骨形态发生蛋白2及碱性成纤维生长因子2对大鼠骨髓间充质干细胞膜片增殖和成骨分化的影响

文题释义： 细胞膜片技术：是在体外接种培养高密度的细胞，使其相互融合生长至100%而形成的透明致密膜状物。该技术不需要胰酶消化即可收集细胞，因此保留了大量的胞外基质、细胞间连接以及细胞-基质连接等结构。目前细胞膜片技术已成为组织工程领域的研究热点，已被推广应用于牙周膜、角膜、心脏、软骨、食管等多种组织器官修复。 成骨细胞：主要由内外骨膜和间充质始祖细胞分化而来，在复杂的骨形成过程中发挥着主要的功能，承担着骨基质的合成、分泌和矿化。骨髓间充质干细胞具有多向分化潜能，能定向分化为成骨细胞，其成骨分化过程可受多种因素的影响，如细胞因子的调控、遗传因素和激素水平等。背景：现阶段骨形态发生蛋白2和碱性成纤维生长因子2对骨髓间充质干细胞膜片增殖、成骨分化的影响和作用机制还尚未可知，如何将生长因子与组织工程细胞膜片技术相整合，最终将其用于骨缺损修复具有重要意义。 目的：探讨单独及联合应用骨形态发生蛋白2和碱性成纤维生长因子2对骨髓间充质干细胞膜片增殖和成骨分化的影响。 方法：体外分离培养鉴定SD大鼠骨髓间充质干细胞并构建细胞膜片，选用不同质量浓度的骨形态发生蛋白2和碱性成纤维生长因子2单独及联合诱导骨髓间充质干细胞膜片，CCK-8法结合碱性磷酸酶活性检测确定2种因子促进膜片增殖和成骨分化的最佳有效质量浓度；然后对骨髓间充质干细胞膜片进行成骨诱导，通过大体及显微镜观察、Vonkossa染色、茜素红染色、RT-PCR检测相关成骨标志物来评估诱导效果。 结果与结论：单独应用骨形态发生蛋白2可增强骨髓间充质干细胞膜片的碱性磷酸酶活性，最佳质量浓度为100 μg/L(P < 0.001)，单独应用碱性成纤维生长因子2能加速骨髓间充质干细胞膜片的增殖，最佳质量浓度为20 μg/L(P < 0.001)，而联合应用既可以促进膜片增殖又能提高其碱性磷酸酶活性(P < 0.001)；经成骨诱导后，4组膜片在形态学上无明显差异，均能诱导骨髓间充质干细胞膜片的成骨分化，其中联合组钙结节最明显(P < 0.001)，可显著促进膜片晚期成骨分化并抑制其早期成骨分化，具有明显的协同促进作用(P < 0.001)。结果表明，骨形态发生蛋白2和碱性成纤维生长因子2联合应用时具有协同作用，既可以促进骨髓间充质干细胞膜片增殖，又能显著增强其成骨诱导能力。ORCID: 0000-0003-1918-579X(何惠宇) 中国组织工程研究杂志出版内容重点：干细胞；骨髓干细胞；造血干细胞；脂肪干细胞；肿瘤干细胞；胚胎干细胞；脐带脐血干细胞；干细胞诱导；干细胞分化；组织工程     

Utility of Tissue Similarity Maps Based on Relative Cerebral Blood Volume for Grading Gliomas as Validated by Histological Results

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Effects of ω-3 Fatty Acids and Catechins on Fatty Acid Synthase in the Prostate: A Randomized Controlled Trial

Animal and human studies suggest fish oil and green tea may have protective effect on prostate cancer. Fatty acid synthase (FAS) has been hypothesized to be linked to chemoprotective effects of both compounds. This study evaluated the independent and joint effects of fish oil (FO) and green tea supplement (epigallocatechin-3-gallate, EGCG) on FAS and Ki-67 levels in prostate tissue. Through a double-blinded, randomized controlled trial with 2 × 2 factorial design, 89 men scheduled for repeat prostate biopsy following an initial negative prostate biopsy were randomized into either FO alone (1.9 g DHA + EPA/day), EGCG alone (600 mg/day), a combination of FO and EGCG, or placebo. We used linear mixed-effects models to test the differences of prostate tissue FAS and Ki-67 by immunohistochemistry between pre- and post-intervention within each group, as well as between treatment groups. Results did not show significant difference among treatment groups in pre-to-post-intervention changes of FAS (P = 0.69) or Ki-67 (P = 0.26). Comparing placebo group with any of the treatment groups, we did not find significant difference in FAS or Ki-67 changes (all P > 0.05). Results indicate FO or EGCG supplementation for a short duration may not be sufficient to produce biologically meaningful changes in FAS or Ki-67 levels in prostate tissue.     

BCAN基因在肾透明细胞癌中的表达及临床意义

目的以基因表达数据集资料为研究对象,分析BCAN基因在肾透明细胞癌中的表达情况以及对患者预后的影响。方法在Oncomine数据库中挖掘BCAN在肾透明细胞癌(ccRCC)中的表达情况。从TCGA数据库中获取ccRCC患者临床资料和目的基因的表达信息并进行统计分析。利用GEO数据库中GSE73731数据集的ccRCC样本进行基因富集分析。利用String数据库分析与BCAN相关的蛋白。结果BCAN低表达组的ccRCC患者在病理分期及T分期方面低于高表达组(P<0.001;P=0.001);N分期及M分期差异无统计学意义(P>0.05)。BCAN低表达组患者的总生存期优于高表达组(P=0.033)。BCAN基因高表达组的样本主要富集在KRAS信号通路。结论BCAN可以通过多种途径来促进肿瘤细胞的侵袭能力,有望成为ccRCC不良预后的重要生物标志物之一。     

Optimal tracking control for non-zero-sum games of linear discrete-time systems via off-policy reinforcement learning

In this article, a model-free off-policy reinforcement learning algorithm is applied to address the optimal tracking problem based on multiplayer non-zero-sum games for discrete-time linear systems. In contrast to the traditional method and the policy iteration method for solving the optimal tracking problems, the proposed algorithm operates with the system data rather than the knowledge of the system dynamics. For performing the proposed algorithm, an auxiliary augmented system is constructed via assembling the original system and the reference trajectory while a discount factor is introduced into the performance indexes. It is analyzed that the solutions of the proposed algorithm converge to the Nash equilibrium and the result is not influenced by the probing noise. Two simulations are presented to verify the feasibility and effectiveness of the proposed algorithm.