TcVac1 vaccine delivery by intradermal electroporation enhances vaccine induced immune protection against Trypanosoma cruzi infection in mice

The efforts for the development and testing of vaccines against Trypanosoma cruzi infection have increased during the past years. We have designed a TcVac series of vaccines composed of T. cruzi derived, GPI-anchored membrane antigens. The TcVac vaccines have been shown to elicit humoral and cellular mediated immune responses and provide significant (but not complete) control of experimental infection in mice and dogs. Herein, we aimed to test two immunization protocols for the delivery of DNA-prime/DNA-boost vaccine (TcVac1) composed of TcG2 and TcG4 antigens in a BALB/c mouse model. Mice were immunized with TcVac1 through intradermal/electroporation (IDE) or intramuscular (IM) routes, challenged with T. cruzi, and evaluated during acute phase of infection. The humoral immune response was evaluated through the assessment of anti-TcG2 and anti-TcG4 IgG subtypes by using an ELISA. Cellular immune response was assessed through a lymphocyte proliferation assay. Finally, clinical and morphopathological aspects were evaluated for all experimental animals. Our results demonstrated that when comparing TcVac1 IDE delivery vs IM delivery, the former induced significantly higher level of antigen-specific antibody response (IgG2a?+?IgG2b?>?IgG1) and lymphocyte proliferation, which expanded in response to challenge infection. Histological evaluation after challenge infection showed infiltration of inflammatory cells (macrophages and lymphocytes) in the heart and skeletal tissue of all infected mice. However, the largest increase in inflammatory infiltrate was observed in TcVac1_IDE/Tc mice when compared with TcVac1_IM/Tc or non-vaccinated/infected mice. The extent of tissue inflammatory infiltrate was directly associated with the control of tissue amastigote nests in vaccinated/infected (vs. non-vaccinated/infected) mice. Our results suggest that IDE delivery improves the protective efficacy of TcVac1 vaccine against T. cruzi infection in mice when compared with IM delivery of the vaccine.     

Penalized variable selection for accelerated failure time models with random effects

Accelerated failure time (AFT) models allowing for random effects are linear mixed models under the log-transformation of survival time with censoring and describe dependence in correlated survival data. It is well known that the AFT models are useful alternatives to frailty models. To the best of our knowledge, however, there is no literature on variable selection methods for such AFT models. In this paper, we propose a simple but unified variable-selection procedure of fixed effects in the AFT random-effect models using penalized h-likelihood (HL). We consider four penalty functions (ie, least absolute shrinkage and selection operator (LASSO), adaptive LASSO, smoothly clipped absolute deviation (SCAD), and HL). We show that the proposed method can be easily implemented via a slight modification to existing h-likelihood estimation procedures. We thus demonstrate that the proposed method can also be easily extended to AFT models with multilevel (or nested) structures. Simulation studies also show that the procedure using the adaptive LASSO, SCAD, or HL penalty performs well. In particular, we find via the simulation results that the variable selection method with HL penalty provides a higher probability of choosing the true model than other three methods. The usefulness of the new method is illustrated using two actual datasets from multicenter clinical trials.     

Impact of vaccination delay on deaths averted by pneumococcal conjugate vaccine: Modeled effects in 8 country scenarios

Delay in vaccination from schedule has been frequently documented and varies by vaccine, dose, and setting. Vaccination delay may result in the failure to prevent deaths that would have been averted by on-schedule vaccination.We constructed a model to assess the impact of delay in vaccination with pneumococcal conjugate vaccine (PCV) on under-five mortality. The model accounted for the week of age-specific risk of pneumococcal mortality, direct effect of vaccination, and herd protection. For each model run, a cohort of children were exposed to the risk of mortality and protective effect of PCV for each week of age from birth to age five. The model was run with and without vaccination delay and difference in number of deaths averted was calculated. We applied the model to eight country-specific vaccination scenarios, reflecting variations in observed vaccination delay, PCV coverage, herd effect, mortality risk, and vaccination schedule. As PCV is currently being scaled up in India, we additionally evaluated the impact of vaccination delay in India under various delay scenarios and coverage levels.We found deaths averted by PCV with and without delay to be comparable in all of the country scenarios when accounting for herd protection. In India, the greatest relative difference in deaths averted was observed at low coverage levels and greatest absolute difference was observed around 60% vaccination coverage. Under moderate delay scenarios, vaccination delay had modest impact on deaths averted by PCV in India across levels of coverage or vaccination schedule. Without accounting for herd protection, vaccination delay resulted in much greater failure to avert deaths.Our model suggests that realistic vaccination delay has a minimal impact on the number of deaths averted by PCV when accounting for herd effect. High population coverage can largely over-ride the deleterious effect of vaccination delay through herd protection.     

Using a monotone single-index model to stabilize the propensity score in missing data problems and causal inference

The augmented inverse weighting method is one of the most popular methods for estimating the mean of the response in causal inference and missing data problems. An important component of this method is the propensity score. Popular parametric models for the propensity score include the logistic, probit, and complementary log-log models. A common feature of these models is that the propensity score is a monotonic function of a linear combination of the explanatory variables. To avoid the need to choose a model, we model the propensity score via a semiparametric single-index model, in which the score is an unknown monotonic nondecreasing function of the given single index. Under this new model, the augmented inverse weighting estimator (AIWE) of the mean of the response is asymptotically linear, semiparametrically efficient, and more robust than existing estimators. Moreover, we have made a surprising observation. The inverse probability weighting and AIWEs based on a correctly specified parametric model may have worse performance than their counterparts based on a nonparametric model. A heuristic explanation of this phenomenon is provided. A real-data example is used to illustrate the proposed methods.     

深圳市龙岗区大气污染物与医院呼吸系统疾病门诊量的广义相加模型分析

目的 探讨深圳市龙岗区主要大气污染物(SO₂、NO₂、PM₁₀与PM_2.5)与医院呼吸系统疾病门诊量的关系。 方法 收集2013年1月1日－2015年12月31日深圳市龙岗区2家公立医院呼吸系统疾病逐日门诊量资料,深圳市龙岗区逐日大气污染物浓度及逐日气象资料分别来自深圳市环境监测站及气象局,运用时间序列分析广义相加模型对大气污染物日均浓度与呼吸系统疾病门诊量的关系及滞后效应进行分析。 结果 深圳市龙岗区2013－2015年SO₂ 、NO₂ 、PM₁₀ 与PM_2.5浓度中位数分别为8.08、38.08、46.05 μg/m³及31.04 μg/m³。2家医院三年呼吸系统门诊总量为549 169人次,日门诊量中位数为499人次/d。广义相加模型分析结果表明,除NO₂对呼吸系统疾病门诊量影响差异无统计学意义外,其余三种污染物对呼吸系统疾病门诊量影响均存在滞后效应,污染物每升高10 μg/m³,滞后2 d时SO₂对门诊量影响最强(相对危险度RR为1.030 7,95%CI:1.015 7~1.045 9),滞后3 d时PM₁₀与PM_2.5浓度对呼吸系统疾病门诊量影响最强(PM₁₀:RR=1.005 4,95%CI:1.002 8~1.008 0,PM_2.5:RR=1.006 0, 95%CI:1.002 7~1.009 4)。 结论 深圳市龙岗区大气SO₂、PM₁₀与PM_2.5浓度对医院呼吸系统疾病门诊量影响存在滞后效应。     

Medical waste management and environmental assessment in the Rio University Hospital,Western Greece

At this study a multi-criteria model was developed to examine the available procedures, techniques and methods of handling infectious waste in the large healthcare unit of University Regional General Hospital of Patras, Western Greece. Particularly, this study examined the: a) current legislation and Directives issued for medical waste management at Greece and among the other EU-members, b) contribution of healthcare wastes (HCW) generation rate on social and economic parameters in selected European countries, c) available procedures, techniques, and methods upon the disposal of infectious wastes at the healthcare studied, and, d) propositions for integrated management of such hazardous wastes. Specifically, the Analytic Hierarchy Process (AHP) methodology was applied under pair wise comparison matrices in two stages: 1) the scale factors and the indicators, and 2) the criteria and their sub–criteria. The assessment of these pair wise matrices included the indicators and the sub–criteria. Subsequently, two pair wise comparison matrices, upon a) the “Fulfillment of environmental objectives” indicator and b) the “Energy consumption” sub criterion, were denoted. The AHP methodology yielded good results; however there is still space of improving the environmental performance. The normalized relative weights obtained for the criteria and sub criteria motivated specific actions that have to be handled. Particularly, the results indicated a very good value in environmental management criteria due the values obtained for the commitment towards the environmental policy standards and the waste management procedures. However, further improvements on staff awareness (such as development programs to enhance sensitivity) and more green purchasing suppliers, should be further addressed.     

多种放射性肺损伤动物模型的建立和效应评价研究

放射性肺损伤是胸部肿瘤放疗后常见并发症,随着对放射性肺炎研究不断深入,如何为基础研究和药物干预寻求最佳的动物模型和效应评价已成为当前亟待解决的难题之一。通过检索近10年文献,对不同放射性肺损伤模型动物及放射部位的选择、照射剂量的确定、照射方法的比较和动物模型效应评价进行了对比研究,以期寻找建立放射性肺损伤动物模型的稳定方法和较为明确的效应机制,为防护和减缓放射性肺损伤的发生发展而进行的基础研究和药物研制提供可靠的方法。     

A low cost smart system to analyze different types of edible Bird's nest adulteration based on colorimetric sensor array

This study was performed to develop a low-cost smart system for identification and quantification of adulterated edible bird's nest (EBN). The smart system was constructed with a colorimetric sensor array (CSA), a smartphone and a multi-layered network model. The CSA were used to collect the odor character of EBN and the response signals of CSA were captured by the smartphone systems. The principal component analysis (PCA) and hierarchical cluster analysis (HAC) were used to inquiry the similarity among authentic and adulterated EBNs. The multi-layered network model was constructed to analyze EBN adulteration. In this model, discrimination of authentic EBN and adulterated EBN was realized using back-propagation neural networks (BPNN) algorithm. Then, another BPNN-based model was developed to identify the type of adulterant in the mixed EBN. Finally, adulterated percentage prediction model for each kind of adulterate EBN was built using partial least square (PLS) method. Results showed that recognition rates of the authentic EBN and adulterated EBN was as high as 90%. The correlation coefficient of percentage prediction model for calibration set was 0.886, and 0.869 for prediction set. The low-cost smart system provides a real-time, nondestructive tool to authenticate EBN for customers and retailers.     

Reducing unnecessary biopsies while detecting clinically significant prostate cancer including cribriform growth with the ERSPC Rotterdam risk calculator and 4Kscore

Introduction

The use of risk calculators predicting clinically significant prostate cancer (csCaP) on biopsy reduces unnecessary biopsies and overdiagnosis of indolent disease compared to a Prostate Specific Antigen (PSA) strategy. Updating these tools using more specific outcome measures and contemporary predictors could potentially lead to further reductions. Our objective was to assess clinical impact of the 4 kallikrein (4K) score, the Rotterdam Prostate Cancer Risk Calculator (RPCRC), and the combination of both for predicting csCaP based on the latest International Society of Urological Pathology grading system and cribriform growth pattern.