课题基金 / 基金详情

OxDX: Machine learning enhanced point of care diagnosis platform for infectious diseases: faster, more accessible, user intuitive, leveraging novel fluorescent labelling of enveloped viruses.

OxDX: Machine learning enhanced point of care diagnosis platform for infectious diseases: faster, more accessible, user intuitive, leveraging novel fluorescent labelling of enveloped viruses.
OxDX:机器学习增强型传染病护理诊断平台:更快、更容易使用、用户直观,利用包膜病毒的新型荧光标记。
批准号:
10032103
负责人:
金额:
$36.59万
依托单位:
依托单位国家:
英国
项目类别:
Collaborative R&D
财政年份:
2022
资助国家:
英国
项目状态:
已结题
起止时间:
2022 至 --

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中文摘要
翻译
英国初创企业OxDX有限公司将利用OxDX独特的病原体识别技术,通过开发传染病诊断平台,改变现有的人类病毒检测方法。由流感、副流感和呼吸道合胞病毒等病原体引起的传染病每年造成数千人死亡。目前的检测工作流程严重依赖于集中实验室,在需求高峰期间,检测能力和物流网络难以维持。目前用于病毒检测的诊断方法,如病毒培养、RT-PCR或基于抗原的快速诊断测试,往往因等待时间长或灵敏度和特异性有限而受到阻碍。因此,迫切需要分散检测解决方案,将检测从实验室转移到提供直接、快速和敏感的病毒检测方法的护理点。该项目的重点是开发OxDX的诊断平台,在降低成本和提供实验室质量结果方面实现阶段性变化。该平台适合临床以外的非临床专家使用,可以快速部署,提高全球对超快速、准确的传染病诊断的可及性。
英文摘要
UK-based start-up OxDX Ltd will transform existing approaches to human virus detection, through the development of an infectious disease diagnosis platform, leveraging OxDX's unique pathogen identification technology.Infectious diseases caused by pathogens like influenza, parainfluenza and respiratory syncytial virus cause many thousands of deaths annually. Current testing workflows rely heavily on centralised laboratories, with testing capacity and logistics networks struggling during peak demand spikes.Current diagnostic approaches for virus detection, such as virus culture, RT-PCR or antigen-based rapid diagnostic tests, are often hampered by long waiting times or limited sensitivity and specificity. As a result, there is an urgent need for decentralising testing solutions, moving testing away from laboratories to the point of care that provides straightforward, fast and sensitive viral detection methods.This project focuses on the development of OxDX's diagnostic platform, providing a step-change in cost reduction and delivering laboratory quality results at the point of care.Suitable for utilisation by non-clinical experts beyond clinical settings, the platform can be rapidly deployed, improving global accessibility to ultra-fast, accurate infectious disease diagnostics.
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国内基金
海外基金
Understanding structural evolution of galaxies with machine learning
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2022
  • 负责人:
    Nicola Rosario Napolitano
  • 依托单位: