Fusarium Head Blight Control and Prevention of Mycotoxin Contamination in Wheat with Botanicals and Tannic Acid

Suspensions or solutions with 1% of Chinese galls (Galla chinensis, GC) or 1% of tannic acid (TA), inhibited germination of conidia or mycelium growth of Fusarium graminearum (FG) by 98%–100% or by 75%–80%, respectively, whereas dried bark from buckthorn (Frangula alnus, FA) showed no effect at this concentration. In climate chamber experiments where the wheat variety “Apogee” was artificially inoculated with FG and F. crookwellense (FCr) and treated with 5% suspensions of TA, GC and FA, the deoxynivalenol (DON) content in grains was reduced by 81%, 67% and 33%, respectively. In field experiments with two commercial wheat varieties and artificial or semi-natural inoculations, mean DON reductions of 66% (TA) and 58% (FA), respectively, were obtained. Antifungal toxicity can explain the high efficacies of TA and GC but not those of FA. The Fusarium head blight (FHB) and mycotoxin reducing effect of FA is probably due to elicitation of resistance in wheat plants. With semi-natural inoculation, a single FA application in the first half of the flowering period performed best. However, we assume that applications of FA at the end of ear emergence and a treatment, triggered by an infection period, with TA or GC during flowering, might perform better than synthetic fungicides.     

护士开展护理科研选题的思考:建模与预测

目的:基于灰色建模预测技术,探索护士开展护理科研选题的途径。方法:根据所研究问题的客观物理特性,定性选用恰当的灰色模型,利用等时距观测数据序列,定量建立灰色预测模型。结果:基于建模预测结果,定性分析所研究问题的发展态势。结论:预测问题的建模工作,必须基于"数学建模"的思想来进行。     

Forecasting age-specific breast cancer mortality using functional data models

Accurate estimates of future age-specific incidence and mortality are critical for allocation of resources to breast cancer control programmes and evaluation of screening programmes. The purpose of this study is to apply functional data analysis techniques to model age-specific breast cancer mortality time trends, and forecast entire age-specific mortality functions using a state-space approach.We use annual unadjusted breast cancer mortality rates in Australia, from 1921 to 2001 in 5 year age groups (45 to 85+). We use functional data analysis techniques where mortality and incidence are modelled as curves with age as a functional covariate varying by time. Data are smoothed using non-parametric smoothing methods then decomposed (using principal components analysis) to estimate basis functions that represent the functional curve. Period effects from the fitted coefficients are forecast then multiplied by the basis functions, resulting in a forecast mortality curve with prediction intervals. To forecast, we adopt a state-space approach and an automatic modelling framework for selecting among exponential smoothing methods.Overall, breast cancer mortality rates in Australia remained relatively stable from 1960 to the late 1990s, but have declined over the last few years. A set of four basis functions minimized the mean integrated squared forecasting error and account for 99.3 per cent of variation around the mean mortality curve. Twenty year forecasts suggest a continuing decline, but at a slower rate, and stabilizing beyond 2010. Forecasts show a decline in all age groups with the greatest decline in older women.The proposed methods have the potential to incorporate important covariates such as hormone replacement therapy and interventions to represent mammographic screening. This would be particularly useful for evaluating the impact of screening on mortality and incidence from breast cancer.     

Forecasting malaria incidence from historical morbidity patterns in epidemic-prone areas of Ethiopia: a simple seasonal adjustment method performs best

The aim of this study was to assess the accuracy of different methods of forecasting malaria incidence from historical morbidity patterns in areas with unstable transmission. We tested five methods using incidence data reported from health facilities in 20 areas in central and north-western Ethiopia. The accuracy of each method was determined by calculating errors resulting from the difference between observed incidence and corresponding forecasts obtained for prediction intervals of up to 12 months. Simple seasonal adjustment methods outperformed a statistically more advanced autoregressive integrated moving average method. In particular, a seasonal adjustment method that uses mean deviation of the last three observations from expected seasonal values consistently produced the best forecasts. Using 3 years' observation to generate forecasts with this method gave lower errors than shorter or longer periods. Incidence during the rainy months of June-August was the most predictable with this method. Forecasts for the normally dry months, particularly December-February, were less accurate. The study shows the limitations of forecasting incidence from historical morbidity patterns alone, and indicates the need for improved epidemic early warning by incorporating external predictors such as meteorological factors.     

新型颈动脉狭窄预测模型在短暂性脑缺血发作患者中的应用

目的探讨颈动脉狭窄预测模型在短暂性脑缺血发作(TIA)患者中的实用性与准确性。方法选取2014年1月~2018年12月任丘康济新图医院住院的TIA患者299例,采用颈动脉超声检查颈动脉狭窄程度,根据颈动脉狭窄程度分为无颈动脉狭窄组(A组)186例,颈动脉轻度狭窄组(B组)76例,中度和重度狭窄或闭塞组(C组)37例,采集一般临床资料,采用ROC曲线分析评价颈动脉预测狭窄模型对TIA患者颈动脉狭窄的预测价值。结果 A组、B组、C组年龄、性别、已婚、高血压、糖尿病、冠心病、脑卒中/TIA、吸烟、抗血小板药物、抗凝药物、舒张压及HDL-C水平呈明显升高趋势,差异有统计学意义(P<0.05)。ROC曲线分析显示,颈动脉狭窄预测模型对A组、B组、C组预测的ROC曲线下面积分别为0.415(95%CI:0.346~0.484)、0.475(95%CI:0.398~0.552)、0.728(95%CI:0.639~0.818),差异有统计学意义(P<0.05)。颈动脉狭窄预测模型对C组预测敏感性较A组、B组更高(P<0.05)。结论颈动脉狭窄预测模型对TIA患者颈动脉狭窄的预测价值较高,此模型的使用效能较高,值得在脑卒中防控措施中推广。     

Rodent reservoirs of future zoonotic diseases

The increasing frequency of zoonotic disease events underscores a need to develop forecasting tools toward a more preemptive approach to outbreak investigation. We apply machine learning to data describing the traits and zoonotic pathogen diversity of the most speciose group of mammals, the rodents, which also comprise a disproportionate number of zoonotic disease reservoirs. Our models predict reservoir status in this group with over 90% accuracy, identifying species with high probabilities of harboring undiscovered zoonotic pathogens based on trait profiles that may serve as rules of thumb to distinguish reservoirs from nonreservoir species. Key predictors of zoonotic reservoirs include biogeographical properties, such as range size, as well as intrinsic host traits associated with lifetime reproductive output. Predicted hotspots of novel rodent reservoir diversity occur in the Middle East and Central Asia and the Midwestern United States.Infectious agents transmitted from animals to humans account for most outbreaks of novel pathogens worldwide (1–3). With over 1 billion cases of human illness attributable to zoonotic disease each year, identifying wild reservoirs of zoonotic pathogens is a perennial public health priority (4). Until now, investigations of disease outbreaks have mostly been reactive, with surveillance efforts targeting a broad host range (5), but because human activities precipitating these events continue to accelerate (4, 6), a more proactive approach is necessary (7, 8). Identifying which wildlife species are most likely to serve as reservoirs of future zoonotic diseases and in which regions new outbreaks are most likely to occur are necessary steps toward a preemptive approach to minimizing zoonotic disease risk in humans. To this end, trait profiles inferred from large datasets that distinguish reservoirs from nonreservoir species can play a major role in guiding the search for novel disease reservoirs in the wild. Identifying these distinguishing, intrinsic features of zoonotic reservoirs also has the potential to generate testable hypotheses that can explain why some host species are more permissive to zoonotic infections.To accomplish these goals, we applied generalized boosted regressions (9, 10), a type of machine learning that builds ensembles of classification/regression trees to identify variables that are most important for prediction—in our case, predicting zoonotic reservoir status and hyperreservoir status (species known to carry two or more zoonotic infections). These methods and similar methods have particular use for comparative ecological studies because they accommodate multiple data types as covariates, nonrandom patterns of data missingness, and hidden, nonlinear interactions. The explanatory power of decision tree methods is unaffected by variations in data coverage that may arise because of sampling bias or when species share a particular trait because of shared evolutionary history (11, 12). We examined intrinsic traits of host species, which are inherently less susceptible to sampling biases common in large-scale epidemiological analyses—for example, important differences in gross domestic product, research productivity, or diagnostic capacity that are certain to influence detection of zoonotic disease reservoirs among countries are unlikely to influence biological or life history characteristics of the wild species found in these countries. Machine learning methods generally do not assume an underlying model, a priori designation of interacting variables, or independence among data points, thereby precluding the need for phylogenetic corrections (11, 13). Such model-free approaches allow the data to speak for themselves and enable statistical learning algorithms to achieve superior predictive accuracy (10) and identify prominent patterns in the data that spark novel hypotheses.     

群体药动学应用于个体化给药的研究进展

群体药动学运用经典的药动学原理结合统计学方法,以稀疏数据预测患者血药浓度,可达到监测药物体内过程和指导临床个性化给药的目的。查阅近年来的相关文献,结合实例综述群体药动学应用于治疗窗窄的药物、个体差异大的药物、联合用药、特殊人群用药等方面的最新进展,为其深入研究和指导临床用药提供参考。     

血 NGAL、CysC检测对外科重症急性肾损伤患者的早期诊断价值

目的：为了评估和比较血中性粒细胞明胶酶相关脂质运载蛋白（NGAL）、血清半胱氨酸蛋白酶抑制剂C（CysC）水平在外科重症监护室（SICU ）内成年急性肾损伤（AKI）患者的早期诊治中的应用价值。方法100例患者被分入两个组：非AKI组63例和AKI组37例,应用酶联免疫吸附法（ELISA）、胶乳增强免疫比浊法（PETIA）分别测定两组患者入SICU 时（T0）、入SICU后24 h（T1）两个时间点血NGAL及CysC水平。结果 AKI组患者在 T0、T1点的血NGAL、CysC水平明显高于非 AKI组（P＜0．01）。T0时血NGAL值对于AKI的诊断价值较好,在截断值为65．95 ng／mL时,受试者工作曲线下面积（AUROC）为0．85,其诊断的敏感性为81．8％,特异性为76．2％;T1时血 NGAL水平对于 AKI的诊断价值较差,在截断值为92 ng／mL 时, AUROC为0．69,其诊断的敏感性为70．3％,特异性为57．1％。而T0、T1时的血CysC水平对于AKI的诊断价值更佳,在截断值分别为1．49 mg／L和1．47 mg／L时,其AUROC相应为0．90和0．88,其诊断的敏感性分别为89．2％、82．5％,特异性分别为83．8％、76．2％。结论血NGAL、CysC是SICU内患者预测AKI发生的早期有效新型标记物,入室时血CysC检测对于AKI的早期诊断价值要优于血NGAL。     

中国人群HLA—Ⅰ类限制性HCV多表位疫苗理论免疫应答率的预测

[目的]预测中国人群HLA-Ⅰ类限制性HCV特异性CTL表位组合的理论免疫应答率,指导丙型肝炎多表位疫苗的研制和应用．[方法]利用“中国多表位疫苗设计的HLA-Ⅰ积累表型频率空间预测系统”,计算HLA—A、B限制性HCV特异性CTL表位组合在中国人群中的累积表型频率（CPF）,得出理论免疫应答率,绘制等值线图并进行评价。[结果]HLA—A或B单一超型组合针对中国人群的理论免疫应答率偏低,HLA-A和B超型组合的理论免疫应答率较好。[结论]根据不同HLA组合设计的多表位疫苗在中国人群中的免疫应答效果不同,进行疫苗理论免疫应答率空间预测研究,对研制适合中国人群的丙型肝炎多表位疫苗有重要的指导意义。     

短暂性脑缺血发作ABCDIS评分的前瞻性研究

目的利用ABCDIS评分对24 h内进入急诊室的短暂性脑缺血发作(TIA)患者进行前瞻性的研究,从而发现高危的TIA患者,为TIA患者提供个体化处理方案。方法在12个月期间,所有进入山东菏泽昆仑脑血管病医院、菏泽市第三人民医院、菏泽市疾控中心3家医院急诊室的329例TIA患者根据8分制ABCDIS评分进行分层。结果在329例患者中,7天内发生缺血性脑卒中12例(3.6%),30天内发生缺血性脑卒中18例(5.5%)。ABCDIS评分可预测7天和30天的脑卒中风险。7天时对各评分点发生脑卒中的风险为3.6(P=0.004 3),30天时为5.5(P=0.0003)。结论ABCDIS评分的前瞻性研究证实了其预测价值,有助于在急诊室迅速得到每例患者可靠易得的风险预测信息,有助于指导患者的个体化处理。