新型冠状病毒肺炎疫情下国家药品抽检工作防控要点分析与建议

目的为新型冠状病毒肺炎(简称新冠肺炎)疫情下做好国家药品抽检工提供参考。方法梳理国家药品抽检工作中的职业暴露环节,分析新冠肺炎疫情防控要点和继续做好防控工作所面临的形势,提出针对性建议。结果与结论目前,国家药品抽检工作中的新冠肺炎疫情防控状况良好,但疫情仍未解除。建议在下一步抽检工作中建议提高网络技术利用率,优化工作安排,全面落实防控措施,重视工作外的防控,继续做好新冠肺炎疫情防控。     

盐酸高乌甲素通过pRb通路介导人宫颈癌Hela细胞周期阻滞

目的 对《药品质量抽查检验管理办法》中监督管理和信息公开方面的新要求进行解读,为相关工作提供参考。方法 对比研究2019年8月颁布的《药品质量抽查检验管理办法》和2006年7月颁布的《药品质量抽查检验管理规定》中监督管理和信息公开相关内容,分析新要求的必要性及意义,并提出实施建议。结果 《药品质量抽查检验管理办法》在监督管理方面新增了追溯不合格药品来源、风险研判及处理、流通环节处罚、工作督促指导、生产经营和使用单位的义务和责任等要求,在信息公开方面新增了公开内容、重大影响研判、信息化管理等要求。结论 新增的要求有利于加强假劣药品和潜在风险的控制处置,改进抽检结果信息公开,应得到药监部门的重视并落实到位。     

头孢他啶与头孢吡肟治疗药物监测的高效液相色谱方法探索及其临床采样流程建立

目的： 建立同时测定头孢他啶和头孢吡肟血药浓度的高效液相色谱（high performance liquid chromatography,HPLC）法及其临床采样流程,并应用于临床治疗药物监测。方法： 采用CAPCELL PAK C₁₈（4.6 mm×250 mm,5.0 μm）色谱柱进行色谱分离,流动相A为50 mmol·L^-1磷酸二氢钾溶液,流动相B为混合有机相（乙腈：甲醇：水=7：2：1）,A：B（V/V,93：7）,流速1.0 mL·min^-1,波长为254 nm,盐酸雷尼替丁为内标,以ACP-1去蛋白剂沉淀蛋白,旋涡离心后进样30 μL分析,同时考察全血中两药在不同抗凝管、不同温度下放置不同时间的稳定性。结果： 头孢他啶和头孢吡肟的血浆质量浓度线性范围分别是0.57~267.34 μg·mL^-1、0.54~208.49 μg·mL^-1,低、中、高质控样品的日内、日间精密度均小于15%,萃取回收率分别为90.9%~95.4%、88.6%~97.7%;全血稳定性试验中,以EDTA-K2管采血的头孢他啶与头孢吡肟血浆在6℃及24℃下均能稳定48 h,37℃下稳定10 h;而以肝素钠管采血的头孢他啶和头孢吡肟血浆在6℃及24℃下能稳定24 h,37℃下能稳定4 h。结论： 所建立的方法具有灵敏度高、稳定性好、操作简便等优点,并根据全血稳定性结果建立了一套临床采样流程,为头孢他啶和头孢吡肟的TDM标准化与规范化建设提供参考依据。     

复杂随机抽样数据的多重线性回归分析方法及其应用

目的：探讨综合权重在复杂随机抽样数据线性回归分析中的意义和作用。方法基于蒙特卡洛随机模拟思想,采用SAS中REG和SURVEYREG两个不同的多重线性回归分析过程,分别对同一批复杂随机抽样数据（ n＝6756）在不同随机抽样率条件下进行回归建模,对所得结果进行比较。结果在未考虑和考虑观测权重与抽样权重的多重线性回归模型拟合的结果中,自变量的偏回归系数、标准误及P值的大小均有所不同。结论在对基于不同抽样率的复杂随机抽样资料,尤其是分层随机抽样调查资料的回归建模中,采用多重线性回归模型拟合资料时,将调查数据的综合权重纳入统计分析,方能更准确、灵敏地进行回归系数的参数估计和对结果变量的统计预测。     

A case of jejunal gastrinoma diagnosed by percutaneous transhepatic portal venous sampling

A 63-year-old male was admitted to our department for further examination of hypergastrinemia. Secretin provocation test and calcium infusion test suggested Zollinger-Ellison syndrome and percutaneous transhepatic portal venous sampling (PTPVS) demonstrated gastrinoma in the jejunum, although CT, ultrasonography and angiography could not accurately detect the location of the gastrinoma. Laparatomy findings showed a solid tumor 1.5 cm in diameter in the jejunal mesentery 5 cm distal to the ligament of Treitz, and primary gastrinoma was confirmed in the submucosa of the jejunum immediately adjacent to this tumor. An immunohistochemical study using the PAP method revealed gastrin secreting cells in the tumor. In addition to this case of jejunal gastrinoma, a review of literature in Japan and other countries was presented.     

Prevalence and behavioural risks for HIV and HCV infections in a population of drug users of Dakar,Senegal: the ANRS 12243 UDSEN study

Objectives

Data on the extent of drug use and associated HIV, hepatitis C and hepatitis B infection in West Africa are lacking. The objectives of ANRS12244 UDSEN study were to estimate the size of the heroin and/or cocaine drug user (DU) population living in the Dakar area (Senegal), and assess the prevalence and risk factors of HIV, hepatitis C virus (HCV) and hepatitis B virus (HBV), including behavioural determinants in this population, in order to set up an integrated prevention and treatment programme for DUs.

Design and methods

A capture-recapture method was applied for population size estimation, whereas the respondent-driven sampling (RDS) method was used to recruit a sample of DUs living in the Dakar area and determine HIV, HBV and HCV prevalence. Behavioural data were gathered during face-to-face interviews, and blood samples were collected on dried blood spots for analysis in a central laboratory. Data analysis was performed using the RDS analysis tool, and risk factors were determined by logistic regression. Access to laboratory results was organized for the participants.

Results

The size of the DU population in the Dakar area was estimated to reach 1324 (95% confidence interval (95% CI: 1281–1367)). Based on the 506 DUs included in the study, the HIV, HCV and HBV prevalence were 5.2% (95% CI: 3.8–6.3), 23.3% (95% CI: 21.2–25.2) and 7.9% (95% CI: 5.2–11.1), respectively. In people who inject drugs (PWID), prevalence levels increased to 9.4% for HIV and 38.9% for HCV (p=0.001 when compared to those who never injected). Women were more at risk of being HIV infected (prevalence: 13.04% versus 2.97% in males, p=0.001). Being PWID was a risk factor for HCV and HIV infection (odds ratio, OR: 2.7, 95% CI: 1.7–4.3, and OR: 4.3, 95% CI: 1.7–10.7, respectively), whereas older age and female sex were additional risk factors for HIV infection (10% increase per year of age, p=0.03 and OR: 4.9, 95% CI: 1.6–156, respectively). No specific determinant was associated with the risk of HBV infection.

Conclusions

High HIV and HCV prevalence were estimated in this population of DUs (including non-injectors) living in the Dakar area, Senegal, whereas HBV prevalence was close to that of the global Senegalese population, reflecting a risk of infection independent of drug use. Women seem to be highly vulnerable and deserve targeted interventions for decreasing exposure to HIV, while behavioural risk factors for HIV and HCV include the use of unsafe injections, reflecting the urgent need for developing harm reduction interventions and access to opioid substitution therapy services.     

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.     

Prevalence of, and risk factors for, hepatitis C virus infection among recent initiates to injecting in London and Glasgow: cross sectional analysis

Our aim was to compare the prevalence of antibody to hepatitis C virus (anti-HCV) among recently initiated injecting drug users (IDUs) in London and Glasgow, and to identify risk factors which could explain differences in prevalence between the cities. Complementary studies of community recruited IDUs who had initiated injection drug use since 1996 were conducted during 2001-2002. Data on HCV risk behaviours were gathered using structured questionnaires with identical core questions and respondents were asked to provide an oral fluid specimen which was tested anonymously for anti-HCV but was linked to the questionnaire. Sensitivities of the anti-HCV assays for oral fluid were 92-96%. Prevalence of anti-HCV was 35% (122/354) in London and 57% (207/366) in Glasgow (P < 0.001). Multifactorially, factors significantly associated with raised odds of anti-HCV positivity were increasing length of injecting career, daily injection, polydrug use, having had a needlestick injury, and having served a prison sentence. In addition lower odds of anti-HCV positivity were associated with non-injection use of crack cocaine and recruitment from drug agencies. After adjustment for these factors, the increased odds of anti-HCV associated with being a Glasgow IDU were diminished but remained significant. HCV continues to be transmitted among the IDU population of both cities at high rates despite the availability of syringe exchange and methadone maintenance. Effectiveness of harm reduction interventions may be compromised by inadequate coverage and failure to reduce sufficiently the frequency of sharing different types of injecting equipment, as well as the high background prevalence of HCV, and its high infectivity. Comprehensive action is urgently required to reduce the incidence of HCV among injectors.     

The use of coroner's autopsy reports to validate the use of targeted swabbing rather than tissue collection for rapid confirmation of virological causes of sudden death in the community

Background and objectivesIn this study, coroner's autopsy reports were used to validate results obtained from respiratory virus screening of swabs rather than tissue collected during autopsy in cases of adult death of unknown cause.Study designCoroner's autopsy samples collected for respiratory virus screening between October 2010 and February 2011, were identified. Autopsy reports were requested from cases positive for a virus. Each report was reviewed to correlate findings at autopsy with the virology result and to determine whether the virus found was listed as a contributing factor in the death.ResultsSixty-four coroner's autopsy cases were identified and a respiratory virus was found in 25 cases. Influenza A(H1N1)pdm09 virus was found most frequently, then RSV and influenza B with a dual influenza A and B infection and a parainfluenza type 1. Where multiple sites were swabbed, the virus was detected in all sites. Autopsy reports for 12 cases were obtained each reporting findings consistent with respiratory infection. Influenza A was always listed as a contributing factor in the death whereas RSV was listed once and influenza B was omitted in one case. The quality of the reports was variable and full histology was less likely to be performed in the elderly.ConclusionsWhile coroner's reports supported the use of swabbing rather than tissue collection, the lack of consistency and omission of the virology findings as contributing factors to death means that the burden of viruses on mortality statistics will remain under-estimated particularly in the elderly.