药品的属性划分研究

目的探讨药品的属性划分,以推动药物政策的研究。方法采用演绎推理方法,先分析药品的特殊性,再根据经济学中物品属性的理论对药品的属性进行划分。结果与结论根据药品的特殊性,并考虑到药品的公平性、消费特点和疾病的外部性特征,可将药品分为私益产品、公共资源和公益产品。     

Using a stated preference discrete choice experiment to assess societal value from the perspective of decision-makers in Europe. Does it work for rare diseases?

Objectives: To pilot the feasibility of using a discrete choice experiment (DCE) design to investigate individual preferences from the decision-maker perspective regarding the use of public funding for orphan drugs and generate prior information for future experimental designs.Methods: A DCE was used on a convenience sample of participants from five European countries (England, France, Germany, Italy and Spain), exploring their preferences in distinct healthcare scenarios involving orphan drugs. A preliminary review of the empirical literature on distributive preferences informed the selection of attributes and their levels in the design. An online questionnaire was used to conduct the DCE survey.Results: A total of 199 questionnaires were completed. The five country model showed relative preference for some attributes over others: cost of treatment, improvement in health, value for money and availability of treatment alternatives received the greatest attention. However, disease severity, beginning of life, waiting times and side effects were also shown to be important social values that should not be ignored.Conclusions: The ?ndings presented in this study provide insight about the preferences that can influence decisions on orphan drugs in different countries. This study also provides valuable prior information that could inform future DCE designs in this area.     

和谐医患关系的伦理维度与法律支撑

我国医患关系尚未实现真正的和谐,其原因是多重的。就伦理层面而言,多数医疗纠纷源于医患双方缺乏信任,以自我为中心,过分强调经济利益,缺少人文关怀。就法律层面而言,医患关系重大涉法问题研究薄弱,调整、规制医患关系的法律滞后甚至缺失,医疗纠纷处理机制不健全、不完备以及医患双方法律意识失衡与法律知识不健全。构建和谐医患关系,应当加快立法进程,保障医患双方利益;加强德育,明确医患关系的伦理诉求。     

The forgetting of stimulus attributes in latent inhibition

Numerous studies have demonstrated that the forgetting of stimulus attributes is a common occurrence; that is, organisms forget the specific characteristics of training stimuli over long retention intervals, while retaining general information of the training stimuli themselves. However, most studies have examined this effect after a learning episode, and there have been virtually no accounts to test whether the forgetting of attributes occurs for stimuli presented prior to training. Therefore, this experiment was designed to test that possibility, and it examined whether the forgetting of stimulus attributes occurred prior to training for the flavor stimulus in a conditioned taste aversion (CTA) procedure. Specifically, a latent inhibition (LI) procedure was used to measure the extent of forgetting for a pre-exposed flavor over short and long retention intervals. The results indicate that rats forgot the specific characteristics of the flavor stimulus (CS) while retaining memory for pre-exposure sessions over a long retention interval. That is, subjects pre-exposed and conditioned with different concentrations of sucrose showed no LI effect with a 1-day delay between pre-exposure and training, but demonstrated a generalized LI with an 8-day delay between pre-exposure and conditioning. This experiment provides further evidence for the robustness of the forgetting of stimulus attributes, and demonstrates that this specific type of forgetting also occurs prior to the learning of a CTA task.     

对“医源性人体脱离物”分类及其权利归属的伦理探讨

首先从医疗废弃物的概念出发分析了该概念的不合理性,并提出了“医源性人体脱离物”的概念,阐述了“医源性人体脱离物”具有“物”的属性,根据其有无价值和传染性进行了四种分类,最后对这四种分类的权利归属进行了伦理探讨。     

略谈名词定语的特殊性

英语中,名词作定语具有一定特殊性.作者阐述与辨析了名词定语的双重功能及其修饰变化,这对掌握名词定语非常关键.     

基于粗糙集的类方配伍规律研究的决策规则模型

为了挖掘类方配伍与其证候指标之间的相关关系,将粗糙集理论应用于类方配伍规律的研究,建立了类方配伍规律研究的决策规则模型。根据此模型通过属性约简可以得到类方中影响药效的核心药物,通过挖掘有效的决策规则可以分析类方内各药物之间的相互作用,并且可以利用决策规则对药效进行预测。该模型的建立对优化类方配伍及临床实践具有一定的指导意义。     

Automatic detection of microaneurysms in color fundus images

This paper addresses the automatic detection of microaneurysms in color fundus images, which plays a key role in computer assisted diagnosis of diabetic retinopathy, a serious and frequent eye disease. The algorithm can be divided into four steps. The first step consists in image enhancement, shade correction and image normalization of the green channel. The second step aims at detecting candidates, i.e. all patterns possibly corresponding to MA, which is achieved by diameter closing and an automatic threshold scheme. Then, features are extracted, which are used in the last step to automatically classify candidates into real MA and other objects; the classification relies on kernel density estimation with variable bandwidth. A database of 21 annotated images has been used to train the algorithm. The algorithm was compared to manually obtained gradings of 94 images; sensitivity was 88.5% at an average number of 2.13 false positives per image.     

Validation of a machine learning approach using FIB-4 and APRI scores assessed by the metavir scoring system: A cohort study

Background and Study AimThe study aim was to improve and validate the accuracy of the fibrosis-4 (FIB-4) and aspartate aminotransferase-to-platelet ratio index (APRI) scores for use in a potential machine-learning (ML) method that accurately predicts the extent of liver fibrosis.Patients and MethodsThis retrospective multicenter study included 69,106 patients with chronic hepatitis C planned for antiviral therapy from January 2010–December 2014 with liver biopsy results. FIB-4 and APRI scores were calculated and their performance for predicting significant liver fibrosis (F3–F4) assessed against the Metavir scoring system. ML was used for feature selection and reduction to identify the most relevant attributes (CfsSubseteval/best first) for prediction.ResultsIn this study, 57,492 (83.2%) patients were F0–F2, and 11,615 (16.8%) patients were F3–F4. The revalidation of FIB-4 and APRI showed lower accuracy and higher disagreement with the biopsy results, with AUCs of 0.68 and 0.58, respectively. FIB-4 diagnosed fewer (14%) F3–F4 patients, and the high specificity and negative predictive values of FIB-4 and APRI reflected the low prevalence of F3–F4 in the study population. Out of 15 attributes, age (>35 years), AFP (>6.5 ng/ml), and platelet count (<150,000/mm³) were the most relevant risk attributes, and patients with one or more of these risk factors were likely to be F3–F4, with a classification accuracy of ≤ 92% and receiver operating characteristics area of 0.74.ConclusionFIB-4 and APRI scores were not very accurate and missed diagnosing most of the F3–F4 patients. ML implementation improved medical decisions and minimized the required clinical data to three risk factors.