Population Pharmacokinetics of High-dose Methotrexate After Intravenous Administration in Chinese Osteosarcoma Patients from a Single Institution

Background:High-dose methotrexate (HD-MTX) with folinic acid (leucovorin) rescue is the gold standard therapy in the treatment of osteosarcoma.The plasma concentration of MTX is closely related to effi...     

The Survey of Birth Defects Rate Based on Birth Registration System

Background:To investigate the surveillance trend of birth defects,incidence,distribution,occurrence regularity,and their relevant factors in Xi'an City in the last 10 years for proposing control measur...     

Association of Estrogen Receptor Gene Polymorphisms and Primary Biliary Cirrhosis in a Chinese Population: A Case–Control Study

Background:

Primary biliary cirrhosis (PBC) is a chronic and slowly progressive cholestatic liver disease characterized by destruction of the interlobular bile ducts and a striking female predominance. The aim of this study was to identify associations between estrogen receptor (ESR) gene polymorphisms with the risk of developing PBC and abnormal serum liver tests in a Chinese population.

Methods:

Thirty-six patients with PBC (case group) and 35 healthy individuals (control group) from the First Hospital of Jilin University were studied. Whole genomic DNA was extracted from all the participants. Three single-nucleotide polymorphisms (rs2234693, rs2228480, and rs3798577) from ESR1 and two (rs1256030 and rs1048315) from ESR2 were analyzed by a pyrosequencing method. Demographic data and liver biochemical data were collected.

Results:

Subjects with the T allele at ESR2 rs1256030 had 1.5 times higher risk of developing PBC than those with the C allele (odds ratio [OR] = 2.1277, 95% confidence interval [CI] = 1.1872–4.5517). Haplotypes TGC of ESR1 rs2234693, rs2228480, and rs3798577 were risk factors for having PBC. The C allele at ESR1 rs2234693 was associated with abnormal alkaline phosphatase (OR = 5.2469, 95% CI = 1.3704–20.0895) and gamma-glutamyl transferase (OR = 3.4286, 95% CI = 1.0083–13.6578) levels in PBC patients.

Conclusions:

ESR2 rs1256030 T allele may be a significant risk factor for the development of PBC. Screening for patients with gene polymorphisms may help to make early diagnoses in patients with PBC.     

乳腺导管原位癌的发病危险因素及生物学指标ER、PR、C-erbB-2表达的研究

目的探讨年龄、初绝经年龄、生育史及哺乳情况等危险因素与乳腺癌发病之间的关系及生物学指标ER、PR、C-erb B-2在乳腺导管原位癌的表达。方法用Logistic回归分析年龄、初潮绝经年龄、生育史及哺乳情况等与乳腺癌发病风险的相关性;同时分析56例乳腺导管原位癌ER、PR、C-erb B-2的表达情况。结果 1在女性的月经、生育、哺乳等因素中,无生育史为乳腺癌发病的危险因素,而哺乳时间长、初潮年龄晚、生产数多则为乳腺癌的保护因素。2乳腺癌组织中ER、PR、C-erb B-2的表达水平明显高于乳腺良性病变。结论女性的月经、生育、哺乳情况与乳腺癌的患病风险有一定关系。通过ER、PR、C-erb B-2表达水平可以对癌组织的生物学行为和预后进行评估,并为内分泌治疗提供依据。     

ARFI破坏微泡增强Fe3O4纳米粒子在大鼠皮下移植瘤靶向递送的实验研究

目的 制备一种具有磁共振显像功能的Fe3O4纳米粒子,通过声脉冲辐射成像(acoustic radiation force impulse,ARFI)辐照脂质微泡(microbubbles,MBs),探讨ARFI辐照微泡对Fe3O4纳米粒子在肿瘤组织分布的影响.方法 采用高温水热法制备Fe3O4纳米粒子,检测其形态、大小、分布等,观察其体外磁共振显像效果.选取60只SD雌性大鼠,体质量为170 ～200 g,制备Walker256皮下移植瘤模型,分为6组(n=10):单纯ARFI辐照组、单纯MBs组、ARFI辐照MBs组、单纯Fe3O4纳米粒子组、ARFI辐照Fe3O4纳米粒子组、ARFI辐照MBs和Fe3O4纳米粒子组.微泡0.2 mL及5 mg/kg Fe3O4纳米粒子经大鼠尾静脉推注,ARFI辐照条件为探头间隔5 s辐照肿瘤部位,累计辐照5 min.将处理后的SD大鼠肿瘤部位行MRI扫描观察肿瘤组织信号变化;处死SD大鼠,取组织标本行病理学分析.结果 制备的Fe3O4纳米粒子形态规则,粒径分布均匀,具有磁共振显像功能.SD大鼠肿瘤组织普鲁士蓝染色结果为单纯Fe3O4纳米粒子组、ARFI辐照Fe3O4纳米粒子组、ARFI辐照MBs和Fe3O4纳米粒子组均可见点状蓝染颗粒.其中ARFI辐照MBs和Fe3O4纳米粒子组蓝染颗粒计数明显多于其余2组(P＜0.05).SD大鼠肿瘤部位磁共振成像结果为单纯Fe3O4纳米粒子组、ARFI辐照Fe3O4纳米粒子组、ARFI辐照MBs和Fe3O4纳米粒子组经相应处理后T2*WI均可见肿瘤内部低信号部分增加.结论 ARFI辐照MBs能够有效地提高Fe3O4纳米粒子在SD大鼠皮下移植瘤组织的分布,增强Fe3O4纳米粒子在活体肿瘤内的靶向递送效果.     

DX方案与DF方案治疗老年进展期胃癌的对比研究

目的比较多烯紫杉醇联合卡培他滨(DX)方案与多烯紫杉醇联合5-氟尿嘧啶/亚叶酸钙(DF)方案治疗老年进展期胃癌的临床疗效及安全性。方法 96例患者随机分为治疗组和对照组。治疗组(DX组)49例,应用多烯紫杉醇联合卡培他滨方案化疗:多烯紫杉醇60mg/m2,静脉滴注1h,第1天;卡培他滨1 000mg/m2,口服,2次/日,第1~14天。对照组(DF组)47例,应用多烯紫杉醇联合5-氟尿嘧啶/亚叶酸钙方案化疗:多烯紫杉醇60mg/m2,静脉滴注1h,第1天;亚叶酸钙200mg/m2,静脉滴注2h后予5-氟尿嘧啶500mg/m2,持续静脉输注22h,第1~5天;两组均3周为1周期。2周期后评价疗效。随访观察疾病进展时间和生存期。结果DX组总有效率42.86%,DF组总有效率31.91%,差异无统计学意义(P>0.05);DX组、DF组疾病控制率分别为79.59%、63.83%,两组相比差异无统计学意义(P>0.05);DX组体力状况Karnofsky评分明显高于DF组(P<0.05)。DX组手足综合征发生率明显高于DF组(P<0.05),而Ⅲ/Ⅳ度中性粒细胞下降发生率则明显低于DF组(P<0.05)。结论多烯紫杉醇联合卡培他滨方案疗效肯定、不良反应易耐受,是老年进展期胃癌较理想的化疗方案,值得临床进一步研究。     

血压变异性与hs-CRP的相关性探讨

目的 探讨高血压患者血压变异性(blood pressure variability,BPV)与血清高敏C反应蛋白(high sensitivityC reactive protein,hs-CRP)之间的关系.方法 收集2012年12月至2014年7月玉溪市人民医院心内科就诊的216例高血压患者按高血压分级分为3组,所有入选对象均进行生化及hs-CRP、血压水平及血压变异性、心脏、血管相关超声指标测定.结果 (1)3组间hs-CRP水平差异有统计学意义(P＜0.05); (2)3组间颈动脉内-中膜厚度(TIM)、升主动脉内径(AAOD)、左心房内径(LAId)、左室后壁厚度(LVPWB)高血压3级与高血压1级组比较有统计学差异(P＜0.05); (3)3组间血压变异性比较差异有统计学意义(P＜0.05); (4)相关回归分析显示血压变异性指标与hs-CRP水平呈正相关.结论 推测血清hs-CRP水平和血压变异性可能对高血压伴相应器官损害的患者治疗和预后有着较高的临床价值.     

磁场对AMI大鼠心肌ATP及血浆cAMP、cGMP的影响

目的探讨磁场对AM I的保护作用。方法将大鼠随机分为5组即:空白对照组、磁场对照组、AM I组、AM I药物(心得安)治疗组和AM I磁场治疗组。采用虫荧光素酶法及放射免疫技术法对实验性大鼠进行心肌ATP含量、及血浆cAMP、cGMP的测定。结果AM I磁场治疗组心肌ATP明显高于AM I组(P<0.01)与药物治疗组相近似。AM I的血浆cAMP、cGMP含量明显高于空白对照组(P<0.01)及磁场对照组,AM I磁场治疗组与药物治疗组明显低于AM I组(P<0.01)。结论磁场能降低AM I大鼠cAMP、cGMP的含量,增加心肌ATP含量,对心肌具有保护作用,这也为磁场用于AM I的治疗提供了实验依据。     

Density estimation using deep generative neural networks

Density estimation is one of the fundamental problems in both statistics and machine learning. In this study, we propose Roundtrip, a computational framework for general-purpose density estimation based on deep generative neural networks. Roundtrip retains the generative power of deep generative models, such as generative adversarial networks (GANs) while it also provides estimates of density values, thus supporting both data generation and density estimation. Unlike previous neural density estimators that put stringent conditions on the transformation from the latent space to the data space, Roundtrip enables the use of much more general mappings where target density is modeled by learning a manifold induced from a base density (e.g., Gaussian distribution). Roundtrip provides a statistical framework for GAN models where an explicit evaluation of density values is feasible. In numerical experiments, Roundtrip exceeds state-of-the-art performance in a diverse range of density estimation tasks.

Let p(·) be a density on a n-dimensional Euclidean space χ. The task of density estimation is to estimate p(·) based on a set of independently and identically distributed data points {xi}i=1Ndrawn from this density.Traditional density estimators such as histograms (1, 2) and kernel density estimators (KDEs) (3, 4) typically perform well only in low dimension. Recently, neural network-based approaches were proposed for density estimation and yielded promising results in problems with high-dimensional data points such as images. There are mainly two families of such neural density estimators: autoregressive models (5–7) and normalizing flows (8–11). Autoregression-based neural density estimators decompose the density into the product of conditional densities based on probability chain rule p(x)=∏ip(xi|x1:i−1). Each conditional probability p(xi|x1:i−1) is modeled by a parametric density (e.g., Gaussian or mixture of Gaussian), of which the parameters are learned by neural networks. Density estimators based on normalizing flows represent x as an invertible transformation of a latent variable z with known density, where the invertible transformation is a composition of a series of simple functions whose Jacobian is easy to compute. The parameters of these component functions are then learned by neural networks.As suggested in ref. 12, both of these are special cases of the following general framework. Given a differentiable and invertible mapping G:Rn→Rn and a base density pzz, the density of x=G(z) can be represented using the change of variable rule as follows:pxx=pzz|detJz|−1,[1]where Jz=∂Gz/∂zT is the Jacobian matrix of function G(·) at point z. Density estimation at x can be solved if the base density pzz is known and the determinant of Jacobian matrix is feasible to calculate. To achieve this, previous neural density estimators have to impose heavy constraints on the model architecture. For example, refs. 7, 10, and 12 require the Jacobian to be triangular, ref. 13 constructed low rank perturbations of a diagonal matrix as the Jacobian, and ref. 14 proposed a circular convolution where the Jacobian is a circulant matrix. These strong constraints diminish the expressiveness of neural networks, which may lead to poor performance. For example, autoregressive neural density estimators based on learning p(xi|x1:i−1) are naturally sensitive to the order of the features. Moreover, the change of variable rule is not applicable when the domain dimension in base density differs from target density. However, experiences from deep generative models [e.g., GAN (15) and VAE (16)] suggested that it is often desirable to use a latent space of smaller dimension than the data space.To overcome the limitations above, we propose a neural density estimator called Roundtrip. Our approach is motivated by recent advances in deep generative neural networks (15, 17, 18). Roundtrip differs from previous neural density estimators in two ways. 1) It allows the direct use of a deep generative network to model the transformation from the latent variable space to the data space, while previous neural density estimators use neural networks only to learn the parameters in the component functions that are used for building up an invertible transformation. 2) It can efficiently model data densities that are concentrated near learned manifolds, which is difficult to achieve by previous approaches as they require the latent space to have the same dimension as the data space. Importantly, we also provide methods, based on either importance sampling and Laplace approximation, for the pointwise evaluation of the density estimate. We summarize our major contributions in this study as follows: 1) We propose a general-purpose neural density estimator based on deep generative models, which requires less restrictive model assumptions compared to previous neural density estimators. 2) We show that the principle in previous neural density estimators can be regarded as a special case in our Roundtrip framework. 3) We demonstrate state-of-the-art performance of Roundtrip model through a series of experiments, including density estimation tasks in simulations as well as in real data applications ranging from image generation to outlier detection.