Genomic variants within chromosome 14q32.32 regulate bone mass through MARK3 signaling in osteoblasts

Bone mineral density (BMD) is a highly heritable predictor of osteoporotic fracture. GWAS have identified hundreds of loci influencing BMD, but few have been functionally analyzed. In this study, we show that SNPs within a BMD locus on chromosome 14q32.32 alter splicing and expression of PAR-1a/microtubule affinity regulating kinase 3 (MARK3), a conserved serine/threonine kinase known to regulate bioenergetics, cell division, and polarity. Mice lacking Mark3 either globally or selectively in osteoblasts have increased bone mass at maturity. RNA profiling from Mark3-deficient osteoblasts suggested changes in the expression of components of the Notch signaling pathway. Mark3-deficient osteoblasts exhibited greater matrix mineralization compared with controls that was accompanied by reduced Jag1/Hes1 expression and diminished downstream JNK signaling. Overexpression of Jag1 in Mark3-deficient osteoblasts both in vitro and in vivo normalized mineralization capacity and bone mass, respectively. Together, these findings reveal a mechanism whereby genetically regulated alterations in Mark3 expression perturb cell signaling in osteoblasts to influence bone mass.     

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.     

A comparison of the academic impact of plastic surgery units in the United Kingdom and Ireland using bibliometric analysis

Academic output is just one aspect of a successful career as a plastic surgeon. However, for those with a strong interest in academia, the academic output of a department will likely be a key factor when deciding how to rank jobs. The aim of this study was to quantify and rank the academic output of plastic surgery units across the UK and Ireland. The Institute for Scientific Information (ISI) Web of Science Bibliometric analysis tool was used to collate cumulative (1950–2016), 10 year (2006–2016) and 3 years (2013–2015) research output data for plastic surgery units in the UK and Ireland. Sixty-six plastic surgery units were identified. Departments were ranked for each time period according to the number of papers produced, number of citations (Nc) and h-index (a measure of the impact of scientific output). The top 3 departments for number of papers in the last 10 years were The Royal Free Hospital, London (226) Broomfield Hospital, Chelmsford (218), and Morriston Hospital and Swansea (188). The top 3 for h-number were The Royal Free Hospital (21) Wythenshawe Hospital, Manchester (18) and Morriston Hospital (17). Academic output varies across plastic surgery units in the UK and Ireland. A number of departments have consistently maintained high academic outputs across the years and will be of interest to surgeons hoping to pursue a career in academia.     

玳玳果黄酮降脂提取物体内组织分布规律及特征研究

目的：建立UPLC-MS/MS分析方法同时测定玳玳果黄酮降脂提取物效应组分新橙皮苷和柚皮苷在大鼠10种脏器组织中含量，分布规律及特征。方法：采用UPLC-MS/MS技术建立提取物效应组分新橙皮苷及柚皮苷在大鼠心、肝、脾、肺、肾、脑、胃、小肠、脂质、肌肉组织中的定量分析方法；大鼠给药后分别于0.33，0.67，1，4，8 h的5个时间点，分别摘取以上10种脏器组织，测定脏器组织及血液中效应组分的质量浓度，采用DAS（V 2.0）药动学软件对各样本的药物浓度-时间数据进行房室拟合，并计算不同组织效应组分的药-时曲线下面积（AUC）及平均滞留时间（MRT）。结果：所建立的UPLC-MS/MS定量分析方法具备良好的专属性、标准曲线及线性范围良好、方法准确度与精密度、定量下限均符合有关规定；玳玳果黄酮降脂提取物效应组分在血液中的分布符合一室模型，除肾脏及脑组织外，其余脏器中提取物效应组分的房室特征多为静脉注射的二室模型，柚皮苷在肾脏中的拟合结果为非静脉注射的二室模型，新橙皮苷在脑组织拟合结果为静脉注射的三室模型，给药后8 h各组织中效应组分新橙皮苷及柚皮苷AUC值大小顺序均为小肠 > 胃 > 肾 > 脂质 ≈ 脾脏 > 肺 > 肌肉 > 肝 > 心 > 脑，效应组分在各脏器中均无明显蓄积；效应组分在血液、肾脏、肝脏中的滞留时间较长，MRT均大于2 h，脂质最短，MRT不足1 h；各脏器中新橙皮苷的药-时曲线下面积约是柚皮苷的3倍，而心、肝、肾中则是3.5，2.1和3.4倍。结论：玳玳果黄酮降脂提取物效应组分在大鼠组织中分布迅速，达峰时间早于血液；效应组分在肠道内消除缓慢，给药8 h后在各脏器中的含量均显著下降且无特异的蓄积部位。研究结果揭示玳玳果黄酮降脂提取物效应组分在大鼠体内的分布特征及规律，为进一步理解玳玳果黄酮降脂提取物在体内的作用靶点及机制奠定了基础。