New frontiers in applied veterinary point‐of‐capture diagnostics: Toward early detection and control of zoonotic influenza

Among the chief limitations in achieving early detection and control of animal‐origin influenza of pandemic potential in high‐risk livestock populations is the existing lag time between sample collection and diagnostic result. Advances in molecular diagnostics are permitting deployment of affordable, rapid, highly sensitive, and specific point‐of‐capture assays, providing opportunities for targeted surveillance driving containment strategies with potentially compelling returns on investment. Interrupting disease transmission at source holds promise of disrupting cycles of animal‐origin influenza incursion to endemicity and limiting impact on animal production, food security, and public health. Adoption of new point‐of‐capture diagnostics should be undertaken in the context of promoting robust veterinary services systems and parallel support for operationalizing pre‐authorized plans and communication strategies that will ensure that the full potential of these new platforms is realized.     

环境水样中痕量铜的浊点萃取-火焰原子吸收光谱测定法

目的建立环境水样中痕量铜的浊点萃取(cloud point extraction,CPE)-火焰原子吸收光谱(flame atomic absorption spectrometry,FAAS)测定法。方法样品在p H 9.5的条件下,加入0.4 ml的1 mmol/L 2-(5-溴-2-吡啶偶氮)-5-二乙氨基酚(5-Br-PADAP)溶液,0.1%氯化钙溶液0.1 ml,5%(W/V)Triton X-114溶液0.8 ml,40℃加热15 min后离心,采用火焰原子吸收光谱法进行检测。结果在2~240μg/L的线性范围内,所得回归方程为A=0.002 7c+0.024 6,r=0.995 8。以3倍信噪比计算,方法的检出限为0.62μg/L,富集倍数为36.58倍,平均加标回收率为96.28%~98.08%,RSD为1.67%~3.13%。结论该方法简单、灵敏,具有良好的重现性,适用于环境水样中痕量铜的测定。     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

Recurrent herpes labialis in the pediatric population: Prevalence,therapeutic studies,and associated complications

Recurrent herpes labialis (RHL) is an incredibly common condition, though the medical literature evaluating pediatric aspects is limited. This paper assesses prevalence and therapeutic studies of pediatric RHL as well as disease complications. A comprehensive literature search of English‐language citations based on PubMed queries of selected terms was performed, with exclusion if methodology was not discussed, or if studies had 10 or fewer patients. RHL prevalence in pediatrics has been assessed by measures of point and periodic prevalence, though methodologic limitations may under‐ or over‐estimate the true prevalence of RHL. Studies have been conducted to evaluate therapeutic safety, tolerability, and efficacy of antivirals in the pediatric population. Pediatric RHL point prevalence ranges from 0.72% to 5.2% depending on the population study and the methodologies used. Pediatric RHL carries a significant public health burden and is often implicated in patients with eczema herpeticum, erythema multiforme, reactive infectious mucositis eruptions, and hypersensitivity reactions. There are few studies that evaluate the rates of occurrence of these sequelae associated with pediatric RHL.     

亚麻籽胶的胶凝性质

采用流变学法测定了亚麻籽胶溶液的胶凝点、熔化点，并采用质构仪、扫描电镜和原子力显微镜等手段研究了影响亚麻籽胶凝胶强度的因素，结果表明亚麻籽胶具有胶凝性，它能形成一种热可逆的冷致凝胶，亚麻籽胶溶液的胶凝点低于其凝胶的熔化点，且亚麻籽胶溶液的胶凝点及其凝胶的熔化点均随冷却的起始温度的升高而升高。亚麻籽胶浓度、溶解温度、pH、NaCl、CaCl2及复合磷酸盐能影响亚麻籽胶的凝胶强度，亚麻籽胶的凝胶强度随着浓度的增加及溶解温度的升高而增强；在pH6～9的范围内，亚麻籽胶的凝胶强度达到最大；NaCl和复合磷酸盐可以降低亚麻籽胶的凝胶强度，低浓度（〈0．3％）的CaCl2可以增强亚麻籽胶的凝胶强度，而高质量分数（〉0．3％）的CaCl2能降低亚麻籽胶的凝胶强度。     

等电聚焦电泳分析红细胞Gd(-)壮族富宁型〔Gd(-)Zhuang-Funing〕

本文采用聚丙烯酰胺凝胶等电聚焦电泳(IEF),借助园盘电泳仪的简易装置,对正常 B 型G6PD 及变异型 G6PD 进行分析,获得了满意效果。     

医院检验科设备的维修分析

医院检验科设备是为临床提供诊断依据最基础也是最常用的检验手段之一，本文简要介绍了检验科设备在日常工作中容易发生的故障判别及修复时的要点。