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您的检索式：作者名="Nicholas Rees"
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| 1 | 快速报告标准之二:严重性及因果关系评价显示文摘1引言按照明确的、国际公认的管理规定,制药公司必须收集药品安全性信息以便进行安全性评估并向上级主管部门报告。虽然不同国家的管理部门有不同的报告标准及时限要求,但共同要求严重的及存在因果关系的不良事件报告应通过速报形式提交行政管理部门。本文的目的是提供如何在个案病例回顾中进行严重性及因果关系评价的实用性指导。 | Manel De Silva Wijayasinghe Inna Pendrak Nicholas Rees 西安杨森药品安全部(译) | 2009 | 中国药物警戒2009，6，5： | 3 |
| 2 | Dialysis Accelerates Medial Vascular Calcification in Part by Triggering Smooth Muscle Cell Apoptosis显示文摘 | Rukshana C. Shroff Rosamund McNair Nichola Figg Jeremy N. Skepper Leon Schurgers Ashmeet Gupta Melanie Hiorns Ann E. Donald John Deanfield Lesley Rees Catherine M. Shanahan | 2008 | Circulation2008，，17： | 1 |
| 3 | Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions显示文摘Background:Substantial research is underway to develop next-generation interventions that address current malaria control challenges.As there is limited testing in their early development,it is difficult to predefine intervention properties such as efficacy that achieve target health goals,and therefore challenging to prioritize selection of novel candidate interventions.Here,we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning.Our analysis identifies requirements of efficacy,coverage,and duration of effect for five novel malaria interventions to achieve targeted reductions in malaria prevalence.Methods:A mathematical model of malaria transmission dynamics is used to simulate deployment and predict potential impact of new malaria interventions by considering operational,health-system,population,and disease characteristics.Our method relies on consultation with product development stakeholders to define the putative space of novel intervention specifications.We couple the disease model with machine learning to search this multi-dimensional space and efficiently identify optimal intervention properties that achieve specified health goals.Results:We apply our approach to five malaria interventions under development.Aiming for malaria prevalence reduction,we identify and quantify key determinants of intervention impact along with their minimal properties required to achieve the desired health goals.While coverage is generally identified as the largest driver of impact,higher efficacy,longer protection duration or multiple deployments per year are needed to increase prevalence reduction.We show that interventions on multiple parasite or vector targets,as well as combinations the new interventions with drug treatment,lead to significant burden reductions and lower efficacy or duration requirements.Conclusions:Our approach uses disease dynamic models and machine learning to support decision-making and resource investment,facilitating development of new malaria interventions.By evaluating the intervention capabilities in relation to the targeted health goal,our analysis allows prioritization of interventions and of their specifications from an early stage in development,and subsequent investments to be channeled cost-effectively towards impact maximization.This study highlights the role of mathematical models to support intervention development.Although we focus on five malaria interventions,the analysis is generalizable to other new malaria interventions. | Monica Golumbeanu Guo-Jing Yang Flavia Camponovo Erin M.Stuckey Nicholas Hamon Mathias Mondy Sarah Rees Nakul Chitnis Ewan Cameron Melissa A.Penny | 2022 | Infectious Diseases of Poverty2022，11，3： | 0 |
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