Search

Zhouxun Chen,1,* Jiahao Lin,2,* Shuyi Feng,2,* Xuxu Chen,2 Hanzhang Huang,3 Chen Wang,3 Yujun Yu,3 Yu He,3 Shaoliang Han,3 Linfeng Zheng,4 Guoyu Huang3 1Department of Colorectal and Anal Surgery, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; 2School of The First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China; 3Department of Gastrointestinal Surgery, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China; 4Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China *These authors contributed equally to this work Background: SIRT4, a protein localized in the mitochondria, is one of the least characteristic members of the sirtuin family. It is known that SIRT4 has deacetylase activity and plays a role in energy metabolism, but little is known about its possible role in carcinogenesis. Recently, several studies have suggested that SIRT4 may function as either a tumor oncogene or a tumor suppressor gene. However, its relationship with thyroid cancer remains unclear. Methods: We stably overexpressed SIRT4 or silenced its expression in the human thyroid cancer cell line BCPAP by means of lentiviral vectors. We conducted a variety of tests, such as CCK-8, wound healing, migration, and invasion assays, to investigate the role of SIRT4 in the proliferation, migration, and invasion abilities of thyroid cancer cells. We also investigated the effects of SIRT4 overexpression on cell cycle progression and apoptosis of BCPAP cells and studied the role of glutamine metabolism in the effects of SIRT4 on BCPAP cell migration and invasion. Finally, we analyzed SIRT4 expression levels in thyroid cancer specimens by immunohistochemistry and investigated their association with clinicopathological features. Results: Overexpression of SIRT4 inhibited the proliferation, migration, and invasion abilities of BCPAP thyroid cancer cells, blocked the cell cycle in the G0/G1 phase, and induced apoptosis. Mechanistically, SIRT4 inhibited BCPAP migration and invasion by inhibiting glutamine metabolism. Moreover, we found that SIRT4 protein levels in thyroid cancer tissues were markedly lower than in their non-neoplastic tissue counterparts (P

IQGAP2 was recently reported as a tumor suppressor of prostate cancer (PC). Nonetheless, its clinical implications remain unknown. To address this issue, we extracted data related to IQGAP2 mRNA expression and genomic alterations from multiple large datasets within the Oncomine and cBioPortal databases and performed in silico analyses to determine a potential association of IQGAP2 mRNA expression and its genomic alterations with PC progression. In 4 cohorts consisting of 118 normal prostate tissues and 277 PCs, IQGAP2 mRNA expression was significantly elevated particularly in low-grade (primary Gleason score ≤3) PCs; these changes separate PC from normal tissues with area under curve values of 0.7-0.8. Significant reductions in IQGAP2 mRNA levels and gene copy number occurred in more than 70 metastases compared to at least 230 local PCs. This duo-alteration in IQGAP2 expression supports IQGAP2 elevation suppressing and its downregulation facilitating PC progression. Deletion and missense mutations were detected in 23 of 492 primary PCs; these alterations significantly associate with PC recurrence (HR=2.71; 95% CI: 1.35-5.44; P=.005) after adjusting for known risk factors and correlate with reductions in disease-free survival (DFS, P=.002). IQGAP2 (5q13.3) genomic alterations were observed in SPOP-marked PCs and co-occurred with deletion in the RN7SK (16p12.2), SNORA50A (16q21), and SNORA50C (17q23.3) genes; the co-occurrence associated with reductions in DFS (P=4.14e-4). In two independent PC populations, MSKCC (n=130) and TCGA provisional (n=490), reductions in IQGAP2 mRNA expression were significantly associated with DFS. Collectively, this investigation reveals an association of IQGAP2 with PC progression.

University of Technology Sydney. Faculty of Engineering and Information Technology. Lithium-ion batteries are being widely used as an enabling energy storage for electric vehicles, renewable energy storage systems, and power grids, etc., as they always exhibit high energy density and long life cycle along with environmental friendliness. However, overly pessimistic or optimistic estimates of lithium-ion battery states would result in waste or abuse of battery available capabilities and may even lead to fire and explosion risks. The safety and reliability of battery utilization necessitate the accurate and reliable state estimation techniques in battery management systems (BMSs). This thesis focuses on the development of the estimation methods of lithium-ion battery states of interest, which are capable of determining internal battery status accurately. The first phase of this thesis centers on battery electrochemical model simplification and discretization for incorporating the co-estimation algorithm of battery state of charge (SOC), capacity, and resistance based on the proportional-integral (PI) observers. A physics-based battery model that has the capability to describe the electrochemical reaction process inside the battery is first developed. Trinal PI observers are then employed to implement the co-estimation task. It takes the influence of battery aging on SOC estimation by furnishing the state equations with up-to-date capacity and resistance estimates into account, thereby improving the SOC estimation accuracy. To achieve high estimation accuracy with low computation costs, SOC and capacity estimation approaches based on the incremental capacity analysis and differential voltage analysis are subsequently investigated. Feature points extracted from the SOC based incremental capacity/differential voltage (DV) curves are applied for developing the estimation algorithm of battery SOC and capacity. Besides, an extended Kalman filter and a particle filter are served as the state observers in an SOC estimator based on the DV model for further improving the performance of estimation. With the credible SOC estimates, a state of energy (SOE) estimator based on a quantitative relationship between SOC and SOE is proposed in the next step, and a moving-window energy-integral technique is then incorporated to estimate the battery maximum available energy. Through the analysis of ambient temperature, battery discharge/charge current rate, and cell aging level dependencies of SOE, the relationship between SOC and SOE can be quantified as a quadratic function for SOE estimation. The simplicity of the proposed SOE estimation method can avoid the heavy computation cost required by the conventional model-based SOE estimation methods. Finally, two state of power capability predictors are designed for a battery to sufficiently absorb or deliver a certain amount of power within its safe operating region. A battery direct current resistance model for quantitatively describing its temperature dependence is proposed and implemented on the battery capability prediction in the first method, which is beneficial to reduce the memory-consumption and dimension of the power characteristic map embedded in BMSs for applications. Different from the conventional methods using the limits of macroscopically observed variables for power prediction, the second method investigates a physical mechanism-based power prediction method and quantifies the relationship between battery power capability and surface lithium concentration for instantaneous peak power prediction. The proposed methods are experimentally verified with various cell aging levels and ambient temperatures. The proposed approaches for accurately modelling and estimating lithium-ion battery states in this thesis can contribute to safe, reliable and sufficient utilization of the battery. The developed methods are pretty general, and therefore are promising to provide valuable insight to the investigations of other types of batteries with various chemistries.