promote tumor growth and metastasis. This study aims to study the potential involvement of Axl signal in the protumoral effect of TAM. We carried out coculture experiment by incubation of oral cancer cells (YD38) and macrophages (THP1). The expression of gas6 and Axl were examined in both cells. Characterized gene expression for M1 and M2 macrophage and EMT gene for cancer were also examined. The effect of Axl signal on cancer cells were further investigated by knockdown Axl expression and neutralized gas6 antibody in coculture system. The association Axl activation (pAxl) and TAM distribution were analyzed by Immunohistochemistry in cancer tissues. Our data indicated that upon coculture with cancer cells, TAM polarized to M2 phenotype with high IL10/low IL12 expression and elevated in gas6 secretion. After coculture with TAM, activation of Axl signal in cancer cells was noted. Furthermore, cancer increased mesenchymal markers expression and invasion/migration ability. While neutralzied gas6 or knockdown Axl, the coculture effects was diminished. In addition our result also demonstrated gas6/Axl signal elevated MMP-2 and -9 through NF-kb pathway to promote tumor invasion/migration, in which coculture of TAM increased NF-kb nuclear translocation and its bonding to MMPs promoter in cancer cells. Finally, in vivo cancer tissues showed significant association between TAM distribution and Axl (phosphorylated) expression. In conclusion, our results showed that TAMs play a protumor role in OSCC. TAM activated Axl signal and promoted tumor progression through NF-kb pathway. Therefore, Gas6/Axl and NF-kb signals in OSCC could be a putative target for therapeutic intervention. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 53. doi:1538-7445.AM2012-53
As energy efficiency has become a primary concern, system designers have greater need for a flexible and highly accurate power estimation method for evaluating different architecture options. Since memory is an increasingly dominant power consumer, we reexamine existing memory power models and propose a highly efficient microcomponent-based approach with data-aware refinement for accurate system-level power estimations. The key contribution of our approach is that the proposed microcomponent method allows designers to use flexible architecture compositions. Our approach identifies the common microcomponents used by internal memory commands and accurately pre-calibrates the power consumption pattern of each microcomponent. We decompose target design architectures into these microcomponents to easily derive accurate power estimates. To achieve very high accuracy, we consider the data variation effect by leveraging the fact that memory circuit is mainly doing data passing and hence a simple interpolation technique can further boost accuracy. Our experiments show that the proposed approach produces accurate results of less than 2% error rate in average for system power analysis.
We propose a new power simulation technique that effectively considers dynamic program execution behaviors such as cache hit/miss or branch predicted/mis-predicted and achieves fast and accurate power estimation results. Traditionally, accurate software power estimation relies on slower fine-grained simulations while faster coarse-grained simulations often lead to inaccurate estimation results. We pre-characterize detailed circuit power consumption, pipeline and branch effects of each basic block for accuracy and then apply efficient instruction-set simulators to compute total software power consumption. The experimental result shows that our approach achieves over 200 MIPS performance with a less than 3% error rate.
Presented is a novel half Gb DRAM device for 3D stacked systems utilizing TSV. It is designed through the use of a new computer-aided design methodology and which realizes 819 Gb/s bandwidth across 16 channels and <;10ns read latency on a 45nm DRAM process. The architecture is based on small subarrays with short WL and BL to realize the low latency and energy efficiency. We also integrated several circuit techniques, including adaptive power to speed-up access time and banks rotation to reduce thermal issues. The proposed device is also estimated in a system simulation that shows that the power efficiency is higher than comparable systems.
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